WorldWideScience

Sample records for monthly rainfall estimates

  1. Estimating Monthly Rainfall from Geostationary Satellite Imagery Over Amazonia, Brazil.

    Science.gov (United States)

    Cutrim, Elen Maria Camara

    The infrared regression and the grid-history satellite rainfall estimating techniques were utilized to estimate monthly rainfall in Amazonia during one month of the rainy season (March, 1980) and one month of the dry season (September, 1980). The estimates were based on 3-hourly SMS-II infrared and visible images. Three sets of coefficients for the grid history method (Marajo, Arabian Sea, and GATE) were used to estimate rainfall. The estimated rain was compared with gauge measurements over the region. The infrared regression technique overestimated by a factor of 1.5. The Marajo coefficients yielded the best estimate, especially for eastern Amazonia. In the wet month Marajo coefficients overestimated rain by 10% and in the dry month by 70%. The Arabian Sea coefficients overestimated rain and the GATE coefficients slightly underestimated rain for Amazonia. Two maps of monthly rainfall over Amazonia were constructed for March and September, 1980, combining the ground station and satellite inferred rainfall of the grid history method using the Marajo coefficients. The satellite observations and ground data were mutually compatible and were contourable on these final, composite maps. Monthly rainfall was found to be much more inhomogeneous than previously reported. In March there was a belt of high precipitation trending southwest, with higher values and sharpest gradients in the coastal area. The upper Amazon was also an area of high precipitation, both north and south of the equator. In Roraima rainfall decreased drastically to the north. In September, the area of highest precipitation was the northwestern part of Amazonas State (northern hemisphere). Rainfall elsewhere was very localized and in northeastern Amazonia varied from 0 to 150 mm. Even though the grid history method presented better results for estimating rainfall over Amazonia, the IR model could be utilized more efficiently and economically on an operational basis if the calibration were properly made

  2. Mapping monthly rainfall erosivity in Europe

    DEFF Research Database (Denmark)

    Ballabio, C; Meusburger, K; Klik, A

    2017-01-01

    Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and...

  3. Mapping monthly rainfall erosivity in Europe.

    Science.gov (United States)

    Ballabio, Cristiano; Borrelli, Pasquale; Spinoni, Jonathan; Meusburger, Katrin; Michaelides, Silas; Beguería, Santiago; Klik, Andreas; Petan, Sašo; Janeček, Miloslav; Olsen, Preben; Aalto, Juha; Lakatos, Mónika; Rymszewicz, Anna; Dumitrescu, Alexandru; Tadić, Melita Perčec; Diodato, Nazzareno; Kostalova, Julia; Rousseva, Svetla; Banasik, Kazimierz; Alewell, Christine; Panagos, Panos

    2017-02-01

    Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha(-1)h(-1)) compared to winter (87MJmmha(-1)h(-1)). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R(2) values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be

  4. Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

    Directory of Open Access Journals (Sweden)

    S. Yin

    2015-05-01

    Full Text Available Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event, EI30, is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models that relate more commonly available rainfall data resolutions, such as daily or monthly totals, to rainfall erosivity. Eleven stations with one-minute temporal resolution rainfall data collected from 1961 through 2000 in the eastern water-erosion areas of China were used to develop and calibrate 21 models. Seven independent stations, also with one-minute data, were utilized to validate those models, together with 20 previously published equations. Results showed that models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Prediction capabilities, as determined using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, were demonstrated for the 41 models including those for estimating erosivity at event, daily, monthly, yearly, average monthly and average annual time scales. Prediction capabilities were generally better using higher resolution rainfall data as inputs. For example, models with rainfall amount and maximum 60 min rainfall amount as inputs performed better than models with rainfall amount and maximum daily rainfall amount, which performed better than those with only rainfall amount. Recommendations are made for choosing the appropriate estimation equation, which depend on objectives and data availability.

  5. Artificial Neural Network for Monthly Rainfall Rate Prediction

    Science.gov (United States)

    Purnomo, H. D.; Hartomo, K. D.; Prasetyo, S. Y. J.

    2017-03-01

    Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.

  6. Modeling of the Monthly Rainfall-Runoff Process Through Regressions

    Directory of Open Access Journals (Sweden)

    Campos-Aranda Daniel Francisco

    2014-10-01

    Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.

  7. Spatial interpolation methods for monthly rainfalls and temperatures in Basilicata

    Directory of Open Access Journals (Sweden)

    Ferrara A

    2008-12-01

    measured temperatures at eight sites from an independent dataset was done. There was a good agreement with mean R2 = 0.99 (mean RMSE = 0.6 °C. Based on this results universal kriging estimates and RST were used to produce monthly rainfall and temperature maps for Basilicata region aimed at using as quality input in forest modeling.

  8. Urban rainfall estimation employing commercial microwave links

    Science.gov (United States)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko; ten Veldhuis, Marie-claire

    2015-04-01

    Urban areas often lack rainfall information. To increase the number of rainfall observations in cities, microwave links from operational cellular telecommunication networks may be employed. Although this new potential source of rainfall information has been shown to be promising, its quality needs to be demonstrated more extensively. In the Rain Sense kickstart project of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS), sensors and citizens are preparing Amsterdam for future weather. Part of this project is rainfall estimation using new measurement techniques. Innovative sensing techniques will be utilized such as rainfall estimation from microwave links, umbrellas for weather sensing, low-cost sensors at lamp posts and in drainage pipes for water level observation. These will be combined with information provided by citizens in an active way through smartphone apps and in a passive way through social media posts (Twitter, Flickr etc.). Sensor information will be integrated, visualized and made accessible to citizens to help raise citizen awareness of urban water management challenges and promote resilience by providing information on how citizens can contribute in addressing these. Moreover, citizens and businesses can benefit from reliable weather information in planning their social and commercial activities. In the end city-wide high-resolution rainfall maps will be derived, blending rainfall information from microwave links and weather radars. This information will be used for urban water management. This presentation focuses on rainfall estimation from commercial microwave links. Received signal levels from tens of microwave links within the Amsterdam region (roughly 1 million inhabitants) in the Netherlands are utilized to estimate rainfall with high spatial and temporal resolution. Rainfall maps will be presented and compared to a gauge-adjusted radar rainfall data set. Rainfall time series from gauge(s), radars and links will be compared.

  9. Comparison of semivariogram models for Kriging monthly rainfall in eastern China

    Institute of Scientific and Technical Information of China (English)

    汤燕冰

    2002-01-01

    An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.

  10. Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

    Directory of Open Access Journals (Sweden)

    Panos Panagos

    2016-03-01

    Full Text Available As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices as well as natural hazard protection (landslides and flood prediction. We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland or former data density was not satisfactory (Austria, France, and Spain. The different time resolutions (from 5 to 60 min of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54 than in summer (August: 2.13, applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France, the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France show the largest from February to April. The average monthly erosivity density is very large in August (1.67 and July (1.63, while very small in January and February (0.37. This study addresses

  11. Comparison of semivariogram models for kriging monthly rainfall in eastern China

    Institute of Scientific and Technical Information of China (English)

    汤燕冰

    2002-01-01

    An exploratory spatial data analysis method(ESDA) was designed Apr.28,2002 for kriging monthly rainfall.Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998.Comparison of five semivariogram models(Spherical,Exponential,Linear,Gaussian and Rational Quadratic)indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation.ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the erea.All five models were appropriate for monthly rainflaa interpolation but under different circumstances.Spherical,Exponential and Linear models perform as smoothing interpolator of the data,whereas Gaussian and Rational Quadratic models serve as an exact interpolator.Spherical,Exponential and Linear models tend to underestimate the values,On the contrayr,Gaussian and Rational Quadratic models tend to overestimate the values.On the contrary,Gaussian and Rational Quadratic models tend to overestimate the values,Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months,an ESDA is recommended for a better interpolation result.

  12. Regionalized rainfall-runoff model to estimate low flow indices

    Science.gov (United States)

    Garcia, Florine; Folton, Nathalie; Oudin, Ludovic

    2016-04-01

    Estimating low flow indices is of paramount importance to manage water resources and risk assessments. These indices are derived from river discharges which are measured at gauged stations. However, the lack of observations at ungauged sites bring the necessity of developing methods to estimate these low flow indices from observed discharges in neighboring catchments and from catchment characteristics. Different estimation methods exist. Regression or geostatistical methods performed on the low flow indices are the most common types of methods. Another less common method consists in regionalizing rainfall-runoff model parameters, from catchment characteristics or by spatial proximity, to estimate low flow indices from simulated hydrographs. Irstea developed GR2M-LoiEau, a conceptual monthly rainfall-runoff model, combined with a regionalized model of snow storage and melt. GR2M-LoiEau relies on only two parameters, which are regionalized and mapped throughout France. This model allows to cartography monthly reference low flow indices. The inputs data come from SAFRAN, the distributed mesoscale atmospheric analysis system, which provides daily solid and liquid precipitation and temperature data from everywhere in the French territory. To exploit fully these data and to estimate daily low flow indices, a new version of GR-LoiEau has been developed at a daily time step. The aim of this work is to develop and regionalize a GR-LoiEau model that can provide any daily, monthly or annual estimations of low flow indices, yet keeping only a few parameters, which is a major advantage to regionalize them. This work includes two parts. On the one hand, a daily conceptual rainfall-runoff model is developed with only three parameters in order to simulate daily and monthly low flow indices, mean annual runoff and seasonality. On the other hand, different regionalization methods, based on spatial proximity and similarity, are tested to estimate the model parameters and to simulate

  13. Deterministic Approach for Estimating Critical Rainfall Threshold of Rainfall-induced Landslide in Taiwan

    Science.gov (United States)

    Chung, Ming-Chien; Tan, Chih-Hao; Chen, Mien-Min; Su, Tai-Wei

    2013-04-01

    Taiwan is an active mountain belt created by the oblique collision between the northern Luzon arc and the Asian continental margin. The inherent complexities of geological nature create numerous discontinuities through rock masses and relatively steep hillside on the island. In recent years, the increase in the frequency and intensity of extreme natural events due to global warming or climate change brought significant landslides. The causes of landslides in these slopes are attributed to a number of factors. As is well known, rainfall is one of the most significant triggering factors for landslide occurrence. In general, the rainfall infiltration results in changing the suction and the moisture of soil, raising the unit weight of soil, and reducing the shear strength of soil in the colluvium of landslide. The stability of landslide is closely related to the groundwater pressure in response to rainfall infiltration, the geological and topographical conditions, and the physical and mechanical parameters. To assess the potential susceptibility to landslide, an effective modeling of rainfall-induced landslide is essential. In this paper, a deterministic approach is adopted to estimate the critical rainfall threshold of the rainfall-induced landslide. The critical rainfall threshold is defined as the accumulated rainfall while the safety factor of the slope is equal to 1.0. First, the process of deterministic approach establishes the hydrogeological conceptual model of the slope based on a series of in-situ investigations, including geological drilling, surface geological investigation, geophysical investigation, and borehole explorations. The material strength and hydraulic properties of the model were given by the field and laboratory tests. Second, the hydraulic and mechanical parameters of the model are calibrated with the long-term monitoring data. Furthermore, a two-dimensional numerical program, GeoStudio, was employed to perform the modelling practice. Finally

  14. Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework

    Science.gov (United States)

    Hasan, Mohammad Mahadi; Sharma, Ashish; Mariethoz, Gregoire; Johnson, Fiona; Seed, Alan

    2016-11-01

    While the value of correcting raw radar rainfall estimates using simultaneous ground rainfall observations is well known, approaches that use the complete record of both gauge and radar measurements to provide improved rainfall estimates are much less common. We present here two new approaches for estimating radar rainfall that are designed to address known limitations in radar rainfall products by using a relatively long history of radar reflectivity and ground rainfall observations. The first of these two approaches is a radar rainfall estimation algorithm that is nonparametric by construction. Compared to the traditional gauge adjusted parametric relationship between reflectivity (Z) and ground rainfall (R), the suggested new approach is based on a nonparametric radar rainfall estimation method (NPR) derived using the conditional probability distribution of reflectivity and gauge rainfall. The NPR method is applied to the densely gauged Sydney Terrey Hills radar network, where it reduces the RMSE in rainfall estimates by 10%, with improvements observed at 90% of the gauges. The second of the two approaches is a method to merge radar and spatially interpolated gauge measurements. The two sources of information are combined using a dynamic combinatorial algorithm with weights that vary in both space and time. The weight for any specific period is calculated based on the error covariance matrix that is formulated from the radar and spatially interpolated rainfall errors of similar reflectivity periods in a cross-validation setting. The combination method reduces the RMSE by about 20% compared to the traditional Z-R relationship method, and improves estimates compared to spatially interpolated point measurements in sparsely gauged areas.

  15. Uncertainty of Areal Rainfall Estimation Using Point Measurements

    Science.gov (United States)

    McCarthy, D.; Dotto, C. B. S.; Sun, S.; Bertrand-Krajewski, J. L.; Deletic, A.

    2014-12-01

    The spatial variability of precipitation has a great influence on the quantity and quality of runoff water generated from hydrological processes. In practice, point rainfall measurements (e.g., rain gauges) are often used to represent areal rainfall in catchments. The spatial rainfall variability is difficult to be precisely captured even with many rain gauges. Thus the rainfall uncertainty due to spatial variability should be taken into account in order to provide reliable rainfall-driven process modelling results. This study investigates the uncertainty of areal rainfall estimation due to rainfall spatial variability if point measurements are applied. The areal rainfall is usually estimated as a weighted sum of data from available point measurements. The expected error of areal rainfall estimates is 0 if the estimation is an unbiased one. The variance of the error between the real and estimated areal rainfall is evaluated to indicate the uncertainty of areal rainfall estimates. This error variance can be expressed as a function of variograms, which was originally applied in geostatistics to characterize a spatial variable. The variogram can be evaluated using measurements from a dense rain gauge network. The areal rainfall errors are evaluated in two areas with distinct climate regimes and rainfall patterns: Greater Lyon area in France and Melbourne area in Australia. The variograms of the two areas are derived based on 6-minute rainfall time series data from 2010 to 2013 and are then used to estimate uncertainties of areal rainfall represented by different numbers of point measurements in synthetic catchments of various sizes. The error variance of areal rainfall using one point measurement in the centre of a 1-km2 catchment is 0.22 (mm/h)2 in Lyon. When the point measurement is placed at one corner of the same-size catchment, the error variance becomes 0.82 (mm/h)2 also in Lyon. Results for Melbourne were similar but presented larger uncertainty. Results

  16. Spatial estimation of debris flows-triggering rainfall and its dependence on rainfall severity

    Science.gov (United States)

    Destro, Elisa; Marra, Francesco; Nikolopoulos, Efthymios; Zoccatelli, Davide; Creutin, Jean-Dominique; Borga, Marco

    2016-04-01

    Forecasting the occurrence of landslides and debris flows (collectively termed 'debris flows' hereinafter) is fundamental for issuing hazard warnings, and focuses largely on rainfall as a triggering agent. Debris flow forecasting relies very often on the identification of combinations of depth and duration of rainfall - rainfall thresholds - that trigger widespread debris flows. Rainfall estimation errors related to the sparse nature of raingauge data are enhanced in case of convective rainfall events characterized by limited spatial extent. Such errors have been shown to cause underestimation of the rainfall thresholds and, thus, less efficient forecasts of debris flows occurrence. This work examines the spatial organization of debris flows-triggering rainfall around the debris flow initiation points using high-resolution, carefully corrected radar data for a set of short duration (debris-flow triggering rainfall events that occurred in the study area between 2005 and 2014. The selected events are among the most severe in the region during this period and triggered a total of 99 debris flows that caused significant damage to people and infrastructures. We show that the spatial rainfall organisation depends on the severity (measured via the estimated return time-RT) of the debris flow-triggering rainfall. For more frequent events (RTdebris flow location coincides with a local minimum, whereas for less frequent events (RT>20 yrs) the triggering rainfall presents a local peak corresponding to the debris flow initiation point. Dependence of these features on rainfall duration is quite limited. The characteristics of the spatial rainfall organisation are exploited to understand the performances and results of three different rainfall interpolation techniques: nearest neighbour (NN), inverse distance weighting (IDW) and ordinary kriging (OK). We show that the features of the spatial organization of the debris flow triggering rainfall explain the biases in the

  17. Generating monthly rainfall amount using multivariate skew-t copula

    Science.gov (United States)

    Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Zanariah Satari, Siti

    2017-09-01

    This study aims to generate rainfall data in cases where the data is not available or not enough for a certain area of study. In general, the rainfall data is rightly skewed, so the multivariate skew-t copula is used as it able to model rainfall amount and capture the spatial dependence in the data. To illustrate the methodology, three rainfall stations in Kelantan are used. Firstly, the observed data is transformed to uniform unit. The Spearman’s correlation coefficient is calculated between the three stations. It is found that the correlations between the stations are significance at α = 0.05. The next step involved generating the synthetic rainfall data using the multivariate skew-t copula. The data is then transformed to uniform unit and the correlation coefficient is calculated for the generated data. Finally, the correlation coefficient of the observed and the generated data are compared. The Kolmogorov-Smirnov goodness of fit test is used to assess the fit between theoretical and empirical copula and supported by graphical representation. The results show that there is no significant difference between empirical and theoretical copula at 5% significance level. Thus, the multivariate skew-t copula is suitable to generate synthetic rainfall data that can mimic the observed rainfall data. It can also be used to present different rainfall scenarios by changing the value of the parameters in the model.

  18. Rainfall Fields: Estimation, Analysis, and Prediction

    Science.gov (United States)

    The problem of predicting rainfall and its characteristics has always been one of overriding concern for both hydrologists and meteorologists. Yet, for decades the two disciplines have pursued its solution using radically different techniques and communicating relatively little about recent advances in understanding rainfall processes, new technology, and improvements in predictive skill.Meteorologists tend to publish in journals that deal almost exclusively with atmospheric processes, while hydrologists prefer media which focus on the Earth's surface and below. Meteorologists tend to concentrate on developing and improving numerical hydrodynamical models of the atmospheric processes that generate rainfall. Their approach is essentially to solve an initial value problem where the observed three-dimensional state of the atmosphere is input to the model and the rainfall is one of the output parameters.

  19. Estimation of rainfall using remote sensing for Riyadh climate, KSA

    Science.gov (United States)

    AlHassoun, Saleh A.

    2013-05-01

    Rainfall data constitute an important parameter for studying water resources-related problems. Remote sensing techniques could provide rapid and comprehensive overview of the rainfall distribution in a given area. Thus, the infrared data from the LandSat satellite in conjunction with the Scofield-oliver method were used to monitor and model rainfall in Riyadh area as a resemble of any area in the Kingdom of Saudi Arabia(KSA). Four convective clouds that covered two rain gage stations were analyzed. Good estimation of rainfall was obtained from satellite images. The results showed that the satellite rainfall estimations were well correlated to rain gage measurements. The satellite climate data appear to be useful for monitoring and modeling rainfall at any area where no rain gage is available.

  20. Spatial estimation of debris flows-triggering rainfall and its dependence on rainfall return period

    Science.gov (United States)

    Destro, Elisa; Marra, Francesco; Nikolopoulos, Efthymios I.; Zoccatelli, Davide; Creutin, Jean Dominique; Borga, Marco

    2017-02-01

    Forecasting the occurrence of debris flows is fundamental for issuing hazard warnings, and often focuses on rainfall as a triggering agent and on the use of empirical rainfall thresholds based on rain gauge observations. A recognized component of the uncertainty associated with the use of rainfall thresholds is related to the sampling of strongly varying rainfall variability with sparse rain gauge networks. In this work we examine the spatial distribution of rainfall depth in areas up to 10 km from the debris flow initiation points as a function of return period, and we exploit this information to analyze the errors expected in the estimation of debris flow triggering rainfall when rain gauge data are used. In particular, we investigate the impact of rain gauge density and of the use of different interpolation methods. High-resolution, adjusted radar rainfall estimates, representing the best available spatially-distributed rainfall estimates at the debris flows initiation point and in the surrounding area, are sampled by stochastically generated rain gauge networks characterized by varying densities. Debris flow triggering rainfall is estimated by means of three rainfall interpolation methods: nearest neighbor, inverse distance weighting and ordinary kriging. On average, triggering rainfall shows a local peak corresponding to the debris flow initiation point, with a decay of rainfall with distance which increases with the return period of the triggering rainfall. Interpolation of the stochastically generated rain gauge measurements leads to an underestimation of the triggering rainfall that, irrespective of the interpolation methods, increases with the return period and decreases with the rain gauge density. For small return period events and high rain gauge density, the differences among the methods are minor. With increasing the return period and decreasing the rain gauge density, the nearest neighbor method is less biased, because it makes use only of the

  1. Curve number estimation from Brazilian Cerrado rainfall and runoff data

    Science.gov (United States)

    The Curve Number (CN) method has been widely used to estimate runoff from rainfall events in Brazil, however, CN values for use in the Brazilian savanna (Cerrado) are poorly documented. In this study we used experimental plots to measure natural rainfall-driven rates of runoff under undisturbed Cerr...

  2. A 305 year monthly rainfall series for the Island of Ireland (1711-2016)

    Science.gov (United States)

    Murphy, Conor; Burt, Tim P.; Broderick, Ciaran; Duffy, Catriona; Macdonald, Neil; Matthews, Tom; McCarthy, Mark P.; Mullan, Donal; Noone, Simon; Ryan, Ciara; Thorne, Peter; Walsh, Seamus; Wilby, Robert L.

    2017-04-01

    This paper derives a continuous 305-year monthly rainfall series for the Island of Ireland (IoI) for the period 1711-2016. Two key data sources are employed: i) a previously unpublished UK Met Office Note which compiled annual rainfall anomalies and corresponding monthly per mille amounts from weather diaries and early observational records for the period 1711-1977; and ii) a long-term, homogenised monthly IoI rainfall series for the period 1850-2016. Using estimates of long-term average precipitation sampled from the quality assured series, the full record is reconstituted and insights drawn regarding notable periods and the range of climate variability and change experienced. Consistency with other long records for the region is examined, including: the England and Wales Precipitation series (EWP; 1766-2016); the early EWP Glasspoole series (1716-1765) and the Central England Temperature series (CET; 1711-2016). Strong correspondence between all records is noted from 1780 onwards. While disparities are evident between the early EWP and Ireland series, the latter shows strong decadal consistency with CET throughout the record. In addition, independent, early observations from Cork and Dublin, along with available documentary sources, corroborate the derived series and add confidence to our reconstruction. The new IoI rainfall record reveals that the wettest decades occurred in the early 18th Century, despite the fact that IoI has experienced a long-term winter wetting trend consistent with climate model projections. These exceptionally wet winters of the 1720s and 1730s were concurrent with almost unprecedented warmth in the CET, glacial advance throughout Scandinavia, and glacial retreat in West Greenland, consistent with a wintertime NAO-type forcing. Our study therefore demonstrates the value of long-term observational records for providing insight to the natural climate variability of the North Atlantic region.

  3. [Seasonality of rotavirus infection in Venezuela: relationship between monthly rotavirus incidence and rainfall rates].

    Science.gov (United States)

    González Chávez, Rosabel

    2015-09-01

    In general, it has been reported that rotavirus infection was detected year round in tropical countries. However, studies in Venezuela and Brazil suggest a seasonal behavior of the infection. On the other hand, some studies link infection with climatic variables such as rainfall. This study analyzes the pattern of behavior of the rotavirus infection in Carabobo-Venezuela (2001-2005), associates the seasonality of the infection with rainfall, and according to the seasonal pattern, estimates the age of greatest risk for infection. The analysis of the rotavirus temporal series and accumulated precipitation was performed with the software SPSS. The infection showed two periods: high incidence (November-April) and low incidence (May-October). Accumulated precipitation presents an opposite behavior. The highest frequency of events (73.8% 573/779) for those born in the period with a low incidence of the virus was recorded at an earlier age (mean age 6.5 +/- 2.0 months) when compared with those born in the station of high incidence (63.5% 568/870, mean age 11.7 +/- 2.2 months). Seasonality of the infection and the inverse relationship between virus incidence and rainfall was demonstrated. In addition, it was found that the period of birth determines the age and risk of infection. This information generated during the preaccine period will be helpful to measure the impact of the vaccine against the rotavirus.

  4. Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications

    Science.gov (United States)

    Husak, Gregory J.; Michaelsen, Joel C.; Funk, Christopher C.

    2007-01-01

    Evaluating a range of scenarios that accurately reflect precipitation variability is critical for water resource applications. Inputs to these applications can be provided using location- and interval-specific probability distributions. These distributions make it possible to estimate the likelihood of rainfall being within a specified range. In this paper, we demonstrate the feasibility of fitting cell-by-cell probability distributions to grids of monthly interpolated, continent-wide data. Future work will then detail applications of these grids to improved satellite-remote sensing of drought and interpretations of probabilistic climate outlook forum forecasts. The gamma distribution is well suited to these applications because it is fairly familiar to African scientists, and capable of representing a variety of distribution shapes. This study tests the goodness-of-fit using the Kolmogorov–Smirnov (KS) test, and compares these results against another distribution commonly used in rainfall events, the Weibull. The gamma distribution is suitable for roughly 98% of the locations over all months. The techniques and results presented in this study provide a foundation for use of the gamma distribution to generate drivers for various rain-related models. These models are used as decision support tools for the management of water and agricultural resources as well as food reserves by providing decision makers with ways to evaluate the likelihood of various rainfall accumulations and assess different scenarios in Africa. 

  5. Rainfall estimation using moving cars as rain gauges - laboratory experiments

    Science.gov (United States)

    Rabiei, E.; Haberlandt, U.; Sester, M.; Fitzner, D.

    2013-11-01

    The spatial assessment of short time-step precipitation is a challenging task. Low density of observation networks, as well as the bias in radar rainfall estimation motivated the new idea of exploiting cars as moving rain gauges with windshield wipers or optical sensors as measurement devices. In a preliminary study, this idea has been tested with computer experiments (Haberlandt and Sester, 2010). The results have shown that a high number of possibly inaccurate measurement devices (moving cars) provide more reliable areal rainfall estimations than a lower number of precise measurement devices (stationary gauges). Instead of assuming a relationship between wiper frequency (W) and rainfall intensity (R) with an arbitrary error, the main objective of this study is to derive valid W-R relationships between sensor readings and rainfall intensity by laboratory experiments. Sensor readings involve the wiper speed, as well as optical sensors which can be placed on cars and are usually made for automating wiper activities. A rain simulator with the capability of producing a wide range of rainfall intensities is designed and constructed. The wiper speed and two optical sensors are used in the laboratory to measure rainfall intensities, and compare it with tipping bucket readings as reference. Furthermore, the effect of the car speed on the estimation of rainfall using a car speed simulator device is investigated. The results show that the sensor readings, which are observed from manual wiper speed adjustment according to the front visibility, can be considered as a strong indicator for rainfall intensity, while the automatic wiper adjustment show weaker performance. Also the sensor readings from optical sensors showed promising results toward measuring rainfall rate. It is observed that the car speed has a significant effect on the rainfall measurement. This effect is highly dependent on the rain type as well as the windshield angle.

  6. Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon

    Science.gov (United States)

    Zheng, Y.; Ali, M.; Bourassa, M. A.

    2015-12-01

    Indian Summer Monsoon Rainfall (ISMR: June-September) has both temporal and spatial variability causing floods/droughts in different seasons/locations leading to a strong or weak monsoon. Here, we present the contribution of all-India monthly, seasonal and regional rainfall to the ISMR, with special reference to the strong and weak monsoons. For this purpose, rainfall data provided by the India Meteorological Department (IMD: http://www.imd.gov.in/section/nhac/dynamic/Monsoon_frame.htm) for 1901-2013 have been used. The IMD divided the Indian sub-continent into four homogeneous regions of northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). Rainfall during July-August contributes the most to the total seasonal rainfall, whether it is a strong or weak monsoon. Although the NEI has the maximum area-weighted rainfall, its contribution is the least toward a strong or weak monsoon. The rainfall in the remaining three regions (NWI, CI, and SPIN) controls whether an ISMR is strong or weak. Compared to the monthly rainfall, the regional rainfall dominates the strong or weak rainfall periods.

  7. Monitoring Niger River Floods from satellite Rainfall Estimates : overall skill and rainfall uncertainty propagation.

    Science.gov (United States)

    Gosset, Marielle; Casse, Claire; Peugeot, christophe; boone, aaron; pedinotti, vanessa

    2015-04-01

    Global measurement of rainfall offers new opportunity for hydrological monitoring, especially for some of the largest Tropical river where the rain gauge network is sparse and radar is not available. Member of the GPM constellation, the new French-Indian satellite Mission Megha-Tropiques (MT) dedicated to the water and energy budget in the tropical atmosphere contributes to a better monitoring of rainfall in the inter-tropical zone. As part of this mission, research is developed on the use of satellite rainfall products for hydrological research or operational application such as flood monitoring. A key issue for such applications is how to account for rainfall products biases and uncertainties, and how to propagate them into the end user models ? Another important question is how to choose the best space-time resolution for the rainfall forcing, given that both model performances and rain-product uncertainties are resolution dependent. This paper analyses the potential of satellite rainfall products combined with hydrological modeling to monitor the Niger river floods in the city of Niamey, Niger. A dramatic increase of these floods has been observed in the last decades. The study focuses on the 125000 km2 area in the vicinity of Niamey, where local runoff is responsible for the most extreme floods recorded in recent years. Several rainfall products are tested as forcing to the SURFEX-TRIP hydrological simulations. Differences in terms of rainfall amount, number of rainy days, spatial extension of the rainfall events and frequency distribution of the rain rates are found among the products. Their impacts on the simulated outflow is analyzed. The simulations based on the Real time estimates produce an excess in the discharge. For flood prediction, the problem can be overcome by a prior adjustment of the products - as done here with probability matching - or by analysing the simulated discharge in terms of percentile or anomaly. All tested products exhibit some

  8. Satellite-rainfall estimation for identification of rainfall thresholds used for landslide/debris flow prediction

    Science.gov (United States)

    Maggioni, Viviana; Nikolopoulos, Efthymios I.; Marra, Francesco; Destro, Elisa; Borga, Marco

    2016-04-01

    Rainfall-induced landslides and debris flows pose a significant and widespread hazard, resulting in a large number of casualties and enormous economic damages worldwide. Rainfall thresholds are often used to identify the local or regional rainfall conditions that, when reached or exceeded, are likely to result in landslides or debris flows. Rain gauge data are the typical source of information for the definition of these rainfall thresholds. However, in-situ observations over mountainous areas, where these hazards mainly occur, are very sparse or inexistent. Therefore identification and use of gauge-based rainfall thresholds is impossible in many landslide prone areas over the globe. The vast advancements in satellite-based precipitation estimation over the last couple of decades have lead to the creation of a number of global precipitation datasets at various spatiotemporal resolutions. Although several investigations have shown that these datasets can be associated with considerable uncertainty, they provide the only source of precipitation information over many areas around the globe. Therefore it is important to assess their performance in the context of landslide/debris flow prediction and investigate how we can potentially benefit from the information they provide. In this work, we evaluate the performance of three widely used quasi-global satellite precipitation products (3B42v7, PERSIANN and CMORPH) for the identification of rainfall threshold for landslide/debris flow triggering. Products are available at 0.25deg/3h resolution. The study region is focused over the Upper Adige river basin, northern Italy where a detailed database of more than 400 identified debris flows (during period 2000-2015) and a raingauge network of 95 stations, is available. Rain-gauge based rainfall thresholds are compared against satellite-based thresholds to evaluate strengths and limitations in using satellite precipitation estimates for defining rainfall thresholds. Analysis of

  9. Theoretical framework to estimate spatially averaged rainfalls conditional on river discharges and point rainfall measurements from a single location: an application to Western Greece

    Directory of Open Access Journals (Sweden)

    A. Langousis

    2012-11-01

    Full Text Available We focus on the special case of catchments covered by a single raingauge, and develop a theoretical framework to obtain estimates of spatial rainfall averages conditional on rainfall measurements from a single location, and the flow conditions at the catchment outlet. In doing so we use: (a statistical tools to identify and correct inconsistencies between daily rainfall occurrence and amount and the flow conditions at the outlet of the basin, (b concepts from multifractal theory to relate the fraction of wet intervals in point rainfall measurements and that in spatial rainfall averages, while accounting for the shape and size of the catchment, the size, lifetime and advection velocity of rainfall generating features and the location of the raingauge inside the basin, and (c semi-theoretical arguments to assure consistency between rainfall and runoff volumes at an inter-annual level, implicitly accounting for spatial heterogeneities of rainfall caused by orographic influences. In an application study, using point rainfall records from Glafkos river basin in Western Greece, we find the suggested approach to demonstrate significant skill in resolving rainfall-runoff incompatibilities at a daily level, while reproducing the statistics of spatial rainfall averages at both monthly and annual time scales, independently of the location of the raingauge and the magnitude of the observed deviations between point rainfall measurements and spatial rainfall averages. The developed scheme should serve as an important tool for the effective calibration of rainfall-runoff models in basins covered by a single raingauge and, also, improve hydrologic impact assessment at a river basin level under changing climatic conditions.

  10. A review of statistical analyses on monthly and daily rainfall in Catalonia

    Directory of Open Access Journals (Sweden)

    X. Lana

    2009-01-01

    Full Text Available A review on recent studies about monthly and daily rainfall in Catalonia is presented. Monthly rainfall is analysed along the west Mediterranean Coast and in Catalonia, quantifying aspects as the irregularity of monthly amounts and the spatial distribution of the Standard Precipitation Index. Several statistics are applied to daily rainfall series such as their extreme value and intraannual spatial distributions, the variability of the average and standard deviation rain amounts for each month, their amount and time distributions, and time trends affecting four pluviometric indices for different percentiles and class intervals. All these different analyses constitute the continuity of the scientific study of Catalan rainfall, which started about a century ago.

  11. Spatiotemporal monthly rainfall forecasts for south-eastern and eastern Australia using climatic indices

    Science.gov (United States)

    Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex

    2016-05-01

    Knowledge about future rainfall is important for agriculture management and planning in arid and semi-arid regions. Australia has complex variations in rainfall patterns in time and space, arising from the combination of the geographic structure and the dual effects of Indian and Pacific Ocean. This study aims to develop a forecasting model of spatiotemporal monthly rainfall totals using lagged climate indices and historical rainfall data from 1950-2011 for south-eastern and eastern Australia. Data were obtained from the Australian Bureau of Meteorology (BoM) from 136 high-quality weather stations. To reduce spatial complexity, climate regionalization was used to divide the stations in homogenous sub-regions based on similarity of rainfall patterns and intensity using principal component analysis (PCA) and K-means clustering. Subsequently, a fuzzy ranking algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. Selected predictors by FRA were found to vary by sub-region. After these two stages of pre-processing, an artificial neural network (ANN) model was developed and optimized separately for each sub-region and the entire area. The results indicate that climate regionalization can improve a monthly spatiotemporal rainfall forecast model. The location and number of sub-regions were important for ranking predictors and modeling. This further suggests that the impact of climate variables on Australian rainfall is more variable in both time and space than indicated thus far.

  12. Spatiotemporal monthly rainfall forecasting for south-eastern and eastern Australia using climatic indices

    Science.gov (United States)

    Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex

    2014-05-01

    Knowledge about future rainfall would significantly benefit land, water resources and agriculture management, as this assists with planning and management decisions. Forecasting spatiotemporal monthly rainfall is difficult, especially in Australia where there is a complex interaction between topography and the effect of Indian and Pacific Ocean. This study describes a method for spatiotemporal monthly rainfall forecasting in south-eastern and eastern part of Australia using climatic and non-climatic variables. Rainfall data were obtained from Bureau of Meteorology (BoM) from 136 high quality weather stations from the south-eastern and eastern part of Australia with monthly rainfall records from 1879 to 2012. To reduce spatial complexity of the area and improve model accuracy, spatial classification (regionalization) was considered as first step. Significant predictors for each sub-region among lagged climatic input variables were selected using Fuzzy Ranking Algorithm (FRA). Climate classification: 1) discovered homogenous sub-regions with a similar rainfall patterns and investigated spatiotemporal rainfall variations in the area, 2) allowed selection of significant predictors with a fine resolution for each area, 3) improved the prediction model and increased model accuracy. PCA was used to reduce the dimensions of the dataset and to remove the rainfall time series correlation. K-means clustering was used on the loadings of PCs describing 93% of long-term monthly rainfall variations. The analysis was repeated for different numbers of sub-regions (3 - 8) to identify the best number of clusters to improve the forecast model performance. Subsequently, a Fuzzy Ranking Algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. After these two stages of pre-processing, a Neural Network model was developed and optimized for each of the sub-regions as well as for the entire area. It is concluded

  13. Preliminary results on uncertainties in rainfall interception estimation

    Energy Technology Data Exchange (ETDEWEB)

    Muzylo, A.; Llorens, P.; Domingo, F.; Valente, Fe.; Beven, K.; Gallart, F.

    2009-07-01

    This work deals with some aspects of rainfall interception estimation uncertainty in a deciduous forest. The importance of interception loss measurement error is stressed. Confidence limits of Rutter original and sparse interception model parameters obtained from regressions for leafed and leafless period are presented, as well as free throughfall coefficient variability with event weather conditions. (Author) 8 refs.

  14. Generalized linear model for estimation of missing daily rainfall data

    Science.gov (United States)

    Rahman, Nurul Aishah; Deni, Sayang Mohd; Ramli, Norazan Mohamed

    2017-04-01

    The analysis of rainfall data with no missingness is vital in various applications including climatological, hydrological and meteorological study. The issue of missing data is a serious concern since it could introduce bias and lead to misleading conclusions. In this study, five imputation methods including simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate were used to estimate the missing data. However, these imputation methods ignored the seasonality in rainfall dataset which could give more reliable estimation. Thus this study is aimed to estimate the missingness in daily rainfall data by using generalized linear model with gamma and Fourier series as the link function and smoothing technique, respectively. Forty years daily rainfall data for the period from 1975 until 2014 which consists of seven stations at Kelantan region were selected for the analysis. The findings indicated that the imputation methods could provide more accurate estimation values based on the least mean absolute error, root mean squared error and coefficient of variation root mean squared error when seasonality in the dataset are considered.

  15. Rainfall estimation using raingages and radar — A Bayesian approach: 1. Derivation of estimators

    Science.gov (United States)

    Seo, D.-J.; Smith, J. A.

    1991-03-01

    Procedures for estimating rainfall from radar and raingage observations are constructed in a Bayesian framework. Given that the number of raingage measurements is typically very small, mean and variance of gage rainfall are treated as uncertain parameters. Under the assumption that log gage rainfall and log radar rainfall are jointly multivariate normal, the estimation problem is equivalent to lognormal co-kriging with uncertain mean and variance of the gage rainfall field. The posterior distribution is obtained under the assumption that the prior for the mean and inverse of the variance of log gage rainfall is normal-gamma 2. Estimate and estimation variance do not have closed-form expressions, but can be easily evaluated by numerically integrating two single integrals. To reduce computational burden associated with evaluating sufficient statistics for the likelihood function, an approximate form of parameter updating is given. Also, as a further approximation, the parameters are updated using raingage measurements only, yielding closed-form expressions for estimate and estimation variance in the Gaussian domain. With a reduction in the number of radar rainfall data in constructing covariance matrices, computational requirements for the estimation procedures are not significantly greater than those for simple co-kriging. Given their generality, the estimation procedures constructed in this work are considered to be applicable in various estimation problems involving an undersampled main variable and a densely sampled auxiliary variable.

  16. Network-derived inhomogeneity in monthly rainfall analyses over western Tasmania

    Science.gov (United States)

    Fawcett, Robert; Trewin, Blair; Barnes-Keoghan, Ian

    2010-08-01

    Monthly rainfall in the wetter western half of Tasmania was relatively poorly observed in the early to middle parts of the 20th century, and this causes a marked inhomogeneity in the operational gridded monthly rainfall analyses generated by the Australian Bureau of Meteorology up until the end of 2009. These monthly rainfall analyses were generated for the period 1900 to 2009 in two forms; a national analysis at 0.25° latitude-longitude resolution, and a southeastern Australia regional analysis at 0.1° resolution. For any given month, they used all the monthly data from the standard Bureau rainfall gauge network available in the Australian Data Archive for Meteorology. Since this network has changed markedly since Federation (1901), there is obvious scope for network-derived inhomogeneities in the analyses. In this study, we show that the topography-resolving techniques of the new Australian Water Availability Project analyses, adopted as the official operational analyses from the start of 2010, substantially diminish those inhomogeneities, while using largely the same observation network. One result is an improved characterisation of recent rainfall declines across Tasmania. The new analyses are available at two resolutions, 0.25° and 0.05°.

  17. Network-derived inhomogeneity in monthly rainfall analyses over western Tasmania

    Energy Technology Data Exchange (ETDEWEB)

    Fawcett, Robert; Trewin, Blair [National Climate Centre, Australian Bureau of Meteorology, Docklands, Victoria 3008 (Australia); Barnes-Keoghan, Ian, E-mail: r.fawcett@bom.gov.a, E-mail: b.trewin@bom.gov.a, E-mail: i.barnes-keoghan@bom.gov.a [Tasmanian Regional Office, Australian Bureau of Meteorology, Hobart, Tasmania 3000 (Australia)

    2010-08-15

    Monthly rainfall in the wetter western half of Tasmania was relatively poorly observed in the early to middle parts of the 20th century, and this causes a marked inhomogeneity in the operational gridded monthly rainfall analyses generated by the Australian Bureau of Meteorology up until the end of 2009. These monthly rainfall analyses were generated for the period 1900 to 2009 in two forms; a national analysis at 0.25{sup 0} latitude-longitude resolution, and a southeastern Australia regional analysis at 0.1{sup 0} resolution. For any given month, they used all the monthly data from the standard Bureau rainfall gauge network available in the Australian Data Archive for Meteorology. Since this network has changed markedly since Federation (1901), there is obvious scope for network-derived inhomogeneities in the analyses. In this study, we show that the topography-resolving techniques of the new Australian Water Availability Project analyses, adopted as the official operational analyses from the start of 2010, substantially diminish those inhomogeneities, while using largely the same observation network. One result is an improved characterisation of recent rainfall declines across Tasmania. The new analyses are available at two resolutions, 0.25{sup 0} and 0.05{sup 0}.

  18. Rainfall estimation from microwave links in São Paulo, Brazil.

    Science.gov (United States)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2017-04-01

    Rainfall estimation from microwave link networks has been successfully demonstrated in countries such as the Netherlands, Israel and Germany. The path-averaged rainfall intensity can be computed from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities to retrieve rainfall rates at high spatiotemporal resolutions very close to the ground surface. High spatiotemporal resolutions and closer-to-ground measurements are highly appreciated, especially in urban catchments where high-impact events such as flash-floods develop in short time scales. We evaluate here this rainfall measurement technique for a tropical climate, something that has hardly been done previously. This is highly relevant since many countries with few surface rainfall observations are located in the tropics. The test-bed is the Brazilian city of São Paulo. The performance of 16 microwave links was evaluated, from a network of 200 links, for the last 3 months of 2014. The open software package RAINLINK was employed to obtain link rainfall estimates. The evaluation was done through a dense automatic gauge network. Results are promising and encouraging, especially for short links for which a high correlation (> 0.9) and a low bias (< 5%) were obtained.

  19. Investigating rainfall estimation from radar measurements using neural networks

    Directory of Open Access Journals (Sweden)

    A. Alqudah

    2013-03-01

    Full Text Available Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar ground radar (KMLB over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting.

  20. Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil

    Science.gov (United States)

    Brito, Thábata T.; Oliveira-Júnior, José F.; Lyra, Gustavo B.; Gois, Givanildo; Zeri, Marcelo

    2016-10-01

    Spatial and temporal patterns of rainfall were identified over the state of Rio de Janeiro, southeast Brazil. The proximity to the coast and the complex topography create great diversity of rainfall over space and time. The dataset consisted of time series (1967-2013) of monthly rainfall over 100 meteorological stations. Clustering analysis made it possible to divide the stations into six groups (G1, G2, G3, G4, G5 and G6) with similar rainfall spatio-temporal patterns. A linear regression model was applied to a time series and a reference. The reference series was calculated from the average rainfall within a group, using nearby stations with higher correlation (Pearson). Based on t-test (p < 0.05) all stations had a linear spatiotemporal trend. According to the clustering analysis, the first group (G1) contains stations located over the coastal lowlands and also over the ocean facing area of Serra do Mar (Sea ridge), a 1500 km long mountain range over the coastal Southeastern Brazil. The second group (G2) contains stations over all the state, from Serra da Mantiqueira (Mantiqueira Mountains) and Costa Verde (Green coast), to the south, up to stations in the Northern parts of the state. Group 3 (G3) contains stations in the highlands over the state (Serrana region), while group 4 (G4) has stations over the northern areas and the continent-facing side of Serra do Mar. The last two groups were formed with stations around Paraíba River (G5) and the metropolitan area of the city of Rio de Janeiro (G6). The driest months in all regions were June, July and August, while November, December and January were the rainiest months. Sharp transitions occurred when considering monthly accumulated rainfall: from January to February, and from February to March, likely associated with episodes of "veranicos", i.e., periods of 4-15 days of duration with no rainfall.

  1. Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

    Science.gov (United States)

    Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

    2016-05-01

    Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

  2. Estimation of local rainfall erosivity using artificial neural network

    Directory of Open Access Journals (Sweden)

    Paulo Tarso Sanches Oliveira

    2011-08-01

    Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.

  3. Forecasting and Analysis of Monthly Rainfalls in Ardabil Province by Arima, Autoregrressive, and Winters Models

    Directory of Open Access Journals (Sweden)

    B. Salahi

    2017-01-01

    Full Text Available Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced rainfall fluctuations and shortage of water supply. Precipitation is one of the most important features of the environment. The amount of precipitation over time and in different places is subject to large fluctuations which may be periodical. Studies show that, due to the certain complexities of rainfall, the models which used to predict future values will also need greater accuracy and less error. Among the forecasting models, Arima has more applications and it has replaced with other models. Materials and Methods: In this research, through order 2 Autoregrressive, Winters, and Arima models, monthly rainfalls of Ardabil synoptic station (representing Ardabil province for a 31-year period (1977-2007 were investigated. To assess the presence or absence of significant changes in mean precipitation of Ardabil synoptic station, rainfall of this station was divided into two periods: 1977-1993 and 1994-2010. T-test was used to statistically examine the difference between the two periods. After adjusting the data, descriptive statistics were applied. In order to model the total monthly precipitation of Ardabil synoptic station, Winters, Autoregressive, and Arima models were used. Among different models, the best options were chosen to predict the time series including the mean absolute deviation (MAD, the mean squared errors (MSE, root mean square errors (RMSE and mean absolute percentage errors (MAPE. In order to select the best model among the available options under investigation, the predicted value of the deviation of the actual value was utilized for the months of 2006-2010. Results and Discussion: Statistical characteristics of the total monthly

  4. Recent Improvements in Estimating Convective and Stratiform Rainfall in Amazonia

    Science.gov (United States)

    Negri, Andrew J.

    1999-01-01

    In this paper we present results from the application of a satellite infrared (IR) technique for estimating rainfall over northern South America. Our main objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. We apply the technique of Anagnostou et al (1999). In simple functional form, the estimated rain area A(sub rain) may be expressed as: A(sub rain) = f(A(sub mode),T(sub mode)), where T(sub mode) is the mode temperature of a cloud defined by 253 K, and A(sub mode) is the area encompassed by T(sub mode). The technique was trained by a regression between coincident microwave estimates from the Goddard Profiling (GPROF) algorithm (Kummerow et al, 1996) applied to SSM/I data and GOES IR (11 microns) observations. The apportionment of the rainfall into convective and stratiform components is based on the microwave technique described by Anagnostou and Kummerow (1997). The convective area from this technique was regressed against an IR structure parameter (the Convective Index) defined by Anagnostou et al (1999). Finally, rainrates are assigned to the Am.de proportional to (253-temperature), with different rates for the convective and stratiform

  5. TRMM Science Highlights and Status of Precipitation Estimates on Monthly and Finder Time Scales

    Science.gov (United States)

    Adler, Robert; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The Tropical Rainfall Measuring Mission (TRMM) has completed three years in orbit. A summary of research highlights will be presented focusing on application of TRMM data to topics ranging from climate analysis, through improving forecasts, to microphysical research. Monthly surface rainfall estimates over the ocean based on different instruments on TRMM currently differ by 20%. The difference is not surprising considering the different type of observations available for the first time from TRMM with both the passive and active microwave sensors. Resolving this difference will strengthen the validity and utility of ocean rainfall estimates and is the topic of ongoing research utilizing various facets of the TRMM validation and field experiment programs. The TRMM rainfall estimates are intercompared among themselves and with other estimates, including those of the standard, monthly Global Precipitation Climatology Project (GPCP) analysis. The GPCP analysis agrees roughly in magnitude with the passive microwave-based TRMM estimates which is not surprising considering GPCP over-ocean estimates are based on passive microwave observations. A three year TRMM rainfall climatology is presented based on the TRMM merged product, including anomaly fields related to the changing ENSO situation during the mission. Results of merging TRMM, other passive microwave observations, and geosynchronous infrared rainfall estimates into a global, tropical 3-hour time resolution analysis will also be described.

  6. A TRMM Rainfall Estimation Method Applicable to Land Areas

    Science.gov (United States)

    Prabhakara, C.; Iacovazzi, R.; Weinman, J.; Dalu, G.

    1999-01-01

    . Based on this observation, in this study we have developed a method to estimate the mesoscale-average rain rate over land utilizing microwave radiometer data. Because of the high degree of geographic and seasonal variability in the nature and intensity of rain, this method requires some tuning with 15-minute rain gauge data on land. After tuning the method, it can be applied to an independent set of rain events that are close in time and space. We find that the mesoscale rain rates retrieved over the period of a month on land with this method shows a correlation of about 0.85 with respect to the surface rain-gauge observations. This mesoscale-average rain rate estimation method can be useful to extend the spatial and temporal coverage of the rainfall data provided by the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM) satellite.

  7. Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

    DEFF Research Database (Denmark)

    Panagos, Panos; Borrelli, Pasquale; Spinoni, Jonathan

    2016-01-01

    , for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland...

  8. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2017-08-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  9. Gauge-adjusted rainfall estimates from commercial microwave links

    Science.gov (United States)

    Fencl, Martin; Dohnal, Michal; Rieckermann, Jörg; Bareš, Vojtěch

    2017-01-01

    Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of ground truth from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error 0.75) and (ii) that the combination of both sensor types clearly outperforms each individual

  10. Frequency Analysis of the Monthly Rainfall Data at Sulaimania Region, Iraq

    Directory of Open Access Journals (Sweden)

    Prof. Dr. Rafa H Al-Suhili

    2016-08-01

    Full Text Available Different frequency distributions models were fitted to the monthly rainfall data in Sulaimania region, north Iraq. Three rainfall gauging stations data were used, Sulaimania city, Dokan Dam, and Derbendikhan Dam metrological stations, for the period (1984-2010. The distributions models fitted are of Normal, Log-normal, Wiebull, Exponential and Two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit. The fittings were done for the overall data and for each month separately. The Gamma, Exponential and Weibull distributions were found as the best fits for the three stations respectively for the overall models, while for the monthly models different distribution type was found as the best fit for each month and each station, however the Gamma distributions was found to have the highest percent of best fit. The best fitted distributions were used to forecast three sets of monthly rainfall data for each station and compared to the observed ones for the last 7- years of data. The t-test,F-test and KolmogorovSmirnov test indicate the capability of these models to produce data that has the same frequency distribution of the observed one. Comparison between the performances of the overall and periodic models reveals that there no distinguishable improvement of the monthly model over the overall one.

  11. Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

    Science.gov (United States)

    Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

    2016-12-01

    Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong

  12. Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software

    Indian Academy of Sciences (India)

    Soma Sen Roy; Subhendu Brata Saha; Hashmi Fatima; S K Roy Bhowmik; P K Kundu

    2012-10-01

    The INSAT Multispectral Rainfall Algorithm (IMSRA) technique for rainfall estimation, has recently been developed to meet the shortcomings of the Global Precipitation Index (GPI) technique of rainfall estimation from the data of geostationary satellites; especially for accurate short period rainfall estimates. This study evaluates the 3-hourly precipitation estimates by this technique as well as the rainfall estimates by the GPI technique using data of the Kalpana-1 satellite, over the Indian region for the south-west monsoon season of 2010 to understand their relative strengths and weaknesses in estimating short period rainfall. The gridded 3 hourly accumulated TRMM satellite (3B42 V6 product or TMPA product) and surface raingauge data for stations over the Indian region for the same period is used as the standard measure of rainfall estimates. The Method for Object-based Diagnostic Evaluation (MODE) utility of the METv3.0 software, has been used for the evaluation purpose. The results show that the new IMSRA technique is closer to the TMPA rainfall estimate, in terms of areal spread, geometric shape and location of rainfall areas, as compared to the GPI technique. The overlap of matching rainfall areas with respect to TMPA rainfall patches is also higher for the IMSRA estimates as compared to the GPI values. However, both satellite rainfall estimates are observed to be generally higher compared to the TMPA measurements. However, the values for the highest 10% of the rainfall rates in any rainfall patch, is generally higher for rainfall measured by the IMSRA technique, as compared to the estimates by the GPI technique. This may partly be due to the capping maximum limit of 3 mm/hr for rainfall measured by the GPI technique limits the total 3-hour accumulation to 9 mm even during heavy rainfall episodes. This is not so with IMSRA technique, which has no such limiting value. However, this general overestimation of the rainfall amount, measured by both techniques

  13. Daily disaggregation of simulated monthly flows using different rainfall datasets in southern Africa

    Directory of Open Access Journals (Sweden)

    D.A. Hughes

    2015-09-01

    New hydrological insights for the region: There are substantial regional differences in the success of the monthly hydrological model, which inevitably affects the success of the daily disaggregation results. There are also regional differences in the success of using global rainfall data sets (Climatic Research Unit (CRU datasets for monthly, National Oceanic and Atmospheric Administration African Rainfall Climatology, version 2 (ARC2 satellite data for daily. The overall conclusion is that the disaggregation method presents a parsimonious approach to generating daily flow simulations from existing monthly simulations and that these daily flows are likely to be useful for some purposes (e.g. water quality modelling, but less so for others (e.g. peak flow analysis.

  14. Multivariate Logistic Model to estimate Effective Rainfall for an Event

    Science.gov (United States)

    Singh, S. K.; Patil, Sachin; Bárdossy, A.

    2009-04-01

    Multivariate logistic models are widely used in biological, medical, and social sciences but logistic models are seldom applied to hydrological problems. A logistic function behaves linear in the mid range and tends to be non-linear as it approaches to the extremes, hence it is more flexible than a linear function and capable of dealing with skew-distributed variables. They seem to bear good potential to handle asymmetrically distributed hydrological variables of extreme occurrence. In this study, logistic regression approach is implemented to derive a multivariate logistic function for effective rainfall; in the process runoff coefficient is assumed to be a Bernoulli-distributed dependent variable. A backward stepwise logistic regression procedure was performed to derive the logistic transfer function between runoff coefficient and catchment as well as event variables (e.g. drainage density, soil moisture etc). The investigation was carried out using data base for 244 rainfall-runoff events from 42 mesoscale catchments located in south-west Germany. The performance of the derived logistic transfer function was compared with that of SCS method for estimation of effective rainfall.

  15. Architectures for Rainfall Property Estimation From Polarimetric Radar

    Science.gov (United States)

    Collis, S. M.; Giangrande, S. E.; Helmus, J.; Troemel, S.

    2014-12-01

    Radars that transmit and receive signals in polarizations aligned both horizontal and vertical to the horizon collect a number of measurements. The relation both between these measurements and between measurements and desired microphysical quantities (such as rainfall rate) is complicated due to a number of scattering mechanisms. The result is that there ends up being an intractable number of often incompatible techniques for extracting geophysical insight. This presentation will discuss methods developed by the Atmospheric Measurement Climate (ARM) Research Facility to streamline the creation of application chains for retrieving rainfall properties for the purposes of fine scale model evaluation. By using a Common Data Model (CDM) approach and working in the popular open source Python scientific environment analysis techniques such as Linear Programming (LP) can be bought to bear on the task of retrieving insight from radar signals. This presentation will outline how we have used these techniques to detangle polarimetric phase signals, estimate a three-dimensional precipitation field and then objectively compare to cloud resolving model derived rainfall fields from the NASA/DoE Mid-Latitude Continental Convective Clouds Experiment (MC3E). All techniques show will be available, open source, in the Python-ARM Radar Toolkit (Py-ART).

  16. Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

    Science.gov (United States)

    Brocca, Luca; Pellarin, Thierry; Crow, Wade T.; Ciabatta, Luca; Massari, Christian; Ryu, Dongryeol; Su, Chun-Hsu; Rüdiger, Christoph; Kerr, Yann

    2016-10-01

    Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different "bottom up" techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well-known "top down" rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge-corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root-mean-square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches

  17. Satellite-based estimation of rainfall erosivity for Africa

    NARCIS (Netherlands)

    Vrieling, A.; Sterk, G.; Jong, S.M. de

    2010-01-01

    Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energy determines the erosivity and is in turn greatly dependent on rainfall intensity. Attempts for its large-scale mapping are rare. Most are based on interpolation of erosivity values derived from rain gauge data.

  18. The all-year rainfall region of South Africa: Satellite rainfall-estimate perspective

    CSIR Research Space (South Africa)

    Engelbrecht, CJ

    2012-09-01

    Full Text Available Climate predictability and variability studies over South Africa typically focus on the summer rainfall region and to a lesser extent on the winter rainfall region. The all-year rainfall region of South Africa, a narrow strip located along the Cape...

  19. Monthly-scale palaeo-rainfall reconstructed using a Belizean stalagmite.

    Science.gov (United States)

    Ridley, H.; Baldini, J. U. L.; Macpherson, C. G.; Prufer, K. M.; Kennett, D. J.; Amserom, Y.

    2012-04-01

    Stable isotope variations and visible growth layers in a fast growing, U-Th dated, aragonitic stalagmite from southern Belize provide an extraordinarily high resolution proxy palaeo-rainfall record for the Central American Atlantic region over the last 1,400 years. The δ18O and δ13C of speleothem carbonate at this location appears to respond primarily to rainfall variability over the cave site. A surprising result is that annual δ13C cycles are present within the stalagmite, conceivably reflecting seasonality in rainfall. With a bi-monthly resolution the record allows the inference of palaeo-tropical cyclone events as well as intra-annual rainfall variations. The record is also sufficiently long as to lend itself to helping decipher long-term behavioural modes of the tropical Atlantic beyond the instrumental record. The annual variability in stalagmite growth rate over the last 1,400 years is feasibly recording ITCZ migration through time. This study therefore has important implications for deconvolving the Atlantic tropical cyclone record, while also increasing our understanding of the links between ENSO, the ITCZ, and Central American climate.

  20. Finite Element Method Application in Areal Rainfall Estimation Case Study; Mashhad Plain Basin

    Directory of Open Access Journals (Sweden)

    M. Irani

    2016-10-01

    Full Text Available Introduction: The hydrological models are very important tools for planning and management of water resources. These models can be used for identifying basin and nature problems and choosing various managements. Precipitation is based on these models. Calculations of rainfall would be affected by displacement and region factor such as topography, etc. Estimating areal rainfall is one of the basic needs in meteorological, water resources and others studies. There are various methods for the estimation of rainfall, which can be evaluated by using statistical data and mathematical terms. In hydrological analysis, areal rainfall is so important because of displacement of precipitation. Estimating areal rainfall is divided to three methods: 1- graphical. 2-topographical. 3-numerical. This paper represented calculating mean precipitation (daily, monthly and annual using Galerkin’s method (numerical method and it was compared with other methods such as kriging, IDW, Thiessen and arithmetic mean. In this study, there were 42 actual gauges and thirteen dummies in Mashhad plain basin which is calculated by Galerkin’s method. The method included the use of interpolation functions, allowing an accurate representation of shape and relief of catchment with numerical integration performed by Gaussian quadrature and represented the allocation of weights to stations. Materials and Methods:The estimation of areal rainfall (daily, monthly,… is the basic need for meteorological project. In this field ,there are various methods that one of them is finite element method. Present study aimed to estimate areal rainfall with a 16-year period (1997-2012 by using Galerkin method ( finite element in Mashhad plain basin for 42 station. Therefore, it was compared with other usual methods such as arithmetic mean, Thiessen, Kriging and IDW. The analysis of Thiessen, Kriging and IDW were in ArcGIS10.0 software environment and finite element analysis did by using of Matlab

  1. USING THE FOURNIER INDEXES IN ESTIMATING RAINFALL EROSIVITY. CASE STUDY - THE SECAŞUL MARE BASIN

    Directory of Open Access Journals (Sweden)

    M. COSTEA

    2012-03-01

    Full Text Available Using the Fournier Index in Estimating Rainfall Erosivity. Case Study - The Secaşul Mare Basin. Climatic aggressiveness is one of the most important factors in relief dynamic. Of all climatic parameters, rainfall is directly involved in versant dynamic, in the loss of soil quality and through pluvial denudation and the processes associated with it, through the erosivity of torrential rain. We analyzed rainfall aggressiveness based on monthly and annual average values through the Fournier's index (1970 and Fournier's index modified by Arnoldus (1980. They have the advantage that they can be used not only for evaluating the land susceptibility to erosion and the calculation of erodibility of land and soil losses, but also in assessing land susceptibility to sliding (Aghiruş, 2010. The literature illustrates the successful use of this index which provides a summary assessment of the probability of rainfall with significant erosive effects. The results obtained allow observation of differences in space and time of the distribution of this index.

  2. Dynamics of monthly rainfall-runoff process at the Gota basin: A search for chaos

    Science.gov (United States)

    Sivakumar, B.; Berndtsson, R.; Olsson, J.; Jinno, K.; Kawamura, A.

    Sivakumar et al. (2000a), by employing the correlation dimension method, provided preliminary evidence of the existence of chaos in the monthly rainfall-runoff process at the Gota basin in Sweden. The present study verifies and supports the earlier results and strengthens such evidence. The study analyses the monthly rainfall, runoff and runoff coefficient series using the nonlinear prediction method, and the presence of chaos is investigated through an inverse approach, i.e. identifying chaos from the results of the prediction. The presence of an optimal embedding dimension (the embedding dimension with the best prediction accuracy) for each of the three series indicates the existence of chaos in the rainfall-runoff process, providing additional support to the results obtained using the correlation dimension method. The reasonably good predictions achieved, particularly for the runoff series, suggest that the dynamics of the rainfall-runoff process could be understood from a chaotic perspective. The predictions are also consistent with the correlation dimension results obtained in the earlier study, i.e. higher prediction accuracy for series with a lower dimension and vice-versa, so that the correlation dimension method can indeed be used as a preliminary indicator of chaos. However, the optimal embedding dimensions obtained from the prediction method are considerably less than the minimum dimensions essential to embed the attractor, as obtained by the correlation dimension method. A possible explanation for this could be the presence of noise in the series, since the effects of noise at higher embedding dimensions could be significantly greater than that at lower embedding dimensions.

  3. Sampling errors in rainfall estimates by multiple satellites

    Science.gov (United States)

    North, Gerald R.; Shen, Samuel S. P.; Upson, Robert

    1993-01-01

    This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.

  4. Application of the rainfall infiltration breakthrough (RIB) model for groundwater recharge estimation in west coastal South Africa

    CSIR Research Space (South Africa)

    Sun, X

    2013-04-01

    Full Text Available level fluctuations (WLF) on a monthly basis was proposed in the rainfall infiltration breakthrough (RIB) model for the purpose of groundwater recharge estimation. In this paper, the physical meaning of parameters in the CRD and previous RIB models...

  5. Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall

    Directory of Open Access Journals (Sweden)

    Eunmi Kim

    2014-09-01

    Full Text Available Recently, flood damage by frequent localized downpours in cities is on the increase on account of abnormal climate phenomena and the growth of impermeable areas due to urbanization. This study suggests a method to estimate real-time flood risk on roads for drivers based on the accumulated rainfall. The amount of rainfall of a road link, which is an intensive type, is calculated by using the revised method of missing rainfall in meteorology, because the rainfall is not measured on roads directly. To process in real time with a computer, we use the inverse distance weighting (IDW method, which is a suitable method in the computing system and is commonly used in relation to precipitation due to its simplicity. With real-time accumulated rainfall, the flooding history, rainfall range causing flooding from previous rainfall information and frequency probability of precipitation are used to determine the flood risk on roads. The result of simulation using the suggested algorithms shows the high concordance rate between actual flooded areas in the past and flooded areas derived from the simulation for the research region in Busan, Korea.

  6. Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation

    Directory of Open Access Journals (Sweden)

    Y. Tramblay

    2011-01-01

    Full Text Available A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2 in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.

  7. a Statistically Dependent Approach for the Monthly Rainfall Forecastfrom One Point Observations

    Science.gov (United States)

    Pucheta, J.; Patiño, D.; Kuchen, B.

    In this work an adaptive linear filter model in a autoregressive moving average (ARMA) topology for forecasting time series is presented. The time series are composed by observations of the accumulative rainfall every month during several years. The learning rule used to adjust the filter coefficients is mainly based on the gradient-descendent method. In function of the long and short term stochastic dependence of the time series, we propose an on-line heuristic law to set the training process and to modify the filter topology. The input patterns for the predictor filter are the values of the time series after applying a time-delay operator. Hence, the filter's output will tend to approximate the current value available from the data series. The approach is tested over a time series obtained from measures of the monthly accumulative rainfall from La Perla, Cordoba, Argentina. The performance of the presented approach is shown by forecasting the following 18 months from a hypothetical actual time for four time series of 102 data length.

  8. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

    Science.gov (United States)

    Kneis, D.; Chatterjee, C.; Singh, R.

    2014-07-01

    The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64-0.74 for 3B42 and 0.59-0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2-0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall-runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash-Sutcliffe index of 0.76-0.88 at gauges not affected by

  9. Constraining continuous rainfall simulations for derived design flood estimation

    Science.gov (United States)

    Woldemeskel, F. M.; Sharma, A.; Mehrotra, R.; Westra, S.

    2016-11-01

    Stochastic rainfall generation is important for a range of hydrologic and water resources applications. Stochastic rainfall can be generated using a number of models; however, preserving relevant attributes of the observed rainfall-including rainfall occurrence, variability and the magnitude of extremes-continues to be difficult. This paper develops an approach to constrain stochastically generated rainfall with an aim of preserving the intensity-durationfrequency (IFD) relationships of the observed data. Two main steps are involved. First, the generated annual maximum rainfall is corrected recursively by matching the generated intensity-frequency relationships to the target (observed) relationships. Second, the remaining (non-annual maximum) rainfall is rescaled such that the mass balance of the generated rain before and after scaling is maintained. The recursive correction is performed at selected storm durations to minimise the dependence between annual maximum values of higher and lower durations for the same year. This ensures that the resulting sequences remain true to the observed rainfall as well as represent the design extremes that may have been developed separately and are needed for compliance reasons. The method is tested on simulated 6 min rainfall series across five Australian stations with different climatic characteristics. The results suggest that the annual maximum and the IFD relationships are well reproduced after constraining the simulated rainfall. While our presentation focusses on the representation of design rainfall attributes (IFDs), the proposed approach can also be easily extended to constrain other attributes of the generated rainfall, providing an effective platform for post-processing of stochastic rainfall generators.

  10. Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

    Directory of Open Access Journals (Sweden)

    Justine Ringard

    2015-12-01

    Full Text Available Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis V7 (Version 7 and RT (real time, CMORPH (Climate Prediction Center (CPC MORPHing technique and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network, for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio. Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and

  11. Adjustment of rainfall estimates from weather radars using in-situ stormwater drainage sensors

    DEFF Research Database (Denmark)

    Ahm, Malte

    importance as long as the estimated flow and water levels are correct. It makes sense to investigate the possibility of adjusting weather radar data to rainfall-runoff measurements instead of rain gauge measurements in order to obtain better predictions of flow and water levels. This Ph.D. study investigates...... challenges for using the data in urban drainage applications. There are discrepancies between radar-rainfall measured in the atmosphere and the “true” rainfall at ground level. Consequently, radar-rainfall estimates are usually adjusted to rainfall observations at ground level from rain gauges. When radar-rain...... gauge adjusted data is applied for urban drainage models, discrepancies between radar-estimated runoff and observed runoff still occur. The aim of urban drainage applications is to estimate flow and water levels in critical points in the system. The “true” rainfall at ground level is, therefore, of less...

  12. Estimating Flood Quantiles on the Basis of Multi-Event Rainfall Simulation – Case Study

    Directory of Open Access Journals (Sweden)

    Jarosińska Elżbieta

    2015-12-01

    Full Text Available This paper presents an approach to estimating the probability distribution of annual discharges Q based on rainfall-runoff modelling using multiple rainfall events. The approach is based on the prior knowledge about the probability distribution of annual maximum daily totals of rainfall P in a natural catchment, random disaggregation of the totals into hourly values, and rainfall-runoff modelling. The presented Multi-Event Simulation of Extreme Flood method (MESEF combines design event method based on single-rainfall event modelling, and continuous simulation method used for estimating the maximum discharges of a given exceedance probability using rainfall-runoff models. In the paper, the flood quantiles were estimated using the MESEF method, and then compared to the flood quantiles estimated using classical statistical method based on observed data.

  13. Radar rainfall estimation for the identification of debris-flow precipitation thresholds

    Science.gov (United States)

    Marra, Francesco; Nikolopoulos, Efthymios I.; Creutin, Jean-Dominique; Borga, Marco

    2014-05-01

    Identification of rainfall thresholds for the prediction of debris-flow occurrence is a common approach for warning procedures. Traditionally the debris-flow triggering rainfall is derived from the closest available raingauge. However, the spatial and temporal variability of intense rainfall on mountainous areas, where debris flows take place, may lead to large uncertainty in point-based estimates. Nikolopoulos et al. (2014) have shown that this uncertainty translates into a systematic underestimation of the rainfall thresholds, leading to a step degradation of the performances of the rainfall threshold for identification of debris flows occurrence under operational conditions. A potential solution to this limitation lies on use of rainfall estimates from weather radar. Thanks to their high spatial and temporal resolutions, these estimates offer the advantage of providing rainfall information over the actual debris flow location. The aim of this study is to analyze the value of radar precipitation estimations for the identification of debris flow precipitation thresholds. Seven rainfall events that triggered debris flows in the Adige river basin (Eastern Italian Alps) are analyzed using data from a dense raingauge network and a C-Band weather radar. Radar data are elaborated by using a set of correction algorithms specifically developed for weather radar rainfall application in mountainous areas. Rainfall thresholds for the triggering of debris flows are identified in the form of average intensity-duration power law curves using a frequentist approach by using both radar rainfall estimates and raingauge data. Sampling uncertainty associated to the derivation of the thresholds is assessed by using a bootstrap technique (Peruccacci et al. 2012). Results show that radar-based rainfall thresholds are largely exceeding those obtained by using raingauge data. Moreover, the differences between the two thresholds may be related to the spatial characteristics (i.e., spatial

  14. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi

    Directory of Open Access Journals (Sweden)

    D. Kneis

    2014-01-01

    Full Text Available The paper examines the quality of satellite-based precipitation estimates for the Lower Mahanadi River Basin (Eastern India. The considered data sets known as 3B42 and 3B42-RT (version 7/7A are routinely produced by the tropical rainfall measuring mission (TRMM from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gage-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gage data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analyzing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2 the satellite-based areal precipitation estimates were found to be moderately correlated with the gage-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT. Significant discrepancies between TRMM data and ground observations were identified at high intensity levels. The rainfall depth derived from rain gage data is often not reflected by the TRMM estimates (hit rate 80 mm day−1. At the same time, the remotely sensed rainfall rates frequently exceed the gage-based equivalents (false alarm ratios of 0.2–0.6. In addition, the real time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalization of rain gage data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gage data were used as model input (Nash–Sutcliffe Index of 0.76–0.88 at gages not

  15. Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

    Directory of Open Access Journals (Sweden)

    M. P. Mittermaier

    2008-05-01

    Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.

    The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

  16. Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

    Science.gov (United States)

    Mittermaier, M. P.

    2008-05-01

    A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

  17. Transfer function modeling of the monthly accumulated rainfall series over the Iberian Peninsula

    Energy Technology Data Exchange (ETDEWEB)

    Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)

    2002-10-01

    In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos

  18. The Use of Radar to Improve Rainfall Estimation over the Tennessee and San Joaquin River Valleys

    Science.gov (United States)

    Petersen, Walter A.; Gatlin, Patrick N.; Felix, Mariana; Carey, Lawrence D.

    2010-01-01

    This slide presentation provides an overview of the collaborative radar rainfall project between the Tennessee Valley Authority (TVA), the Von Braun Center for Science & Innovation (VCSI), NASA MSFC and UAHuntsville. Two systems were used in this project, Advanced Radar for Meteorological & Operational Research (ARMOR) Rainfall Estimation Processing System (AREPS), a demonstration project of real-time radar rainfall using a research radar and NEXRAD Rainfall Estimation Processing System (NREPS). The objectives, methodology, some results and validation, operational experience and lessons learned are reviewed. The presentation. Another project that is using radar to improve rainfall estimations is in California, specifically the San Joaquin River Valley. This is part of a overall project to develop a integrated tool to assist water management within the San Joaquin River Valley. This involves integrating several components: (1) Radar precipitation estimates, (2) Distributed hydro model, (3) Snowfall measurements and Surface temperature / moisture measurements. NREPS was selected to provide precipitation component.

  19. A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

    Science.gov (United States)

    Tan, H.; Chandra, C. V.; Chen, H.

    2016-12-01

    Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge

  20. Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil

    Directory of Open Access Journals (Sweden)

    A. O. Cardoso

    2007-04-01

    Full Text Available Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961–2005 in the Rio Grande do Sul – Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation.

  1. Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

    Science.gov (United States)

    Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; da Cunha, Elias Rodrigues; Correa, Caio Cezar Guedes; Torres, Francisco Eduardo; Bacani, Vitor Matheus; Gois, Givanildo; Ribeiro, Larissa Pereira

    2016-04-01

    The State of Mato Grosso do Sul (MS) located in Brazil Midwest is devoid of climatological studies, mainly in the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Atlantic Forest and Pantanal. This study aimed to apply the cluster analysis using Ward's algorithm and identify those meteorological systems that affect the rainfall regime in the biomes. The rainfall data of 32 stations (sites) of the MS State were obtained from the Agência Nacional de Águas (ANA) database, collected from 1954 to 2013. In each of the 384 monthly rainfall temporal series was calculated the average and applied the Ward's algorithm to identify spatial and temporal variability of rainfall. Bartlett's test revealed only in January homogeneous variance at all sites. Run test showed that there was no increase or decrease in trend of monthly rainfall. Cluster analysis identified five rainfall homogeneous regions in the MS State, followed by three seasons (rainy, transitional and dry). The rainy season occurs during the months of November, December, January, February and March. The transitional season ranges between the months of April and May, September and October. The dry season occurs in June, July and August. The groups G1, G4 and G5 are influenced by South Atlantic Subtropical Anticyclone (SASA), Chaco's Low (CL), Bolivia's High (BH), Low Levels Jet (LLJ) and South Atlantic Convergence Zone (SACZ) and Maden-Julian Oscillation (MJO). Group G2 is influenced by Upper Tropospheric Cyclonic Vortex (UTCV) and Front Systems (FS). The group G3 is affected by UTCV, FS and SACZ. The meteorological systems' interaction that operates in each biome and the altitude causes the rainfall spatial and temporal diversity in MS State.

  2. Radar rainfall estimates in an alpine environment using inverse hydrological modelling

    Directory of Open Access Journals (Sweden)

    A. Marx

    2006-01-01

    Full Text Available The quality of hydrological modelling is limited due to the restricted availability of high resolution temporal and spatial input data such as temperature, global radiation, and precipitation. Radar-based rain measurements provide good spatial information. On the other hand, using radar data is accompanied by basic difficulties such as clutter, shielding, variations of Z/R-relationships, beam-resolution and attenuation. Instead of accounting for all errors involved separately, a robust Z/R-relationship is estimated in this study for the short range (up to 40 km distance using inverse hydrological modelling for a continuous period of three months in summer 2001. River gauge measurements from catchment sizes around 100 km2 are used to estimate areal precipitation and finally Z/R-relationships using a calibrated hydrological model. The study is performed in the alpine Ammer catchment with very short reaction times of the river gauges to rainfall events.

  3. A New Method for Radar Rainfall Estimation Using Merged Radar and Gauge Derived Fields

    Science.gov (United States)

    Hasan, M. M.; Sharma, A.; Johnson, F.; Mariethoz, G.; Seed, A.

    2014-12-01

    Accurate estimation of rainfall is critical for any hydrological analysis. The advantage of radar rainfall measurements is their ability to cover large areas. However, the uncertainties in the parameters of the power law, that links reflectivity to rainfall intensity, have to date precluded the widespread use of radars for quantitative rainfall estimates for hydrological studies. There is therefore considerable interest in methods that can combine the strengths of radar and gauge measurements by merging the two data sources. In this work, we propose two new developments to advance this area of research. The first contribution is a non-parametric radar rainfall estimation method (NPZR) which is based on kernel density estimation. Instead of using a traditional Z-R relationship, the NPZR accounts for the uncertainty in the relationship between reflectivity and rainfall intensity. More importantly, this uncertainty can vary for different values of reflectivity. The NPZR method reduces the Mean Square Error (MSE) of the estimated rainfall by 16 % compared to a traditionally fitted Z-R relation. Rainfall estimates are improved at 90% of the gauge locations when the method is applied to the densely gauged Sydney Terrey Hills radar region. A copula based spatial interpolation method (SIR) is used to estimate rainfall from gauge observations at the radar pixel locations. The gauge-based SIR estimates have low uncertainty in areas with good gauge density, whilst the NPZR method provides more reliable rainfall estimates than the SIR method, particularly in the areas of low gauge density. The second contribution of the work is to merge the radar rainfall field with spatially interpolated gauge rainfall estimates. The two rainfall fields are combined using a temporally and spatially varying weighting scheme that can account for the strengths of each method. The weight for each time period at each location is calculated based on the expected estimation error of each method

  4. Rainfall analysis for Indian monsoon region using the merged rain gauge observations and satellite estimates: Evaluation of monsoon rainfall features

    Indian Academy of Sciences (India)

    S K Roy Bhowmik; Ananda K Das

    2007-06-01

    Objective analysis of daily rainfall at the resolution of 1° grid for the Indian monsoon region has been carried out merging dense land rainfall observations and INSAT derived precipitation estimates. This daily analysis, being based on high dense rain gauge observations was found to be very realistic and able to reproduce detailed features of Indian summer monsoon. The inter-comparison with the observations suggests that the new analysis could distinctly capture characteristic features of the summer monsoon such as north–south oriented belt of heavy rainfall along the Western Ghats with sharp gradient of rainfall between the west coast heavy rain region and the rain shadow region to the east, pockets of heavy rainfall along the location of monsoon trough/low, over the east central parts of the country, over north–east India, along the foothills of Himalayas and over the north Bay of Bengal. When this product was used to assess the quality of other available standard climate products (CMAP and ECMWF reanalysis) at the grid resolution of 2.5°, it was found that the orographic heavy rainfall along Western Ghats of India was poorly identified by them. However, the GPCC analysis (gauge only) at the resolution of 1° grid closely discerns the new analysis. This suggests that there is a need for a higher resolution analysis with adequate rain gauge observations to retain important aspects of the summer monsoon over India. The case studies illustrated show that the daily analysis is able to capture large-scale as well as mesoscale features of monsoon precipitation systems. This study with data of two seasons (2001 and 2003) has shown sufficiently promising results for operational application, particularly for the validation of NWP models.

  5. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi

    Directory of Open Access Journals (Sweden)

    D. Kneis

    2014-07-01

    Full Text Available The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India. The considered data sets known as 3B42 and 3B42-RT (version 7/7A are routinely produced by the tropical rainfall measuring mission (TRMM from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2 the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT. Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate 80 mm day-1. At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6. In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at

  6. A regression-kriging model for estimation of rainfall in the Laohahe basin

    Science.gov (United States)

    Wang, Hong; Ren, Li L.; Liu, Gao H.

    2009-10-01

    This paper presents a multivariate geostatistical algorithm called regression-kriging (RK) for predicting the spatial distribution of rainfall by incorporating five topographic/geographic factors of latitude, longitude, altitude, slope and aspect. The technique is illustrated using rainfall data collected at 52 rain gauges from the Laohahe basis in northeast China during 1986-2005 . Rainfall data from 44 stations were selected for modeling and the remaining 8 stations were used for model validation. To eliminate multicollinearity, the five explanatory factors were first transformed using factor analysis with three Principal Components (PCs) extracted. The rainfall data were then fitted using step-wise regression and residuals interpolated using SK. The regression coefficients were estimated by generalized least squares (GLS), which takes the spatial heteroskedasticity between rainfall and PCs into account. Finally, the rainfall prediction based on RK was compared with that predicted from ordinary kriging (OK) and ordinary least squares (OLS) multiple regression (MR). For correlated topographic factors are taken into account, RK improves the efficiency of predictions. RK achieved a lower relative root mean square error (RMSE) (44.67%) than MR (49.23%) and OK (73.60%) and a lower bias than MR and OK (23.82 versus 30.89 and 32.15 mm) for annual rainfall. It is much more effective for the wet season than for the dry season. RK is suitable for estimation of rainfall in areas where there are no stations nearby and where topography has a major influence on rainfall.

  7. Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

    Science.gov (United States)

    Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  8. Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

    Directory of Open Access Journals (Sweden)

    Carolien Toté

    2015-02-01

    Full Text Available Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT v2.0, Famine Early Warning System NETwork (FEWS NET Rainfall Estimate (RFE v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS are compared to independent gauge data (2001–2012. This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  9. Rainfall spatial variability observed by X-band weather radar and its implication for the accuracy of rainfall estimates

    Science.gov (United States)

    Moreau, E.; Testud, J.; Le Bouar, E.

    2009-07-01

    The main objective of this paper is to estimate the error in the rainfall derived from a polarimetric X-band radar, by comparison with the corresponding estimate of a rain gauge network. However the present analysis also considers the errors inherent to rain gauge, in particular instrumental and representativeness errors. A special emphasis is addressed to the spatial variability of the rainfall in order to appreciate the representativeness error of the rain gauge with respect to the 1 km square average, typical of the radar derived estimate. For this purpose the spatial correlation function of the rainfall is analyzed. The data set consists of 1-year radar data collected by the X-band polarimetric radar HYDRIX ®, located in Beauce region (80 km south of Paris). All data were processed in real time using the ZPHI ® algorithm. A dense 25 rain gauge network provided ground comparison data. The various sources of uncertainties (instrumental and representativeness) are then analyzed and quantified for each sensor.

  10. Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

    Science.gov (United States)

    Friedel, M.J.

    2008-01-01

    A regularized joint inverse procedure is presented and used to estimate the magnitude of extreme rainfall events in ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. Since streamflow measurements reflect temporal and spatial rainfall information, peak-flow discharge is hypothesized to represent a similarity measure suitable for regionalization. To test this hypothesis, peak-flow discharge values determined from streamflow recurrence information (10-year, 25-year, and 100-year) collected outside the study basins are used to develop regional (country-wide) regression equations. Peak-flow discharge derived from these equations together with preferred spatial parameter relations as soft prior information are used to constrain the simultaneous calibration of 20 tributary basin models. The nonlinear range of uncertainty in estimated parameter values (1 curve number and 3 recurrent rainfall amounts for each model) is determined using an inverse calibration-constrained Monte Carlo approach. Cumulative probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase in magnitude and range among basins with increasing return interval. Comparison of the estimated median rainfall amounts for all return periods were reasonable but larger (3.2-26%) than rainfall estimates computed using the frequency-duration (traditional) approach and individual rain gauge data. The observed 25-year recurrence rainfall amount at La Hachadura in the Paz River basin during Hurricane Mitch (1998) is similar in value to, but outside and slightly less than, the estimated rainfall confidence limits. The similarity in joint inverse and traditionally computed rainfall events, however, suggests that the rainfall observation may likely be due to under-catch and not model bias. ?? Springer Science+Business Media B.V. 2007.

  11. Monthly oceanic rainfall based on METH techniques: DMSP SSM/I V6 and SSMIS continuity

    Science.gov (United States)

    Chiu, L. S.; Gao, S.; Shin, D.-B.; Cho, Y.-J.; Adler, R. F.; Huffman, G.; Bolvin, D.; Nelkin, E.

    2012-04-01

    As part of the Global Precipitation Climatology Project (GPCP), our group have been producing oceanic rainfall over 2.5 and 5 degree boxes by applying the Microwave Emission brightness Temperature (Tb) Histogram, or METH technique to the Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellite series. Recently, the rainfall series have been updated using the V6 SSM/I provided by RSS (Chiu and Chokngamwong., 2010). With the demise of the F15 SSM/I sensor, we examine the use of the SSMIS series to continue the DMSP time series. With its long duration, the DMSP satellite sensors constitute a unique data set capable of producing microwave-based products for climate studies. We compared the F13 SSM/I and F17 SSMIS for the period January 2008 - September 2009. The METH technique matches the histogram of Tb (twice 19V minus 22V) to a mixed-distribution of rain rates and estimates the parameters of the rain rate distribution. Mathematical convergence of the matching procedure is reached when a certain Chi-square threshold is reached. The important parameters are the Tb of the non-raining pixels (To) and the freezing level (FL) of the grid box considered. The sample size of the SSMIS is much larger than the SSM/I, hence the convergence criteria is relaxed by changing the Chi-square threshold. Preliminary results show a slight shift of the To (~0.8K). By adjusting To by a constant, the domain average SSMIS rain rates and FL are computed to within 2% and 1% of the SSM/I rain rates, respectively. Further investigation of the SSMIS METH rain rate will involve the comparison of the 19V and 22V and fine tuning the Chi-square parameter.

  12. ESTIMATION AND MAPPING OF EXTREME RAINFALL IN THE CATCHMENT AREA OF BATNA (ALGERIA

    Directory of Open Access Journals (Sweden)

    Guellouh SAMI

    2016-06-01

    Full Text Available Statistical estimation of rainfall associated with extreme events is of major interest for hydrologists in terms of risk prevention. Comprehending the spatial distribution of extreme rainfalls that cover the entire catchment area, the impluvium, of Batna, requires as a first step a frequency analysis of annual maximum daily rainfall time series with the application of empirical distributions, namely the GEV distribution, the Gumbel distribution and the log-normal distribution. This has allowed us to estimate the quantiles of extreme rainfall with return periods of 5, 10, 20, 50 and 100 years for ten rainfall stations. Subsequently, this has allowed us to map the quantiles matching the centennial return period using three types of interpolations.

  13. Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation

    Science.gov (United States)

    Nathan, Rory; Jordan, Phillip; Scorah, Matthew; Lang, Simon; Kuczera, George; Schaefer, Melvin; Weinmann, Erwin

    2016-12-01

    If risk-based criteria are used in the design of high hazard structures (such as dam spillways and nuclear power stations), then it is necessary to estimate the annual exceedance probability (AEP) of extreme rainfalls up to and including the Probable Maximum Precipitation (PMP). This paper describes the development and application of two largely independent methods to estimate the frequencies of such extreme rainfalls. One method is based on stochastic storm transposition (SST), which combines the "arrival" and "transposition" probabilities of an extreme storm using the total probability theorem. The second method, based on "stochastic storm regression" (SSR), combines frequency curves of point rainfalls with regression estimates of local and transposed areal rainfalls; rainfall maxima are generated by stochastically sampling the independent variates, where the required exceedance probabilities are obtained using the total probability theorem. The methods are applied to two large catchments (with areas of 3550 km2 and 15,280 km2) located in inland southern Australia. Both methods were found to provide similar estimates of the frequency of extreme areal rainfalls for the two study catchments. The best estimates of the AEP of the PMP for the smaller and larger of the catchments were found to be 10-7 and 10-6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.

  14. Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets.

    Science.gov (United States)

    Panagos, Panos; Ballabio, Cristiano; Meusburger, Katrin; Spinoni, Jonathan; Alewell, Christine; Borrelli, Pasquale

    2017-05-01

    The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha(-1) h(-1) yr(-1) till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

  15. Adjustment of rainfall estimates from weather radars using in-situ stormwater drainage sensors

    DEFF Research Database (Denmark)

    Ahm, Malte

    importance as long as the estimated flow and water levels are correct. It makes sense to investigate the possibility of adjusting weather radar data to rainfall-runoff measurements instead of rain gauge measurements in order to obtain better predictions of flow and water levels. This Ph.D. study investigates......The topic of this Ph.D. thesis is adjustment of weather radar rainfall measurements for urban drainage applications by the use of in-situ stormwater runoff measurements. It is possible to obtain the high spatiotemporal resolution rainfall data desired for advanced distributed urban drainage...... applications by the use of weather radars. Rainfall data representing the spatiotemporal distribution is a necessity for accurate modelling and real-time control of distributed urban drainage systems. Weather radar measurements are indirect measurements of the rainfall in the atmosphere, which poses some...

  16. Antecedent precipitation index determined from CST estimates of rainfall

    Science.gov (United States)

    Martin, David W.

    1992-01-01

    This paper deals with an experimental calculation of a satellite-based antecedent precipitation index (API). The index is also derived from daily rain images produced from infrared images using an improved version of GSFC's Convective/Stratiform Technique (CST). API is a measure of soil moisture, and is based on the notion that the amount of moisture in the soil at a given time is related to precipitation at earlier times. Four different CST programs as well as the Geostationary Operational Enviroment Satellite (GOES) Precipitation Index developed by Arkin in 1979 are compared to experimental results, for the Mississippi Valley during the month of July. Rain images are shown for the best CST code and the ARK program. Comparisons are made as to the accuracy and detail of the results for the two codes. This project demonstrates the feasibility of running the CST on a synoptic scale. The Mississippi Valley case is well suited for testing the feasibility of monitoring soil moisture by means of CST. Preliminary comparisons of CST and ARK indicate significant differences in estimates of rain amount and distribution.

  17. A Geostatistical Approach to Reducing Uncertainty in Rainfall Estimates Using Terrain Characteristics: A Case Study in the Central-North Regional Water Administration of Mozambique (ARA Centro-Norte)

    Science.gov (United States)

    Raheem, Y. T.; Freyberg, D. L.

    2012-12-01

    Many data-sparse regions, such as southern Africa, will likely face significant changes in water resources availability and timing in the future due land use change, climate change and population growth. Improved estimates of rainfall and streamflow are necessary to improve water resources decision-making, risk management, and uncertainty quantification. In this study, we use a universal kriging framework to associate various watershed terrain characteristics with gauged rainfall data to improve the estimation of monthly rainfall fields. We focus on available gauge data because other precipitation data sources, such as satellite precipitation products, are unavailable over historic periods of interest and exhibit bias in our context of streamflow estimation. Our study area is the 185,000 sq km Central-North Regional Water Administration of Mozambique (ARA Centro-Norte), which is predominantly rural tropical savanna. We use fifty years of spatially and temporally sparse monthly rainfall observations from 316 rainfall gauges. Most of the watershed terrain characteristics we use are derived from the 90m HydroSHEDS Digital Elevation Model dataset. These include elevation, aspect, slope, distance to large water bodies, distance to ridges, and distance to watershed rainfall maximum. We quantify the importance of these characteristics for reducing uncertainty in rainfall estimates using the Bayes information criterion (BIC) approach. Future work includes using these rainfall estimates to drive the semi-distributed monthly time-step Pitman rainfall-runoff model, in order to reduce uncertainty in streamflow estimates in gauged and ungauged basins.

  18. Areal rainfall estimation using moving cars - computer experiments including hydrological modeling

    Science.gov (United States)

    Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel; Wallner, Markus

    2016-09-01

    The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.

  19. An assessment of the performance of global rainfall estimates without ground-based observations

    Directory of Open Access Journals (Sweden)

    C. Massari

    2017-09-01

    Full Text Available Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS, showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern

  20. An integrative estimation model of summer rainfall-band patterns in China

    Institute of Scientific and Technical Information of China (English)

    WEI Fengying

    2007-01-01

    Three variation indices are defined to objectively and quantitatively represent fluctuations of three rainfall-band patterns in summers in China for the period from 1951 to 2005, and the variation features of these indices are analyzed on both of interdecadal and interannual scales. A new method is proposed to establish an integrative estimation model based on the analysis of rainfall-band indices, and the model is applied to air, ocean factors to estimate their roles on variations of three rainfall-band patterns on different time-scales. The tests of estimation effects show that the fluctuations of three rainfall-band patterns are composed of variations on both significant interdecadal and interannual scales, of which the interannual variation is mainly influenced by the Elnino/Lanina events, the East Asia monsoon and the ridge locations of subtropical high pressures in western pacific, while the interdecadal variation is mainly controlled by the Pacific decadal oscillation and interdecadal oscillations of the Arctic oscillation, ENSO, Nino3 sea surface temperature and summer monsoon. The estimated results from the integrative estimation model of rainfall-band patterns suggest that the way of estimation first according to each time scale of both the interdecadal and interannual scales, then estimating with an integration, which is proposed in this paper, has an obvious improvement on that without separation of time scales.

  1. Gauge-adjusted rainfall estimates from commercial microwave links

    Directory of Open Access Journals (Sweden)

    M. Fencl

    2017-01-01

    experimental layouts of ground truth from rain gauges (RGs with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i provide precise high-resolution QPEs (relative error  < 7 %, Nash–Sutcliffe efficiency coefficient  >  0.75 and (ii that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

  2. Radar Rainfall Estimation using a Quadratic Z-R equation

    Science.gov (United States)

    Hall, Will; Rico-Ramirez, Miguel Angel; Kramer, Stefan

    2016-04-01

    The aim of this work is to test a method that enables the input of event based drop size distributions to alter a quadratic reflectivity (Z) to rainfall (R) equation that is limited by fixed upper and lower points. Results will be compared to the Marshall-Palmer Z-R relation outputs and validated by a network of gauges and a single polarisation weather radar located close to Essen, Germany. The time window over which the drop size distribution measurements will be collected is varied to note any effect on the generated quadratic Z-R relation. The new quadratic algorithm shows some distinct improvement over the Marshall-Palmer relationship through multiple events. The inclusion of a minimum number of Z-R points helped to decrease the associated error by defaulting back to the Marshall-Palmer equation if the limit was not reached. More research will be done to discover why the quadratic performs poorly in some events as there appears to be little correlation between number of drops or mean rainfall amount and the associated error. In some cases it seems the spatial distribution of the disdrometers has a significant effect as a large percentage of the rain bands pass to the north of two of the three disdrometers, frequently in a slightly north-easterly direction. However during widespread precipitation events the new algorithm works very well with reductions compared to the Marshall-Palmer relation.

  3. Improved radar data processing algorithms for quantitative rainfall estimation in real time.

    Science.gov (United States)

    Krämer, S; Verworn, H R

    2009-01-01

    This paper describes a new methodology to process C-band radar data for direct use as rainfall input to hydrologic and hydrodynamic models and in real time control of urban drainage systems. In contrast to the adjustment of radar data with the help of rain gauges, the new approach accounts for the microphysical properties of current rainfall. In a first step radar data are corrected for attenuation. This phenomenon has been identified as the main cause for the general underestimation of radar rainfall. Systematic variation of the attenuation coefficients within predefined bounds allows robust reflectivity profiling. Secondly, event specific R-Z relations are applied to the corrected radar reflectivity data in order to generate quantitative reliable radar rainfall estimates. The results of the methodology are validated by a network of 37 rain gauges located in the Emscher and Lippe river basins. Finally, the relevance of the correction methodology for radar rainfall forecasts is demonstrated. It has become clearly obvious, that the new methodology significantly improves the radar rainfall estimation and rainfall forecasts. The algorithms are applicable in real time.

  4. The issues of current rainfall estimation techniques in mountain natural multi-hazard investigation

    Science.gov (United States)

    Zhuo, Lu; Han, Dawei; Chen, Ningsheng; Wang, Tao

    2017-04-01

    Mountain hazards (e.g., landslides, debris flows, and floods) induced by rainfall are complex phenomena that require good knowledge of rainfall representation at different spatiotemporal scales. This study reveals rainfall estimation from gauges is rather unrepresentative over a large spatial area in mountain regions. As a result, the conventional practice of adopting the triggering threshold for hazard early warning purposes is insufficient. The main reason is because of the huge orographic influence on rainfall distribution. Modern rainfall estimation methods such as numerical weather prediction modelling and remote sensing utilising radar from the space or on land are able to provide spatially more representative rainfall information in mountain areas. But unlike rain gauges, they only indirectly provide rainfall measurements. Remote sensing suffers from many sources of errors such as weather conditions, attenuation and sampling methods, while numerical weather prediction models suffer from spatiotemporal and amplitude errors depending on the model physics, dynamics, and model configuration. A case study based on Sichuan, China is used to illustrate the significant difference among the three aforementioned rainfall estimation methods. We argue none of those methods can be relied on individually, and the challenge is on how to make the full utilisation of the three methods conjunctively because each of them only provides partial information. We propose that a data fusion approach should be adopted based on the Bayesian inference method. However such an approach requires the uncertainty information from all those estimation techniques which still need extensive research. We hope this study will raise the awareness of this important issue and highlight the knowledge gap that should be filled in so that such a challenging problem could be tackled collectively by the community.

  5. Sampling error study for rainfall estimate by satellite using a stochastic model

    Science.gov (United States)

    Shin, Kyung-Sup; North, Gerald R.

    1988-01-01

    In a parameter study of satellite orbits, sampling errors of area-time averaged rain rate due to temporal sampling by satellites were estimated. The sampling characteristics were studied by accounting for the varying visiting intervals and varying fractions of averaging area on each visit as a function of the latitude of the grid box for a range of satellite orbital parameters. The sampling errors were estimated by a simple model based on the first-order Markov process of the time series of area averaged rain rates. For a satellite of nominal Tropical Rainfall Measuring Mission (Thiele, 1987) carrying an ideal scanning microwave radiometer for precipitation measurements, it is found that sampling error would be about 8 to 12 pct of estimated monthly mean rates over a grid box of 5 X 5 degrees. It is suggested that an observation system based on a low inclination satellite combined with a sunsynchronous satellite simultaneously might be the best candidate for making precipitation measurements from space.

  6. Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

    Science.gov (United States)

    Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

    2000-01-01

    Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

  7. Rainfall estimation using an optical and a microwave link in the Ardèche catchment.

    Science.gov (United States)

    Pietersen, Henk; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2013-04-01

    The Mediterranean basin is considered to be one of the "hotspots" for climate change. One of the main factors in these changes is the availability and distribution of water, both in time and space. To gain more understanding about the hydrological cycle in the Mediterranean basin and to quantify the related processes, the HYdrological cycle in the Mediterranean EXperiment (HyMeX) was set up. This experiment focuses on inter-annual to decadal variability in the coupled Mediterranean system, running during the second decade of the 21st century. During this long experiment, special intensive observation periods are planned, of which the first passed during the autumn of 2012. Within the HyMeX framework, one working group pays special attention to (flash) floods and heavy rainfall. To investigate this, several (small) catchments were heavily instrumented during the first special observation period. We show the first results on rainfall estimation employing an optical link, a microwave link, and a disdrometer in the Ardèche catchment in the south of France for the first special observation period of HyMeX. Optical and microwave links can be employed to estimate path-averaged rain intensities along a transect of several kilometers, similar in length to the cross-section of a small catchment. The transmitted signal is attenuated by rain along the link path causing a decrease in received power at the end of the link. The attenuation of this signal has a power-law relation to the average rainfall intensity along the link. As a reference, the disdrometer is placed at one end of the link. Link-based rainfall intensities are compared to those based on disdrometer data. However, due to the nature of the observational technique (point measurement vs. average along a link) errors in representation may occur. The estimation of rainfall intensity from attenuation can be hampered by a number of factors. Principal among these are: moisture on the antennae that is perceived to be

  8. A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate

    Science.gov (United States)

    Lima, Carlos H. R.; Kwon, Hyun-Han; Kim, Jin-Young

    2016-09-01

    The estimation of intensity-duration-frequency (IDF) curves for rainfall data comprises a classical task in hydrology studies to support a variety of water resources projects, including urban drainage and the design of flood control structures. In a changing climate, however, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to poor estimates of rainfall intensity quantiles. Climate change scenarios built on General Circulation Models offer a way to access and estimate future changes in spatial and temporal rainfall patterns at the daily scale at the utmost, which is not as fine temporal resolution as required (e.g. hours) to directly estimate IDF curves. In this paper we propose a novel methodology based on a four-parameter beta distribution to estimate IDF curves conditioned on the observed (or simulated) daily rainfall, which becomes the time-varying upper bound of the updated nonstationary beta distribution. The inference is conducted in a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters when building the IDF curves. The proposed model is tested using rainfall data from four stations located in South Korea and projected climate change Representative Concentration Pathways (RCPs) scenarios 6 and 8.5 from the Met Office Hadley Centre HadGEM3-RA model. The results show that the developed model fits the historical data as good as the traditional Generalized Extreme Value (GEV) distribution but is able to produce future IDF curves that significantly differ from the historically based IDF curves. The proposed model predicts for the stations and RCPs scenarios analysed in this work an increase in the intensity of extreme rainfalls of short duration with long return periods.

  9. Development of a censored modelling approach for stochastic estimation of rainfall extremes at fine temporal scales

    Science.gov (United States)

    Cross, David; Onof, Christian; Bernardara, Pietro

    2016-04-01

    With the COP21 drawing to a close in December 2015, storms Desmond, Eva and Frank which swept across the UK and Ireland causing widespread flooding and devastation have acted as a timely reminder of the need for reliable estimation of rainfall extremes in a changing climate. The frequency and intensity of rainfall extremes are predicted to increase in the UK under anthropogenic climate change, and it is notable that the UK's 24 hour rainfall record of 316mm set in Seathwaite, Cumbria in 2009 was broken on the 5 December 2015 with 341mm by storm Desmond at Honister Pass also in Cumbria. Immediate analysis of the latter by the Centre for Ecology and Hydrology (UK) on the 8 December 2015 estimated that this is approximately equivalent to a 1300 year return period event (Centre for Ecology & Hydrology, 2015). Rainfall extremes are typically estimated using extreme value analysis and intensity duration frequency curves. This study investigates the potential for using stochastic rainfall simulation with mechanistic rectangular pulse models for estimation of extreme rainfall. These models have been used since the late 1980s to generate synthetic rainfall time-series at point locations for scenario analysis in hydrological studies and climate impact assessment at the catchment scale. Routinely they are calibrated to the full historical hyetograph and used for continuous simulation. However, their extremal performance is variable with a tendency to underestimate short duration (hourly and sub-hourly) rainfall extremes which are often associated with heavy convective rainfall in temporal climates such as the UK. Focussing on hourly and sub-hourly rainfall, a censored modelling approach is proposed in which rainfall below a low threshold is set to zero prior to model calibration. It is hypothesised that synthetic rainfall time-series are poor at estimating extremes because the majority of the training data are not representative of the climatic conditions which give rise to

  10. Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

    Science.gov (United States)

    Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

    2016-02-01

    Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

  11. Variability of raindrop size distributions in a squall line and implications for radar rainfall estimation

    NARCIS (Netherlands)

    Uijlenhoet, R.; Steiner, M.; Smith, J.A.

    2003-01-01

    The intrastorm variability of raindrop size distributions as a source of uncertainty in single-parameter and dual-parameter radar rainfall estimates is studied using time series analyses of disdrometer observations. Two rain-rate (R) estimators are considered: the traditional single-parameter

  12. A Comparison of De-noising Methods for Diff erential Phase Shift and Associated Rainfall Estimation

    Institute of Scientific and Technical Information of China (English)

    胡志群; 刘察平; 吴林林; 魏庆

    2015-01-01

    Measured diff erential phase shift Φ DP is known to be a noisy unstable polarimetric radar variable, such that the quality of Φ DP data has direct impact on specifi c diff erential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many Φ DP de-noising methods have been developed; however, the de-noising eff ects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy Φ DP data were generated and de-noised by using several methods such as fi nite-impulse response (FIR), Kalman, wavelet, traditional mean, and median fi lters. The biases were compared between KDP from simulated and observedΦ DP radial profi les after de-noising by these methods. The results suggest that the complicated FIR, Kalman, and wavelet methods have a better de-noising eff ect than the traditional methods. AfterΦ DP was de-noised, the accuracy of the KDP-based rainfall estimation increased signifi cantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated withΦ DP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h−1. However, the improved estimation was not signifi cant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h−1. The performance of wavelet analysis was found to be the most stable of these fi lters.

  13. Influence of rainfalls on heat and steam fluxes of fumarolic zones: Six months records along the Ty fault (Soufrière of Guadeloupe, Lesser Antilles)

    Science.gov (United States)

    Gaudin, Damien; Finizola, Anthony; Delcher, Eric; Beauducel, François; Allemand, Pascal; Delacourt, Christophe; Brothelande, Elodie; Peltier, Aline; Di Gangi, Fabio

    2015-09-01

    Fumarolic zones are permeable areas where both steam and heat are expelled to the atmosphere. Surface fluxes and flows, which are representative of the intensity of the hydrothermal circulation in depth, can be monitored by thermometers, thermal infrared cameras, spectrometers, or condensers. However, the superficial activity of fumarolic zones can be modified by the meteorological conditions, in particular the rainfalls, which might result in erroneous estimations. From this perspective, we developed a set of physical equations to quantify the effects of rainfalls on the thermal behavior of fumarolic zones. Results were faced to continuous measurements achieved at the Ty fault fumarolic zone (La Soufrière volcano, Guadeloupe, Lesser Antilles) during six months in 2010, using six vertical series of thermometers measuring the heat transfer in the ground and one condenser measuring the rising steam flux. Results demonstrate that in the absence of rainfalls, heat and steam flux reach an equilibrium that is representative of the geothermal flux in depth. Conversely, after the rainfalls, the cooling of the ground provokes a deepening of the condensation level. The related soil temperature drop can be estimated by computing the heat required to warm the infiltrated water up to boiling temperature while the recovery rate is directly linked to the geothermal flux. Our observations allow defining in which conditions flux are at steady state, but also to build a first-order numerical model allowing estimating both the physical parameters of the ground (thermal conductivity, precipitation efficiency coefficient and surface flux constant) and the long-term thermal behavior of the hydrothermal system. In particular, our results predict that the hydrothermal activity must vanish on the zones where the geothermal flux drops under a certain threshold (60 W/m2 at La Soufrière). The existence of this limit may have strong implications for the precipitation rate of minerals and the

  14. Radar Estimation of Intense Rainfall Rates through Adaptive Calibration of the Z-R Relation

    Directory of Open Access Journals (Sweden)

    Andrea Libertino

    2015-10-01

    Full Text Available Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z to rainfall rate (R relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses.

  15. Rainfall estimation from soil moisture data: crash test for SM2RAIN algorithm

    Science.gov (United States)

    Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia

    2015-04-01

    Soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that soil moisture observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, soil moisture data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from soil moisture data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite soil moisture products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (Soil Moisture and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled soil moisture data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled soil moisture data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root

  16. ANALYSIS OF THE STATISTICAL BEHAVIOUR OF DAILY MAXIMUM AND MONTHLY AVERAGE RAINFALL ALONG WITH RAINY DAYS VARIATION IN SYLHET, BANGLADESH

    Directory of Open Access Journals (Sweden)

    G. M. J. HASAN

    2014-10-01

    Full Text Available Climate, one of the major controlling factors for well-being of the inhabitants in the world, has been changing in accordance with the natural forcing and manmade activities. Bangladesh, the most densely populated countries in the world is under threat due to climate change caused by excessive use or abuse of ecology and natural resources. This study checks the rainfall patterns and their associated changes in the north-eastern part of Bangladesh mainly Sylhet city through statistical analysis of daily rainfall data during the period of 1957 - 2006. It has been observed that a good correlation exists between the monthly mean and daily maximum rainfall. A linear regression analysis of the data is found to be significant for all the months. Some key statistical parameters like the mean values of Coefficient of Variability (CV, Relative Variability (RV and Percentage Inter-annual Variability (PIV have been studied and found to be at variance. Monthly, yearly and seasonal variation of rainy days also analysed to check for any significant changes.

  17. Multi-satellite rainfall sampling error estimates – a comparative study

    Directory of Open Access Journals (Sweden)

    A. Loew

    2012-10-01

    Full Text Available This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is used to simulate realistic satellite overpasses with orbital shifts taken into account. With this model we resampled rain gauge timeseries to simulate satellites rainfall estimates free of retrieval and calibration errors. We concentrate on two regions, Germany and Benin, areas with different precipitation regimes. Our results show that sampling uncertainty for all satellite constellations does not differ greatly depending on the area despite the differences in local precipitation patterns. Addition of 3 hourly geostationary observations provides equal performance improvement in Germany and Benin, reducing rainfall undersampling by 20–25% of the total rainfall amount. Authors do not find a significant difference in rainfall sampling between conical imager and cross-track sounders.

  18. Parameter estimation in stochastic rainfall-runoff models

    DEFF Research Database (Denmark)

    Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

    2006-01-01

    the parameters, including the noise terms. The parameter estimation method is a maximum likelihood method (ML) where the likelihood function is evaluated using a Kalman filter technique. The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

  19. Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network - the development of the REIINN model

    Science.gov (United States)

    Ang, M. R. C. O.; Gonzalez, R. M.; Castro, P. P. M.

    2014-03-01

    Rainfall, one of the important elements of the hydrologic cycle, is also the most difficult to model. Thus, accurate rainfall estimation is necessary especially in localized catchment areas where variability of rainfall is extremely high. Moreover, early warning of severe rainfall through timely and accurate estimation and forecasting could help prevent disasters from flooding. This paper presents the development of two rainfall estimation models that utilize a NARX-based neural network architecture namely: REIINN 1 and REIINN 2. These REIINN models, or Rainfall Estimation by Information Integration using Neural Networks, were trained using MTSAT cloud-top temperature (CTT) images and rainfall rates from the combined rain gauge and TMPA 3B40RT datasets. Model performance was assessed using two metrics - root mean square error (RMSE) and correlation coefficient (R). REIINN 1 yielded an RMSE of 8.1423 mm/3h and an overall R of 0.74652 while REIINN 2 yielded an RMSE of 5.2303 and an overall R of 0.90373. The results, especially that of REIINN 2, are very promising for satellite-based rainfall estimation in a catchment scale. It is believed that model performance and accuracy will greatly improve with a denser and more spatially distributed in-situ rainfall measurements to calibrate the model with. The models proved the viability of using remote sensing images, with their good spatial coverage, near real time availability, and relatively inexpensive to acquire, as an alternative source for rainfall estimation to complement existing ground-based measurements.

  20. Estimating monthly temperature using point based interpolation techniques

    Science.gov (United States)

    Saaban, Azizan; Mah Hashim, Noridayu; Murat, Rusdi Indra Zuhdi

    2013-04-01

    This paper discusses the use of point based interpolation to estimate the value of temperature at an unallocated meteorology stations in Peninsular Malaysia using data of year 2010 collected from the Malaysian Meteorology Department. Two point based interpolation methods which are Inverse Distance Weighted (IDW) and Radial Basis Function (RBF) are considered. The accuracy of the methods is evaluated using Root Mean Square Error (RMSE). The results show that RBF with thin plate spline model is suitable to be used as temperature estimator for the months of January and December, while RBF with multiquadric model is suitable to estimate the temperature for the rest of the months.

  1. Underwater Acoustic Measurements to Estimate Wind and Rainfall in the Mediterranean Sea

    Directory of Open Access Journals (Sweden)

    Sara Pensieri

    2015-01-01

    Full Text Available Oceanic ambient noise measurements can be analyzed to obtain qualitative and quantitative information about wind and rainfall phenomena over the ocean filling the existing gap of reliable meteorological observations at sea. The Ligurian Sea Acoustic Experiment was designed to collect long-term synergistic observations from a passive acoustic recorder and surface sensors (i.e., buoy mounted rain gauge and anemometer and weather radar to support error analysis of rainfall rate and wind speed quantification techniques developed in past studies. The study period included combination of high and low wind and rainfall episodes and two storm events that caused two floods in the vicinity of La Spezia and in the city of Genoa in 2011. The availability of high resolution in situ meteorological data allows improving data processing technique to detect and especially to provide effective estimates of wind and rainfall at sea. Results show a very good correspondence between estimates provided by passive acoustic recorder algorithm and in situ observations for both rainfall and wind phenomena and demonstrate the potential of using measurements provided by passive acoustic instruments in open sea for early warning of approaching coastal storms, which for the Mediterranean coastal areas constitutes one of the main causes of recurrent floods.

  2. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    Science.gov (United States)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  3. Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

    Science.gov (United States)

    Veerakachen, Watcharee; Raksapatcharawong, Mongkol

    2015-09-01

    Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

  4. Estimation of rainfall-runoff using curve number: a GIS based development of Sathanur reservoir catchment.

    Science.gov (United States)

    Vijay, Ritesh; Pareek, Ashutosh; Gupta, Apurba

    2006-10-01

    A GIS based algorithm has been developed to estimate the rainfall-runoff relationship of Sathanur reservoir catchment based on Soil Conservation Service (SCS) model. The landuse and soil maps were prepared in Arc/Info 9.0 and an arc macro language (AML) programme was developed to assign curve number based on landuse and soil classification including hydrological condition of the area. The algorithm was executed successfully by rainfall data for computation of runoff depth in all the sub watersheds. The study is important for a watershed, which does not have runoff records and can be used for planning of various water conservation measures.

  5. Curve Number Estimation for a Small Urban Catchment from Recorded Rainfall-Runoff Events

    Directory of Open Access Journals (Sweden)

    Banasik Kazimierz

    2014-12-01

    Full Text Available Runoff estimation is a key component in various hydrological considerations. Estimation of storm runoff is especially important for the effective design of hydraulic and road structures, for the flood flow management, as well as for the analysis of land use changes, i.e. urbanization or low impact development of urban areas. The curve number (CN method, developed by Soil Conservation Service (SCS of the U.S. Department of Agriculture for predicting the flood runoff depth from ungauged catchments, has been in continuous use for ca. 60 years. This method has not been extensively tested in Poland, especially in small urban catchments, because of lack of data. In this study, 39 rainfall-runoff events, collected during four years (2009–2012 in a small (A=28.7 km2, urban catchment of Służew Creek in southwest part of Warsaw were used, with the aim of determining the CNs and to check its applicability to ungauged urban areas. The parameters CN, estimated empirically, vary from 65.1 to 95.0, decreasing with rainfall size and, when sorted rainfall and runoff separately, reaching the value from 67 to 74 for large rainfall events.

  6. Ability of a dual polarized X-band radar to estimate rainfall

    Science.gov (United States)

    Diss, S.; Testud, J.; Lavabre, J.; Ribstein, P.; Moreau, E.; Parent du Chatelet, J.

    2009-07-01

    The aim of this study is to assess rainfall estimates by a dual polarized X-band radar. This study was part of the European project FRAMEA (Flood forecasting using Radar in Alpine and Mediterranean Areas). Two radars were set up near the small town of Collobrières in South Eastern France. The first radar was a dual polarized X-band radar (Hydrix ®) associated with a ZPHI ® algorithm while the second one was an S-band radar (Météo France). We compared radar rainfall data with measurements obtained by two rain gauge networks (Météo France and Cemagref). During the experiments from February 2006 to June 2007, four significant rainfall events occurred. The accuracy of the rain rate obtained with both S-band and X-band radars decreased significantly beyond 60 km, in particular for the X-band radar. At closer ranges, such as 30-60 km from the radars, the X-band and the S-band radar retrievals showed similar performance with Nash criteria around 0.80 for the X-band radar and 0.75 for the S-band radar. Furthermore, the X-band radar did not require calibration on rainfall records, which tends to make it a useful method to assess rainfall in areas without a rain gauge network.

  7. Performance and Probabilistic Verification of Regional Parameter Estimates for Conceptual Rainfall-runoff Models

    Science.gov (United States)

    Franz, K.; Hogue, T.; Barco, J.

    2007-12-01

    Identification of appropriate parameter sets for simulation of streamflow in ungauged basins has become a significant challenge for both operational and research hydrologists. This is especially difficult in the case of conceptual models, when model parameters typically must be "calibrated" or adjusted to match streamflow conditions in specific systems (i.e. some of the parameters are not directly observable). This paper addresses the performance and uncertainty associated with transferring conceptual rainfall-runoff model parameters between basins within large-scale ecoregions. We use the National Weather Service's (NWS) operational hydrologic model, the SACramento Soil Moisture Accounting (SAC-SMA) model. A Multi-Step Automatic Calibration Scheme (MACS), using the Shuffle Complex Evolution (SCE), is used to optimize SAC-SMA parameters for a group of watersheds with extensive hydrologic records from the Model Parameter Estimation Experiment (MOPEX) database. We then explore "hydroclimatic" relationships between basins to facilitate regionalization of parameters for an established ecoregion in the southeastern United States. The impact of regionalized parameters is evaluated via standard model performance statistics as well as through generation of hindcasts and probabilistic verification procedures to evaluate streamflow forecast skill. Preliminary results show climatology ("climate neighbor") to be a better indicator of transferability than physical similarities or proximity ("nearest neighbor"). The mean and median of all the parameters within the ecoregion are the poorest choice for the ungauged basin. The choice of regionalized parameter set affected the skill of the ensemble streamflow hindcasts, however, all parameter sets show little skill in forecasts after five weeks (i.e. climatology is as good an indicator of future streamflows). In addition, the optimum parameter set changed seasonally, with the "nearest neighbor" showing the highest skill in the

  8. Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

    Science.gov (United States)

    Bell, Thomas L.; Kundu, Prasun K.; Kummerow, Christian D.; Einaudi, Franco (Technical Monitor)

    2000-01-01

    Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

  9. Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

    Science.gov (United States)

    Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

    2015-12-01

    Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual.

  10. Analysis of a method for radar rainfall estimation considering the freezing level height

    Directory of Open Access Journals (Sweden)

    R. Bordoy

    2010-01-01

    Full Text Available Quantitative precipitation estimation provided by weather radars plays a vital role in many hydrometeorological applications. The complexity of all the factors that contribute, on the one hand, to rainfall processes, and on the other hand, to the behavior of the energy beam emitted by the radar in its traverse through the atmosphere, mean that current estimates generally differ from the precipitation observed on surface. The aim of this study was to validate the SRI product (Surface Rain Intensity, which is a method of radar rainfall estimation that applies a correction considering a vertical profile of reflectivity (VPR. The VPR takes into account the freezing level height to make a correction in areas affected by the phenomenon known as “bright band”. Precipitation estimates obtained through this method were compared with other methodscurrently operational in the Meteorological Service of Catalonia in five representative episodes of convective and stratiform rainfall. In general, better results were obtained when compared with raingauge observations. Although this is a preliminary assessment that will have to be completed with more case studies, the results indicate good prospects for an operational use of this method.

  11. Estimation of Rainfall Associated with Typhoons over the Ocean Using TRMM/TMI and Numerical Models

    Directory of Open Access Journals (Sweden)

    Nan-Ching Yeh

    2015-11-01

    Full Text Available This study quantitatively estimated the precipitation associated with a typhoon in the northwestern Pacific Ocean by using a physical algorithm which included the Weather Research and Forecasting model, Radiative Transfer for TIROS Operational Vertical Sounder model, and data from the Tropical Rainfall Measuring Mission (TRMM/TRMM Microwave Imager (TMI and TRMM/Precipitation Radar (PR. First, a prior probability distribution function (PDF was constructed using over three million rain rate retrievals from the TRMM/PR data for the period 2002–2010 over the northwestern Pacific Ocean. Subsequently, brightness temperatures for 15 typhoons that occurred over the northwestern Pacific Ocean were simulated using a microwave radiative transfer model and a conditional PDF was obtained for these typhoons. The aforementioned physical algorithm involved using a posterior PDF. A posterior PDF was obtained by combining the prior and conditional PDFs. Finally, the rain rate associated with a typhoon was estimated by inputting the observations of the TMI (attenuation indices at 10, 19, 37 GHz into the posterior PDF (lookup table. Results based on rain rate retrievals indicated that rainband locations with the heaviest rainfall showed qualitatively similar horizontal distributions. The correlation coefficient and root-mean-square error of the rain rate estimation were 0.63 and 4.45 mm·h−1, respectively. Furthermore, the correlation coefficient and root-mean-square error for convective rainfall were 0.78 and 7.25 mm·h−1, respectively, and those for stratiform rainfall were 0.58 and 9.60 mm·h−1, respectively. The main contribution of this study is introducing an approach to quickly and accurately estimate the typhoon precipitation, and remove the need for complex calculations.

  12. Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data

    Science.gov (United States)

    Ebrahimian, Ali; Wilson, Bruce N.; Gulliver, John S.

    2016-05-01

    Impervious surfaces are useful indicators of the urbanization impacts on water resources. Effective impervious area (EIA), which is the portion of total impervious area (TIA) that is hydraulically connected to the drainage system, is a better catchment parameter in the determination of actual urban runoff. Development of reliable methods for quantifying EIA rather than TIA is currently one of the knowledge gaps in the rainfall-runoff modeling context. The objective of this study is to improve the rainfall-runoff data analysis method for estimating EIA fraction in urban catchments by eliminating the subjective part of the existing method and by reducing the uncertainty of EIA estimates. First, the theoretical framework is generalized using a general linear least square model and using a general criterion for categorizing runoff events. Issues with the existing method that reduce the precision of the EIA fraction estimates are then identified and discussed. Two improved methods, based on ordinary least square (OLS) and weighted least square (WLS) estimates, are proposed to address these issues. The proposed weighted least squares method is then applied to eleven urban catchments in Europe, Canada, and Australia. The results are compared to map measured directly connected impervious area (DCIA) and are shown to be consistent with DCIA values. In addition, both of the improved methods are applied to nine urban catchments in Minnesota, USA. Both methods were successful in removing the subjective component inherent in the analysis of rainfall-runoff data of the current method. The WLS method is more robust than the OLS method and generates results that are different and more precise than the OLS method in the presence of heteroscedastic residuals in our rainfall-runoff data.

  13. Application of radar data to estimate distributed return periods of extreme rainfall events over Trondheim

    Energy Technology Data Exchange (ETDEWEB)

    Abdella, Yisak Sultan

    2013-07-01

    The return period of a given rainfall intensity is an important parameter for the Trondheim municipality since the drainage systems in Trondheim have been and are still being designed on the basis of a selected return period. Since rainfall is a spatially distributed phenomenon, a single event passing over a city can yield different return periods at different locations in the same city. In order to account for this spatial variability, a tool has been developed in this project for determining distributed return periods for rainfall events over Trondheim using the measurements from Rissa radar. The tool includes a method for adjusting radar rainfall using rain gauge measurements and an accumulation technique which accounts for storm movement and temporal variation in intensity. The tool has been tested on two extreme events which occurred on July 29 2007 and August 13 2007. The application on the two events has demonstrated a fully-automated estimation of distributed return periods using readily available data. For the particular rain gauge network in Trondheim, it has also been shown how areas of maximum intensity observed by the radar can be missed by all the gauges. (author)

  14. A new approach for estimating groundwater table fluctuation response to rainfall events in North China Plain

    Science.gov (United States)

    Liao, Z.; Xie, X.; Ma, Z.

    2015-12-01

    A rise or decline in water table in response to water budget is a function of rainfall volume and groundwater depletion intensity. Most research have focus on estimating water table fluctuations among various shallow aquifer resulting from recharge and discharge change, however, the methods commonly applied are limited in that the subsurface system is more complex. In this paper, a reliable approach based on statistics theory is presented for quantifying the correlation relationship among water table, rainfall events and groundwater depletion process. The detail monitoring data are used to multivariate regression analysis and established the relationship model between water table and groundwater depletion in the proposed method. We further employed the model to obtain water table fluctuation trend with manual controlled depletion in different rainfall conditions. We also identify how this model applied to North China Plain and examine the water table error. The results show that controlling the depletion process based on different rainfall frequency can promote groundwater table recover and the model can provide a reliable method to groundwater management.

  15. Estimation of missing rainfall data using spatial interpolation and imputation methods

    Science.gov (United States)

    Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Azman, Muhammad Az-zuhri

    2015-02-01

    This study is aimed to estimate missing rainfall data by dividing the analysis into three different percentages namely 5%, 10% and 20% in order to represent various cases of missing data. In practice, spatial interpolation methods are chosen at the first place to estimate missing data. These methods include normal ratio (NR), arithmetic average (AA), coefficient of correlation (CC) and inverse distance (ID) weighting methods. The methods consider the distance between the target and the neighbouring stations as well as the correlations between them. Alternative method for solving missing data is an imputation method. Imputation is a process of replacing missing data with substituted values. A once-common method of imputation is single-imputation method, which allows parameter estimation. However, the single imputation method ignored the estimation of variability which leads to the underestimation of standard errors and confidence intervals. To overcome underestimation problem, multiple imputations method is used, where each missing value is estimated with a distribution of imputations that reflect the uncertainty about the missing data. In this study, comparison of spatial interpolation methods and multiple imputations method are presented to estimate missing rainfall data. The performance of the estimation methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).

  16. Estimating probabilistic rainfall and food security outcomes for eastern and southern Africa

    Science.gov (United States)

    Verdin, J.; Funk, C.; Dettinger, M.; Brown, M.

    2009-05-01

    Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains high, and declining per-capita agricultural capacity retards development. In September of 2008, Ethiopia, Kenya, Djibouti, and Somalia faced high or extreme conditions of food insecurity caused by repeated droughts and rapid food price inflation. In this talk we present research, performed for the US Agency for International Development on probabilistic projections of rainfall and food security trends for eastern and southern Africa. Analyses of station data and satellite-based estimates of precipitation have identified another problematic trend: main growing- season rainfall has diminished by ~15% in food-insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain-fed agriculture, these declines constitute a long term danger to subsistence agricultural and pastoral livelihoods. Tracing moisture deficits upstream to an anthropogenically-induced warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus, late 20th century Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, and seed and fertilizer use. Persistence of current trends may result in a 50% increase in undernourished people. On the other hand, modest increases in per-capita agricultural productivity could more than offset the

  17. Effects of sample size on estimation of rainfall extremes at high temperatures

    Directory of Open Access Journals (Sweden)

    B. Boessenkool

    2017-09-01

    Full Text Available High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.

  18. Hydro-meteorological Inverse Problems via Sparse Regularization: Advanced frameworks for rainfall spaceborne estimation

    Science.gov (United States)

    Ebtehaj, Mohammad

    The past decades have witnessed a remarkable emergence of new spaceborne and ground-based sources of multiscale remotely sensed geophysical data. Apart from applications related to the study of short-term climatic shifts, availability of these sources of information has improved dramatically our real-time hydro-meteorological forecast skills. Obtaining improved estimates of hydro-meteorological states from a single or multiple low-resolution observations and assimilating them into the background knowledge of a prognostic model have been a subject of growing research in the past decades. In this thesis, with particular emphasis on precipitation data, statistical structure of rainfall images have been thoroughly studied in transform domains (i.e., Fourier and Wavelet). It is mainly found that despite different underlying physical structure of storm events, there are general statistical signatures that can be robustly characterized and exploited as a prior knowledge for solving hydro-meteorological inverse problems such rainfall downscaling, data fusion, retrieval and data assimilation. In particular, it is observed that in the wavelet domain or derivative space, rainfall images are sparse. In other words, a large number of the rainfall expansion coefficients are very close to zero and only a small number of them are significantly non-zero, a manifestation of the non-Gaussian probabilistic structure of rainfall data. To explain this signature, relevant family of probability models including Generalized Gaussian Density (GGD) and a specific class of conditionally linear Gaussian Scale Mixtures (GSM) are studied. Capitalizing on this important but overlooked property of precipitation, new methodologies are proposed to optimally integrate and improve resolution of spaceborne and ground-based precipitation data. In particular, a unified framework is proposed that ties together the problems of downscaling, data fusion and data assimilation via a regularized variational

  19. Estimating hydrological parameters based on rainfall patterns in river basins with no long-term historical observations

    Science.gov (United States)

    Shi, Haiyun; Li, Tiejian

    2017-10-01

    Small and medium river basins may frequently suffer from the destructive hydrological extremes (e.g., floods). However, the common problem in such regions is a lack of long-term historical observations. Meteorological and hydrological station networks in some river basins in China were newly-built only a few years ago, and it is infeasible to estimate hydrological parameters from calibration and validation with a long time period directly. This paper aims to develop a method to estimate the feasible hydrological parameters based on rainfall patterns in such regions. Digital Yellow River Integrated Model (DYRIM) is adopted as the hydrological model, and the feasible hydrological parameters can be estimated based on limited rainfall-runoff events. First, for each rainfall-runoff event, the parameters are independently calibrated with the observed rainfall and hydrological data using a double-layer parallel system. Then, the performances of the simulation results are comprehensively evaluated, and the value ranges of the parameters can be obtained. Finally, the statistical relationships between hydrological parameters and rainfall patterns (i.e., amount and intensity) are established, which are expressed by the statistical equations and the distribution of hydrological parameters with the rainfall patterns. From a sample demonstration, it is concluded that this parameter estimation method will be useful to estimate the feasible hydrological parameters for future rainfall-runoff events in river basins with no long-term historical observations.

  20. A web service and android application for the distribution of rainfall estimates and Earth observation data

    Science.gov (United States)

    Mantas, V. M.; Liu, Z.; Pereira, A. J. S. C.

    2015-04-01

    The full potential of Satellite Rainfall Estimates (SRE) can only be realized if timely access to the datasets is possible. Existing data distribution web portals are often focused on global products and offer limited customization options, especially for the purpose of routine regional monitoring. Furthermore, most online systems are designed to meet the needs of desktop users, limiting the compatibility with mobile devices. In response to the growing demand for SRE and to address the current limitations of available web portals a project was devised to create a set of freely available applications and services, available at a common portal that can: (1) simplify cross-platform access to Tropical Rainfall Measuring Mission Online Visualization and Analysis System (TOVAS) data (including from Android mobile devices), (2) provide customized and continuous monitoring of SRE in response to user demands and (3) combine data from different online data distribution services, including rainfall estimates, river gauge measurements or imagery from Earth Observation missions at a single portal, known as the Tropical Rainfall Measuring Mission (TRMM) Explorer. The TRMM Explorer project suite includes a Python-based web service and Android applications capable of providing SRE and ancillary data in different intuitive formats with the focus on regional and continuous analysis. The outputs include dynamic plots, tables and data files that can also be used to feed downstream applications and services. A case study in Southern Angola is used to describe the potential of the TRMM Explorer for SRE distribution and analysis in the context of ungauged watersheds. The development of a collection of data distribution instances helped to validate the concept and identify the limitations of the program, in a real context and based on user feedback. The TRMM Explorer can successfully supplement existing web portals distributing SRE and provide a cost-efficient resource to small and medium

  1. Projection of global climate change scenarios onto the Hawaiian Islands: Estimating the characteristics of rainfall for the 21st century

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This project will build on existing experience with statistical downscaling methods to derive comprehensive estimates of the future rainfall changes over the...

  2. Integration of rain gauge measurement errors with the overall rainfall uncertainty estimation using kriging methods

    Science.gov (United States)

    Cecinati, Francesca; Moreno Ródenas, Antonio Manuel; Rico-Ramirez, Miguel Angel; ten Veldhuis, Marie-claire; Han, Dawei

    2016-04-01

    In many research studies rain gauges are used as a reference point measurement for rainfall, because they can reach very good accuracy, especially compared to radar or microwave links, and their use is very widespread. In some applications rain gauge uncertainty is assumed to be small enough to be neglected. This can be done when rain gauges are accurate and their data is correctly managed. Unfortunately, in many operational networks the importance of accurate rainfall data and of data quality control can be underestimated; budget and best practice knowledge can be limiting factors in a correct rain gauge network management. In these cases, the accuracy of rain gauges can drastically drop and the uncertainty associated with the measurements cannot be neglected. This work proposes an approach based on three different kriging methods to integrate rain gauge measurement errors in the overall rainfall uncertainty estimation. In particular, rainfall products of different complexity are derived through 1) block kriging on a single rain gauge 2) ordinary kriging on a network of different rain gauges 3) kriging with external drift to integrate all the available rain gauges with radar rainfall information. The study area is the Eindhoven catchment, contributing to the river Dommel, in the southern part of the Netherlands. The area, 590 km2, is covered by high quality rain gauge measurements by the Royal Netherlands Meteorological Institute (KNMI), which has one rain gauge inside the study area and six around it, and by lower quality rain gauge measurements by the Dommel Water Board and by the Eindhoven Municipality (six rain gauges in total). The integration of the rain gauge measurement error is accomplished in all the cases increasing the nugget of the semivariogram proportionally to the estimated error. Using different semivariogram models for the different networks allows for the separate characterisation of higher and lower quality rain gauges. For the kriging with

  3. Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model

    Directory of Open Access Journals (Sweden)

    Tao Zhang

    2016-01-01

    Full Text Available This study presented a method to estimate areal mean rainfall (AMR using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available.

  4. A dual-polarisation radar rainfall estimation method using a multi-parameter fuzzy logic algorithm

    Science.gov (United States)

    Hall, Will; Rico-Ramirez, Miguel Angel

    2017-04-01

    The emergence of dual-polarisation radar has resulted in a significant enhancement of quantitative precipitation estimation (QPE). It has enabled the measurement of rain drop size and shapes within a volume, the classification of hydrometeors, and the ability to more accurately account for attenuation of the radar beam. Previous methods for QPE have used only the radar reflectivity (Zh) to estimate rainfall, but more recent methods can use a combination of ZH, differential reflectivity (Zdr), specific differential phase (Kdp), and specific attenuation (Ah). The radar variables perform differently depending on rain rate, attenuation, and bright band presence. This has led to the use of fixed threshold values within which the different estimators are used, or the variables are weighted based on performance. This new method to be presented will use fuzzy logic to try to form a more robust algorithm using combinations of the rainfall estimators R(Zh), R(Kdp), and R(Ah). For this a C-band dual-polarised radar based in Hameldon Hill, near Burnley, UK, will be used, alongside a rain gauge network for calibration adn validation.

  5. A multi-scale analysis of Namibian rainfall over the recent decade – comparing TMPA satellite estimates and ground observations

    Directory of Open Access Journals (Sweden)

    Xuefei Lu

    2016-12-01

    New hydrological insights for the region: The agreement between ground and satellite rainfall data was generally good at annual/monthly scales but large variations were observed at the daily scale. Results showed a spatial variability of rainfall trends across the rainfall gradient. We observed significant changes in frequency along with insignificant changes in intensity and no changes in total amount for the driest location, but no changes in any of the rainfall parameters were observed for the three wetter locations. The results also showed increased rainfall variability for the driest location. This study provided a useful approach of using TMPA data associated with trend analysis to extend the data record for ecohydrological studies for similar data scarce conditions. The results of this study will also help constrain IPCC predictions in this region.

  6. Estimation of Future Return Levels for Heavy Rainfall in the Iberian Peninsula: Comparison of Methodologies

    Science.gov (United States)

    Parey, S.

    2014-12-01

    F. J. Acero1, S. Parey2, T.T.H. Hoang2, D. Dacunha-Castelle31Dpto. Física, Universidad de Extremadura, Avda. de Elvas s/n, 06006, Badajoz 2EDF/R&D, 6 quai Watier, 78401 Chatou Cedex, France 3Laboratoire de Mathématiques, Université Paris 11, Orsay, France Trends can already be detected in daily rainfall amount in the Iberian Peninsula (IP), and this will have an impact on the extreme levels. In this study, we compare different ways to estimate future return levels for heavy rainfall, based on the statistical extreme value theory. Both Peaks over Threshold (POT) and block maxima with the Generalized Extreme Value (GEV) distribution will be used and their results compared when linear trends are assumed in the parameters: threshold and scale parameter for POT and location and scale parameter for GEV. But rainfall over the IP is a special variable in that a large number of the values are 0. Thus, the impact of taking this into account is discussed too. Another approach is then tested, based on the evolutions of the mean and variance obtained from the time series of rainy days only, and of the number of rainy days. A statistical test, similar to that designed for temperature in Parey et al. 2013, is used to assess if the trends in extremes can be considered as mostly due to these evolutions when considering only rainy days. The results show that it is mainly the case: the extremes of the residuals, after removing the trends in mean and standard deviation, cannot be differentiated from those of a stationary process. Thus, the future return levels can be estimated from the stationary return level of these residuals and an estimation of the future mean and standard deviation. Moreover, an estimation of the future number of rainy days is used to retrieve the return levels for all days. All of these comparisons are made for an ensemble of high quality rainfall time series observed in the Iberian Peninsula over the period 1961-2010, from which we want to estimate a 20-year

  7. A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

    Directory of Open Access Journals (Sweden)

    Yinping Long

    2016-07-01

    Full Text Available Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM. The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

  8. The role of regional information in estimation of extreme point rainfalls

    DEFF Research Database (Denmark)

    Rosbjerg, Dan; Madsen, Henrik

    1996-01-01

    of regional prior distributions for the PDS-parameters in a Bayesian estimation procedure. The advantages of this theoretically satisfactory, but also somewhat complicated procedure are evaluated by means of a comparison with simplified procedures. These include modelling based on regional pooling of all...... point rainfall data into one sample from a common parent distribution and modelling with disregard of either the dependence between stations or the regional heterogeneity. The different models are analysed and compared with respect to the uncertainty of the predicted extreme events....

  9. A Quality Assurance Procedure and Evaluation of Rainfall Estimates for C-Band Polarimetric Radar

    Institute of Scientific and Technical Information of China (English)

    HU Zhiqun; LIU Liping; WANG Lirong

    2012-01-01

    A mobile C-band dual polarimetric weather radar J type (PCDJ),which adopts simultaneous transmission and simultaneous reception (STSR) of horizontally and vertically polarized signals,was first developed in China in 2008.It was deployed in the radar observation plan in the South China Heavy Rainfall Experiment (SCHeREX) in the summer of 2008 and 2009,as well as in Tropical Western Pacific Ocean Observation Experiments and Research on the Predictability of High Impact Weather Events from 2008 to 2010 in China (TWPOR).Using the observation data collected in these experiments,the radar systematic error and its sources were analyzed in depth.Meanwhile an algorithm that can smooth differential propagation phase (ΦDP) for estimating the high-resolution specific differential phase (KDp) was developed.After attenuation correction of reflectivity in horizontal polarization (ZH) and differential reflectivity (ZDR) of PCDJ radar by means of KDP,the data quality was improved significantly.Using quality-controlled radar data,quantitative rainfall estimation was performed,and the resutls were compared with rain-gauge measurements.A synthetic ZH /KDP-based method was analyzed.The results suggest that the synthetic method has the advantage over the traditional ZH-based method when the rain rate is >5 mm h-1.The more intensive the rain rates,the higher accuracy of the estimation.

  10. A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution

    Directory of Open Access Journals (Sweden)

    F. Uboldi

    2013-09-01

    Full Text Available Estimation of extreme event distributions and depth-duration-frequency (DDF curves is achieved at any target site by repeated sampling among all available raingauge data in the surrounding area. The estimate is computed over a gridded domain in Northern Italy, using precipitation time series from 1929 to 2011, including data from historical analog stations and from the present-day automatic observational network. The presented local regionalisation naturally overcomes traditional station-point methods, with their demand of long historical series and their sensitivity to very rare events occurring at very few stations, possibly causing unrealistic spatial gradients in DDF relations. At the same time, the presented approach allows for spatial dependence, necessary in a geographical domain such as Lombardy, complex for both its topography and its climatology. The bootstrap technique enables evaluating uncertainty maps for all estimated parameters and for rainfall depths at assigned return periods.

  11. Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

    Directory of Open Access Journals (Sweden)

    Basile Pauthier

    2016-01-01

    Full Text Available A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1 PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2 both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3 PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE. This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

  12. Sensitivity of quantitative groundwater recharge estimates to volumetric and distribution uncertainty in rainfall forcing products

    Science.gov (United States)

    Werner, Micha; Westerhoff, Rogier; Moore, Catherine

    2017-04-01

    Quantitative estimates of recharge due to precipitation excess are an important input to determining sustainable abstraction of groundwater resources, as well providing one of the boundary conditions required for numerical groundwater modelling. Simple water balance models are widely applied for calculating recharge. In these models, precipitation is partitioned between different processes and stores; including surface runoff and infiltration, storage in the unsaturated zone, evaporation, capillary processes, and recharge to groundwater. Clearly the estimation of recharge amounts will depend on the estimation of precipitation volumes, which may vary, depending on the source of precipitation data used. However, the partitioning between the different processes is in many cases governed by (variable) intensity thresholds. This means that the estimates of recharge will not only be sensitive to input parameters such as soil type, texture, land use, potential evaporation; but mainly to the precipitation volume and intensity distribution. In this paper we explore the sensitivity of recharge estimates due to difference in precipitation volumes and intensity distribution in the rainfall forcing over the Canterbury region in New Zealand. We compare recharge rates and volumes using a simple water balance model that is forced using rainfall and evaporation data from; the NIWA Virtual Climate Station Network (VCSN) data (which is considered as the reference dataset); the ERA-Interim/WATCH dataset at 0.25 degrees and 0.5 degrees resolution; the TRMM-3B42 dataset; the CHIRPS dataset; and the recently releases MSWEP dataset. Recharge rates are calculated at a daily time step over the 14 year period from the 2000 to 2013 for the full Canterbury region, as well as at eight selected points distributed over the region. Lysimeter data with observed estimates of recharge are available at four of these points, as well as recharge estimates from the NGRM model, an independent model

  13. Scavenging of ultrafine particles by rainfall at a boreal site: observations and model estimations

    Directory of Open Access Journals (Sweden)

    C. Andronache

    2006-05-01

    Full Text Available Values of the scavenging coefficient were determined from observations of ultrafine particles (with diameters in the range 10–510 nm during rain events at a boreal forest site in Southern Finland between 1996 and 2001. The estimated range of values of the scavenging coefficient was [7×10−6–4×10−5] s−1, which is generally higher than model calculations based only on below-cloud processes (Brownian diffusion, interception, and typical charge effects. A new model that includes below-cloud scavenging processes, mixing of ultrafine particles from the boundary layer (BL into cloud, followed by cloud condensation nuclei activation and in-cloud removal by rainfall, is presented. The effective scavenging coefficients estimated from this new model have values comparable with those obtained from observations. Results show that ultrafine particle removal by rain depends on aerosol size, rainfall intensity, mixing processes between BL and cloud elements, in-cloud scavenged fraction, in-cloud collection efficiency, and in-cloud coagulation with cloud droplets. Implications for the treatment of scavenging of BL ultrafine particles in numerical models are discussed.

  14. Dual-polarization radar rainfall estimation in Korea according to raindrop shapes obtained by using a 2-D video disdrometer

    Science.gov (United States)

    Kim, Hae-Lim; Suk, Mi-Kyung; Park, Hye-Sook; Lee, Gyu-Won; Ko, Jeong-Seok

    2016-08-01

    Polarimetric measurements are sensitive to the sizes, concentrations, orientations, and shapes of raindrops. Thus, rainfall rates calculated from polarimetric radar are influenced by the raindrop shapes and canting. The mean raindrop shape can be obtained from long-term raindrop size distribution (DSD) observations, and the shapes of raindrops can play an important role in polarimetric rainfall algorithms based on differential reflectivity (ZDR) and specific differential phase (KDP). However, the mean raindrop shape is associated with the variation of the DSD, which can change depending on precipitation types and climatic regimes. Furthermore, these relationships have not been studied extensively on the Korean Peninsula. In this study, we present a method to find optimal polarimetric rainfall algorithms for the Korean Peninsula by using data provided by both a two-dimensional video disdrometer (2DVD) and the Bislsan S-band dual-polarization radar. First, a new axis-ratio relation was developed to improve radar rainfall estimations. Second, polarimetric rainfall algorithms were derived by using different axis-ratio relations. The rain gauge data were used to represent the ground truth situation, and the estimated radar-point hourly mean rain rates obtained from the different polarimetric rainfall algorithms were compared with the hourly rain rates measured by a rain gauge. The daily calibration biases of horizontal reflectivity (ZH) and differential reflectivity (ZDR) were calculated by comparing ZH and ZDR radar measurements with the same parameters simulated by the 2DVD. Overall, the derived new axis ratio was similar to the existing axis ratio except for both small particles (≤ 2 mm) and large particles (≥ 5.5 mm). The shapes of raindrops obtained by the new axis-ratio relation carried out with the 2DVD were more oblate than the shapes obtained by the existing relations. The combined polarimetric rainfall relations using ZDR and KDP were more efficient than

  15. Application of satellite estimates of rainfall distribution to simulate the potential for malaria transmission in Africa

    Science.gov (United States)

    Yamana, T. K.; Eltahir, E. A.

    2009-12-01

    The Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) is a mechanistic model developed to assess malaria risk in areas where the disease is water-limited. This model relies on precipitation inputs as its primary forcing. Until now, applications of the model have used ground-based precipitation observations. However, rain gauge networks in the areas most affected by malaria are often sparse. The increasing availability of satellite based rainfall estimates could greatly extend the range of the model. The minimum temporal resolution of precipitation data needed was determined to be one hour. The CPC Morphing technique (CMORPH ) distributed by NOAA fits this criteria, as it provides 30-minute estimates at 8km resolution. CMORPH data were compared to ground observations in four West African villages, and calibrated to reduce overestimation and false alarm biases. The calibrated CMORPH data were used to force HYDREMATS, resulting in outputs for mosquito populations, vectorial capacity and malaria transmission.

  16. A probabilistic approach for assessing landslide-triggering event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates

    Science.gov (United States)

    Robbins, J. C.

    2016-10-01

    Large and numerous landslides can result in widespread impacts which are felt particularly strongly in the largely subsistence-orientated communities residing in the most landslide-prone areas of Papua New Guinea (PNG). Understanding the characteristics of rainfall preceding these landslide events is essential for the development of appropriate early warning systems and forecasting models. Relationships between rainfall and landslides are frequently complex and uncertainties tend to be amplified by inconsistent and incomplete landslide catalogues and sparse rainfall data availability. To address some of these uncertainties a modified Bayesian technique has been used, in conjunction with the multiple time frames method, to produce thresholds of landslide probability associated with rainfall events of specific magnitude and duration. Satellite-derived precipitation estimates have been used to derive representative rainfall accumulations and intensities over a range of different rainfall durations (5, 10, 15, 30, 45, 60, 75 and 90 days) for rainfall events which resulted in landslides and those which did not result in landslides. Of the two parameter combinations (accumulation-duration and intensity-duration) analysed, rainfall accumulation and duration provide the best scope for identifying probabilistic thresholds for use in landslide warning and forecasting in PNG. Analysis of historical events and rainfall characteristics indicates that high accumulation (>250 mm), shorter duration (75 days), high accumulation (>1200 mm) rainfall events are more likely to lead to moderate- to high-impact landslides. This analysis has produced the first proxy probability thresholds for landslides in PNG and their application within an early warning framework has been discussed.

  17. Areal rainfall estimation using moving cars as rain gauges - laboratory and field experiment

    Science.gov (United States)

    Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel

    2014-05-01

    Areal precipitation estimation for fine temporal and spatial resolution is still a challenging task. Beside the fact that newly developed instrumentations, e.g. weather radar, provide valuable information with high spatial and temporal resolutions, they are subject to different sources of errors. On the other hand, recording rain gauges provide accurate point rainfall depth, but are still often poor in density. Equipping a car with a GPS device as well as sensors measuring rainfall makes it possible to implement cars on the streets as the moving rain gauges. Initial results from a modeling study assuming arbitrary measurement errors have shown that implementing a reasonable large number of inaccurate measurement devices (raincars) provide more reliable areal precipitations compared to the available rain gauge network. The purpose of this study is to derive relationships between sensor readings and rain rate in a laboratory and quantify the errors. Sensor readings involve wiper frequency and optical sensors which are on the cars to automate wiper activities. Besides, the influence of car speed on the sensor readings is investigated implementing a car-speed simulator. It has been observed that the manual wiper activity adjustment, according to front visibility, shows a strong relationship between rainfall intensity and wiper speed. Two optical sensors calibrated in laboratory showed a relatively strong relationship with the rain intensity recorded by a tipping bucket. A positive relationship between the velocity and the amount of water has been observed meaning that the higher the speed of a car, the higher the amount of water hitting the car. Additionally, some preliminary results of the field experiments are discussed.

  18. Sensitivity of accumulated rainfall and errors estimates to the configuration of microwave imagers constellation for the tropical regions

    Science.gov (United States)

    Chambon, P.; Jobard, I.; Capderou, M.; Roca, R.

    2012-04-01

    Over the intertropical belt, satellites are powerful tools to measure precipitation, as surface networks of rain gauges or radars are scarce over this part of the globe. Rainfall is central to the water and energy cycle of the Tropics and the upcoming GPM program offers a unique perspective on this important challenge. We explore here, via simulations, how sensitive are rainfall accumulation estimates to the design of the details of the observing system. The Megha-Tropiques TAPEER-BRAIN Level-4 product is considered for this study. TAPEER-BRAIN is a technique that builds rainfall accumulation estimations and associated error at the one-degree/one-day scale over the whole Tropical belt. TAPEER-BRAIN relies on the use of infrared imagers onboard a fleet of geostationary satellites and Level-2 instantaneous rainfall estimates derived from passive microwave radiometers onboard a constellation of low Earth orbiting satellites. An error model involving rainfall auto-correlation calculations is then used to characterize sampling uncertainties on accumulated precipitation estimations. This framework is used to simulate the various configuration of the observing system. To this end, Level-2 instantaneous rain products are simulated through the use of an orbit simulator and a sampling method. Rainfall estimations are extracted from the GSMaP rainfall product under the swath of simulated observing systems. One-degree/one-day rain and error estimations are then computed with infrared data and the simulated Level-2 instantaneous rain products for different scenarios of constellation. Sensitivities to the sampling of sun-synchronous satellites as well as observing systems on low-inclination orbits are performed. One of the main findings of this study is that satellites on "tropical" orbits have a high contribution to the improvements of TAPEER-BRAIN quantitative precipitation estimations (rain and error estimations). This study also shows that satellites with local Equator

  19. Risk and size estimation of debris flow caused by storm rainfall in mountain regions

    Institute of Scientific and Technical Information of China (English)

    CHENG Genwei

    2003-01-01

    Debris flow is a common disaster in mountain regions. The valley slope, storm rainfall and amassed sand-rock materials in a watershed may influence the types of debris flow. The bursting of debris flow is not a pure random event. Field investigations show the periodicity of its burst, but no directive evidence has been found yet. A risk definition of debris flow is proposed here based upon the accumulation and the starting conditions of loose material in channel. According to this definition, the risk of debris flow is of quasi-periodicity. A formula of risk estimation is derived. Analysis of relative factors reveals the relationship between frequency and size of debris flow. For a debris flow creek, the longer the time interval between two occurrences of debris flows is, the bigger the bursting event will be.

  20. Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

    Directory of Open Access Journals (Sweden)

    Rômulo Oliveira

    2016-06-01

    Full Text Available Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement and GoAmazon (Observations and Modeling of the Green Ocean Amazon over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG and the Goddard Profiling Algorithm—Version 2014 (GPROF2014 algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM, is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM

  1. Long-range forecast of monthly rainfall over India during summer monsoon season using SST in the north Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Sadhuram, Y.

    Long-range forecasting of summer monsoon rainfall was reported through linear models by Delsole and Shukla3. They showed that minimum number of predictors are sufficient for accurate forecasts. Recent studies4,5 reported long-range prediction...

  2. Hydrological behaviour of the Nilgiri sub-watersheds as affected by bluegum plantations, part II. Monthly water balances at different rainfall and runoff probabilities

    Science.gov (United States)

    Sharda, V. N.; Samraj, P.; Chinnamani, S.; Lakshmanan, V.

    1988-11-01

    Frequency analyses of rainfall and runoff at Ootacamund (the Nilgiris) under conditions of natural grassland and savannah "Shola" land have been carried out. Availability of water at different probabilities during different months after conversion of natural grasslands into bluegum (eucalyptus) plantations has also been worked out. Investigations revealed that the maximum rainfall occurs during the month of July (298.2 mm at 50% chance) and the minimum is received during January (1.5 mm at 50% chance). On an average, the expected total flow and base flow under natural conditions of grasslands and "Shola" are 31% and 22% respectively of the expected rainfall of the region. The expected available total flow is maximum (45.8 mm at 50% chance) during the month of August out of which 31.18 mm is contributed by base flow. The minimum expected available water is observed during January-April (the lowest during March). Plantation of bluegum in natural grasslands further reduces water yield by about 23% (at 50% chance) during these months. These reductions in water yield during lean months may affect the water supply into the downstream hydroelectric reservoirs in the region. Hence, caution may have to be exercised while planning large-scale conversion of natural grasslands into bluegum plantations.

  3. Estimation of oceanic rainfall using passive and active measurements from SeaWinds spaceborne microwave sensor

    Science.gov (United States)

    Ahmad, Khalil Ali

    The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT/ SeaWinds Radiometer (QRad/SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active/passive rain estimates from SeaWinds are presented, and comparisons are made with the standard

  4. Development of Hierarchical Bayesian Model Based on Regional Frequency Analysis and Its Application to Estimate Areal Rainfall in South Korea

    Science.gov (United States)

    Kim, J.; Kwon, H. H.

    2014-12-01

    The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, This study aims to develop a hierarchical Bayesian model based regional frequency analysis in that spatial patterns of the design rainfall with geographical information are explicitly incorporated. This study assumes that the parameters of Gumbel distribution are a function of geographical characteristics (e.g. altitude, latitude and longitude) within a general linear regression framework. Posterior distributions of the regression parameters are estimated by Bayesian Markov Chain Monte Calro (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the Gumbel distribution by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Acknowledgement: This research was supported by a grant (14AWMP-B079364-01) from Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

  5. Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

    Science.gov (United States)

    Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

    2016-04-01

    Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas

  6. Curve Number estimation from rainfall-runoff data in the Brazilian Cerrado Biome

    Science.gov (United States)

    Oliveira, P. S.; Nearing, M.; Rodrigues, D. B.; Panachuki, E.; Wendland, E.

    2013-12-01

    The Brazilian Cerrado (Savanna) is considered one of the most important biomes for Brazilian water resources; meanwhile, it is experiencing major losses of its natural landscapes due to the pressures of food and energy production, which has caused changes in hydrological processes. To evaluate these changes hydrologic models have been used. The Curve Number (SCS-CN) method has been widely employed to estimate direct runoff from a given rainfall event, however, there are some uncertainties for estimating this parameter, particularly for use in areas with native vegetation. The objectives of this study were to measure natural rainfall-driven rates of runoff under native Cerrado vegetation and under the main crops found in this biome, and derive associated CN values from five methods. We used six plots of 5 x 20 m (100 m2) in size, with three replications of undisturbed Cerrado and three under bare soil (Ortic Quartzarenic Neosol, hydrological soil class A) and 10 plots of 3.5 x 22.15 m (77.5 m2), with two replications for pasture, soy, millet, sugarcane and bare soil (Dystrophic Red Argisol, hydrological soil class B). Plots were monitored between October 2011 and April 2013. The five methods used to obtain CN values were median, geometric mean, arithmetic mean, nonlinear, least squares fit, and standard asymptotic fit. We found reasonable results for CN calibration for the undisturbed Cerrado only by using the nonlinear least squares fit. CN obtained from the standard table values was not adequate to estimate runoff for this condition. The standard table and the five CN methods presented satisfactory results for the other land covers studied. From our results we can suggest the best CN values for each land cover: Cerrado 49.8 (47.9-51.1), bare soil class-A 83.9 (74.4-93.4), bare soil class-B 88.3 (81.7-94.8), pasture 73.7 (62.9-84.5), soy 83.5 (80.6-86.4), millet 73.9 (67.4-80.4) and sugarcane 83.9 (80.6-87.3). These CN values and ranges provide guidance for

  7. Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method

    National Research Council Canada - National Science Library

    Hyuck-Jin Park; Jung-Yoon Jang; Jung-Hyun Lee

    2017-01-01

    The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence...

  8. Calibration of a conceptual rainfall-runoff model for flood frequency estimation by continuous simulation

    Science.gov (United States)

    Lamb, Robert

    1999-10-01

    An approach is described to the calibration of a conceptual rainfall-runoff model, the Probability Distributed Model (PDM), for estimating flood frequencies at gauged sites by continuous flow simulation. A first step was the estimation of routing store parameters by recession curve analysis. Uniform random sampling was then used to search for parameter sets that produced simulations achieving the best fit to observed, hourly flow data over a 2-year period. Goodness of fit was expressed in terms of four objective functions designed to give different degrees of weight to peaks in flow. Flood frequency results were improved, if necessary, by manual adjustment of parameters, with reference to peaks extracted from the entire hourly flow record. Although the primary aim was to reproduce observed peaks, consideration was also given to finding parameter sets capable of generating a realistic overall characterization of the flow regime. Examples are shown where the calibrated model generated simulations that reproduced well the magnitude and frequency distribution of peak flows. Factors affecting the acceptability of these simulations are discussed. For an example catchment, a sensitivity analysis shows that there may be more than one set of parameter values well suited to the simulation of peak flows.

  9. Bayesian Assessment of the Uncertainties of Estimates of a Conceptual Rainfall-Runoff Model Parameters

    Science.gov (United States)

    Silva, F. E. O. E.; Naghettini, M. D. C.; Fernandes, W.

    2014-12-01

    This paper evaluated the uncertainties associated with the estimation of the parameters of a conceptual rainfall-runoff model, through the use of Bayesian inference techniques by Monte Carlo simulation. The Pará River sub-basin, located in the upper São Francisco river basin, in southeastern Brazil, was selected for developing the studies. In this paper, we used the Rio Grande conceptual hydrologic model (EHR/UFMG, 2001) and the Markov Chain Monte Carlo simulation method named DREAM (VRUGT, 2008a). Two probabilistic models for the residues were analyzed: (i) the classic [Normal likelihood - r ≈ N (0, σ²)]; and (ii) a generalized likelihood (SCHOUPS & VRUGT, 2010), in which it is assumed that the differences between observed and simulated flows are correlated, non-stationary, and distributed as a Skew Exponential Power density. The assumptions made for both models were checked to ensure that the estimation of uncertainties in the parameters was not biased. The results showed that the Bayesian approach proved to be adequate to the proposed objectives, enabling and reinforcing the importance of assessing the uncertainties associated with hydrological modeling.

  10. Comparison of the TRMM Precipitation Radar rainfall estimation with ground-based disdrometer and radar measurements in South Greece

    Science.gov (United States)

    Ioannidou, Melina P.; Kalogiros, John A.; Stavrakis, Adrian K.

    2016-11-01

    The performance of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) rainfall estimation algorithm is assessed, locally, in Crete island, south Greece, using data from a 2D-video disdrometer and a ground-based, X-band, polarimetric radar. A three-parameter, normalized Gamma drop size distribution is fitted to the disdrometer rain spectra; the latter are classified in stratiform and convective rain types characterized by different relations between distribution parameters. The method of moments estimates more accurately the distribution parameters than the best fit technique, which exhibits better agreement with and is more biased by the observed droplet distribution at large diameter values. Power laws between the radar reflectivity factor (Z) and the rainfall rate (R) are derived from the disdrometer data. A significant diversity of the prefactor and the exponent of the estimated power laws is observed, depending on the scattering model and the regression technique. The Z-R relationships derived from the disdrometer data are compared to those obtained from TRMM-PR data. Generally, the power laws estimated from the two datasets are different. Specifically, the greater prefactor found for the disdrometer data suggests an overestimation of rainfall rate by the TRMM-PR algorithm for light and moderate stratiform rain, which was the main rain type in the disdrometer dataset. Finally, contemporary data from the TRMM-PR and a ground-based, X-band, polarimetric radar are analyzed. Comparison of the corresponding surface rain rates for a rain event with convective characteristics indicates a large variability of R in a single TRMM-PR footprint, which typically comprises several hundreds of radar pixels. Thus, the coarse spatial resolution of TRMM-PR may lead to miss of significant high local peaks of convective rain. Also, it was found that the high temporal variability of convective rain may introduce significant errors in the estimation of bias of

  11. A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale

    Directory of Open Access Journals (Sweden)

    Yaokui Cui

    2014-04-01

    Full Text Available Rainfall interception loss of forest is an important component of water balance in a forested ecosystem. The Gash analytical model has been widely used to estimate the forest interception loss at field scale. In this study, we proposed a simple model to estimate rainfall interception loss of heterogeneous forest at regional scale with several reasonable assumptions using remote sensing observations. The model is a modified Gash analytical model using easily measured parameters of forest structure from satellite data and extends the original Gash model from point-scale to the regional scale. Preliminary results, using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS products, field measured rainfall data, and meteorological data of the Automatic Weather Station (AWS over a picea crassifolia forest in the upper reaches of the Heihe River Basin in northwestern China, showed reasonable accuracy in estimating rainfall interception loss at both the Dayekou experimental site (R2 = 0.91, RMSE = 0.34 mm∙d −1 and the Pailugou experimental site (R2 = 0.82, RMSE = 0.6 mm∙d −1, compared with ground measurements based on per unit area of forest. The interception loss map of the study area was shown to be strongly heterogeneous. The modified model has robust physics and is insensitive to the input parameters, according to the sensitivity analysis using numerical simulations. The modified model appears to be stable and easy to be applied for operational estimation of interception loss over large areas.

  12. A diagnosis of rainfall over South America during 1997/98 El Niño and 1998/99 La Niña events: Comparison between TRMM PR and GPCP rainfall estimates

    Indian Academy of Sciences (India)

    Sergio H Franchito; V Brahmananda Rao; Ana C Vasques; Clovis M E Santo; Jorge C Conforte

    2009-06-01

    A comparison between TRMM PR rainfall estimates and rain gauge data from ANEEL and combined gauge/satellite data from GPCP over South America (SA)is made.In general,the annual and seasonal regional characteristics of rainfall over SA are qualitatively well reproduced by TRMM PR and GPCP.It is found that over most of SA GPCP exceeds TRMM PR rainfall.The largest positive differences between GPCP and TRMM PR data occur in the north SA,northwestern and central Amazonia.However,there are regions where GPCP rainfall is lower than TRMM PR,particularly in the Pacific coastal regions and in southern Brazil.We suggest that the cause for the positive differences GPCP minus TRMM PR rainfall are related to the fact that satellite observations based on infrared radiation and outgoing longwave radiance sensors overestimate convective rainfall in GPCP and the cause for the negative differences are due to the random errors in TRMM PR.Rainfall differences in the latter phases of the 1997/98 El Niño and 1998/99 La Niña are analyzed.The results showed that the rainfall anomalies are generally higher in GPCP than in TRMM PR,however,as in the mean annual case,there are regions where the rainfall in GPCP is lower than in TRMM PR.The higher positive (negative)differences between the rainfall anomalies in GPCP and TRMM PR,which occur in the central Amazonia (southern Brazil),are reduced (increased) in the El Niño event.This is due to the fact that during the El Niño episode the rainfall decreases in the central Amazonia and increases in the southern Brazil.Consequently,the overestimation of the convective rainfall by GPCP is reduced and the overestimation of the rainfall by TRMM PR is increased in these two regions,respectively.

  13. Estimation of rainfall thresholds for the initiation of landslides in the Ialomita Subcarpathians, Romania

    Science.gov (United States)

    Chitu, Z.; Micu, D.; Sandric, I.; Mihai, B.

    2012-04-01

    Landslides are a common feature in the landscape of the Romanian hills and plateaus, affecting around 7% of the national territory (Pusch, 2004). It is general knowledge that landslides represent the combined result of a series of predisposing factors (lithology, faults, slope, land-use, land cover, etc.) with long term impact on slope stability and triggering factors (rainfall, snow melt, earthquakes) that temporarily modify the local hydrogeological conditions (Corominas, 2008). Rainfall represents the most common triggering factor of landslides in the Ialomita Subcarpathians, therefore the determination of rainfall thresholds for landslides initiation would be very useful for landslide hazard assessment and implementation of warning systems. This paper aims to determine regional rainfall thresholds in the Subcarpathian area between the Prahova and Ialomita Valleys, where the most frequent phenomena are: deep seated rotational slides, earth flows and complex movements (rotational slides combined with mudflow or translational slides). The methodology used in studies addressing the regional scale is based on empirical or statistical analysis of rainfall, due to the spatial and temporal variation of landslide factors. Given the lack of hourly measurements of rainfall variables for long periods in Romania we were constrained to determine the corresponding rainfall thresholds based on cumulated precipitation during the landslide events. The rainfall variables were chosen based on the typology of landslides: daily rainfall in the case of shallow landslides usually triggered by short and intense rainfall, normalized total precipitation (antecedent and event rainfall) for deep-seated landslides. After establishing what thresholds correspond to the different types of landslides, we continued by analyzing the spatial and temporal variability of the pluvial regime aiming to understand the over time occurrence of landslides in the Subcarpathian area between the Prahova and

  14. Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

    Science.gov (United States)

    Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

    2017-03-01

    In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

  15. Depth-area-duration characteristics of storm rainfall in Texas using Multi-Sensor Precipitation Estimates

    Science.gov (United States)

    McEnery, J. A.; Jitkajornwanich, K.

    2012-12-01

    This presentation will describe the methodology and overall system development by which a benchmark dataset of precipitation information has been used to characterize the depth-area-duration relations in heavy rain storms occurring over regions of Texas. Over the past two years project investigators along with the National Weather Service (NWS) West Gulf River Forecast Center (WGRFC) have developed and operated a gateway data system to ingest, store, and disseminate NWS multi-sensor precipitation estimates (MPE). As a pilot project of the Integrated Water Resources Science and Services (IWRSS) initiative, this testbed uses a Standard Query Language (SQL) server to maintain a full archive of current and historic MPE values within the WGRFC service area. These time series values are made available for public access as web services in the standard WaterML format. Having this volume of information maintained in a comprehensive database now allows the use of relational analysis capabilities within SQL to leverage these multi-sensor precipitation values and produce a valuable derivative product. The area of focus for this study is North Texas and will utilize values that originated from the West Gulf River Forecast Center (WGRFC); one of three River Forecast Centers currently represented in the holdings of this data system. Over the past two decades, NEXRAD radar has dramatically improved the ability to record rainfall. The resulting hourly MPE values, distributed over an approximate 4 km by 4 km grid, are considered by the NWS to be the "best estimate" of rainfall. The data server provides an accepted standard interface for internet access to the largest time-series dataset of NEXRAD based MPE values ever assembled. An automated script has been written to search and extract storms over the 18 year period of record from the contents of this massive historical precipitation database. Not only can it extract site-specific storms, but also duration-specific storms and

  16. Coupling Radar Rainfall Estimation and Hydrological Modelling For Flash-flood Hazard Mitigation

    Science.gov (United States)

    Borga, M.; Creutin, J. D.

    issues are examined: advantages and caveats of using radar rainfall estimates in operational flash flood forecasting, methodological problems as- sociated to the use of hydrological models for distributed flash flood forecasting with rainfall input estimated from radar.

  17. Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

    Directory of Open Access Journals (Sweden)

    Emad Habib

    2014-07-01

    Full Text Available Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

  18. Ceilometer-based Rainfall Rate estimates in the framework of VORTEX-SE campaign: A discussion

    Science.gov (United States)

    Barragan, Ruben; Rocadenbosch, Francesc; Waldinger, Joseph; Frasier, Stephen; Turner, Dave; Dawson, Daniel; Tanamachi, Robin

    2017-04-01

    During Spring 2016 the first season of the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) was conducted in the Huntsville, AL environs. Foci of VORTEX-SE include the characterization of the tornadic environments specific to the Southeast US as well as societal response to forecasts and warnings. Among several experiments, a research team from Purdue University and from the University of Massachusetts Amherst deployed a mobile S-band Frequency-Modulated Continuous-Wave (FMCW) radar and a co-located Vaisala CL31 ceilometer for a period of eight weeks near Belle Mina, AL. Portable disdrometers (DSDs) were also deployed in the same area by Purdue University, occasionally co-located with the radar and lidar. The NOAA National Severe Storms Laboratory also deployed the Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) consisting of a Doppler lidar, a microwave radiometer, and an infrared spectrometer. The purpose of these profiling instruments was to characterize the atmospheric boundary layer evolution over the course of the experiment. In this paper we focus on the lidar-based retrieval of rainfall rate (RR) and its limitations using observations from intensive observation periods during the experiment: 31 March and 29 April 2016. Departing from Lewandowski et al., 2009, the RR was estimated by the Vaisala CL31 ceilometer applying the slope method (Kunz and Leeuw, 1993) to invert the extinction caused by the rain. Extinction retrievals are fitted against RR estimates from the disdrometer in order to derive a correlation model that allows us to estimate the RR from the ceilometer in similar situations without a disdrometer permanently deployed. The problem of extinction retrieval is also studied from the perspective of Klett-Fernald-Sasano's (KFS) lidar inversion algorithm (Klett, 1981; 1985), which requires the assumption of an aerosol extinction-to-backscatter ratio (the so-called lidar ratio) and calibration in a

  19. Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2014-01-01

    Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date, res...... in this analysis. In conclusion, further research must focus on the development of model structures that allow the proper separation of dry and wet weather uncertainties and simulate runoff uncertainties depending on the rainfall input.......Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date......, research has primarily focused on one-step-ahead flow predictions for identifying, estimating, and evaluating greybox models. For control purposes, however, stochastic predictions are required for longer forecast horizons and for the prediction of runoff volumes, rather than flows. This article therefore...

  20. Estimating daily flow duration curves from monthly streamflow data

    CSIR Research Space (South Africa)

    Smakhtin, VU

    2000-01-01

    Full Text Available The paper describes two techniques by which to establish 1-day (1d) flow duration curves at an ungauged site where only a simulated or calculated monthly flow time series is available. Both methods employ the straightforward relationships between...

  1. Estimation of Model and Parameter Uncertainty For A Distributed Rainfall-runoff Model

    Science.gov (United States)

    Engeland, K.

    The distributed rainfall-runoff model Ecomag is applied as a regional model for nine catchments in the NOPEX area in Sweden. Ecomag calculates streamflow on a daily time resolution. The posterior distribution of the model parameters is conditioned on the observed streamflow in all nine catchments, and calculated using Bayesian statistics. The distribution is estimated by Markov chain Monte Carlo (MCMC). The Bayesian method requires a definition of the likelihood of the parameters. Two alter- native formulations are used. The first formulation is a subjectively chosen objective function describing the goodness of fit between the simulated and observed streamflow as it is used in the GLUE framework. The second formulation is to use a more statis- tically correct likelihood function that describes the simulation errors. The simulation error is defined as the difference between log-transformed observed and simulated streamflows. A statistical model for the simulation errors is constructed. Some param- eters are dependent on the catchment, while others depend on climate. The statistical and the hydrological parameters are estimated simultaneously. Confidence intervals, due to the uncertainty of the Ecomag parameters, for the simulated streamflow are compared for the two likelihood functions. Confidence intervals based on the statis- tical model for the simulation errors are also calculated. The results indicate that the parameter uncertainty depends on the formulation of the likelihood function. The sub- jectively chosen likelihood function gives relatively wide confidence intervals whereas the 'statistical' likelihood function gives more narrow confidence intervals. The statis- tical model for the simulation errors indicates that the structural errors of the model are as least as important as the parameter uncertainty.

  2. Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

    Science.gov (United States)

    Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

    2016-12-01

    Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

  3. High resolution X-Band radar rainfall estimates for a Mediterranean to hyper-arid transition area

    Science.gov (United States)

    Marra, Francesco; Lokshin, Anton; Notarpietro, Riccardo; Gabella, Marco; Branca, Marco; Bonfil, David; Morin, Efrat

    2015-04-01

    Weather radars provide rainfall estimates with high spatial and temporal resolutions over wide areas. X-Band weather radars are of relatively low-cost and easy to be handled and maintained, moreover they offer extremely high spatial and temporal resolutions and are therefore object of particular interest. Main drawback of these instruments lies on the quantitative accuracy, that can be significantly affected by atmospheric attenuation. Distributed rainfall information is a key issue when hydrological applications are needed for small space-time scale phenomena such as flash floods and debris flows. Moreover, such detailed measurements represent a great benefit for agricultural management of areas characterized by substantial rainfall variability. Two single polarization, single elevation, non-Doppler X-Band weather radars are operational since Oct-2012 in the northern Negev (Israel). Mean annual precipitation over the area drops dramatically from 500 mm/yr at the Mediterranean coast to less than 50 mm/yr at the hyper-arid region near the Dead Sea in less than a 100 km distance. The dryer region close to the Dead Sea is prone to flash floods that often cause casualties and severe damage while the western Mediterranean region is extensively used for agricultural purposes. Measures from a C-Band weather radar located 40-120 km away and from a sparse raingauge network (density ~1gauge/450km2) are also available. C-Band rainfall estimates are corrected using combined physically-based and empirical adjustment of data. The aim of this study is to assess the quantitative accuracy of X-Band rainfall estimates with respect to the combined use of in situ measurements and C-Band observations. Results from a set of storms occurred during the first years of measurements are discussed paying particular attention to: (i) wet radome attenuation, (ii) range dependent degradation including attenuation along the path and (iii) systematic effects related to the Mediterranean to hyper

  4. Estimate of annual daily maximum rainfall and intense rain equation for the Formiga municipality, MG, Brazil

    Directory of Open Access Journals (Sweden)

    Giovana Mara Rodrigues Borges

    2016-11-01

    Full Text Available Knowledge of the probabilistic behavior of rainfall is extremely important to the design of drainage systems, dam spillways, and other hydraulic projects. This study therefore examined statistical models to predict annual daily maximum rainfall as well as models of heavy rain for the city of Formiga - MG. To do this, annual maximum daily rainfall data were ranked in decreasing order that best describes the statistical distribution by exceedance probability. Daily rainfall disaggregation methodology was used for the intense rain model studies and adjusted with Intensity-Duration-Frequency (IDF and Exponential models. The study found that the Gumbel model better adhered to the data regarding observed frequency as indicated by the Chi-squared test, and that the exponential model best conforms to the observed data to predict intense rains.

  5. Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

    Science.gov (United States)

    Zambrano-Bigiarini, Mauricio; Nauditt, Alexandra; Birkel, Christian; Verbist, Koen; Ribbe, Lars

    2017-03-01

    Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate - for the first time - the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4-43.7° S) and Central Chile (32.18-36.4° S), in particular at low- and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM-JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere

  6. Quantitative microbial risk assessment combined with hydrodynamic modelling to estimate the public health risk associated with bathing after rainfall events.

    Science.gov (United States)

    Eregno, Fasil Ejigu; Tryland, Ingun; Tjomsland, Torulv; Myrmel, Mette; Robertson, Lucy; Heistad, Arve

    2016-04-01

    This study investigated the public health risk from exposure to infectious microorganisms at Sandvika recreational beaches, Norway and dose-response relationships by combining hydrodynamic modelling with Quantitative Microbial Risk Assessment (QMRA). Meteorological and hydrological data were collected to produce a calibrated hydrodynamic model using Escherichia coli as an indicator of faecal contamination. Based on average concentrations of reference pathogens (norovirus, Campylobacter, Salmonella, Giardia and Cryptosporidium) relative to E. coli in Norwegian sewage from previous studies, the hydrodynamic model was used for simulating the concentrations of pathogens at the local beaches during and after a heavy rainfall event, using three different decay rates. The simulated concentrations were used as input for QMRA and the public health risk was estimated as probability of infection from a single exposure of bathers during the three consecutive days after the rainfall event. The level of risk on the first day after the rainfall event was acceptable for the bacterial and parasitic reference pathogens, but high for the viral reference pathogen at all beaches, and severe at Kalvøya-small and Kalvøya-big beaches, supporting the advice of avoiding swimming in the day(s) after heavy rainfall. The study demonstrates the potential of combining discharge-based hydrodynamic modelling with QMRA in the context of bathing water as a tool to evaluate public health risk and support beach management decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Modified DTW for a quantitative estimation of the similarity between rainfall time series

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Barthès, Laurent; Mallet, Cécile; Chazottes, Aymeric

    2017-04-01

    The Precipitations are due to complex meteorological phenomenon and can be described as intermittent process. The spatial and temporal variability of this phenomenon is significant and covers large scales. To analyze and model this variability and / or structure, several studies use a network of rain gauges providing several time series of precipitation measurements. To compare these different time series, the authors compute for each time series some parameters (PDF, rain peak intensity, occurrence, amount, duration, intensity …). However, and despite the calculation of these parameters, the comparison of the parameters between two series of measurements remains qualitative. Due to the advection processes, when different sensors of an observation network measure precipitation time series identical in terms of intermitency or intensities, there is a time lag between the different measured series. Analyzing and extracting relevant information on physical phenomena from these precipitation time series implies the development of automatic analytical methods capable of comparing two time series of precipitation measured by different sensors or at two different locations and thus quantifying the difference / similarity. The limits of the Euclidean distance to measure the similarity between the time series of precipitation have been well demonstrated and explained (eg the Euclidian distance is indeed very sensitive to the effects of phase shift : between two identical but slightly shifted time series, this distance is not negligible). To quantify and analysis these time lag, the correlation functions are well established, normalized and commonly used to measure the spatial dependences that are required by many applications. However, authors generally observed that there is always a considerable scatter of the inter-rain gauge correlation coefficients obtained from the individual pairs of rain gauges. Because of a substantial dispersion of estimated time lag, the

  8. Rainfall statistics changes in Sicily

    Directory of Open Access Journals (Sweden)

    E. Arnone

    2013-02-01

    Full Text Available Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles which can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood prone areas.

    In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends while for longer durations the trends are mainly negative.

    Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the non parametric Mann–Kendall test.

    Particularly, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration while daily rainfall properties have been analyzed in term of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations

  9. Rainfall statistics changes in Sicily

    Directory of Open Access Journals (Sweden)

    E. Arnone

    2013-07-01

    Full Text Available Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles that can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood-prone areas. In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends, while for longer durations the trends are mainly negative. Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the nonparametric Mann–Kendall test. In particular, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration, while daily rainfall properties have been analyzed in terms of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations, especially for 1 h rainfall

  10. Rainfall Estimation and Performance Characterization Using an X-band Dual-Polarization Radar in the San Francisco Bay Area

    Science.gov (United States)

    Cifelli, R.; Chen, H.; Chandra, C. V.

    2016-12-01

    The San Francisco Bay area is home to over 5 million people. In February 2016, the area also hosted the NFL Super bowl, bringing additional people and focusing national attention to the region. Based on the El Nino forecast, public officials expressed concern for heavy rainfall and flooding with the potential for threats to public safety, costly flood damage to infrastructure, negative impacts to water quality (e.g., combined sewer overflows) and major disruptions in transportation. Mitigation of the negative impacts listed above requires accurate precipitation monitoring (quantitative precipitation estimation-QPE) and prediction (including radar nowcasting). The proximity to terrain and maritime conditions as well as the siting of existing NEXRAD radars are all challenges in providing accurate, short-term near surface rainfall estimates in the Bay area urban region. As part of a collaborative effort between the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory, Colorado State University (CSU), and Santa Clara Valley Water District (SCVWD), an X-band dual-polarization radar was deployed in Santa Clara Valley in February of 2016 to provide support for the National Weather Service during the Super Bowl and NOAA's El Nino Rapid Response field campaign. This high-resolution radar was deployed on the roof of one of the buildings at the Penitencia Water Treatment Plant. The main goal was to provide detailed precipitation information for use in weather forecasting and assists the water district in their ability to predict rainfall and streamflow with real-time rainfall data over Santa Clara County especially during a potentially large El Nino year. The following figure shows the radar's coverage map, as well as sample reflectivity observations on March 06, 2016, at 00:04UTC. This paper presents results from a pilot study from February, 2016 to May, 2016 demonstrating the use of X-band weather radar for quantitative precipitation

  11. Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

    Science.gov (United States)

    Todorovic, Andrijana; Plavsic, Jasna

    2015-04-01

    A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters

  12. Estimation of evapotranspiration for a small catchment as an input for rainfall-runoff model

    Science.gov (United States)

    Hejduk, Leszek; Banasik, Kazimierz; Krajewski, Adam; Mackiewicz, Marta

    2014-05-01

    One of the methods for determination of floods is application of mathematical rainfall-runoff models. Usually, it is possible to distinguish a number of steps for calculation of hydrograph of the flood. The first step is the calculation of effective rainfall which is a difference between total rainfall and losses (amount of water which do not participate in flood formation like interception, infiltration, evaporation etc.) . One of the most common method for determination of effective rainfall is a USDA-SCS method were losses are connected with type of the soils, vegetation and soil moisture. Those factors includes the Curve Number factor (CN). However there is also different approach for determination of losses were soil moisture is calculated as a function of evapotranspiration. In this study, the meteorological data from year 2002-2012 were used for determination of daily evapotranspiration (ETo) by use of FAO Penmana-Monteitha model for Zagozdzonka river catchment in central Poland. Due to gaps in metrological data, some other simpler methods of ETo calculation were applied like Hargraves model and Grabarczyk (1976) model. Based on received results the uncertainty of ETo was calculated. Grabarczyk S., 1976. Polowe zuzycie wody a czynniki meteorologiczne. Zesz. Probl. Post. Nauk Rol. 181, 495-511 ACKNOWLEDGMENTS The investigation described in the poster is part of the research project KORANET founded by PL-National Center for Research and Development (NCBiR).

  13. Observation-Based Estimates of Surface Cooling Inhibition by Heavy Rainfall under Tropical Cyclones

    Digital Repository Service at National Institute of Oceanography (India)

    Jourdain, N.C.; Lengaigne, M.; Vialard, J.; Madec, G.; Menkes, C.E.; Vincent, E.M.; Jullien, E.; Barnier, B.

    Tropical cyclones drive intense ocean vertical mixing that explains most of the surface cooling observed in their wake (the "cold wake"). The influence of cyclonic rainfall on the cold wake at a global scale over the 2002-09 period is investigated...

  14. Estimation of underground river water availability based on rainfall in the Maros karst region, South Sulawesi

    Science.gov (United States)

    Arsyad, Muhammad; Ihsan, Nasrul; Tiwow, Vistarani Arini

    2016-02-01

    Maros karst region, covering an area of 43.750 hectares, has water resources that determine the life around it. Water resources in Maros karst are in the rock layers or river underground in the cave. The data used in this study are primary and secondary data. Primary data includes characteristics of the medium. Secondary data is rainfall data from BMKG, water discharge data from the PSDA, South Sulawesi province in 1990-2010, and the other characteristics data Maros karst, namely cave, flora and fauna of the Bantimurung Bulusaraung National Park. Data analysis was conducted using laboratory test for medium characteristics Maros karst, rainfall and water discharge were analyzed using Minitab Program 1.5 to determine their profile. The average rainfall above 200 mm per year occurs in the range of 1999 to 2005. The availability of the water discharge at over 50 m3/s was happened in 1993 and 1995. Prediction was done by modeling Autoregressive Integrated Moving Average (ARIMA), with the rainfall data shows that the average precipitation for four years (2011-2014) will sharply fluctuate. The prediction of water discharge in Maros karst region was done for the period from January to August in 2011, including the type of 0. In 2012, the addition of the water discharge started up in early 2014.

  15. Radar Rainfall Estimates for Modeling Flood Response to Orographic Thunderstorms in the Central Appalachians

    Science.gov (United States)

    Hicks, N. S.; Smith, J. A.

    2001-12-01

    We examine the hydrometeorology and hydrology of extreme flooding from orographic convective systems in the central Appalachian region. Analyses of flood response are based on rainfall and discharge observations for major flood events along the western margin of the central Appalachians (16-17 May 1996, 18-19 July 1996, 30-31 July 1996, 28-29 June 1998, and 7-8 July 2001). A distributed hydrologic model is used to access flood response in Appalachian basins with diverse physiographic properties. High-resolution (1 km, 5 minutes) rainfall fields derived from WSR-88D radars in Charleston, West Virginia and Pittsburgh, Pennsylvania are used for model analyses. Cloud-to-ground lightning and the IFLOWs raingage network provide additional information for hydrometeorological analyses. Flood response is viewed in the context of land surface hydrologic processes and frequency of extreme precipitation events. Orographic convective systems in the Appalachians have produced some of the largest rainfall accumulations in the world for time intervals less than 6 hours and some of the largest unit discharge flood peaks for the U.S. east of the Mississippi River. The 18 July 1942 Smethport, Pennsylvania storm, for example, produced the world record rainfall accumulation of 780 mm in 4.5 hours.

  16. Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

    NARCIS (Netherlands)

    Tote, C.; Patricio, D.; Boogaard, H.L.; Wijngaart, van der R.; Tarnavsky, E.; Funk, C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and

  17. Rainfall-runoff modelling for estimating Latonyanda River flow contributions to Luvuvhu River downstream of Albasini Dam

    Science.gov (United States)

    Odiyo, J. O.; Phangisa, J. I.; Makungo, R.

    Rainfall-runoff modelling was conducted to estimate the flows that Latonyanda River contribute to Luvuvhu River downstream of Albasini Dam. The confluence of Latonyanda and Luvuvhu Rivers is ungauged. The contributed flows compensate for upstream water abstractions and periodic lack of releases from Albasini Dam. The flow contributions from tributaries to Luvuvhu River are important for ecosystem sustenance, meeting downstream domestic and agricultural water demand and ecological water requirements particularly in Kruger National Park. The upper Latonyanda River Quaternary Catchment (LRQC), with streamflow gauging station number A9H027 was delineated and used for rainfall-runoff modelling. The simulation was done using Mike 11 NAM rainfall-runoff model. Calibration and verification runs of Mike 11 NAM rainfall-runoff model were carried out using data for periods of 4 and 2 years, respectively. The model was calibrated using shuffled complex evolution optimizer. The model efficiency was tested using coefficient of determination (R2), root mean square error (RMSE), overall water balance error (OWBE) and percentage bias (PBIAS). The model parameters obtained from the upper LRQC were transferred and used together with rainfall and evaporation data for 40 years period in the simulation of runoff for the LRQC. The flows that Latonyanda River contribute to Luvuvhu River were computed by subtracting irrigation abstractions and runoff drained to Tshakhuma Dam from the simulated runoff time series of the LRQC. The observed and the simulated runoff showed similar trends and measures of performances for both calibration and verification runs fell within acceptable ranges. The pairs of values obtained for R2, RMSE, OWBE and PBIAS for calibration and verification were 0.86 and 0.73, 0.21 and 0.2, 2.1 and 1.3, and 4.1 and 3.4, respectively. The simulated runoff for LRQC correlated well with the areal rainfall showing that the results are reasonable. The mean and maximum daily

  18. The application of an analytical probabilistic model for estimating the rainfall-runoff reductions achieved using a rainwater harvesting system.

    Science.gov (United States)

    Kim, Hyoungjun; Han, Mooyoung; Lee, Ju Young

    2012-05-01

    Rainwater harvesting systems cannot only supplement on-site water needs, but also reduce water runoff and lessen downstream flooding. In this study, an existing analytic model for estimating the runoff in urban areas is modified to provide a more economical and effective model that can be used for describing rainwater harvesting. This model calculates the rainfall-runoff reduction by taking into account the catchment, storage tank, and infiltration facility of a water harvesting system; this calculation is based on the water balance equation, and the cumulative distribution, probability density, and average rainfall-runoff functions. This model was applied to a water harvesting system at the Seoul National University in order to verify its practicality. The derived model was useful for evaluating runoff reduction and for designing the storage tank capacity.

  19. Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines

    Directory of Open Access Journals (Sweden)

    Catherine Cristobal Abon

    2016-07-01

    Full Text Available This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs for the simulation of streamflow in the Marikina River Basin (MRB, the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radar-based QPE, we used spatially interpolated rain gauge observations (gauge-only (GO product as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radar-based QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product – even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.

  20. Dynamical forecast vs Ensemble Streamflow Prediction (ESP): how sensitive are monthly and seasonal hydrological forecasts to the quality of rainfall drivers?

    Science.gov (United States)

    Tanguy, Maliko; Prudhomme, Christel; Harrigan, Shaun; Smith, Katie

    2017-04-01

    Seasonal forecasting of hydrological extremes is challenging for the hydro-meteorological modelling community, and the performance of hydrological forecasts at lead times over 1 month is still poor especially for catchments with limited hydrological memory. A considerable amount of effort is being invested within the meteorological community to improve dynamic meteorological forecasting which can then be used to drive hydrological models to produce physically-driven hydrological forecasts. However, currently for the UK, these meteorological forecasts are being produced at 1 month or seasonal time-step, whereas hydrological models often require daily or sub-daily time-steps. A simpler way to get seasonal forecasts is to use historical climate data to drive hydrological models using Ensemble Streamflow Prediction (ESP). This gives a range of possible future hydrological status given known initial conditions, but it does not contain any information on the future dynamic of the atmosphere. The error is highly dependent on the type of catchment, but ESP is an improvement compared to simply using climatology of river flows, especially in groundwater dominated catchments. The objective of this study is to find out how accurate the seasonal rainfall forecast has to be (in terms of total rainfall and temporal distribution) for the dynamical seasonal forecast to beat ESP. To this aim, we have looked at the sensitivity of hydrological models to the quality of driving rainfall input, proxy of 'best possible' forecasts. Study catchments representative of the range of UK's hydro-climatic conditions were selected. For these catchments, synthetic rainfall time series derived from observed data were created by increasingly degrading the data. The number of rainy days, their intensity and their sequencing were artificially modified to analyse which of these characteristics is most important to get a better hydrological forecast using a simple lumped hydrological model (GR4J), and

  1. An integrated approach for identifying homogeneous regions of extreme rainfall events and estimating IDF curves in Southern Ontario, Canada: Incorporating radar observations

    Science.gov (United States)

    Paixao, Edson; Mirza, M. Monirul Qader; Shephard, Mark W.; Auld, Heather; Klaassen, Joan; Smith, Graham

    2015-09-01

    Reliable extreme rainfall information is required for many applications including infrastructure design, management of water resources, and planning for weather-related emergencies in urban and rural areas. In this study, in situ TBRG sub-daily rainfall rate observations have been supplemented with weather radar information to better capture the spatial and temporal variability of heavy rainfall events regionally. Comparison of extreme rainfall events show that the absolute differences between the rain gauge and radar generally increase with increasing rainfall. Better agreement between the two observations is found when comparing the collocated radar and TBRG annual maximum values. The median difference is <18% for the annual maximum rainfall values ⩽50 mm. The median of difference of IDF estimates obtained through the Gumbel distribution for 10-year return period values computed from TBRG and radar are also found to be 4%. The overall results of this analysis demonstrates the potential value of incorporating remotely sensed radar with traditional point source TBRG network observations to provide additional insight on extreme rainfall events regionally, especially in terms of identifying homogeneous regions of extreme rainfall. The radar observations are particularly useful in areas where there is insufficient TBRG station density to statistically capture the extreme rainfall events.

  2. Radar-based rainfall estimation: Improving Z/R relations through comparison of drop size distributions, rainfall rates and radar reflectivity patterns

    Science.gov (United States)

    Neuper, Malte; Ehret, Uwe

    2014-05-01

    The relation between the measured radar reflectivity factor Z and surface rainfall intensity R - the Z/R relation - is profoundly complex, so that in general one speaks about radar-based quantitative precipitation estimation (QPE) rather than exact measurement. Like in Plato's Allegory of the Cave, what we observe in the end is only the 'shadow' of the true rainfall field through a very small backscatter of an electromagnetic signal emitted by the radar, which we hope has been actually reflected by hydrometeors. The meteorological relevant and valuable Information is gained only indirectly by more or less justified assumptions. One of these assumptions concerns the drop size distribution, through which the rain intensity is finally associated with the measured radar reflectivity factor Z. The real drop size distribution is however subject to large spatial and temporal variability, and consequently so is the true Z/R relation. Better knowledge of the true spatio-temporal Z/R structure therefore has the potential to improve radar-based QPE compared to the common practice of applying a single or a few standard Z/R relations. To this end, we use observations from six laser-optic disdrometers, two vertically pointing micro rain radars, 205 rain gauges, one rawindsonde station and two C-band Doppler radars installed or operated in and near the Attert catchment (Luxembourg). The C-band radars and the rawindsonde station are operated by the Belgian and German Weather Services, the rain gauge data was partly provided by the French, Dutch, Belgian, German Weather Services and the Ministry of Agriculture of Luxembourg and the other equipment was installed as part of the interdisciplinary DFG research project CAOS (Catchment as Organized Systems). With the various data sets correlation analyzes were executed. In order to get a notion on the different appearance of the reflectivity patterns in the radar image, first of all various simple distribution indices (for example the

  3. A method for estimating maximum static rainfall retention in pebble mulches used for soil moisture conservation

    Science.gov (United States)

    Peng, Hongtao; Lei, Tingwu; Jiang, Zhiyun; Horton, Robert

    2016-06-01

    Mulching of agricultural fields and gardens with pebbles has long been practiced to conserve soil moisture in some semi-arid regions with low precipitation. Rainfall interception by the pebble mulch itself is an important part of the computation of the water balance for the pebble mulched fields and gardens. The mean equivalent diameter (MED) was used to characterize the pebble size. The maximum static rainfall retention in pebble mulch is based on the water penetrating into the pores of pebbles, the water adhering to the outside surfaces of pebbles and the water held between pebbles of the mulch. Equations describing the water penetrating into the pores of pebbles and the water adhering to the outside surface of pebbles are constructed based on the physical properties of water and the pebble characteristics. The model for the water between pebbles of the mulch is based on the basic equation to calculate the water bridge volume and the basic coordination number model. A method to calculate the maximum static rainfall retention in the pebble mulch is presented. Laboratory rain simulation experiments were performed to test the model with measured data. Paired sample t-tests showed no significant differences between the values calculated with the method and the measured data. The model is ready for testing on field mulches.

  4. Rainfall estimates for hydrological models: Comparing rain gauge, radar and microwave link data as input for the Wageningen Lowland Runoff Simulator (WALRUS)

    Science.gov (United States)

    Brauer, Claudia; Overeem, Aart; Uijlenhoet, Remko

    2015-04-01

    Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution. We investigated the effect of differences in rainfall estimates on discharge simulations in a lowland catchment by forcing a novel rainfall-runoff model (WALRUS) with rainfall data from gauges, radars and microwave links. The hydrological model used for this analysis is the recently developed Wageningen Lowland Runoff Simulator (WALRUS). WALRUS is a rainfall-runoff model accounting for hydrological processes relevant to areas with shallow groundwater (e.g. groundwater-surface water feedback). Here, we used WALRUS for case studies in the Hupsel Brook catchment. We used two automatic rain gauges with hourly resolution, located inside the catchment (the base run) and 30 km northeast. Operational (real-time) and climatological (gauge-adjusted) C-band radar products and country-wide rainfall maps derived from microwave link data from a cellular telecommunication network were also used. Discharges simulated with these different inputs were compared to observations. Traditionally, the precipitation research community places emphasis on quantifying spatial errors and uncertainty, but for hydrological applications, temporal errors and uncertainty should be quantified as well. Its memory makes the hydrologic system sensitive to missed or badly timed rainfall events, but also emphasizes the effect of a bias in rainfall estimates. Systematic underestimation of rainfall by the uncorrected operational radar product leads to very dry model states and an increasing underestimation of discharge. Using the rain gauge 30 km northeast of the catchment yields good results for climatological studies, but not for forecasting individual floods. Simulating discharge using the maps derived from microwave link data and the gauge-adjusted radar product yields good results for both events and climatological studies. This indicates that these products can be

  5. Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface

    Directory of Open Access Journals (Sweden)

    S.A. Margulis

    2001-01-01

    Full Text Available Global estimates of precipitation can now be made using data from a combination of geosynchronous and low earth-orbit satellites. However, revisit patterns of polar-orbiting satellites and the need to sample mixed-clouds scenes from geosynchronous satellites leads to the coarsening of the temporal resolution to the monthly scale. There are prohibitive limitations to the applicability of monthly-scale aggregated precipitation estimates in many hydrological applications. The nonlinear and threshold dependencies of surface hydrological processes on precipitation may cause the hydrological response of the surface to vary considerably based on the intermittent temporal structure of the forcing. Therefore, to make the monthly satellite data useful for hydrological applications (i.e. water balance studies, rainfall-runoff modelling, etc., it is necessary to disaggregate the monthly precipitation estimates into shorter time intervals so that they may be used in surface hydrology models. In this study, two simple statistical disaggregation schemes are developed for use with monthly precipitation estimates provided by satellites. The two techniques are shown to perform relatively well in introducing a reasonable temporal structure into the disaggregated time series. An ensemble of disaggregated realisations was routed through two land surface models of varying complexity so that the error propagation that takes place over the course of the month could be characterised. Results suggest that one of the proposed disaggregation schemes can be used in hydrological applications without introducing significant error. Keywords: precipitation, temporal disaggregation, hydrological modelling, error propagation

  6. Exploring the relationship between malaria, rainfall intermittency, and spatial variation in rainfall seasonality

    Science.gov (United States)

    Merkord, C. L.; Wimberly, M. C.; Henebry, G. M.; Senay, G. B.

    2014-12-01

    Malaria is a major public health problem throughout tropical regions of the world. Successful prevention and treatment of malaria requires an understanding of the environmental factors that affect the life cycle of both the malaria pathogens, protozoan parasites, and its vectors, anopheline mosquitos. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distribution of rainfall is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates (TRMM and GPM), evapotranspiration derived from a simplified surface energy balance, and estimates of soil moisture and fractional water cover from passive microwave imagery. Previous studies have found links between malaria cases and total monthly or weekly rainfall in areas where both are highly seasonal. However it is far from clear that monthly or weekly summaries are the best metrics to use to explain malaria outbreaks. It is possible that particular temporal or spatial patterns of rainfall result in better mosquito habitat and thus higher malaria risk. We used malaria case data from the Amhara region of Ethiopia and satellite-derived rainfall estimates to explore the relationship between malaria outbreaks and rainfall with the goal of identifying the most useful rainfall metrics for modeling malaria occurrence. First, we explored spatial variation in the seasonal patterns of both rainfall and malaria cases in Amhara. Second, we assessed the relative importance of different metrics of rainfall intermittency, including alternation of wet and dry spells, the strength of intensity fluctuations, and spatial variability in these measures, in determining the length and severity of malaria outbreaks. We also explored the sensitivity of our results to the choice of method for describing rainfall intermittency and the spatial and temporal scale at which metrics were calculated. Results

  7. Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

    Science.gov (United States)

    Lau, William K. M. (Technical Monitor); Bell, Thomas L.; Steiner, Matthias; Zhang, Yu; Wood, Eric F.

    2002-01-01

    The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

  8. Distributed specific sediment yield estimations in Japan attributed to extreme-rainfall-induced slope failures under a changing climate

    Directory of Open Access Journals (Sweden)

    K. Ono

    2011-01-01

    Full Text Available The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure probability accounts for the effects of topography (as relief energy, geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario for the future and by accounting for the slope failure probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan.

    Time series analyses of annual sediment yields covering 15–20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5–7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r2 = 0.65. The verification demonstrated that the model is effective for use in simulating specific sediment yields with r2 = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially averaged sediment

  9. Distributed specific sediment yield estimations in Japan attributed to extreme-rainfall-induced slope failures under a changing climate

    Directory of Open Access Journals (Sweden)

    K. Ono

    2010-09-01

    Full Text Available The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure hazard probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure hazard probability accounts for the effects of topography (as relief energy, geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario for the future and by accounting for the slope failure hazard probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan.

    Time series analyses of annual sediment yields covering 15–20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5–7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r2 = 0.65. The verification demonstrated that the model is effective for use in simulating specific sediment yields with r2 = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially

  10. Distributed specific sediment yield estimations in Japan attributed to extreme-rainfall-induced slope failures under a changing climate

    Science.gov (United States)

    Ono, K.; Akimoto, T.; Gunawardhana, L. N.; Kazama, S.; Kawagoe, S.

    2011-01-01

    The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure probability accounts for the effects of topography (as relief energy), geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations) and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario) for the future and by accounting for the slope failure probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan. Time series analyses of annual sediment yields covering 15-20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5-7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r2 = 0.65). The verification demonstrated that the model is effective for use in simulating specific sediment yields with r2 = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially averaged sediment yield for all of Japan is considered, both scenarios produced an

  11. Statistical Analysis of 30 Years Rainfall Data: A Case Study

    Science.gov (United States)

    Arvind, G.; Ashok Kumar, P.; Girish Karthi, S.; Suribabu, C. R.

    2017-07-01

    Rainfall is a prime input for various engineering design such as hydraulic structures, bridges and culverts, canals, storm water sewer and road drainage system. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. A rain gauge station located closely in Trichy district is selected for statistical analysis where agriculture is the prime occupation. The daily rainfall data for a period of 30 years is used to understand normal rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall of the selected circle headquarters. Further various plotting position formulae available is used to evaluate return period of monthly, seasonally and annual rainfall. This analysis will provide useful information for water resources planner, farmers and urban engineers to assess the availability of water and create the storage accordingly. The mean, standard deviation and coefficient of variation of monthly and annual rainfall was calculated to check the rainfall variability. From the calculated results, the rainfall pattern is found to be erratic. The best fit probability distribution was identified based on the minimum deviation between actual and estimated values. The scientific results and the analysis paved the way to determine the proper onset and withdrawal of monsoon results which were used for land preparation and sowing.

  12. Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG

    Science.gov (United States)

    Lazri, Mourad; Ameur, Zohra; Ameur, Soltane; Mohia, Yacine; Brucker, Jean Michel; Testud, Jacques

    2013-10-01

    The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.

  13. Hydraulic Geometry, GIS and Remote Sensing, Techniques against Rainfall-Runoff Models for Estimating Flood Magnitude in Ephemeral Fluvial Systems

    Directory of Open Access Journals (Sweden)

    Rafael Garcia-Lorenzo

    2010-11-01

    Full Text Available This paper shows the combined use of remotely sensed data and hydraulic geometry methods as an alternative to rainfall-runoff models. Hydraulic geometric data and boolean images of water sheets obtained from satellite images after storm events were integrated in a Geographical Information System. Channel cross-sections were extracted from a high resolution Digital Terrain Model (DTM and superimposed on the image cover to estimate the peak flow using HEC-RAS. The proposed methodology has been tested in ephemeral channels (ramblas on the coastal zone in south-eastern Spain. These fluvial systems constitute an important natural hazard due to their high discharges and sediment loads. In particular, different areas affected by floods during the period 1997 to 2009 were delimited through HEC-GeoRAs from hydraulic geometry data and Landsat images of these floods (Landsat‑TM5 and Landsat-ETM+7. Such an approach has been validated against rainfall-surface runoff models (SCS Dimensionless Unit Hydrograph, SCSD, Témez gamma HU Tγ and the Modified Rational method, MRM comparing their results with flood hydrographs of the Automatic Hydrologic Information System (AHIS in several ephemeral channels in the Murcia Region. The results obtained from the method providing a better fit were used to calculate different hydraulic geometry parameters, especially in residual flood areas.

  14. Rainfall-runoff modelling using different estimators of precipitation data in the Carpathian mountain catchments (South Poland)

    Science.gov (United States)

    Kasina, Michal; Ziemski, Michal; Niedbala, Jerzy; Malota, Agnieszka

    2013-04-01

    Precipitation observations are an essential element of flood forecasting systems. Rain gauges, radars, satellite sensors and forecasts from high resolution numerical weather prediction models are a part of precipitation monitoring networks. These networks collect rainfall data that are further provided to hydrological models to produce forecasts. The main goal of this work is to assess the usage of different precipitation data sources in rainfall-runoff modelling with reference to Flash Flood Early Warning System. STUDY AREA Research was carried out in the upper parts of the Sola and Raba river catchments. Both of the rivers begin their course in the southern part of the Western Beskids (Outer Eastern Carpathians; southern Poland). For the purpose of this study, both rivers are taken to comprise the catchments upstream of the gauging stations at Zywiec (Sola) and Stroza (Raba). The upper Sola river catchment encompasses an area of 785 sq. km with an altitude ranging from 342 to 1236 m above sea level, while the Raba river catchment occupies an area of 644 sq. km with an altitude ranging from 300 to 1266 m above sea level. The catchments are underlain mainly by flysch sediments. The average annual amount of precipitation for the Sola River catchment is between 750 and 1300 mm and for the Raba river catchment is in the range of 800-1000 mm. METHODS AND RESULTS This work assesses the sensitivity of a lumped hydrological model DHI's Nedbør-Afrstrømnings-Model (NAM) to different sources of rainfall estimates: rain gauges, radar and satellite as well as predicted precipitation amount from high resolution numerical weather prediction models (e.g. ALADIN). The main steps of validation procedure are: i) comparison of rain gauge data with other precipitation data sources, ii) calibration of the hydrological model (using historical, long time series of rain gauge data treated as "ground truth"), iii) validation using different precipitation data sources as an input, iii

  15. ACCURACY OF MILK YIELD ESTIMATION IN DAIRY CATTLE FROM MONTHLY RECORD BY REGRESSION METHOD

    Directory of Open Access Journals (Sweden)

    I.S. Kuswahyuni

    2014-10-01

    Full Text Available This experiment was conducted to estimate the actual milk yield and to compare the estimation accuracyof cumulative monthly record to actual milk yield by regression method. Materials used in this experimentwere records relating to milk yield and pedigree. The obtained data were categorized into 2 groups i.e. AgeGroup I (AG I that was cow calving at < 36 months old as many as 33 cows with 33 lactation records andAG II that cows calving e” 36 months old as many as 44 cows with 105 lactation records. The first three toseven months data were used to estimate actual milk yield. Results showed that mean of milk yield/ head/lactation at AG I (2479.5 ± 461.5 kg was lower than that of AG II (2989,7 ± 526,8 kg. Estimated milk yieldsfor three to seven months at AG I were 2455.6±419.7; 2455.7±432.9; 2455.5±446.4; 2455.6±450.8; 2455,64± 450,8; 2455,5 ± 459,3 kg respectively, meanwhile at AG II was 2972.3±479.8; 2972.0±497.2; 2972.4±509.6;2972.5±523.6 and 2972.5±535.1 respectively. Correlation coefficients between estimated and actual milkyield at AG I were 0.79; 0.82; 0.86; 0.86 and 0.88, respectively, meanwhile at AG II were 0.65; 0.66; 0.67;0.69 and 0.72 respectively. In conclusion, the mean of estimated milk yield at AG I was lower than AG II.The best record to estimate actual milk yield both at AG I and AG II were the seven cumulative months.

  16. Estimating impact of rainfall change on hydrological processes in Jianfengling rainforest watershed, China using BASINS-HSPF-CAT modeling system

    Science.gov (United States)

    Zhang Zhou; Ying Ouyang; Yide Li; Zhijun Qiu; Matt Moran

    2017-01-01

    Climate change over the past several decades has resulted in shifting rainfall pattern and modifying rain-fall intensity, which has exacerbated hydrological processes and added the uncertainty and instability tothese processes. This study ascertained impacts of potential future rainfall change on hydrological pro-cesses at the Jianfengling (JFL) tropical mountain...

  17. Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales

    Science.gov (United States)

    Katiraie-Boroujerdy, Pari-Sima; Akbari Asanjan, Ata; Hsu, Kuo-lin; Sorooshian, Soroosh

    2017-09-01

    In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648-0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349-0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7

  18. Rainfall Erosivity Database on the European Scale (REDES): A product of a high temporal resolution rainfall data collection in Europe

    Science.gov (United States)

    Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine

    2016-04-01

    The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the (R)USLE model. The R-factor is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minutes rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years) which are not readily available at European scale. The European Commission's Joint Research Centre(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland in order to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,541 rainfall stations in 2014 and has been updated with 134 additional stations in 2015. The interpolation of those point R-factor values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511, 801-815). The intra-annual variability of rainfall erosivity is crucial for modelling soil erosion on a monthly and seasonal basis. The monthly feature of rainfall erosivity has been added in 2015 as an advancement of REDES and the respective mean annual R-factor map. Almost 19,000 monthly R-factor values of REDES contributed to the seasonal and monthly assessments of rainfall erosivity in Europe. According to the first results, more than 50% of the total rainfall erosivity in Europe takes place in the period from June to September. The spatial patterns of rainfall erosivity have significant differences between Northern and Southern Europe as summer is the most erosive period in Central and Northern Europe and autumn in the

  19. EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

    DEFF Research Database (Denmark)

    Proietti, Tommaso; Marczak, Martyna; Mazzi, Gianluigi

    EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model...... parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes...

  20. Correlations for the estimation of monthly mean hourly diffuse solar radiation: a time dependent approach

    Directory of Open Access Journals (Sweden)

    A. K. Katiyar, Akhilesh Kumar, C. K. Pandey, V. K. Katiyar, S. H. Abdi

    2010-09-01

    Full Text Available The time dependent monthly mean hourly diffuse solar radiation on a horizontal surface has been estimated for Lucknow (latitude26.75 degree, longitude 80.50 degree using least squares regression analysis. The monthly and annually regression constants are obtained. The present results are compared with the estimation of Orgill-Holands (Sol. Energy, 19 (4, 357 (1977, Erbs et. al (Sol. Energy 28 (4, 293-304(1982 and Spencer (Sol. Energy 29 (1, 19-32(1982 as well as with experimental value. The proposed constant provides better estimation for the entire year over others. Spencer, who correlate hourly diffuse fraction with clearness index, estimates lowest value except in summers when insolation in this region is very high. The accuracy of the regression constants are also checked with statistical tests of root mean square error (RMSE, mean bias error (MBE and t –statistic tests.

  1. Comparing TRMM 3B42, CFSR and ground-based rainfall estimates as input for hydrological models, in data scarce regions: the Upper Blue Nile Basin, Ethiopia

    Directory of Open Access Journals (Sweden)

    A. W. Worqlul

    2015-02-01

    Full Text Available Accurate prediction of hydrological models requires accurate spatial and temporal distribution of rainfall observation network. In developing countries rainfall observation station network are sparse and unevenly distributed. Satellite-based products have the potential to overcome these shortcomings. The objective of this study is to compare the advantages and the limitation of commonly used high-resolution satellite rainfall products as input to hydrological models as compared to sparsely populated network of rain gauges. For this comparison we use two semi-distributed hydrological models Hydrologiska Byråns Vattenbalansavdelning (HBV and Parameter Efficient Distributed (PED that performed well in Ethiopian highlands in two watersheds: the Gilgel Abay with relatively dense network and Main Beles with relatively scarce rain gauge stations. Both are located in the Upper Blue Nile Basin. The two models are calibrated with the observed discharge from 1994 to 2003 and validated from 2004 to 2006. Satellite rainfall estimates used includes Climate Forecast System Reanalysis (CFSR, Tropical Rainfall Measuring Mission (TRMM 3B42 version 7 and ground rainfall measurements. The results indicated that both the gauged and the CFSR precipitation estimates were able to reproduce the stream flow well for both models and both watershed. TRMM 3B42 performed poorly with Nash Sutcliffe values less than 0.1. As expected the HBV model performed slightly better than the PED model, because HBV divides the watershed into sub-basins resulting in a greater number of calibration parameters. The simulated discharge for the Gilgel Abay was better than for the less well endowed (rain gauge wise Main Beles. Finally surprisingly, the ground based gauge performed better for both watersheds (with the exception of extreme events than TRMM and CFSR satellite rainfall estimates. Undoubtedly in the future, when improved satellite products will become available, this will change.

  2. Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2014-01-01

    , research has primarily focused on one-step-ahead flow predictions for identifying, estimating, and evaluating greybox models. For control purposes, however, stochastic predictions are required for longer forecast horizons and for the prediction of runoff volumes, rather than flows. This article therefore...

  3. Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices

    Science.gov (United States)

    Haiganoush K. Preisler; Shyh-Chin Chen; Francis Fujioka; John W. Benoit; Anthony L. Westerling

    2008-01-01

    The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography...

  4. On the consideration of scaling properties of extreme rainfall in Madrid (Spain) for developing a generalized intensity-duration-frequency equation and assessing probable maximum precipitation estimates

    Science.gov (United States)

    Casas-Castillo, M. Carmen; Rodríguez-Solà, Raúl; Navarro, Xavier; Russo, Beniamino; Lastra, Antonio; González, Paula; Redaño, Angel

    2016-11-01

    The fractal behavior of extreme rainfall intensities registered between 1940 and 2012 by the Retiro Observatory of Madrid (Spain) has been examined, and a simple scaling regime ranging from 25 min to 3 days of duration has been identified. Thus, an intensity-duration-frequency (IDF) master equation of the location has been constructed in terms of the simple scaling formulation. The scaling behavior of probable maximum precipitation (PMP) for durations between 5 min and 24 h has also been verified. For the statistical estimation of the PMP, an envelope curve of the frequency factor (k m ) based on a total of 10,194 station-years of annual maximum rainfall from 258 stations in Spain has been developed. This curve could be useful to estimate suitable values of PMP at any point of the Iberian Peninsula from basic statistical parameters (mean and standard deviation) of its rainfall series.

  5. Relationships between statistics of rainfall extremes and mean annual precipitation: an application for design-storm estimation in northern central Italy

    Directory of Open Access Journals (Sweden)

    G. Di Baldassarre

    2006-01-01

    Full Text Available Several hydrological analyses need to be founded on a reliable estimate of the design storm, which is the expected rainfall depth corresponding to a given duration and probability of occurrence, usually expressed in terms of return period. The annual series of precipitation maxima for storm duration ranging from 15 min to 1 day, observed at a dense network of raingauges sited in northern central Italy, are analyzed using an approach based on L-moments. The analysis investigates the statistical properties of rainfall extremes and detects significant relationships between these properties and the mean annual precipitation (MAP. On the basis of these relationships, we developed a regional model for estimating the rainfall depth for a given storm duration and recurrence interval in any location of the study region. The applicability of the regional model was assessed through Monte Carlo simulations. The uncertainty of the model for ungauged sites was quantified through an extensive cross-validation.

  6. Remote Sensing-based Rainfall Estimates in Data-Scarce Himalaya: Performance Assessment of TRMM_3B42v7, TRMM_3B42RT v7 & GPM_3IMERGHH v03 using Ground Rainfall and Stream Hydrographs in Sikkim Himalaya, India

    Science.gov (United States)

    Kumar, M.; Krishnaswamy, J.; Badiger, S.

    2016-12-01

    Sikkim Himalaya are characterised by high altitudinal gradients and greatly varying precipitation patterns, both across space and time, which further influences vegetation distribution and their hydrologic functioning. However, in the absence of long-term and spatially-distributed precipitation data, very little is known about the inherent climatic variability of the region, its impact on the ecosystem complexities and services; and their response to climate change. Recently, satellite rainfall estimates (SREs) have emerged as useful substitute in hydrological studies from Himalayas but require careful validation based on ground observations. In the study, we assess the performance of three gridded SREs TRMM_3B42v7 (TRMM), TRMM_3B42RT v7 (TRMM_RT) & GPM_3IMERGHH v03 (GPM) using ground observations of rainfall and streamflow at Khangchendzonga Bioshpere Reserve in Sikkim, India. TRMM and TRMM_RT were available at 3-hourly temporal resolution and 0.50 spatial resolution, GPM at half-hourly and 0.10; and rainfall from two closely placed tipping bucket raingauges (TBRG) at 1 minute. All datasets were aggregated at 3-hourly, daily and monthly for validation against TBRG. Statistical performance metrics like bias, correlation coefficient, false alarm ration, accuracy etc. were calculated. In a novel approach, hydrograph-based performance assessment (HPA) was carried out by plotting flow data from two nearby streams was as hydrographs using the SREs and TBRG at 3-hourly resolution. Independent categorical assessment of each rainfall source in explaining individual storm events in the streams was done. Statistically, all SREs performed below-par at sub-daily scales (accuracy 0.6). Among SREs, TRMM performed the best overall, whereas GPM was better at detecting high-intensity rainfall but poorest at low-intensity rainfall. Both, TRMM and TRMM_RT significantly underestimated rainfall. The performance of SREs based on HPA was much better as SREs were able to explain more than

  7. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

    Science.gov (United States)

    Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.

    2014-09-01

    The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

  8. Month-wise estimates of tobacco smoking during pregnancy for the United States, 2002-2009.

    Science.gov (United States)

    Alshaarawy, Omayma; Anthony, James C

    2015-05-01

    The timing of prenatal exposure to tobacco cigarette smoking can be crucial for the developing fetus. Pushing the field beyond prior pregnancy trimester-focused smoking estimates, we estimated month-specific prevalence proportions for tobacco cigarette smoking among pregnant and non-pregnant women of the United States, with consideration of tobacco dependence (TD) as well. In advance, we posited that pregnancy onset might prompt smoking cessation in early months, before the end of the 1st trimester, and that TD might account for sustained smoking in later months, especially months 8-9, when there are added reasons to quit. Estimates are from the 2002-2009 National Surveys on Drug Use and Health Restricted-Data Analysis System (R-DAS), with large nationally representative samples of US civilians, including 12-44 year old women (n ~ 70,000) stratified by pregnancy status and month of pregnancy, with multi-item assessment of TD as well as recently active smoking. Age was held constant via the Breslow-Day indirect standardization approach, a methodological detail of potential interest to other research teams conducting online R-DAS analyses. Among 12-44 year old women in Month 1 of pregnancy, as well as non-pregnant women, just over one in four was a recently active smoker (26-27 %), and approximately one-half of these smokers qualified as a TD case (52 %). Corresponding estimates for women in Month 3 were 17.6 % and two-thirds, respectively, lending some support for our advance hypotheses. Nonetheless, our a priori TD hypothesis about Months 8-9 seems to be contradicted: an increased concentration of TD among smokers surfaced early in pregnancy. Evidence of a possible ameliorative pregnancy effect on smoking prevalence as well as TD's effect on smoking persistence might be seen quite early in pregnancy. Substitution of a month-specific view for the traditional trimester view sheds new light on how pregnancy might shape smoking behavior before the end of trimester 1

  9. Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Michala Jakubcová

    2015-01-01

    Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

  10. Estimation of monthly Angstrom-Prescott equation coefficients from measured daily data in Toledo, Spain

    Energy Technology Data Exchange (ETDEWEB)

    Almorox, J.; Hontoria, C. [Universidad Politecnica de Madrid (Spain). Dpto. De Edafologia; Benito, M. [Universidad Politecnica de Madrid (Spain). Dpto. De Silvopasicultura

    2005-05-01

    In this study, daily global radiation for Toledo (39{sup o}53'05''N, 4{sup o}02'58''W, Spain) were utilized to determine monthly-specific equations for estimating global solar radiation from sunshine hours and to obtain improved fits to monthly Angstrom-Prescott's coefficients. Models were compared using the root mean square error (RMSE), the mean bias error (MBE) and the t-statistic. According to our results, all the models fitted the data adequately and can be used to estimate the specific monthly global solar radiation. Average RMSE and MBE for comparison between observed and estimated global radiation were 1.260 and -0.002 MJ m{sup -2} day{sup -1}, respectively. The t-statistic was used as the best indicator, this indicator depends on both, and is more effective for determining the model performance. The agreement between the estimated and the measured data were remarkable and the method was recommended for use in Toledo (Spain). (author)

  11. Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation

    Science.gov (United States)

    Tang, Guoqiang; Wen, Yixin; Gao, Jinyu; Long, Di; Ma, Yingzhao; Wan, Wei; Hong, Yang

    2017-05-01

    Precipitation is one of the most important components in the water and energy cycles. Radars are considered the best available technology for observing the spatial distribution of precipitation either from the ground since the 1980s or from space since 1998. This study, for the first time ever, compares and evaluates the only three existing spaceborne precipitation radars, i.e., the Ku-band precipitation radar (PR), the W-band Cloud Profiling Radar (CPR), and the Ku/Ka-band Dual-frequency Precipitation Radar (DPR). The three radars are matched up globally and intercompared in the only period which they coexist: 2014-2015. In addition, for the first time ever, TRMM PR and GPM DPR are evaluated against hourly rain gauge data in Mainland China. Results show that DPR and PR agree with each other and correlate very well with gauges in Mainland China. However, both show limited performance in the Tibetan Plateau (TP) known as the Earth's third pole. DPR improves light precipitation detectability, when compared with PR, whereas CPR performs best for light precipitation and snowfall. DPR snowfall has the advantage of higher sampling rates than CPR; however, its accuracy needs to be improved further. The future development of spaceborne radars is also discussed in two complementary categories: (1) multifrequency radar instruments on a single platform and (2) constellations of many small cube radar satellites, for improving global precipitation estimation. This comprehensive intercomparison of PR, CPR, and DPR sheds light on spaceborne radar precipitation retrieval and future radar design.

  12. Trend analysis and forecast of precipitation, reference evapotranspiration, and rainfall deficit in the Blackland Prairie of eastern Mississippi

    Science.gov (United States)

    Gary Feng; Stacy Cobb; Zaid Abdo; Daniel K. Fisher; Ying Ouyang; Ardeshir Adeli; Johnie N. Jenkins

    2016-01-01

    Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ET, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ET, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894-2014) were analyzed for annual, seasonal, and monthly...

  13. Estimates of peak flood discharge for 21 sites in the Front Range in Colorado in response to extreme rainfall in September 2013

    Science.gov (United States)

    Moody, John A.

    2016-03-21

    Extreme rainfall in September 2013 caused destructive floods in part of the Front Range in Boulder County, Colorado. Erosion from these floods cut roads and isolated mountain communities for several weeks, and large volumes of eroded sediment were deposited downstream, which caused further damage of property and infrastructures. Estimates of peak discharge for these floods and the associated rainfall characteristics will aid land and emergency managers in the future. Several methods (an ensemble) were used to estimate peak discharge at 21 measurement sites, and the ensemble average and standard deviation provided a final estimate of peak discharge and its uncertainty. Because of the substantial erosion and deposition of sediment, an additional estimate of peak discharge was made based on the flow resistance caused by sediment transport effects.Although the synoptic-scale rainfall was extreme (annual exceedance probability greater than 1,000 years, about 450 millimeters in 7 days) for these mountains, the resulting peak discharges were not. Ensemble average peak discharges per unit drainage area (unit peak discharge, [Qu]) for the floods were 1–2 orders of magnitude less than those for the maximum worldwide floods with similar drainage areas and had a wide range of values (0.21–16.2 cubic meters per second per square kilometer [m3 s-1 km-2]). One possible explanation for these differences was that the band of high-accumulation, high-intensity rainfall was narrow (about 50 kilometers wide), oriented nearly perpendicular to the predominant drainage pattern of the mountains, and therefore entire drainage areas were not subjected to the same range of extreme rainfall. A linear relation (coefficient of determination [R2]=0.69) between Qu and the rainfall intensity (ITc, computed for a time interval equal to the time-of-concentration for the drainage area upstream from each site), had the form: Qu=0.26(ITc-8.6), where the coefficient 0.26 can be considered to be an

  14. Development of tools for evaluating rainfall estimation models in real- time using the Integrated Meteorological Observation Network in Castilla y León (Spain)

    Science.gov (United States)

    Merino, Andres; Guerrero-Higueras, Angel Manuel; López, Laura; Gascón, Estibaliz; Sánchez, José Luis; Lorente, José Manuel; Marcos, José Luis; Matía, Pedro; Ortiz de Galisteo, José Pablo; Nafría, David; Fernández-González, Sergio; Weigand, Roberto; Hermida, Lucía; García-Ortega, Eduardo

    2014-05-01

    The integration of various public and private observation networks into the Observation Network of Castile-León (ONet_CyL), Spain, allows us to monitor the risks in real-time. One of the most frequent risks in this region is severe precipitation. Thus, the data from the network allows us to determine the area where precipitation was registered and also to know the areas with precipitation in real-time. The observation network is managed with a LINUX system. The observation platform makes it possible to consult the observation data in a specific point in the region, or otherwise to see the spatial distribution of the precipitation in a user-defined area and time interval. In this study, we compared several rainfall estimation models, based on satellite data for Castile-León, with precipitation data from the meteorological observation network. The rainfall estimation models obtained from the meteorological satellite data provide us with a precipitation field covering a wide area, although its operational use requires a prior evaluation using ground truth data. The aim is to develop a real-time evaluation tool for rainfall estimation models that allows us to monitor the accuracy of its forecasting. This tool makes it possible to visualise different Skill Scores (Probability of Detection, False Alarm Ratio and others) of each rainfall estimation model in real time, thereby not only allowing us to know the areas where the rainfall models indicate precipitation, but also the validation of the model in real-time for each specific meteorological situation. Acknowledgements The authors would like to thank the Regional Government of Castile-León for its financial support through the project LE220A11-2. This study was supported by the following grants: GRANIMETRO (CGL2010-15930); MICROMETEO (IPT-310000-2010-22).

  15. Continuous rainfall generation for a warmer climate using observed temperature sensitivities

    Science.gov (United States)

    Wasko, Conrad; Sharma, Ashish

    2017-01-01

    Continuous rainfall sequences are often used as inputs in hydrologic modeling, particularly where a probabilistic assessment is required. Continuous rainfall sequences provide a means for accounting of all aspects of rainfall that produce flooding, for example, not just the design rainfall event but also the rainfall prior to the extreme rainfall event. With the advent of climate change, higher temperatures have been associated with changes in rainfall, in particular intensifying rainfall extremes with less uniform temporal patterns. Given these demonstrated changes to extreme rainfall with temperature rise, there is a need to modify continuous rainfall generators to account for current and likely future changes in temperature. In this work we propose a novel method for simulating continuous rainfall sequences for a future warmer climate by conditioning parameters on their historical sensitivity with temperature. To demonstrate the proposed technique we use a one-dimensional Neyman-Scott Rectangular Pulses model at two locations across Australia. The statistics used in the parameter estimation are conditioned on their historical sensitivity to average monthly temperature to simulate rainfall for a change in temperature. The results are validated by comparing the simulated rainfall against observations originating from differing temperatures and it is shown that the model captures the relative difference in the mean monthly rainfall and monthly maxima. Encouraged by these results we simulate rainfall for higher temperatures and capture expected changes to annual maxima and design temporal patterns for a warmer climate. While we demonstrate our methodology in the simulation of sub-daily rainfall using a specific model, the approach presented here can be applied to all weather generation schemes for projection in a warmer climate.

  16. Satellite-derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: implications for groundwater decline in a drying climate.

    Science.gov (United States)

    Smettem, Keith R J; Waring, Richard H; Callow, John N; Wilson, Melissa; Mu, Qiaozhen

    2013-08-01

    There is increasing concern that widespread forest decline could occur in regions of the world where droughts are predicted to increase in frequency and severity as a result of climate change. The average annual leaf area index (LAI) is an indicator of canopy cover and the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. In this study, we analyzed satellite-derived estimates of monthly LAI across forested coastal catchments of southwest Western Australia over a 12 year period (2000-2011) that included the driest year on record for the last 60 years. We observed that over the 12 year study period, the spatial pattern of average annual satellite-derived LAI values was linearly related to mean annual rainfall. However, interannual changes to LAI in response to changes in annual rainfall were far less than expected from the long-term LAI-rainfall trend. This buffered response was investigated using a physiological growth model and attributed to availability of deep soil moisture and/or groundwater storage. The maintenance of high LAIs may be linked to a long-term decline in areal average underground water storage and diminished summer flows, with an emerging trend toward more ephemeral flow regimes.

  17. A probabilistic approach for estimating monthly catchment water balances from satellite and ground data

    Science.gov (United States)

    Schoups, Gerrit

    2017-04-01

    A probabilistic model is developed to estimate monthly basin-scale precipitation, evaporation, storage and river discharge from open-source data and water balance constraints. Both random and systematic deviations between observed and "true" water balance components are included in the model to account for measurement/processing errors and differences in scale. Model parameters comprise data standard deviations (random noise) and scaling factors (systematic bias). Water balance terms and parameters are estimated using Bayesian inference, yielding posterior distributions for all unknowns. The model is applied to MOPEX basins across the continental US using the following data sources: TRMM-3B43 (precipitation), SSEBop (evaporation), GRACE (storage), and USGS stream gauges (river discharge). Results provide optimal estimates and uncertainty of water balance components and data errors across a range of basin characteristics (size, wetness, etc).

  18. An estimate of the effects of climate change on the rainfall of Mediterranean Spain by the late twenty first century

    Energy Technology Data Exchange (ETDEWEB)

    Sumner, G.N. [Centre for Geography, University of Wales, Lampeter, Ceredigion, Wales (United Kingdom); Romero, R.; Homar, V.; Ramis, C.; Alonso, S. [Departament de Fisica, Universitat de les Illes Balears, Palma de Mallorca (Spain); Zorita, E. [Institut fuer Gewaesserphysik GKSS, Geesthacht (Germany)

    2003-05-01

    Heading Abstract. The study uses a GCM (ECHAM-OPYC3) and the association between the atmospheric circulation at 925 and 500 hPa and the distribution of daily precipitation for Mediterranean Spain (from earlier analyses) to give estimates of the probable annual precipitation for the late twenty first century. A down-scaling technique is used which involves the matching of daily circulation output from the model for a sequence of years in the late twentieth century (1971-90) and for a corresponding period in the late twenty first century (2080-99) to derive probable regional atmospheric pattern (AP) frequencies for this latter period, and thence to estimate likely changes in annual precipitation. Model days are classified by searching for the closest analogue amongst 19 previously identified APs from an earlier study. Future annual precipitation distribution is derived using previously established relationships between circulation type and daily precipitation distribution. Predicted AP frequencies and precipitation amounts and distribution are compensated by comparing model output with ECMWF data for a decade (1984-93) within the 1971-90 sequence, so that the analysis also provides a verification of the performance of the model. In general the agreement between model output and actual AP frequencies is very good for the present day, though for this southerly region the model appears slightly to under-estimate the frequency of easterly type circulations, many of which yield some of the most significant autumn severe storm rainfalls along the Mediterranean coast. The model tends to over-estimate the frequency of westerly type situations. The study utilises a 'moving window' technique in an attempt to derive measures of inter-decadal variability within the two 20 year periods. This avoids use of data from outside the periods, which would incorporate changing AP frequencies during a period of sustained climate change. Quite pronounced changes in frequency are

  19. Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

    Science.gov (United States)

    Fisher, Brad; Wolff, David B.

    2010-01-01

    Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

  20. Influence of satellite-derived rainfall patterns on plague occurrence in northeast Tanzania.

    Science.gov (United States)

    Debien, Annekatrien; Neerinckx, Simon; Kimaro, Didas; Gulinck, Hubert

    2010-12-13

    In the tropics, rainfall data are seldom accurately recorded, and are often discontinuous in time. In the scope of plague-research in northeast Tanzania, we adapted previous research to reconstruct rainfall patterns at a suitable resolution (1 km), based on time series of NDVI: more accurate satellite imagery was used, in the form of MODIS NDVI, and rainfall data were collected from the TRMM sensors instead of in situ data. First, we established a significant relationship between monthly rainfall and monthly composited MODIS NDVI. The established linear relationship was then used to reconstruct historic precipitation patterns over a mountainous area in northeastern Tanzania. We validated the resulting precipitation estimates with in situ rainfall time series of three meteorological stations located in the study area. Taking the region's topography into account, a correlation coefficient of 0.66 was obtained for two of the three meteorological stations. Our results suggest that the adapted strategy can be applied fruitfully to estimate rainfall variability and seasonality, despite the underestimation of overall rainfall rates. Based on this model, rainfall in previous years (1986) is modelled to obtain a dataset with which we can compare plague occurrence in the area. A positive correlation of 82% is obtained between high rainfall rates and plague incidence with a two month lag between rainfall and plague cases. We conclude that the obtained results are satisfactory in support of the human plague research in which this study is embedded, and that this approach can be applied in other studies with similar goals.

  1. An assessment of El Niño and La Niña impacts focused on monthly and seasonal rainfall and extreme dry/precipitation events in mountain regions of Colombia and México

    Science.gov (United States)

    Pinilla Herrera, María Carolina; Andrés Pinzón Correa, Carlos

    2016-03-01

    The influence of El Niño and La Niña on monthly and seasonal rainfall over mountain landscapes in Colombia and México was assessed based on the Oceanic Niño Index (ONI). A statistical analysis was develop to compare the extreme dry/precipitation events between El Niño, La Niña and Neutral episodes. For both areas, it was observed that El Niño and La Niña episodes are associated with important increases or decreases in rainfall. However, Neutral episodes showed the highest occurrence of extreme precipitation/dry events. For a better understanding of the impact of El Niño and La Niña on seasonal precipitation, we did a compound and a GIS analyses to define the high/low probability of above, below or normal seasonal precipitation under El Niño, La Niña and cold/warm Neutral episodes. In San Vicente, Colombia the below-normal seasonal rainfall was identified during El Niño and Neutral episodes in the dry season JJA. In this same municipality we also found above-normal seasonal rainfall during La Niña and Neutral episodes, especially in the dry season DJF. In Tancítaro México the below-normal seasonal rainfall was identified during La Niña winters (DJF) and El Niño summers (JJA), the above-normal seasonal rainfall was found during La Niña summers (JJA) and El Niño winters (DJF).

  2. An empirical formula to estimate rainfall intensity in Kupwara region of Kashmir valley, J and K, India

    Directory of Open Access Journals (Sweden)

    Dar Abdul Qayoom

    2016-01-01

    Full Text Available Knowledge of rainfall extremes particularly their magnitude and frequency, as embodied in Intensity-Duration-Frequency (IDF relationships and IDF curves is fundamental to many engineering problems such as design of hydraulic structures, urban drainage system, water resources projects and many others. The objective of this study is to obtain IDF relationships and curves for the Kupwara region of Kashmir valley in Jammu and Kashmir, India. Three different frequency distributions namely the Gumbel distribution, the Pearson Type III distribution and the Log-Pearson Type III distribution were fitted to the rainfall data to obtain rainfall intensities for selected return periods (2, 5, 10, 25, 50 and 100 years and durations (0.16, 0.5, 1, 3, 6, 12 and 24 hours. Regional constants in IDF relations were found using the Sherman Morrison method and results were compared based on the Chi-square goodness-of-fit test. Results obtained using all distributions showed a similar trend. However, the Pearson Type III distribution emerges to be the best fit for the rainfall data of the region. Results revealed that higher rainfall intensities have shorter durations. Maximum rainfall intensity 81.13 mm/hr as per the best fit relation occurs with a return period of 100 years for 0.16 hours duration.

  3. Worldwide assessment of the Penman-Monteith temperature approach for the estimation of monthly reference evapotranspiration

    Science.gov (United States)

    Almorox, Javier; Senatore, Alfonso; Quej, Victor H.; Mendicino, Giuseppe

    2016-11-01

    When not all the meteorological data needed for estimating reference evapotranspiration ETo are available, a Penman-Monteith temperature (PMT) equation can be adopted using only measured maximum and minimum air temperature data. The performance of the PMT method is evaluated and compared with the Hargreaves-Samani (HS) equation using the measured long-term monthly data of the FAO global climatic dataset New LocClim. The objective is to evaluate the quality of the PMT method for different climates as represented by the Köppen classification calculated on a monthly time scale. Estimated PMT and HS values are compared with FAO-56 Penman-Monteith ETo values through several statistical performance indices. For the full dataset, the approximated PMT expressions using air temperature alone produce better results than the uncalibrated HS method, and the performance of the PMT method is even more improved adopting corrections depending on the climate class for the estimation of the solar radiation, especially in the tropical climate class.

  4. Estimation of areal precipitation based on rainfall data and X-band radar images in the Venero-Claro Basin (Ávila, Spain)

    Science.gov (United States)

    Guardiola-Albert, Carolina; River-Honegger, Carlos; Yagüe, Carlos; Agut, Robert Monjo i.; Díez-Herrero, Andrés; María Bodoque, José; José Tapiador, Francisco

    2015-04-01

    The aim of this work is to estimate the spatial-temporal rainfall during precipitation events with hydrological response in Venero-Claro Basin (Avila, Spain). In this small mountainous basin of 15km2, flood events of different magnitudes have been often registered. Therefore, rainfall estimation is essential to calibrate and validate hydrological models, and hence implies an improvement in the objectivity of risk studies and its predictive and preventive capacity. The geostatistical merging method of ordinary kriging of the errors (OKRE) has been applied. This technique has been already used by several authors to merge C-band radar and dense rain gauge networks. Here it is adapted to estimate hourly rainfall accumulations over the area with observations from one of the 5 existing X-band radar in Spain and 7 rain gauges located in the zone. Verification of the results has been performed through cross-validation comparing the estimation error of the OKRE with the one obtained adjusting the Marshall-Palmer relation. Analyzed errors are bias, the Hanseen-Kuiper coefficient and the relative mean root transformed error. Results have an average error of 15%, distinguishing quite well between dry and wet periods.

  5. Random Forests (RFs) for Estimation, Uncertainty Prediction and Interpretation of Monthly Solar Potential

    Science.gov (United States)

    Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

    2017-04-01

    Solar energy is clean, widely available, and arguably the most promising renewable energy resource. Taking full advantage of solar power, however, requires a deep understanding of its patterns and dependencies in space and time. The recent advances in Machine Learning brought powerful algorithms to estimate the spatio-temporal variations of solar irradiance (the power per unit area received from the Sun, W/m2), using local weather and terrain information. Such algorithms include Deep Learning (e.g. Artificial Neural Networks), or kernel methods (e.g. Support Vector Machines). However, most of these methods have some disadvantages, as they: (i) are complex to tune, (ii) are mainly used as a black box and offering no interpretation on the variables contributions, (iii) often do not provide uncertainty predictions (Assouline et al., 2016). To provide a reasonable solar mapping with good accuracy, these gaps would ideally need to be filled. We present here simple steps using one ensemble learning algorithm namely, Random Forests (Breiman, 2001) to (i) estimate monthly solar potential with good accuracy, (ii) provide information on the contribution of each feature in the estimation, and (iii) offer prediction intervals for each point estimate. We have selected Switzerland as an example. Using a Digital Elevation Model (DEM) along with monthly solar irradiance time series and weather data, we build monthly solar maps for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (GHI), and Extraterrestrial Irradiance (EI). The weather data include monthly values for temperature, precipitation, sunshine duration, and cloud cover. In order to explain the impact of each feature on the solar irradiance of each point estimate, we extend the contribution method (Kuz'min et al., 2011) to a regression setting. Contribution maps for all features can then be computed for each solar map. This provides precious information on the spatial variation of the features impact all

  6. Reasonability Analysis of Chaotic Identification of Monthly Rainfall Series%月降雨序列的混沌判定的合理性分析

    Institute of Scientific and Technical Information of China (English)

    路剑飞; 陈子燊

    2011-01-01

    针对目前月降雨序列混沌特性研究中存在的问题,以广东省西江流域高要站月降雨序列为例,运用功率谱方法、主成分分析法、饱和关联维数法、C-C方法进行了混沌特性的判定及特征参数的求取,同时分析了数据长度和噪声对混沌研究的影响.研究结果表明,利用功率谱方法进行混沌判定时,单纯的根据连续多峰的噪声背景作为判定混沌存在的依据并不可靠;饱和关联维数法仅从能量角度对混沌序列进行判定,此外,对混沌序列进行滤波会导致此法判定结果的稳健性降低,C-C方法证明了其计算结果的可靠性;为计算出相对稳定的饱和关联维D2,计算数据的长度至少应为450个点;递归图及相应的各种定量判定标准验证了改进的双小波空域降噪方法可有效去除混沌序列中噪声的影响.%A discussion is conducted due to several problems presented in chaotic research of monthly rainfall series.As an example, monthly precipitation data of Gaoyao Station in West River basin in Guangdong Province is utilized for chaotic identification and calculation of characteristic parameters via power spectrum method, PCA, G-P Algorithm and C-C method.Impacts of data length and noise in the dataset are also studied.The results show that it is not reliable to judge a chaotic feature reflected on the power spectrum as successive multi-peaks; although chaotic series and noise can be distinguished directly with PCA method,it can only be held in the way of energy; besides, filtering process on chaotic series will make identification results derived from PCA method unstable; embedded parameters calculated with C-C method prove the validity of results from G-P Algorithm; take dataset used in this paper for example, a relatively stable D2 can be calculated on the premise that at least 450 data points are considered; recurrence plot and corresponding quantitative analysis indices validate effectiveness of

  7. Using High Resolution Tracer Data to Constrain Storage and Flux Estimates in a Spatially Distributed Rainfall-runoff Model

    Science.gov (United States)

    Van Huijgevoort, M.; Tetzlaff, D.; Sutanudjaja, E.; Soulsby, C.

    2015-12-01

    Models simulating both stream flow and conservative tracers can provide a more realistic representation of flow paths, storage distributions and mixing processes that is advantageous for many predictions. Conceptual models with such integration have provided useful insights, but tend to be lumped and thus crude representations of catchment processes. Using tracers to aid spatially-distributed models has considerable potential to improve the conceptualisation of the dynamics of internal hydrological stores and fluxes. Here, we examine the strengths and weaknesses of a data-driven, spatially-distributed tracer-aided rainfall-runoff model. The model structure allows the assessment of the effect of landscape properties on the routing and mixing of water and tracers. The model was applied to an experimental site (3.2 km2) in the Scottish Highlands with a unique tracer data set; 4 years of daily isotope ratios in stream water and precipitation were available, as well as 2 years of weekly soil and ground water isotopes. The model evolved from an empirically-based, lumped tracer-aided model previously developed for the catchment. The best model runs were selected from Monte Carlo simulations based on a dual calibration criterion that included objective functions for both stream water isotopes and discharge at the outlet. Model results were also tested against observed spatially-distributed soil water isotope data. Model performance for both criteria was good and the model could reproduce the variable isotope signals in steeper hillslopes where storage was low and damped isotope responses in valley bottom cells with high storage. The model also allows us to estimate the age distributions of internal water fluxes and stream flow and has substantially improved spatial and temporal dynamics of process representation. This gives a more robust framework for projecting the effects of environmental change.

  8. Estimation of monthly average daily global solar irradiation using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mubiru, J.; Banda, E.J.K.B. [Department of Physics, Makerere University, P.O. Box 7062, Kampala (Uganda)

    2008-02-15

    This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m{sup 2} and root mean square error of 0.385 MJ/m{sup 2}. The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model. (author)

  9. Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain

    OpenAIRE

    Estelle de Coning

    2013-01-01

    The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of pr...

  10. Estimation of the Variation of Matric Suction with Respect to Depth in a Vertical Unsaturated Soil Trench Associated with Rainfall Infiltration

    Directory of Open Access Journals (Sweden)

    Oh Won Taek

    2016-01-01

    Full Text Available Soil trenching is extensively used in geotechnical, mining, tunneling and geo-environmental infrastructures. Safe height and stand-up time are two key factors that are required for the rational design of soil trenches. Rainfall infiltration has a significant influence on the safe height and stand-up time of unsaturated soil trenches since it can significantly alter the shear strength of soils by influencing the matric suction. In other words, predicting the variation of matric suction of soils associated with rainfall infiltration is vital to the design of unsaturated soil trenches. In this paper, finite element analysis is carried out to reproduce the variation of matric suction profile in unsaturated soil trenches associated with rainfall infiltration using the published results of a full scale instrumented test trench at the site of BBRI at Limelette, Belgium. The analysis results showed that the variation of matric suction in unsaturated soil trenches can be reliably estimated using the information of environmental factors such as the rainfall measurements.

  11. Development and testing of a large, transportable rainfall simulator for plot-scale runoff and parameter estimation

    Directory of Open Access Journals (Sweden)

    T. G. Wilson

    2014-04-01

    Full Text Available There is increased interest in the interplay between vegetation conditions and overland flow generation. The literature is unclear on this relationship and there is little quantitative guidance for modeling efforts. Therefore, experimental efforts are needed and these call for a lightweight transportable plot-scale (>10 m2 rainfall simulator that can be deployed quickly and quickly redeployed over various vegetation cover conditions. Accordingly, a variable intensity rainfall simulator and collection system was designed and tested in the laboratory and in the field. The system was tested with three configurations of common pressure washing nozzles producing rainfall intensities of 62, 43, and 32 mm h−1 with uniformity coefficients of 76, 65, and 62, respectively, over a plot of 15.12 m2. Field tests were carried out in on a grassy field with silt-loam soil in Orroli, Sardinia in July and August 2010, and rainfall, soil moisture, and runoff data were collected. The two-term Philip infiltration model was used to find optimal values for the saturated hydraulic conductivity of the soil surface and bulk soil, soil water retention curve slope, and air entry suction head. Optimized hydraulic conductivity values were comparable to both the measured final infiltration rate and literature values for saturated hydraulic conductivity. This inexpensive rainfall simulator can therefore be used to identify field parameters needed for hydrologic modeling.

  12. Estimating natural monthly streamflows in California and the likelihood of anthropogenic modification

    Science.gov (United States)

    Carlisle, Daren M.; Wolock, David M.; Howard, Jeannette K.; Grantham, Theodore E.; Fesenmyer, Kurt; Wieczorek, Michael

    2016-12-12

    Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be

  13. Predicting global landslide spatiotemporal distribution: Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.govV First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, land cover classification, etc.) using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world.

  14. Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

    Science.gov (United States)

    Sunilkumar, K.; Narayana Rao, T.; Satheeshkumar, S.

    2016-05-01

    This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and sub-daily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ˜ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the

  15. Variation in stable isotope ratios of monthly rainfall in the Douala and Yaounde cities, Cameroon: local meteoric lines and relationship to regional precipitation cycle

    Science.gov (United States)

    Wirmvem, Mengnjo Jude; Ohba, Takeshi; Kamtchueng, Brice Tchakam; Taylor, Eldred Tunde; Fantong, Wilson Yetoh; Ako, Ako Andrew

    2017-09-01

    Hydrogen (D) and oxygen (18O) stable isotopes in precipitation are useful tools in groundwater recharge and climatological investigations. This study investigated the isotopes in rainfall during the 2013 and 2014 hydrological years in the Douala and Yaounde urban cities. The objectives were to generate local meteoric water lines (LMWLs), define the spatial-temporal variations of the isotopes in rainwater and their relationship to the regional precipitation cycle, and determine the factors controlling the isotopic variation. The LWMLs in Douala and Yaounde were δD = 7.92δ18O + 12.99 and δD = 8.35δ18O + 15.29, respectively. The slopes indicate isotopic equilibrium conditions during rain formation and negligible evaporation effect during rainfall. Precipitation showed similar wide ranges in δ18O values from -5.26 to -0.75 ‰ in Douala and -5.8 to +1.81 ‰ in Yaounde suggesting a common moisture source from the Atlantic Ocean. Enriched weighted mean δ18O (wδ18O) values during the low pre- and post-monsoon showers coincided with low convective activity across the entire region. Enriched isotopic signatures also marked the West African monsoon transition phase during each hydrological year. Abrupt wδ18O depletion after the transition coincided with the monsoon onset in the region. Peak periods of monsoonal rainfall, associated with high convective activities, were characterised by the most depleted wδ18O values. Controls on isotopic variations are the amount effect and moisture recycling. The stable isotope data provide a tool for groundwater recharge studies while the isotopic correlation with regional rainfall cycle demonstrate their use as markers of moisture circulation and detecting climatic changes in precipitation.

  16. Variation in stable isotope ratios of monthly rainfall in the Douala and Yaounde cities, Cameroon: local meteoric lines and relationship to regional precipitation cycle

    Science.gov (United States)

    Wirmvem, Mengnjo Jude; Ohba, Takeshi; Kamtchueng, Brice Tchakam; Taylor, Eldred Tunde; Fantong, Wilson Yetoh; Ako, Ako Andrew

    2016-04-01

    Hydrogen (D) and oxygen (18O) stable isotopes in precipitation are useful tools in groundwater recharge and climatological investigations. This study investigated the isotopes in rainfall during the 2013 and 2014 hydrological years in the Douala and Yaounde urban cities. The objectives were to generate local meteoric water lines (LMWLs), define the spatial-temporal variations of the isotopes in rainwater and their relationship to the regional precipitation cycle, and determine the factors controlling the isotopic variation. The LWMLs in Douala and Yaounde were δD = 7.92δ18O + 12.99 and δD = 8.35δ18O + 15.29, respectively. The slopes indicate isotopic equilibrium conditions during rain formation and negligible evaporation effect during rainfall. Precipitation showed similar wide ranges in δ18O values from -5.26 to -0.75 ‰ in Douala and -5.8 to +1.81 ‰ in Yaounde suggesting a common moisture source from the Atlantic Ocean. Enriched weighted mean δ18O (wδ18O) values during the low pre- and post-monsoon showers coincided with low convective activity across the entire region. Enriched isotopic signatures also marked the West African monsoon transition phase during each hydrological year. Abrupt wδ18O depletion after the transition coincided with the monsoon onset in the region. Peak periods of monsoonal rainfall, associated with high convective activities, were characterised by the most depleted wδ18O values. Controls on isotopic variations are the amount effect and moisture recycling. The stable isotope data provide a tool for groundwater recharge studies while the isotopic correlation with regional rainfall cycle demonstrate their use as markers of moisture circulation and detecting climatic changes in precipitation.

  17. Spatio-temporal variability of rainfall regime in the Brahmaputra valley of North East India

    Science.gov (United States)

    Deka, R. L.; Mahanta, C.; Nath, K. K.; Dutta, M. K.

    2016-05-01

    Monthly rainfall data, spanning over 110 years (1901-2010), were utilized for trend analysis at different spatial and temporal scales over the Brahmaputra valley, India. The Mann-Kendall statistic and Sen's slope model were used to identify the trends and estimate the magnitude of change, respectively. Statistical significance of the decadal shifts in rainfall from the overall mean was estimated by using Cramer's test. The analysis revealed decrease in annual as well as monsoon rainfall in the Brahmaputra valley during the last 110 years with large spatial and temporal variations. These decreasing trends of rainfall in the eastern part of the valley were statistically significant. Significant decreasing trend of monsoon rainfall during the recent 30-year period was due to significant decrease of July and September rainfall, and this trend was found to be consistent at different spatial scales. In the last decade (2001-2010) in particular, monsoon rainfall exhibited significant negative deviation from the normal due to three deficient years and absence of excess rainfall years. On the contrary, contribution of pre-monsoon and post-monsoon rainfall to annual total in the Brahmaputra valley increased during the recent 30-year period. Winter rainfall in the valley decreased during the last 30 years due to significant decrease of December rainfall in the eastern and central parts.

  18. Rainfall generation

    Science.gov (United States)

    Sharma, Ashish; Mehrotra, Raj

    This chapter presents an overview of methods for stochastic generation of rainfall at annual to subdaily time scales, at single- to multiple-point locations, and in a changing climatic regime. Stochastic rainfall generators are used to provide inputs for risk assessment of natural or engineering systems that can undergo failure under sustained (high or low) extremes. As a result, generation of rainfall has evolved to provide options that adequately represent such conditions, leading to sequences that exhibit low-frequency variability of a nature similar to the observed rainfall. The chapter consists of three key sections: the first two outlining approaches for rainfall generation using endogenous predictor variables and the third highlighting approaches for generation using exogenous predictors often simulated to represent future climatic conditions. The first section presents approaches for generation of annual and seasonal rainfall and daily rainfall, both at single-point locations and multiple sites, with an emphasis on alternatives that ensure appropriate representation of low-frequency variability in the generated rainfall sequences. The second section highlights advancements in the subdaily rainfall generation procedures including commonly used approaches for daily to subdaily rainfall generation. The final section (generation using exogenous predictors) presents a range of alternatives for stochastic downscaling of rainfall for climate change impact assessments of natural and engineering systems. We conclude the chapter by outlining some of the key challenges that remain to be addressed, especially in generation under climate change conditions, with an emphasis on the importance of incorporating uncertainty present in both measurements and models, in the rainfall sequences that are generated.

  19. Regional estimation of rainfall intensity-duration-frequency curves using generalized least squares regression of partial duration series statistics

    DEFF Research Database (Denmark)

    Madsen, H.; Mikkelsen, Peter Steen; Rosbjerg, Dan

    2002-01-01

    A general framework for regional analysis and modeling of extreme rainfall characteristics is presented. The model is based on the partial duration series (PDS) method that includes in the analysis all events above a threshold level. In the PDS model the average annual number of exceedances, the ...

  20. Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

    DEFF Research Database (Denmark)

    Wied Pedersen, Jonas; Lund, Nadia Schou Vorndran; Borup, Morten;

    2016-01-01

    period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior......High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper...... presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined...

  1. Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables.

    Science.gov (United States)

    Maniquiz, Marla C; Lee, Soyoung; Kim, Lee-Hyung

    2010-01-01

    Rainfall is an important factor in estimating the event mean concentration (EMC) which is used to quantify the washed-off pollutant concentrations from non-point sources (NPSs). Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression (MLR) to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long-term monitoring is needed to gather more data that can be used for the development of estimation models.

  2. Potential of convective rainfall estimation from lightning data in the context of the "Simulation of Meteosat Third Generation - Lightning Imager through Tropical Rainfall Measuring Mission - Lightning Imaging Sensor data

    Science.gov (United States)

    Biron, D.; de Leonibus, L.; Zauli, F.; Melfi, D.; Laquale, P.; Labate, D.

    2009-04-01

    The Centro Nazionale di Meteorologia e Climatologia Aeronautica recently hosted a fellowship sponsored by Selex Galileo, with the intent to study and perform a simulation of Meteosat Third Generation - Lightning Imager (MTG-LI) sensor behavior through Tropical Rainfall Measuring Mission - Lightning Imaging Sensor data (TRMM-LIS). For the next generation of earth observation geostationary satellite, major operating agencies are planning to insert an optical imaging mission, that continuously observes lightning pulses in the atmosphere; EUMETSAT has decided in recent years that one of the candidate mission to be flown on MTG is LI, a Lightning Imager. MTG-LI mission has no Meteosat Second Generation heritage, but users need to evaluate the possible real time data output of the instrument to agree in inserting it on MTG payload. Authors took the expected LI design from MTG Mission Requirement Document, and reprocess real lightning dataset, acquired from space by TRMM-LIS instrument, to produce a simulated MTG-LI lightning dataset. The simulation is performed in several run, varying Minimum Detectable Energy, taking into account processing steps from event detection to final lightning information. A definition of the specific meteorological requirements is given from the potential use in meteorology of lightning final information for convection estimation and numerical cloud modeling. Study results show the range of instrument requirements relaxation which lead to minimal reduction in the final lightning information. Potential in convective rainfall estimation over ocean from space lightning observation is addressed and a retrieval example making use of lightning ground network data is reported both with validation by radar observation.

  3. Estimation of Summer Rainfall over an Arid Area using AMSR-E Measurements:A Case Study in Xinjiang,China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Rainfall estimate in arid region using passive microwave remote sensing techniques has been a complex issue for some time.The main reason for this difficulty is that the high and variable emissivity of land surfaces greatly aggravates the complexity of the signatures from the rain cloud.The Xinjiang area,located in the northwest of China,holds all the typical characteristics of arid climate.A rainfall algorithm has been developed for this region by using the Advanced Microwave Scanning Radiometer for Earth Observing System(AMSR-E) measurements.The algorithm attempts to use all 12 chan-nels on the AMSR-E instrument and a two-step method calibrated over 11 days of hourly rain-gauge data.First,Stepwise Discriminant Analysis(SDA) used to optimally estimate rain pixels based on all 12 channels,although only three channels were found to be necessary.Next,a rain predicator scattering index was used to estimate rain rates.A linear relationship between the rain rates and the scattering index above the threshold of 3.0K was constructed with a simple approximately linear function.The estimated rain rates were compared with the rain-gauge data used to calibrate the method,and a good relationship was found with a root-mean-square error of 2.1mm/h.The numerical calculations and comparisons show that the algorithm works well in the Xinjiang area.

  4. Effects of time-series length and gauge network density on rainfall climatology estimates in Latin America

    OpenAIRE

    MAEDA EDUARDO EIJI; AREVALO TORRES JUAN; CARMONA MORENO Cesar

    2012-01-01

    Despite recent advances in the development of satellite sensors for monitoring precipitation at high spatial and temporal resolutions, the assessment of rainfall climatology still relies strongly on ground-station measurements. The Global Historical Climatology Network (GHCN) is one of the most popular stations database available for the international community. Nevertheless, the spatial distribution of these stations is not always homogeneous and the record length largely varies for each sta...

  5. Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm

    Directory of Open Access Journals (Sweden)

    Brocca Luca

    2015-09-01

    Full Text Available Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013 with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm, and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.

  6. Estimating spatially distributed monthly evapotranspiration rates by linear transformations of MODIS daytime land surface temperature data

    Directory of Open Access Journals (Sweden)

    J. Szilagyi

    2009-05-01

    Full Text Available Under simplifying conditions catchment-scale vapor pressure at the drying land surface can be calculated as a function of its watershed-representative temperature (<Ts> by the wet-surface equation (WSE, similar to the wet-bulb equation in meteorology for calculating the dry-bulb thermometer vapor pressure of the Complementary Relationship of evaporation. The corresponding watershed ET rate, , is obtained from the Bowen ratio with the help of air temperature, humidity and percent possible sunshine data. The resulting (<Ts>, pair together with the wet-environment surface temperature (<Tws> and ET rate (ETw, obtained by the Priestley-Taylor equation, define a linear transformation on a monthly basis by which spatially distributed ET rates can be estimated as a sole function of MODIS daytime land surface temperature, Ts, values within the watershed. The linear transformation preserves the mean which is highly desirable. <Tws>, in the lack of significant open water surfaces within the study watershed (Elkhorn, Nebraska, was obtained as the mean of the smallest MODIS Ts values each month. The resulting period-averaged (2000–2007 catchment-scale ET rate of 624 mm/yr is very close to the water-balance derived ET rate of about 617 mm/yr. The latter is a somewhat uncertain value due to the effects of (a observed groundwater depletion of about 1m over the study period caused by extensive irrigation, and; (b the uncertain rate of net regional groundwater supply toward the watershed. The spatially distributed ET rates correspond well with soil/aquifer properties and the resulting land use type (i.e. rangeland versus center-pivot irrigated crops.

  7. Application of seasonal rainfall forecasts and satellite rainfall observations to crop yield forecasting for Africa

    Science.gov (United States)

    Greatrex, H. L.; Grimes, D. I. F.; Wheeler, T. R.

    2009-04-01

    Rain-fed agriculture is of utmost importance in sub-Saharan Africa; the FAO estimates that over 90% of food consumed in the region is grown in rain-fed farming systems. As the climate in sub-Saharan Africa has a high interannual variability, this dependence on rainfall can leave communities extremely vulnerable to food shortages, especially when coupled with a lack of crop management options. The ability to make a regional forecast of crop yield on a timescale of months would be of enormous benefit; it would enable both governmental and non-governmental organisations to be alerted in advance to crop failure and could facilitate national and regional economic planning. Such a system would also enable individual communities to make more informed crop management decisions, increasing their resilience to climate variability and change. It should be noted that the majority of crops in the region are rainfall limited, therefore the ability to create a seasonal crop forecast depends on the ability to forecast rainfall at a monthly or seasonal timescale and to temporally downscale this to a daily time-series of rainfall. The aim of this project is to develop a regional-scale seasonal forecast for sub-Saharan crops, utilising the General Large Area Model for annual crops (GLAM). GLAM would initially be driven using both dynamical and statistical seasonal rainfall forecasts to provide an initial estimate of crop yield. The system would then be continuously updated throughout the season by replacing the seasonal rainfall forecast with daily weather observations. TAMSAT satellite rainfall estimates are used rather than rain-gauge data due to the scarcity of ground based observations. An important feature of the system is the use of the geo-statistical method of sequential simulation to create an ensemble of daily weather inputs from both the statistical seasonal rainfall forecasts and the satellite rainfall estimates. This allows a range of possible yield outputs to be

  8. Applicability of Doppler weather radar based rainfall data for runoff estimation in Indian watersheds – A case study of Chennai basin

    Indian Academy of Sciences (India)

    V S Josephine; B V Mudgal; S B Thampi

    2014-08-01

    Traditionally, India has been vulnerable to various hazards such as floods, droughts and cyclones. About 8% of the total Indian landmass is prone to cyclones. A number of Doppler weather radars are installed in India and their products are utilized for weather predictions and detection of cyclones approaching the Indian coast. Radar-based hydrological studies in various countries have proven that computation of runoff using radar rainfall data could outperform rain gauge network measurements. There are no reported studies on their utilization for hydrological modelling and/or flood-related studies in Indian river basins. A comparison study between Doppler weather radar (DWR) derived rainfall data and the conventional rain gauge data was carried out with hourly inputs at one of the watersheds of Chennai basin, Tamil Nadu, India using HEC-HMS model. The model calibration and validation were performed by comparing the simulated outflow with the observed daily outflow data. The calibrated model was used to predict runoff from two post-monsoon cyclonic storm events with hourly inputs. It was noticed that the discrepancy in the runoff volume was small, but the difference in the peak flow was substantial. Additionally, there was a variation at the time to peak flow using daily and hourly inputs. The results show that the use of radar data may be optional for runoff volume estimation for the watersheds with sufficient rain gauge density, but highly desirable for peak flow and time to peak estimation. Therefore, the DWR derived rainfall data is a promising input for runoff estimation, especially in urban flood modelling.

  9. Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.

    Science.gov (United States)

    Xiaopeng, Q I; Liang, Wei; Barker, Laurie; Lekiachvili, Akaki; Xingyou, Zhang

    Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R(2), mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R(2) range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.

  10. Spatial dependence of extreme rainfall

    Science.gov (United States)

    Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri

    2017-05-01

    This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.

  11. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    Directory of Open Access Journals (Sweden)

    T. Soares dos Santos

    2016-01-01

    model output and observed monthly precipitation. We used general circulation model (GCM experiments for the 20th century (RCP historical; 1970–1999 and two scenarios (RCP 2.6 and 8.5; 2070–2100. The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  12. Sampling errors for satellite-derived tropical rainfall - Monte Carlo study using a space-time stochastic model

    Science.gov (United States)

    Bell, Thomas L.; Abdullah, A.; Martin, Russell L.; North, Gerald R.

    1990-01-01

    Estimates of monthly average rainfall based on satellite observations from a low earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The size of this error is estimated for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). First, the statistical description of rainfall on scales from 1 to 1000 km is examined in detail, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10 percent of the mean for rainfall averaged over a 500 x 500 sq km area.

  13. Downscaled TRMM Rainfall Time-Series for Catchment Hydrology Applications

    Science.gov (United States)

    Tarnavsky, E.; Mulligan, M.

    2009-04-01

    Hydrology in semi-arid regions is controlled, to a large extent, by the spatial and temporal distribution of rainfall defined in terms of rainfall depth and intensity. Thus, appropriate representation of the space-time variability of rainfall is essential for catchment-scale hydrological models applied in semi-arid regions. While spaceborne platforms equipped with remote sensing instruments provide information on a range of variables for hydrological modelling, including rainfall, the necessary spatial and temporal detail is rarely obtained from a single dataset. This paper presents a new dynamic model of dryland hydrology, DryMOD, which makes best use of free, public-domain remote sensing data for representation of key variables with a particular focus on (a) simulation of spatial rainfall fields and (b) the hydrological response to rainfall, particularly in terms of rainfall-runoff partitioning. In DryMOD, rainfall is simulated using a novel approach combining 1-km spatial detail from a climatology derived from the TRMM 2B31 dataset (mean monthly rainfall) and 3-hourly temporal detail from time-series derived from the 0.25-degree gridded TRMM 3B42 dataset (rainfall intensity). This allows for rainfall simulation at the hourly time step, as well as accumulation of infiltration, recharge, and runoff at the monthly time step. In combination with temperature, topography, and soil data, rainfall-runoff and soil moisture dynamics are simulated over large dryland regions. In order to investigate the hydrological response to rainfall and variable catchment characteristics, the model is applied to two very different catchments in the drylands of North and West Africa. The results of the study demonstrate the use of remote sensing-based estimates of precipitation intensity and volume for the simulation of critical hydrological parameters. The model allows for better spatial planning of water harvesting activities, as well as for optimisation of agricultural activities

  14. Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

    Directory of Open Access Journals (Sweden)

    Jonas W. Pedersen

    2016-09-01

    Full Text Available High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.

  15. Application of a Cloud-Texture Analysis Scheme to the Cloud Cluster Structure Recognition and Rainfall Estimation in a Mesoscale Rainstorm Process

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    It is thought that satellite infrared (IR) images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scale structure on infrared satellite images.The classification using the IR satellite images taken during 4-5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting.

  16. FREQUENCY STRUCTURE OF MAJOR RAINFALL EVENTS IN THE NORTH-EASTERN PART OF BANGLADESH

    Directory of Open Access Journals (Sweden)

    RAQUIBUL ALAM

    2012-12-01

    Full Text Available The amount of rainfall received over an area is an important factor in assessing availability of water to meet various demands for agriculture, industry, irrigation, generation of hydroelectricity and other human activities. The distribution of rainfall in time and space is, therefore, an important factor for the economic development of a country. Due to rapid urbanization in various parts of the north-eastern region of Bangladesh, there is a growing need to study the rainfall pattern, and also frequency of the heavy rainfall events. This study was checked monthly average rainfall from daily records of last 50 years for this region. In order to check the major events, time history of monthly rainfall data were transformed into frequency domain using the Fast Fourier Transform (FFT. Estimated peak frequency (11.98 month depicts that major rainfall events of a year are occurring earlier than the previous year. The variability of rainfall in time scale was also checked from filtered signals, which is very useful for long-term water resources planning, agricultural development and disaster management for Bangladesh.

  17. An estimate of monthly global emissions of anthropogenic CO2: Impact on the seasonal cycle of atmospheric CO2

    Energy Technology Data Exchange (ETDEWEB)

    Erickson, D [Oak Ridge National Laboratory (ORNL); Mills, R [Oak Ridge National Laboratory (ORNL); Gregg, J [University of Maryland; Blasing, T J [ORNL; Hoffman, F [Oak Ridge National Laboratory (ORNL); Andres, Robert Joseph [ORNL; Devries, M [Oak Ridge National Laboratory (ORNL); Zhu, Z [NASA Goddard Space Flight Center; Kawa, S [NASA Goddard Space Flight Center

    2008-01-01

    Monthly estimates of the global emissions of anthropogenic CO2 are presented. Approximating the seasonal CO2 emission cycle using a 2-harmonic Fourier series with coefficients as a function of latitude, the annual fluxes are decomposed into monthly flux estimates based on data for the United States and applied globally. These monthly anthropogenic CO2 flux estimates are then used to model atmospheric CO2 concentrations using meteorological fields from the NASA GEOS-4 data assimilation system. We find that the use of monthly resolved fluxes makes a significant difference in the seasonal cycle of atmospheric CO2 in and near those regions where anthropogenic CO2 is released to the atmosphere. Local variations of 2-6 ppmv CO2 in the seasonal cycle amplitude are simulated; larger variations would be expected if smaller source-receptor distances could be more precisely specified using a more refined spatial resolution. We also find that in the midlatitudes near the sources, synoptic scale atmospheric circulations are important in the winter and that boundary layer venting and diurnal rectifier effects are more important in the summer. These findings have implications for inverse-modeling efforts that attempt to estimate surface source/sink regions especially when the surface sinks are colocated with regions of strong anthropogenic CO2 emissions.

  18. Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls

    Science.gov (United States)

    Bonnema, Matthew; Sikder, Safat; Miao, Yabin; Chen, Xiaodong; Hossain, Faisal; Ara Pervin, Ismat; Mahbubur Rahman, S. M.; Lee, Hyongki

    2016-05-01

    Growing population and increased demand for water is causing an increase in dam and reservoir construction in developing nations. When rivers cross international boundaries, the downstream stakeholders often have little knowledge of upstream reservoir operation practices. Satellite remote sensing in the form of radar altimetry and multisensor precipitation products can be used as a practical way to provide downstream stakeholders with the fundamentally elusive upstream information on reservoir outflow needed to make important and proactive water management decisions. This study uses a mass balance approach of three hydrologic controls to estimate reservoir outflow from satellite data at monthly and annual time scales: precipitation-induced inflow, evaporation, and reservoir storage change. Furthermore, this study explores the importance of each of these hydrologic controls to the accuracy of outflow estimation. The hydrologic controls found to be unimportant could potentially be neglected from similar future studies. Two reservoirs were examined in contrasting regions of the world, the Hungry Horse Reservoir in a mountainous region in northwest U.S. and the Kaptai Reservoir in a low-lying, forested region of Bangladesh. It was found that this mass balance method estimated the annual outflow of both reservoirs with reasonable skill. The estimation of monthly outflow from both reservoirs was however less accurate. The Kaptai basin exhibited a shift in basin behavior resulting in variable accuracy across the 9 year study period. Monthly outflow estimation from Hungry Horse Reservoir was compounded by snow accumulation and melt processes, reflected by relatively low accuracy in summer and fall, when snow processes control runoff. Furthermore, it was found that the important hydrologic controls for reservoir outflow estimation at the monthly time scale differs between the two reservoirs, with precipitation-induced inflow being the most important control for the Kaptai

  19. Improving the rainfall rate estimation in the midstream of the Heihe River Basin using rain drop size distribution

    Directory of Open Access Journals (Sweden)

    G. Zhao

    2009-09-01

    Full Text Available During the intensive observation period of the Watershed Allied Telemetry Experimental Research (WATER, a total of 1074 raindrop size distribution were measured by the Parsivel disdrometer, a latest state of the art optical laser instrument. Because of the limited observation data in Qinghai-Tibet Plateau, the modeling behavior was not well-done. We used raindrop size distributions to improve the rain rate estimator of meteorological radar, in order to obtain many accurate rain rate data in this area. We got the relationship between the terminal velocity of the rain drop and the diameter (mm of a rain drop: v(D=4.67 D0.53. Then four types of estimators for X-band polarimetric radar are examined. The simulation results show that the classical estimator R(Z is most sensitive to variations in DSD and the estimator R (KDP, Z, ZDR is the best estimator for estimating the rain rate. The lowest sensitivity of the rain rate estimator R (KDP, Z, ZDP to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes KDP, is proportional to the 3rd power of the drop diameter. On the other hand, the exponent of the backscatter cross section, which contributes to Z, is proportional to the 6th power of the drop diameter. Because the rain rate R is proportional to the 3.57th power of the drop diameter, KDP is less sensitive to DSD variations than Z.

  20. Evaluation of Satellite Rainfall Products over NASA's Iowa Flood Studies (IFloodS) Domain

    Science.gov (United States)

    ElSaadani, Mohamed; Quintero, Felipe; Krajewski, Witold F.; Goska, Radoslaw; Seo, Bongchul

    2014-05-01

    Iowa Flood Studies (IFloodS) is a NASA Global Precipitation Measurement (GPM) Mission to provide better understanding of the strengths and limitations of satellite products in the context of hydrologic applications. IFloodS took place in the central to north eastern part of Iowa in Midwestern United States during the months of April-June, 2013. Quantifying the physical characteristics, space/time variability and assessing satellite rainfall retrieval uncertainties at instantaneous to daily time scales are of the main objectives of IFloodS field experiment beside assessing hydrologic predictive skills as a function of space/time scales and discerning the relative roles of rainfall quantities in flood genesis. The errors of rainfall estimation of three satellite rainfall products (TRMM's TMPA 3B42 V7, CPC's CMORPH and CHRS at UCI's PERSIANN) have been characterized in space and time using NCEP Stage IV radar-rainfall product as a benchmark for comparison. The satellite rainfall products used in this study represent 3 hourly, quarter degree, rainfall accumulation. The benchmark rainfall accumulation has an hourly, four kilometers, resolutions in time and space respectively. We also investigate the adequacy of satellite rainfall products as inputs for hydrological modeling. To this end, these products were used as forcing for the Iowa Flood Center (IFC) hydrological model and produced discharge simulations in a high-resolution drainage network. The IFC hydrological model has been validated using radar rainfall product and thus, the hydrological outputs becomes the reference of comparison for the other rainfall products. We evaluated the hydrological performance of the rainfall products at different spatial scales, ranging from 2 to 14,000 square miles using stream discharge information from USGS gauges network. We discuss the adequacy of the rainfall products for flood forecasting at different spatial scales.

  1. Can SAPHIR Instrument Onboard MEGHATROPIQUES Retrieve Hydrometeors and Rainfall Characteristics ?

    Science.gov (United States)

    Goyal, J. M.; Srinivasan, J.; Satheesh, S. K.

    2014-12-01

    MEGHATROPIQUES (MT) is an Indo-French satellite launched in 2011 with the main intention of understanding the water cycle in the tropical region and is a part of GPM constellation. MADRAS was the primary instrument on-board MT to estimate rainfall characteristics, but unfortunately it's scanning mechanism failed obscuring the primary goal of the mission.So an attempt has been made to retrieve rainfall and different hydrometeors using other instrument SAPHIR onboard MT. The most important advantage of using MT is its orbitography which is specifically designed for tropical regions and can reach up to 6 passes per day more than any other satellite currently in orbit. Although SAPHIR is an humidity sounder with six channels centred around 183 GHz channel, it still operates in the microwave region which directly interacts with rainfall, especially wing channels and thus can pick up rainfall signatures. Initial analysis using radiative transfer models also establish this fact .To get more conclusive results using observations, SAPHIR level 1 brightness temperature (BT) data was compared with different rainfall products utilizing the benefits of each product. SAPHIR BT comparison with TRMM 3B42 for one pass clearly showed that channel 5 and 6 have a considerable sensitivity towards rainfall. Following this a huge database of more than 300000 raining pixels of spatially and temporally collocated 3B42 rainfall and corresponding SAPHIR BT for an entire month was created to include all kinds of rainfall events, to attain higher temporal resolution collocated database was also created for SAPHIR BT and rainfall from infrared sensor on geostationary satellite Kalpana 1.These databases were used to understand response of various channels of SAPHIR to different rainfall regimes . TRMM 2A12 rainfall product was also used to identify capabilities of SAPHIR to retrieve cloud and ice water path which also gave significant correlation. Conclusively,we have shown that SAPHIR has

  2. Robust Parameter Estimation Framework of a Rainfall-Runoff Model Using Pareto Optimum and Minimax Regret Approach

    Directory of Open Access Journals (Sweden)

    Yeonjoo Kim

    2015-03-01

    Full Text Available This study developed a robust parameter set (ROPS selection framework for a rainfall-runoff model that considers multi-events using the Pareto optimum and minimax regret approach (MRA. The calibrated parameter sets based on the Nash-Sutcliffe coefficient (NSE for two events were derived using a genetic algorithm. We generated 41 combinations for weighting values between two events for the multi-event objective function and derived 41 Pareto optimum points that were considered as the ROPS candidates. Then, two different approaches for parameter selection were proposed to determine the ROPS among the candidates: one uses NSE only and the other uses four performance measures (NSE, peak flow error, root mean square error and percentage of bias. In the NSE-only method, five events, including two events from the calibration set and three events from the evaluation set, were used, and the ROPS was selected based on the regrets of both the calibration and the evaluation sets. In the multiple (i.e., four performance measure method, only three events from the evaluation set were used and the ROPS was determined based on the regrets of twelve different cases, including three events with four measures. As a result, while single- and multi-event optimizations produced satisfying results for the calibration events, the optimized parameters from the single-event calibration do not perform well for another event, even one with the same criteria, such as NSE. The results of this study suggest that the optimized parameter set from the well-weighted objective function can successfully simulate not only hydrographs in general but also others, such as peak flow. In addition, the ROPS can be selected by considering the multiple performance measures of multiple validation events, as well as the NSE only of multiple calibration and validation events. Note that the study provides a framework that could be performed reasonably well with a limited number of events. While

  3. Improving the rainfall rate estimation in the midstream of the Heihe River Basin using raindrop size distribution

    Directory of Open Access Journals (Sweden)

    G. Zhao

    2011-03-01

    Full Text Available During the intensive observation period of the Watershed Allied Telemetry Experimental Research (WATER, a total of 1074 raindrop size distribution were measured by the Parsivel disdrometer, the latest state-of-the-art optical laser instrument. Because of the limited observation data in Qinghai-Tibet Plateau, the modelling behaviour was not well done. We used raindrop size distributions to improve the rain rate estimator of meteorological radar in order to obtain many accurate rain rate data in this area. We got the relationship between the terminal velocity of the raindrop and the diameter (mm of a raindrop: v(D = 4.67D0.53. Then four types of estimators for X-band polarimetric radar are examined. The simulation results show that the classical estimator R (ZH is most sensitive to variations in DSD and the estimator R (KDP, ZH, ZDR is the best estimator for estimating the rain rate. An X-band polarimetric radar (714XDP is used for verifying these estimators. The lowest sensitivity of the rain rate estimator R (KDP, ZH, ZDR to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes KDP, is proportional to the 3rd power of the drop diameter. On the other hand, the exponent of the backscatter cross-section, which contributes to ZH, is proportional to the 6th power of the drop diameter. Because the rain rate R is proportional to the 3.57th power of the drop diameter, KDP is less sensitive to DSD variations than ZH.

  4. Estimation of rainfall inputs and direct recharge to the deep unsaturated zone of southern Niger using the chloride profile method.

    NARCIS (Netherlands)

    Bromley, J.; Edmunds, W.M.; Fellman, E.; Brouwer, J.; Gaze, S.R.; Sudlow, J.; Taupin, J.D.

    1997-01-01

    An estimate of direct groundwater recharge below a region of natural woodland (tiger bush) has been made in south-west Niger using the solute profile technique. Data has been collected from a 77 m deep well dug within the study area covered by HAPEX-Sahel (Hydrological and Atmospheric Pilot Experime

  5. Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall-runoff model

    Science.gov (United States)

    Roy, Tirthankar; Gupta, Hoshin V.; Serrat-Capdevila, Aleix; Valdes, Juan B.

    2017-02-01

    Daily, quasi-global (50° N-S and 180° W-E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

  6. Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall-snowmelt flood regimes

    Science.gov (United States)

    Lawrence, D.; Paquet, E.; Gailhard, J.; Fleig, A. K.

    2014-05-01

    Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom-up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods

  7. Cyclical components of local rainfall data

    Science.gov (United States)

    Mentz, R. P.; D'Urso, M. A.; Jarma, N. M.; Mentz, G. B.

    2000-02-01

    This paper reports on the use of a comparatively simple statistical methodology to study local short time series rainfall data. The objective is to help in agricultural planning, by diminishing the risks associated with some uncertainties affecting this business activity.The analysis starts by assuming a model of unobservable components, trend, cycle, seasonal and irregular, that is well known in many areas of application. When series are in the realm of business and economics, the statistical methods popularized by the US Census Bureau US National Bureau of Economic Research are used for seasonal and cyclical estimation, respectively. The flexibility of these methods makes them good candidates to be applied in the meteorological context, and this is done in this paper for a selection of monthly rainfall time series.Use of the results to help in analysing and forecasting cyclical components is emphasized. The results are interesting. An agricultural entrepreneur, or a group of them located in a single geographical region, will profit by systematically collecting information (monthly in our work) about rainfall, and adopting the scheme of analysis described in this paper.

  8. Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data

    Science.gov (United States)

    Li, Zhanqing; Whitlock, Charles H.; Charlock, Thomas P.

    1995-01-01

    Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.

  9. Modelling rainfall amounts using mixed-gamma model for Kuantan district

    Science.gov (United States)

    Zakaria, Roslinazairimah; Moslim, Nor Hafizah

    2017-05-01

    An efficient design of flood mitigation and construction of crop growth models depend upon good understanding of the rainfall process and characteristics. Gamma distribution is usually used to model nonzero rainfall amounts. In this study, the mixed-gamma model is applied to accommodate both zero and nonzero rainfall amounts. The mixed-gamma model presented is for the independent case. The formulae of mean and variance are derived for the sum of two and three independent mixed-gamma variables, respectively. Firstly, the gamma distribution is used to model the nonzero rainfall amounts and the parameters of the distribution (shape and scale) are estimated using the maximum likelihood estimation method. Then, the mixed-gamma model is defined for both zero and nonzero rainfall amounts simultaneously. The formulae of mean and variance for the sum of two and three independent mixed-gamma variables derived are tested using the monthly rainfall amounts from rainfall stations within Kuantan district in Pahang Malaysia. Based on the Kolmogorov-Smirnov goodness of fit test, the results demonstrate that the descriptive statistics of the observed sum of rainfall amounts is not significantly different at 5% significance level from the generated sum of independent mixed-gamma variables. The methodology and formulae demonstrated can be applied to find the sum of more than three independent mixed-gamma variables.

  10. Climatological aspects of the extreme European rainfall of August 2002 and a trajectory method for estimating the associated evaporative source regions

    Directory of Open Access Journals (Sweden)

    P. James

    2004-01-01

    Full Text Available During the first half of August 2002, a sequence of extreme precipitation episodes affected many regions of central and southern Europe, culminating in one of the most severe flooding events ever experienced along sections of the river Elbe and its tributaries. In this paper, the synoptic meteorological situation during the primary flooding event, 11-13 August 2002, and its recent background is illustrated and discussed. Then, backward trajectory modelling of water vapour transport is employed to determine the sources and transport pathways of the moisture which rained out during the event. The Lagrangian trajectory model FLEXTRA is used together with high resolution operational meteorological analyses from the ECMWF to track a very large number of trajectories, initialized in a dense three-dimensional grid array over the extreme rainfall region. Specific humidity changes along each trajectory are mapped out to yield source-receptor relationships between evaporation and subsequent precipitation for the event. Regions of significant surface evaporation of moisture which later rained out were determined to be parts of the Aegean and Ligurian Seas during the initial stages of the event, while strong evaporation from eastern European land surfaces and from the Black Sea became dominant later on. The method also provides precipitation estimates based solely on specific humidity changes along Lagrangian airmass trajectories, which can be compared to ECMWF model forecast precipitation estimates.

  11. Comparison between Rayleigh and Mie Scattering Assumptions for Z-R Relation and Rainfall Rate Estimation with TRMM/PR Data

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-08-01

    Full Text Available Comparison of the rain rate estimated with the assumptions of Rayleigh and Mie scattering is made. We analyzed the different relationships between the radar reflective factor and rain rate (so-called Z-R relationship with both scattering models for different DSD (droplet size distribution and rainfall types as the wavelength is 2.2cm which is in accord with the band of TRMM/PR. Meanwhile we introduced a discrete ordinates method to retrieve the Z-R relationship for Mie scattering assumption. It is found that the retrieval result can be represented as the sum of some simple Z-R relationships. By the analysis of the Z-R relationships estimated from Rayleigh and Mie scattering assumptions in the rain types, we found that the difference of Z-R relationships between Rayleigh and Mie scattering in the thunderstorm that represents the larger raindrop size is larger than that in the drizzle that represent the smaller raindrop size.

  12. Evaluation of forecast skill of monthly rainfall over Northeast China using multi-models%多模式对东北地区月降水预测性能对比评估

    Institute of Scientific and Technical Information of China (English)

    李永生; 段春锋; 王莹

    2016-01-01

    基于中国、美国、欧洲和日本的4种气候模式对1983—2010年东北地区降水的回报试验结果,利用2011—2014年东北地区业务应用的结果和国家气象信息中心提供的东北地区172个气象站的观测资料,采用距平相关系数(ACC)、趋势异常综合评分(Ps)和距平符号一致率(Pc)3种定量方法对比评估了4种模式对东北地区月降水的预测性能。结果表明:EC模式和CFSv 2模式与BCC模式和TCC模式相比,EC模式和CFSv 2模式对东北地区月降水的总体预测效果较好,具有一定的预测技巧。从空间上来看,CFSv 2模式各月Pc的分布存在较明显的差异,模式仍有较大的改进空间。CFSv 2模式对东北地区初夏典型旱涝年具有一定的预测能力,对典型涝年的预测效果优于典型旱年。%The prediction skill of four climate models for monthly rainfall over Northeast China was evaluated using three qualitative evaluation methods,i.e.,anomaly correlation coefficient (ACC),trend anomaly inspection evalu-ation (Ps)and anomaly symbol consistency rate (Pc).Many data were used in this study,including 172 meteoro-logical stations over Northeast China supplied by the National Meteorological Information Center,the hindcast ex-perimental results of rainfall over Northeast China from 1983 to 2010 according to four climate models from Chi-na,America,Japan and Europe,and the operational application results over Northeast China from 201 1 to 2014. The results indicate that the monthly rainfall prediction skills of EC (European Center for Medium-Range Weather Forecasts)and CFSv 2 (Coupled Forecast System Model Version 2)models are better than those of BCC (Bei-jing Climate Center)and TCC (Tokyo Climate Center)models.Looking at the spatial distribution,there is a sig-nificant difference in the distribution of each monthly Pc for CFSv 2 model,indicating that this model has a big space for its improvement.The CFSv 2 model has

  13. Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation

    Science.gov (United States)

    Erbs, D. G.; Klein, S. A.; Duffie, J. A.

    1982-01-01

    Hourly pyrheliometer and pyranometer data from four U.S. locations are used to establish a relationship between the hourly diffuse fraction and the hourly clearness index. This relationship is compared to the relationship established by Orgill and Hollands (1977) and to a set of data from Highett, Australia, and agreement is within a few percent in both cases. The transient simulation program TRNSYS is used to calculate the annual performance of solar energy systems using several correlations. For the systems investigated, the effect of simulating the random distribution of the hourly diffuse fraction is negligible. A seasonally dependent daily diffuse correlation is developed from the data, and this daily relationship is used to derive a correlation for the monthly-average diffuse fraction.

  14. Radar rainfall estimation for the post-event analysis of a Slovenian flash-flood case: application of the mountain reference technique at C-band frequency

    Directory of Open Access Journals (Sweden)

    L. Bouilloud

    2009-01-01

    Full Text Available This article is dedicated to radar rainfall estimation for the post-event analysis of a Slovenian flash flood that occurred on 18 September 2007. The utility of the Mountain Reference Technique is demonstrated to quantify rain attenuation effects that affect C-band radar measurements in heavy rain. Maximum path-integrated attenuation between 15 and 20 dB were measured thanks to mountain returns for path-averaged rain rates between 10 and 15 mm h−1 over a 120-km path. The proposed technique allowed estimation of an effective radar calibration correction factor, assuming the reflectivity-attenuation relationship to be known. Screening effects were quantified using a geometrical calculation based on a digitized terrain model of the region. The vertical structure of the reflectivity was modelled with a normalized apparent vertical profile of reflectivity. Implementation of the radar data processing indicated that: (1 attenuation correction using the Hitschfeld Bordan algorithm allowed obtaining satisfactory radar rain estimates (Nash criterion of 0.8 at the event time scale; (2 due to the attenuation equation instability, it is however compulsory to limit the maximum path-integrated attenuation to be corrected to about 10 dB; (3 the results also proved to be sensitive on the parameterization of reflectivity-attenuation-rainrate relationships. The convective nature of the precipitation explains the rather good performance obtained. For more contrasted rainy systems with convective and stratiform regions, the combination of the vertical (VPR and radial (attenuation, screening sources of heterogeneity yields a still very challenging problem for radar quantitative precipitation estimation at C-band.

  15. Remote sensing estimates of impervious surfaces for hydrological modelling of changes in flood risk during high-intensity rainfall events

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Fensholt, Rasmus; Drews, Martin

    This paper addresses the accuracy and applicability of medium resolution (MR) remote sensing estimates of impervious surfaces (IS) for urban land cover change analysis. Landsat-based vegetation indices (VI) are found to provide fairly accurate measurements of sub-pixel imperviousness for urban...... areas at different geographical locations within Europe, and to be applicable for cities with diverse morphologies and dissimilar climatic and vegetative conditions. Detailed data on urban land cover changes can be used to examine the diverse environmental impacts of past and present urbanisation...

  16. Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

    Science.gov (United States)

    Creese, A.; Washington, R.

    2016-11-01

    Coupled models are the tools by which we diagnose and project future climate, yet in certain regions they are critically underevaluated. The Congo Basin is one such region which has received limited scientific attention, due to the severe scarcity of observational data. There is a large difference in the climatology of rainfall in global coupled climate models over the basin. This study attempts to address this research gap by evaluating modeled rainfall magnitude and distribution amongst global coupled models in the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Mean monthly rainfall between models varies by up to a factor of 5 in some months, and models disagree on the location of maximum rainfall. The ensemble mean, which is usually considered a "best estimate" of coupled model output, does not agree with any single model, and as such is unlikely to present a possible rainfall state. Moisture flux (qflux) convergence (which is assumed to be better constrained than parameterized rainfall) is found to have a strong relationship with rainfall; strongest correlations occur at 700 hPa in March-May (r = 0.70) and 850 hPa in June-August, September-November, and December-February (r = 0.66, r = 0.71, and r = 0.81). In the absence of observations, this relationship could be used to constrain the wide spectrum of modeled rainfall and give a better understanding of Congo rainfall climatology. Analysis of moisture transport pathways indicates that modeled rainfall is sensitive to the amount of moisture entering the basin. A targeted observation campaign at key Congo Basin boundaries could therefore help to constrain model rainfall.

  17. Modelling Ecuador's rainfall distribution according to geographical characteristics.

    Science.gov (United States)

    Tobar, Vladimiro; Wyseure, Guido

    2017-04-01

    It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting

  18. Estimating Soil Erosion and Carbon Mineralization by Rainfall Erosion for Select Management Practices in Corn-based Cropping Rotations: A Case Study for Iowa

    Science.gov (United States)

    Nelson, R. G.; Sheehan, J. J.; West, T. O.

    2005-12-01

    This paper presents estimates of changes in rainfall-induced soil erosion and soil carbon mineralization of individual land capability class I-VIII soil types in Iowa. Land management considered in this analysis includes various quantities of corn stover removal on continuous corn and corn-soybean rotations that are subject to conventional, reduced, and no-till tillage practices. For each rotation and tillage scenario, calculations of soil erosion and carbon mineralization were made for: 1) a ``baseline'' case (e.g., the annual quantity of rainfall-induced soil erosion (tons per acre) that would have occurred with no corn stover removal), 2) a minimum residue level at harvest such that the USDA-NRCS prescribed tolerable soil loss limit (T) is not exceeded for each individual soil type, and 3) a minimum residue at harvest set at 50 bushels corn stover equivalent. Results indicate a large variation in soil erosion and soil carbon mineralization, with this variation depending on rotation, tillage, residue level at harvest, stover removal, physical characteristics of individual soil types, field topology (average % slope), and localized climate. For each county, soil erosion and carbon mineralization increased within a set tillage practice in the corn-soybean rotation versus continuous corn with a range of 11.5% to nearly 600%. Also, an expected decrease in soil erosion and carbon mineralization occurred as tillage decreased in intensity from conventional to conservation/reduced to no-till. Moving from conventional to no-till in continuous corn and corn/soybean rotations with no stover removal, for example, resulted in average decreases of soil erosion of 60% and 88% respectively, and an average decrease of 0.084 tons of carbon dioxide efflux per acre between the two rotations. Allowing a minimum stover level at harvest based either on T or 50 bushels per acre stover equivalent resulted in average increases in soil erosion and carbon mineralization between 27% to over

  19. Rainfall variability and estimation for hydrologic modeling : a remote sensing based study at the source basin of the Upper Blue Nile river

    NARCIS (Netherlands)

    Haile, A.T.

    2010-01-01

    Rainfall is one of the meteorological forcing terms in hydrologic modelling and therefore its spatial variability in coverage, frequency and intensity affects simulation results. Rainfall variability in particular under the effect of orography adjacent to a large water body is not fully explored.

  20. Rainfall variability and estimation for hydrologic modeling : a remote sensing based study at the source basin of the Upper Blue Nile River

    NARCIS (Netherlands)

    Haile, Alemseged Tamiru

    2010-01-01

    Rainfall is one of the meteorological forcing terms in hydrologic modelling and therefore its spatial variability in coverage, frequency and intensity affects simulation results. Rainfall variability in particular under the effect of orography adjacent to a large water body is not fully explored. Su

  1. Remote sensing of rainfall for debris-flow hazard assessment

    Science.gov (United States)

    Wieczorek, G.F.; Coe, J.A.; Godt, J.W.; ,

    2003-01-01

    Recent advances in remote sensing of rainfall provide more detailed temporal and spatial data on rainfall distribution. Four case studies of abundant debris flows over relatively small areas triggered during intense rainstorms are examined noting the potential for using remotely sensed rainfall data for landslide hazard analysis. Three examples with rainfall estimates from National Weather Service Doppler radar and one example with rainfall estimates from infrared imagery from a National Oceanic and Atmospheric Administration satellite are compared with ground-based measurements of rainfall and with landslide distribution. The advantages and limitations of using remote sensing of rainfall for landslide hazard analysis are discussed. ?? 2003 Millpress,.

  2. Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology

    Indian Academy of Sciences (India)

    Diego Rivera; Yessica Rivas; Alex Godoy

    2015-02-01

    Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s−1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.

  3. Heterogeneity of Dutch rainfall

    NARCIS (Netherlands)

    Witter, J.V.

    1984-01-01

    Rainfall data for the Netherlands have been used in this study to investigate aspects of heterogeneity of rainfall, in particular local differences in rainfall levels, time trends in rainfall, and local differences in rainfall trend. The possible effect of urbanization and industrialization on the

  4. Monthly variation in the fat content of anchovy (Engraulis japonicus) in the Yellow Sea: implications for acoustic abundance estimation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Bo; ZHAO Xianyong; DAI Fangqun

    2011-01-01

    Anchovy is a key species in the Yellow Sea ecosystem. An accurate estimate of anchovy abundance is vital for the management of the anchovy stock and measurement of the ecosystem response to changes in anchovy abundance. However, the acoustic fish abundance estimate may be biased by 30%-40% if the fat-content induced target strength variation is not taken into account. We measured the monthly variation in the fat content of anchovy (Engraulis japonicus) in the Yellow Sea, and evaluated the potential effect of variation in fat content on the acoustic assessment of anchovy abundance. The fat content of anchovy varied seasonally, with two maxima and two minima in a year. The highest fat content (14.75%) was measured in the pre-spawning period in May, and the lowest fat content (2.48%) was measured during the post-spawning period in October. Fat content appeared to correlate with water content,but not body size. Assuming that the target strength is decreased by 0.2dB for every 1% increase in fat content, the seasonal difference in the target strength of anchovy may be as high as 2.45 dB. Given this,the acoustic abundance estimate may be biased by between 43% and 76%. Our results highlight the need for more information on the changes in fat content of fishes whose abundance is estimated by acoustic surveys.

  5. Acute myocardial infarction: estimation of at-risk and salvaged myocardium at myocardial perfusion SPECT 1 month after infarction.

    Science.gov (United States)

    Romero-Farina, Guillermo; Aguadé-Bruix, Santiago; Candell-Riera, Jaume; Pizzi, M Nazarena; Pineda, Victor; Figueras, Jaume; Cuberas, Gemma; de León, Gustavo; Castell-Conesa, Joan; García-Dorado, David

    2013-11-01

    To estimate at-risk and salvaged myocardium by using gated single photon emission computed tomography (SPECT) myocardial perfusion imaging after acute myocardial infarction (AMI). The study was approved by the hospital's Ethical Committee on Clinical Trials (trial register number, PR(HG)36/2000), and all patients gave informed consent. Forty patients (mean age, 61.78 years; eight women) with a first AMI underwent two gated SPECT examinations--one before percutaneous coronary intervention (PCI) and one 4-5 weeks after PCI. Myocardium at risk was estimated by assessing the perfusion defect at the first gated SPECT examination, and salvaged myocardium was estimated by assessing the risk area minus necrosis at the second examination. Myocardium at risk was estimated by determining the discordance between the areas of left ventricular (LV) wall motion and perfusion at the second examination. Concordance between tests was analyzed by means of linear regression analysis, the Pearson correlation, the intraclass correlation coefficient, and Bland-Altman analysis. An improvement in perfusion, wall motion, wall thickening, and LV ejection fraction (P Myocardial perfusion gated SPECT performed 1 month after early PCI in a first AMI provides potentially useful information on at-risk and salvaged myocardium. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122324/-/DC1. RSNA, 2013

  6. Estimativas de chuvas intensas para o Estado de Goiás Intense rainfall estimates for the Goiás State, Brazil

    Directory of Open Access Journals (Sweden)

    Luiz F. C. de Oliveira

    2008-03-01

    Full Text Available A ausência de estações pluviográficas e de séries históricas longas, tem levado os Engenheiros à utilização de metodologias que permitam expressar a relação intensidade-duração-freqüência de precipitações críticas. Com o objetivo de estimar as alturas de chuvas intensas, associadas a uma duração e freqüência, desenvolveu-se uma rotina computacional para ajustar os parâmetros do modelo de Bell para alguns municípios do Estado de Goiás. Para tal, empregaram-se séries históricas de precipitações diárias e de relações intensidade-duração-freqüência disponíveis para alguns municípios. Determinou-se, também, a relação entre a precipitação de 60 minutos e 1 dia de duração, para um período de retorno de dois anos. As informações geradas neste trabalho foram regionalizadas, permitindo a geração de mapas temáticos, visando a estimar a altura precipitada-duração-freqüência para as localidades desprovidas de registros. Para os municípios estudados, o modelo de Bell se ajustou-se perfeitamente, apresentando alternativa interessante na obtenção das alturas de chuvas intensas a partir de séries curtas. A reconstrução do modelo de Bell a partir da regionalização dos parâmetros ajustados permitiu a ampliação das equações que expressam a relação entre a precipitação máxima para diferentes durações e o período de retorno com baixos valores no erro-padrão da estimativa.The absence of pluviograph stations and of long historical series has induced engineers to use methodologies that allow them to express the intensity-duration-frequency relation of critical rainfalls. With the purpose of estimating the height of intense rain associated to a given duration and frequency, it was developed a computational routine to adjust the parameters of the BellÂ’s model to some municipal districts of the Goiás State. For such work it was employed historical series of daily precipitation and intensity

  7. Tropical stratospheric circulation and monsoon rainfall

    Science.gov (United States)

    Sikder, A. B.; Patwardhan, S. K.; Bhalme, H. N.

    1993-09-01

    Interannual variability of both SW monsoon (June September) and NE monsoon (October December) rainfall over subdivisions of Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu have been examined in relation to monthly zonal wind anomaly for 10 hPa, 30 hPa and 50 hPa at Balboa (9°N, 80°W) for the 29 year period (1958 1986). Correlations of zonal wind anomalies to SW monsoon rainfall ( r=0.57, significant at 1% level) is highest with the longer lead time (August of the previous year) at 10 hPa level suggesting some predictive value for Coastal Andhra Pradesh. The probabilities estimated from the contingency table reveal non-occurrence of flood during easterly wind anomalies and near non-occurrence of drought during westerly anomalies for August of the previous year at 10 hPa which provides information for forecasting of performance of SW monsoon over Coastal Andhra Pradesh. However, NE monsoon has a weak relationship with zonal wind anomalies of 10 hPa, 30 hPa and 50 hPa for Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu. Tracks of the SW monsoon storms and depressions in association with the stratospheric wind were also examined to couple with the fluctuations in SW monsoon rainfall. It is noted that easterly / westerly wind at 10 hPa, in some manner, suppresses / enhances monsoon storms and depressions activity affecting their tracks.

  8. Application of statistical weight matrix method in estimation of regional rainfall%统计权重矩阵法在雷达估测降水集成中的应用

    Institute of Scientific and Technical Information of China (English)

    邵月红; 张万昌

    2009-01-01

    In terms of the Doppler radar rainfall estimation algorithms, such as the Z-R relation, the average calibration, the Kalman filter, the optimum interpolation, the variation, the optimum Kalman filter and the variation Kalman, the regional rainfall are estimated and compared with those interpolated from the observations in the automatic precipitation observatories. The results suggest that the performance of each algorithms mentioned above is not very satisfied. The statistical analyses are applied to the estimated results, and the statistical weight matrix approach is employed to improve the accuracy of the regional rainfall estimations. The results reveal that the precision of the regional rainfall estimation from the average calibration, the Kalman filter, the optimum interpolation, the variation, the optimum Kalman filter and the variation Kalman are evidently superior to those from the Z-R relation, and the regional rainfall estimation from the Z-R relation shows the evident underestimation. What's more, the results further show that the accuracy of the estimated regional rainfall derived from the statistical weight matrix approach by integrating all individual algorithm mentioned above is evidently higher than that obtained by any individual ones. The quantitative rainfall estimations with the statistical weight matrix approach are very close to the automatic rain-gage network observed either in the spatial distribution or in the location of the intense precipitation centers. The regional precipitation estimation of the statistic weight matrix approach can truly reflect the precipitation status over the ground surface and might be served as a promising conventional method for estimation of the regional rainfall for the studied region.%Z~R关系法和6种雷达雨量计联合法反演的区域降水量与雨量计观测得到的降水场存在较大的误差,将这7种降水估测结果作为信息源,采用统计权重矩阵法对上述7种反演

  9. Estimated monthly streamflows for selected locations on the Kabul and Logar Rivers, Aynak copper, cobalt, and chromium area of interest, Afghanistan, 1951-2010

    Science.gov (United States)

    Vining, Kevin C.; Vecchia, Aldo V.

    2014-01-01

    The U.S. Geological Survey, in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, used the stochastic monthly water-balance model and existing climate data to estimate monthly streamflows for 1951–2010 for selected streamgaging stations located within the Aynak copper, cobalt, and chromium area of interest in Afghanistan. The model used physically based, nondeterministic methods to estimate the monthly volumetric water-balance components of a watershed. A comparison of estimated and recorded monthly streamflows for the streamgaging stations Kabul River at Maidan and Kabul River at Tangi-Saidan indicated that the stochastic water-balance model was able to provide satisfactory estimates of monthly streamflows for high-flow months and low-flow months even though withdrawals for irrigation likely occurred. A comparison of estimated and recorded monthly streamflows for the streamgaging stations Logar River at Shekhabad and Logar River at Sangi-Naweshta also indicated that the stochastic water-balance model was able to provide reasonable estimates of monthly streamflows for the high-flow months; however, for the upstream streamgaging station, the model overestimated monthly streamflows during periods when summer irrigation withdrawals likely occurred. Results from the stochastic water-balance model indicate that the model should be able to produce satisfactory estimates of monthly streamflows for locations along the Kabul and Logar Rivers. This information could be used by Afghanistan authorities to make decisions about surface-water resources for the Aynak copper, cobalt, and chromium area of interest.

  10. Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth's Radiant Energy System Instrument on the Tropical Rainfall Measuring Mission Satellite. Part II; Validation

    Science.gov (United States)

    Loeb, N. G.; Loukachine, K.; Wielicki, B. A.; Young, D. F.

    2003-01-01

    Top-of-atmosphere (TOA) radiative fluxes from the Clouds and the Earth s Radiant Energy System (CERES) are estimated from empirical angular distribution models (ADMs) that convert instantaneous radiance measurements to TOA fluxes. This paper evaluates the accuracy of CERES TOA fluxes obtained from a new set of ADMs developed for the CERES instrument onboard the Tropical Rainfall Measuring Mission (TRMM). The uncertainty in regional monthly mean reflected shortwave (SW) and emitted longwave (LW) TOA fluxes is less than 0.5 W/sq m, based on comparisons with TOA fluxes evaluated by direct integration of the measured radiances. When stratified by viewing geometry, TOA fluxes from different angles are consistent to within 2% in the SW and 0.7% (or 2 W/sq m) in the LW. In contrast, TOA fluxes based on ADMs from the Earth Radiation Budget Experiment (ERBE) applied to the same CERES radiance measurements show a 10% relative increase with viewing zenith angle in the SW and a 3.5% (9 W/sq m) decrease with viewing zenith angle in the LW. Based on multiangle CERES radiance measurements, 18 regional instantaneous TOA flux errors from the new CERES ADMs are estimated to be 10 W/sq m in the SW and, 3.5 W/sq m in the LW. The errors show little or no dependence on cloud phase, cloud optical depth, and cloud infrared emissivity. An analysis of cloud radiative forcing (CRF) sensitivity to differences between ERBE and CERES TRMM ADMs, scene identification, and directional models of albedo as a function of solar zenith angle shows that ADM and clear-sky scene identification differences can lead to an 8 W/sq m root-mean-square (rms) difference in 18 daily mean SW CRF and a 4 W/sq m rms difference in LW CRF. In contrast, monthly mean SW and LW CRF differences reach 3 W/sq m. CRF is found to be relatively insensitive to differences between the ERBE and CERES TRMM directional models.

  11. Accounting for the uncertainties in radar-raingauge rainfall estimation and the parametric uncertainties of the hydrological model in the prediction of flash floods in the Cévennes-Vivarais region, France

    Science.gov (United States)

    Navas, Rafael; Delrieu, Guy

    2017-04-01

    The Cévennes-Vivarais is a Mediterranean medium-elevation mountainous region of about 32000 km2 located in the south-east of France, prone to heavy precipitation events and subsequent flash floods and floods occurring mainly during the autumn season. Due to this vulnerability, it is a well instrumented region in terms of rainfall (4 weather radars of the French ARAMIS radar network, 250 hourly raingauges) and river discharge (45 stations) observations. A high-resolution (1 km2, 1 hour) radar-raingauge rainfall re-analysis has been established for the period 2007-2014 by using the kriging with external drift (KED) technique (Delrieu et al. 2014; Boudevillain et al. 2016). In the present communication, we present first a geostatistical method aimed at generating radar-raingauge rainfall ensembles based on the KED error standard deviations and the space-time structure of the residuals to the drift. The method is implemented over the four main watersheds of the Cévennes-Vivarais region by considering a spatial segmentation in hydrological meshes of variable sizes from 10 to 300 km2. A distributed hydrological model based on the SCS curve number and unit hydrograph concepts is then implemented in continuous mode for these watersheds. A sensitivity analysis allows us to identify the most sensitive parameters and to generate ensembles of "acceptable" hydrological simulations by using 16 discharge time series. Several results of this simulation framework will be highlighted: (1) the overall quality of the hydrological simulations as a function of the gauged watershed characteristics, (2) the transferability of the acceptable parameter sets from one year to another, (3) the effect of the space and time resolution of rainfall estimations on the hydrological simulations for gauged watersheds, (4) the respective impact of rainfall and model parametric uncertainties over a range of spatial and temporal scales for ungauged watersheds. References: Delrieu, G., A. Wijbrans, B

  12. Application of a Remote Sensing Method for Estimating Monthly Blue Water Evapotranspiration in Irrigated Agriculture

    Directory of Open Access Journals (Sweden)

    Mireia Romaguera

    2014-10-01

    Full Text Available In this paper we show the potential of combining actual evapotranspiration (ETactual series obtained from remote sensing and land surface modelling, to monitor community practice in irrigation at a monthly scale. This study estimates blue water evapotranspiration (ETb in irrigated agriculture in two study areas: the Horn of Africa (2010–2012 and the province of Sichuan (China (2001–2010. Both areas were affected by a drought event during the period of analysis, but are different in terms of water control and storage infrastructure. The monthly ETb results were separated by water source—surface water, groundwater or conjunctive use—based on the Global Irrigated Area Map and were analyzed per country/province. The preliminary results show that the temporal signature of the total ETb allows seasonal patterns to be distinguished within a year and inter-annual ETb dynamics. In Ethiopia, ETb decreased during the dry year, which suggests that less irrigation water was applied. Moreover, an increase of groundwater use was observed at the expense of surface water use. In Sichuan province, ETb in the dry year was of similar magnitude to the previous years or increased, especially in the month of August, which points to a higher amount of irrigation water used. This could be explained by the existence of infrastructure for water storage and water availability, in particular surface water. The application presented in this paper is innovative and has the potential to assess the existence of irrigation, the source of irrigation water, the duration and variability in time, at pixel and country scales, and is especially useful to monitor irrigation practice during periods of drought.

  13. Seasonal variation and climate change impact in Rainfall Erosivity across Europe

    Science.gov (United States)

    Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine; Ballabio, Cristiano

    2017-04-01

    Rainfall erosivity quantifies the climatic effect on water erosion and is of high importance for soil scientists, land use planners, agronomists, hydrologists and environmental scientists in general. The rainfall erosivity combines the influence of rainfall duration, magnitude, frequency and intensity. Rainfall erosivity is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minute rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years). The European Commission's Joint Research Centr(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,675 stations. The interpolation of those point erosivity values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511: 801-815). In 2016, REDES extended with a monthly component, which allowed developing monthly and seasonal erosivity maps and assessing rainfall erosivity both spatially and temporally for European Union and Switzerland. The monthly erosivity maps have been used to develop composite indicators that map both intra-annual variability and concentration of erosive events (Science of the Total Environment, 579: 1298-1315). Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year. Finally, the identification of the most erosive month allows recommending certain agricultural management practices (crop

  14. Modelling rainfall interception in unlogged and logged forest areas of Central Kalimantan, Indonesia

    Directory of Open Access Journals (Sweden)

    C. Asdak

    1998-01-01

    Full Text Available Rainfall interception losses were monitored for twelve months and related to vegetation and rainfall characteristics at the Wanariset Sangai on the upper reaches of the Mentaya river, Central Kalimantan. The rainfall interception losses were quantified for one hectare each of unlogged and logged humid tropical rainforests. The results show that interception loss is higher in the unlogged forest (11% of total gross rainfall than in the logged forest (6%. Interception loss was also simulated by the modified Rutter model and Gash's original and revised models. Both the Rutter and revised Gash models predicted total interception loss over a long period adequately, and resulted in estimates of the interception loss that deviated by 6 to 14% of the measured values, for both the unlogged and logged plots.

  15. Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Sahan Muhittin

    2016-01-01

    Full Text Available In this study, an artificial neural network (ANN model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010. Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin. The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm. Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98% was obtained with root mean square error (RMSE of 0.852 MJ/m2, mean square error (MSE of 0.725 MJ/m2, mean absolute bias error (MABE 10.659MJ/m2, and mean absolute percentage error (MAPE of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.

  16. Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

    Science.gov (United States)

    Sahan, Muhittin; Yakut, Emre

    2016-11-01

    In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 ( 98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2, mean square error (MSE) of 0.725 MJ/m2, mean absolute bias error (MABE) 10.659MJ/m2, and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.

  17. Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

    Science.gov (United States)

    Koo, Choongwan; Hong, Taehoon; Lee, Minhyun; Park, Hyo Seon

    2013-05-07

    The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country's climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

  18. Statistical comparison of models for estimating the monthly average daily diffuse radiation at a subtropical African site

    Energy Technology Data Exchange (ETDEWEB)

    Bashahu, M. [University of Burundi, Bujumbura (Burundi). Institute of Applied Pedagogy, Department of Physics and Technology

    2003-07-01

    Nine correlations have been developed in this paper to estimate the monthly average diffuse radiation for Dakar, Senegal. A 16-year period data on the global (H) and diffuse (H{sub d}) radiation, together with data on the bright sunshine hours (N), the fraction of the sky's (Ne/8), the water vapour pressure in the air (e) and the ambient temperature (T) have been used for that purpose. A model inter-comparison based on the MBE, RMSE and t statistical tests has shown that estimates in any of the obtained correlations are not significantly different from their measured counterparts, thus all the nine models are recommended for the aforesaid location. Three of them should be particularly selected for their simplicity, universal applicability and high accuracy. Those are simple linear correlations between K{sub d} and N/N{sub d}, Ne/8 or K{sub t}. Even presenting adequate performance, the remaining correlations are either simple but less accurate, or multiple or nonlinear regressions needing one or two input variables. (author)

  19. Estimates of monthly streamflow characteristics at selected sites in the upper Missouri River basin, Montana, base period water years 1937-86

    Science.gov (United States)

    Parrett, Charles; Johnson, D.R.; Hull, J.A.

    1989-01-01

    Estimates of streamflow characteristics (monthly mean flow that is exceeded 90, 80, 50, and 20 percent of the time for all years of record and mean monthly flow) were made and are presented in tabular form for 312 sites in the Missouri River basin in Montana. Short-term gaged records were extended to the base period of water years 1937-86, and were used to estimate monthly streamflow characteristics at 100 sites. Data from 47 gaged sites were used in regression analysis relating the streamflow characteristics to basin characteristics and to active-channel width. The basin-characteristics equations, with standard errors of 35% to 97%, were used to estimate streamflow characteristics at 179 ungaged sites. The channel-width equations, with standard errors of 36% to 103%, were used to estimate characteristics at 138 ungaged sites. Streamflow measurements were correlated with concurrent streamflows at nearby gaged sites to estimate streamflow characteristics at 139 ungaged sites. In a test using 20 pairs of gages, the standard errors ranged from 31% to 111%. At 139 ungaged sites, the estimates from two or more of the methods were weighted and combined in accordance with the variance of individual methods. When estimates from three methods were combined the standard errors ranged from 24% to 63 %. A drainage-area-ratio adjustment method was used to estimate monthly streamflow characteristics at seven ungaged sites. The reliability of the drainage-area-ratio adjustment method was estimated to be about equal to that of the basin-characteristics method. The estimate were checked for reliability. Estimates of monthly streamflow characteristics from gaged records were considered to be most reliable, and estimates at sites with actual flow record from 1937-86 were considered to be completely reliable (zero error). Weighted-average estimates were considered to be the most reliable estimates made at ungaged sites. (USGS)

  20. Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

    Science.gov (United States)

    Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir

    2016-08-01

    In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate extreme rainfall indices and annual maximum daily rainfall (AMDR) when downscaled daily rainfall from National Centers for Environmental Prediction (NCEP) reanalysis and Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCM) (four GCMs and two scenarios) output datasets and then their changes were estimated for the future period 2041-2060. The model was able to reproduce the monthly variations in the extreme rainfall indices reasonably well when forced by the NCEP reanalysis datasets. Frequency Adapted Quantile Mapping (FAQM) was used to remove bias in the simulated daily rainfall when forced by CMIP5 GCMs, which reduced the discrepancy between observed and simulated extreme rainfall indices. Although the observed AMDR were within the 2.5th and 97.5th percentiles of the simulated AMDR, the model consistently under-predicted the inter-annual variability of AMDR. A non-stationary model was developed using the generalized linear model for local, shape and scale to estimate the AMDR with an annual exceedance probability of 0.01. The study shows that in general, AMDR is likely to decrease in the future. The Onkaparinga catchment will also experience drier conditions due to an increase in consecutive dry days coinciding with decreases in heavy (>long term 90th percentile) rainfall days, empirical 90th quantile of rainfall and maximum 5-day consecutive total rainfall for the future period (2041-2060) compared to the base period (1961-2000).

  1. Prediction in Ungauged Basins (PUB) for estimating water availability during water scarcity conditions: rainfall-runoff modelling of the ungauged diversion inflows to the Ridracoli water supply reservoir

    Science.gov (United States)

    Toth, Elena

    2013-04-01

    The Ridracoli reservoir is the main drinking water supply reservoir serving the whole Romagna region, in Northern Italy. Such water supply system has a crucial role in an area where the different characteristics of the communities to be served, their size, the mass tourism and the presence of food industries highlight strong differences in drinking water needs. Its operation allows high quality drinking water supply to a million resident customers, plus a few millions of tourists during the summer of people and it reduces the need for water pumping from underground sources, and this is particularly important since the coastal area is subject also to subsidence and saline ingression into aquifers. The system experienced water shortage conditions thrice in the last decade, in 2002, in 2007 and in autumn-winter 2011-2012, when the reservoir water storage fell below the attention and the pre-emergency thresholds, thus prompting the implementation of a set of mitigation measures, including limitations to the population's water consumption. The reservoir receives water not only from the headwater catchment, closed at the dam, but also from four diversion watersheds, linked to the reservoir through an underground water channel. Such withdrawals are currently undersized, abstracting only a part of the streamflow exceeding the established minimum flows, due to the design of the water intake structures; it is therefore crucial understanding how the reservoir water availability might be increased through a fuller exploitation of the existing diversion catchment area. Since one of the four diversion catchment is currently ungauged (at least at the fine temporal scale needed for keeping into account the minimum flow requirements downstream of the intakes), the study first presents the set up and parameterisation of a continuous rainfall-runoff model at hourly time-step for the three gauged diversion watersheds and for the headwater catchment: a regional parameterisation

  2. RAINFALL AGGRESSIVENESS EVALUATION IN REGHIN HILLS USING FOURNIER INDEX

    Directory of Open Access Journals (Sweden)

    J. SZILAGYI

    2016-03-01

    Full Text Available Aggressiveness erosive force of rainfall is the express of kinetic energy and potential energy of rain water runoff on slopes. In the absence of a database for the analysis of parameters that define the torrencial rainfall, the rainfall erosivity factor was calculated by Fournier Index, Modified Fournier Index based on the monthly and annual precipitation.

  3. EVALUATION OF RAINFALL-RUNOFF EROSIVITY FACTOR FOR CAMERON HIGHLAND, PAHANG, MALAYSIA

    Directory of Open Access Journals (Sweden)

    Abdulkadir Taofeeq Sholagberu

    2016-07-01

    Full Text Available Rainfall-runoff is the active agent of soil erosion which often resulted in land degradation and water quality deterioration. Its aggressiveness to induce erosion is usually termed as rainfall erosivity index or factor (R. R-factor is one of the factors to be parameterized in the evaluation of soil loss using the Universal Soil Loss Equation and its reversed versions (USLE/RUSLE. The computation of accurate R-factor for a particular watershed requires high temporal resolution rainfall (pluviograph data with less than 30-minutes intensities for at least 20 yrs, which is available only in a few regions of the world. As a result, various simplified models have been proposed by researchers to evaluate R-factor using readily available daily, monthly or annual precipitation data. This study is thus aimed at estimating R-factor and to establish an approximate relationship between R-factor and rainfall for subsequent usage in the estimation of soil loss in Cameron highlands watershed. The results of the analysis showed that the least and peak (critical R-factors occurred in the months of January and April with 660.82 and 2399.18 MJ mm ha-1 h-1year-1 respectively. Also, it was observed that erosivity power starts to increase from the month of January through April before started falling in the month of July. The monthly and annual peaks (critical periods may be attributed to increased rainfall amount due to climate change which in turn resulted to increased aggressiveness of rains to cause erosion in the study area. The correlation coefficient of 0.985 showed that there was a strong relationship rainfall and R-factor.

  4. Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya

    Directory of Open Access Journals (Sweden)

    M. Oscar Kisaka

    2015-01-01

    Full Text Available This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59 and in number of rainy days (CV = 0.88, 0.49, and 0.53 in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76 with high probabilities (0.67 of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60% in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.

  5. Estimated fluoride intake of 6-month-old infants in four dietary regions of the United States.

    Science.gov (United States)

    Ophaug, R H; Singer, L; Harland, B F

    1980-02-01

    Eleven composite food groups comprising the infant "market basket" food collections for 1977 or 1978 from each of four dietary regions of the United States were analyzed for their fluoride content. Based upon the determined fluoride content of each composite and Food and Drug Administration estimates of food consumption the daily fluoride intake of an average 6-month-old infant residing in each of the dietary regions was calculated. The daily fluoride intake varied from 0.207 mg/day in Grand Rapids, Mich. (north central dietary region) to 0.541 mg/day in Orlando, Fla. (south dietary region). Flouride intakes of 0.272 and 0.354 mg/day were calculated for Philadelphia, Pa. (northeast dietary region) and Los Angeles, Calif. (west dietary region), respectively. The fluoride content of the water supplies ranged from 0.37 ppm (Los Angeles) to 1.04 ppm (Grand Rapids). Drinking water, dairy products and substitutes (other than milk), and grain and cereal products contributed 44 to 80% of the daily fluoride intake. In three of the four dietary regions the daily fluoride intake was less than the optimum level of 0.05 mg/kg body weight.

  6. Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

    Institute of Scientific and Technical Information of China (English)

    WANG Yingbin; ZHENG Ji; WANG Yang; ZHENG Xianzhi

    2012-01-01

    We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch,and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan,China.We used logbooks from five commercial fishing boats and data in government's monthly statistical reports.We developed two GAM models:one included temporal variables (month and hauling time) and spatial variables (longitude and latitude),and another included just two variables,month and the number of fishing boats.Our results suggest that temporal factors explained more of the variability in catch than spatial factors.Furthermore,month explained the majority of variation in catch.Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month,but the area varied among months.The number of boats fishing in each month also explained a large proportion of the variation in catch.Engine power had no effect on catch.The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively,indicating the both had good fits.The model yielded estimates that were very similar to those in the governmental reports between January to September,with relative estimate errors (REE) of <18%.However,the yields in October and November were significantly underestimated,with REEs of 36% and 27%,respectively.

  7. Advances in rainfall-runoff estimation using the NRCS-CN model in a changing climate in semiarid zones in both the northern and southern hemispheres

    Science.gov (United States)

    Durán-Barroso, Pablo; João Simas Guerreiro, Maria; De Andrade, Eunice Maia; González, Javier

    2016-04-01

    Extreme events runoff is one of the most important variables in water resources management, but its quantification in semiarid watersheds is not easy, especially because of their large retention capacity. In the worldwide used NRCS Curve Number model (CN), retention capacity is conditioned by the initial abstraction parameter, for which this manuscript questions its assessment procedure. We propose a more accurate procedure to compute the initial abstractions based on previous cumulative dry days (CDD). We also analyze the combined effect of initial abstractions and climatic characteristics by analyzing CN in a dry (Walnut Gulch, US) and wet (Ceará, Brazil) semiarid environment. With this new methodology and the evolution of rainfall volumes and CDD analysis, it is possible to suggest consequences of climate change on floods forecast of extreme rainfall-runoff events in a semiarid environment.

  8. Estimation of the Variation of Matric Suction with Respect to Depth in a Vertical Unsaturated Soil Trench Associated with Rainfall Infiltration

    OpenAIRE

    Oh Won Taek; Vanapalli Sai K.; Qi Shunchao; Han Zhong

    2016-01-01

    Soil trenching is extensively used in geotechnical, mining, tunneling and geo-environmental infrastructures. Safe height and stand-up time are two key factors that are required for the rational design of soil trenches. Rainfall infiltration has a significant influence on the safe height and stand-up time of unsaturated soil trenches since it can significantly alter the shear strength of soils by influencing the matric suction. In other words, predicting the variation of matric suction of soil...

  9. RAINFALL EROSIVITY IN SOUTHEASTERN NIGERIA *Ezemonye ...

    African Journals Online (AJOL)

    Osondu

    2011-10-13

    Oct 13, 2011 ... annual total amount, and frequency of fall, kinetic energy and ... annual rainfall increases from the northern frontier of the region ... Nigeria Meteorological Agency, Lagos for the ..... Estimation for Australia's Tropics. Aust. J. Soil.

  10. Estimation of reservoir inflow in data scarce region by using Sacramento rainfall runoff model - A case study for Sittaung River Basin, Myanmar

    Science.gov (United States)

    Myo Lin, Nay; Rutten, Martine

    2017-04-01

    The Sittaung River is one of four major rivers in Myanmar. This river basin is developing fast and facing problems with flood, sedimentation, river bank erosion and salt intrusion. At present, more than 20 numbers of reservoirs have already been constructed for multiple purposes such as irrigation, domestic water supply, hydro-power generation, and flood control. The rainfall runoff models are required for the operational management of this reservoir system. In this study, the river basin is divided into (64) sub-catchments and the Sacramento Soil Moisture Accounting (SAC-SMA) models are developed by using satellite rainfall and Geographic Information System (GIS) data. The SAC-SMA model has sixteen calibration parameters, and also uses a unit hydrograph for surface flow routing. The Sobek software package is used for SAC-SMA modelling and simulation of river system. The models are calibrated and tested by using observed discharge and water level data. The statistical results show that the model is applicable to use for data scarce region. Keywords: Sacramento, Sobek, rainfall runoff, reservoir

  11. Modelling and assessment of urban flood hazards based on rainfall intensity-duration-frequency curves reformation

    OpenAIRE

    Ghazavi, Reza; Moafi Rabori, Ali; Ahadnejad Reveshty, Mohsen

    2016-01-01

    Estimate design storm based on rainfall intensity–duration–frequency (IDF) curves is an important parameter for hydrologic planning of urban areas. The main aim of this study was to estimate rainfall intensities of Zanjan city watershed based on overall relationship of rainfall IDF curves and appropriate model of hourly rainfall estimation (Sherman method, Ghahreman and Abkhezr method). Hydrologic and hydraulic impacts of rainfall IDF curves change in flood properties was evaluated via Stormw...

  12. Interpolation of daily rainfall using spatiotemporal models and clustering

    KAUST Repository

    Militino, A. F.

    2014-06-11

    Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1km2 grid in the whole region. © 2014 Royal Meteorological Society.

  13. Entropy of stable seasonal rainfall distribution in Kelantan

    Science.gov (United States)

    Azman, Muhammad Az-zuhri; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Radi, Noor Fadhilah Ahmad

    2017-05-01

    Investigating the rainfall variability is vital for any planning and management in many fields related to water resources. Climate change can gives an impact of water availability and may aggravate water scarcity in the future. Two statistics measurements which have been used by many researchers to measure the rainfall variability are variance and coefficient of variation. However, these two measurements are insufficient since rainfall distribution in Malaysia especially in the East Coast of Peninsular Malaysia is not symmetric instead it is positively skewed. In this study, the entropy concept is used as a tool to measure the seasonal rainfall variability in Kelantan and ten rainfall stations were selected. In previous studies, entropy of stable rainfall (ESR) and apportionment entropy (AE) were used to describe the rainfall amount variability during years for Australian rainfall data. In this study, the entropy of stable seasonal rainfall (ESSR) is suggested to model rainfall amount variability during northeast monsoon (NEM) and southwest monsoon (SWM) seasons in Kelantan. The ESSR is defined to measure the long-term average seasonal rainfall amount variability within a given year (1960-2012). On the other hand, the AE measures the rainfall amounts variability across the months. The results of ESSR and AE values show that stations in east coastline are more variable as compared to other stations inland for Kelantan rainfall. The contour maps of ESSR for Kelantan rainfall stations are also presented.

  14. Maritime Continent rainfall variability during the TRMM era: The role of monsoon, topography and El Niño Modoki

    Science.gov (United States)

    As-syakur, Abd. Rahman; Osawa, Takahiro; Miura, Fusanori; Nuarsa, I. Wayan; Ekayanti, Ni Wayan; Dharma, I. Gusti Bagus Sila; Adnyana, I. Wayan Sandi; Arthana, I. Wayan; Tanaka, Tasuku

    2016-09-01

    Rainfall is among the most important climatic elements of the Maritime Continent. The Maritime Continent rainfall climate is uniquely located in the world's most active convective area. Satellite data measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 based high-resolution rainfall products represent monthly Maritime Continent rainfall characteristics over 16 years. Several statistical scores were employed to analyse annual means, linear trends, seasonal means, and anomalous Maritime Continent rainfall characteristic percentages. The effects of land and topography on rainfall quantities were also studied and compared with the Global Precipitation Climatology Project (GPCP) gridded precipitation estimates which has low-resolution. Comparison also applied on linear correlation and partial correlation techniques to determine the relationship between rainfall and the El Niño Modoki and El Niño-Southern Oscillation (ENSO; hereafter conventional El Niño). The results show that north-south Maritime Continent precipitation is associated with and generated by the northwest and southeast monsoon patterns. In addition, the large-scale circulations are linked with heavy rainfall over this land-ocean region due to large-scale island-topography-induced convective organization. The rainfall responses to El Niño Modoki and conventional El Niño clearly indicated the times at which the conventional El Niño had a higher impact than El Niño Modoki, especially during northern winter and spring, and vice versa during northern fall, and similarly affect during northern summer. Furthermore, the dynamic movements of rainfall anomaly that are caused by El Niño Modoki and the conventional El Niño events spanned from the southwest during June-July-August (JJA) to throughout the northeast ending in March-April-May (MAM).

  15. 多因子方法在雷达降水量估测中的应用%Application of a Multi-Factor Method in Radar Rainfall Estimation

    Institute of Scientific and Technical Information of China (English)

    张乐坚

    2012-01-01

    使用合肥雷达站2007年7月和广州雷达站2008年5-10月的雷达以及雨量计资料提出了使用雷达反射率因子、水平梯度和垂直积分液态水含量测量降水量的方法(简称多因子方法).此方法在人工神经网络构架之上隐含地实现了在降水类型识别基础上的降水量测量,并与使用单一Z-R关系测量的降水量进行比较.结果表明:多因子方法和使用Z=300R1.4测量的降水量相比,前者的计算结果与雨量计观测值相比具有较高的相关系数和较低的均方根误差,即前者测量降水量的精度高于后者.%A method of rainfall estimation by means of artificial neural network with the reflectivity, horizontal gradient and vertical integration of liquid water content is introduced, based on the radar data from the Hefei radar station in July 2007 and the Guangzhou radar station from May to October 2008 corresponding to the rain gauge data. The estimation of rainfall by the method is compared with the result of the Z=30R1.4 relationship. The results show that the rainfall estimation of the multi-factor artificial neural network is better than that of the Z=300R1.4 relationship, according to the correlation coefficients and root mean square errors.

  16. Patching rainfall data using regression methods. 3. Grouping, patching and outlier detection

    Science.gov (United States)

    Pegram, Geoffrey

    1997-11-01

    Rainfall data are used, amongst other things, for augmenting or repairing streamflow records in a water resources analysis environment. Gaps in rainfall records cause problems in the construction of water-balance models using monthly time-steps, when it becomes necessary to estimate missing values. Modest extensions are sometimes also desirable. It is also important to identify outliers as possible erroneous data and to group data which are hydrologically similar in order to accomplish good patching. Algorithms are described which accomplish these tasks using the covariance biplot, multiple linear regression, singular value decomposition and the pseudo-Expectation-Maximization algorithm.

  17. Interrelationship of rainfall, temperature and reference evapotranspiration trends and their net response to the climate change in Central India

    Science.gov (United States)

    Kundu, Sananda; Khare, Deepak; Mondal, Arun

    2016-09-01

    The monthly rainfall data from 1901 to 2011 and maximum and minimum temperature data from 1901 to 2005 are used along with the reference evapotranspiration (ET0) to analyze the climate trend of 45 stations of Madhya Pradesh. ET0 is calculated by the Hargreaves method from 1901 to 2005 and the computed data is then used for trend analysis. The temporal variation and the spatial distribution of trend are studied for seasonal and annual series with the Mann-Kendall (MK) test and Sen's estimator of slope. The percentage of change is used to find the rate of change in 111 years (rainfall) and 105 years (temperatures and ET0). Interrelationships among these variables are analyzed to see the dependency of one variable on the other. The results indicate a decreasing rainfall and increasing temperatures and ET0 trend. A similar pattern is noticeable in all seasons except for monsoon season in temperature and ET0 trend analysis. The highest increase of temperature is noticed during post-monsoon and winter. Rainfall shows a notable decrease in the monsoon season. The entire state of Madhya Pradesh is considered as a single unit, and the calculation of overall net change in the amount of the rainfall, temperatures (maximum and minimum) and ET0 is done to estimate the total loss or gain in monthly, seasonal and annual series. The results show net loss or deficit in the amount of rainfall and the net gain or excess in the temperature and ET0 amount.

  18. Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco

    Directory of Open Access Journals (Sweden)

    Hamza Ouatiki

    2017-01-01

    Full Text Available In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission. In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r of 0.2 and average Root Mean Square Error (RMSE of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm or annual time scale (r of 0.71 and RMSE of 157 mm. Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions.

  19. Accuracy of rainfall measurement for scales of hydrological interest

    Directory of Open Access Journals (Sweden)

    S. J. Wood

    2000-01-01

    Full Text Available The dense network of 49 raingauges over the 135 km2 Brue catchment in Somerset, England is used to examine the accuracy of rainfall estimates obtained from raingauges and from weather radar. Methods for data quality control and classification of precipitation types are first described. A super-dense network comprising eight gauges within a 2 km grid square is employed to obtain a 'true value' of rainfall against which the 2 km radar grid and a single 'typical gauge' estimate can be compared. Accuracy is assessed as a function of rainfall intensity, for different periods of time-integration (15 minutes, 1 hour and 1 day and for two 8-gauge networks in areas of low and high relief. In a similar way, the catchment gauge network is used to provide the 'true catchment rainfall' and the accuracy of a radar estimate (an area-weighted average of radar pixel values and a single 'typical gauge' estimate of catchment rainfall evaluated as a function of rainfall intensity. A single gauge gives a standard error of estimate for rainfall in a 2 km square and over the catchment of 33% and 65% respectively, at rain rates of 4 mm in 15 minutes. Radar data at 2 km resolution give corresponding errors of 50% and 55%. This illustrates the benefit of using radar when estimating catchment scale rainfall. A companion paper (Wood et al., 2000 considers the accuracy of rainfall estimates obtained using raingauge and radar in combination. Keywords: rainfall, accuracy, raingauge, radar

  20. Adequacy of satellite derived rainfall data for stream flow modeling

    Science.gov (United States)

    Artan, G.; Gadain, Hussein; Smith, Jody L.; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

    2007-01-01

    Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

  1. Weather radar rainfall data in urban hydrology

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick

    2017-01-01

    Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology...... necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall...... estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological...

  2. Temporal analysis (1940-2010) of rainfall aggressiveness in the Iberian Peninsula basins

    Science.gov (United States)

    García-Barrón, L.; Camarillo, J. M.; Morales, J.; Sousa, A.

    2015-06-01

    Rainfall aggressiveness causes environmental impacts and it is related to several natural hazards. Therefore, this parameter has been chosen as an environmental indicator. The present study is based on the monthly estimated rainfall using the Precipitation Runoff Integrated Model (SIMPA) for each Spanish hydrographic basin from 1940 to 2010. The main aim is to analyse temporal irregularity of rainfall aggressiveness in large geographic areas and to extract spatio-temporal patterns. For each year the rainfall aggressiveness was calculated using the Modified Fournier Index (IFM) and Oliver's Index of Precipitation Concentration (IPC). The temporal variability of the annual series of these indices was analysed for each zone delimited. The results obtained made it possible to characterize the rainfall aggressiveness in the Iberian Peninsula and to determine its evolution over the past decades. They also reveal that the general pattern of the rainfall aggressiveness is determined by the dual effect of latitude (north-south) and longitude (east-west) as a result of the different maritime influences of the Atlantic and the Mediterranean watersheds. Finally a new variable is proposed, the Annual Aggressiveness Risk RA, which summarizes the information provided by IFM and IPC.

  3. Estimation of infiltration rate, run-off and sediment yield under simulated rainfall experiments in upper Pravara Basin, India: Effect of slope angle and grass-cover

    Indian Academy of Sciences (India)

    Veena U Joshi; Devidas T Tambe

    2010-12-01

    The main objective of this study is to measure the effect of slope and grass-cover on in filtration rate, run-off and sediment yield under simulated rainfall conditions in a badland area located in the upper Pravara Basin in western India. An automatic rainfall simulator was designed following Dunne et al (1980) and considering the local conditions. Experiments were conducted on six selected experimental fields of 2 × 2 m within the catchment with distinct variations in surface characteristics –grass-covered area with gentle slope, recently ploughed gently sloping area, area covered by crop residue (moderate slope), bare badland with steep slope, gravelly surface with near flat slope and steep slope with grass-cover. The results indicate subtle to noteworthy variations amongst the plots depending on their slope angle and surface characteristics. An important finding that emerges from the study is that the grass-cover is the most effective measure in inducing infiltration and in turn minimizing run-off and sediment yield. Sediment yields are lowest in gently sloping grass-covered surfaces and highest in bare badland surfaces with steep slopes. These findings have enormous implication for this area, because over 2/3 area is characterized by bare and steep slopes.

  4. Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2002

    Science.gov (United States)

    Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

    2003-01-01

    NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs. S. America ) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model. Review of other latent heating algorithms will be discussed in the workshop.

  5. The Amazon forest-rainfall feedback: the roles of transpiration and interception

    Science.gov (United States)

    Dekker, Stefan; Staal, Arie; Tuinenburg, Obbe

    2017-04-01

    In the Amazon, deep-rooted trees increase local transpiration and high tree cover increase local interception evaporation. These increased local evapotranspiration fluxes to the atmosphere have both positive effects on forests down-wind, as they stimulate rainfall. Although important for the functioning of the Amazon, we have an inadequate assessment on the strength and the timing of these forest-rainfall feedbacks. In this study we (i) estimate local forest transpiration and local interception evaporation, (ii) simulate the trajectories of these moisture flows through the atmosphere and (iii) quantify their contributions to the forest-rainfall feedback for the whole Amazon basin. To determine the atmospheric moisture flows in tropical South America we use a Lagrangian moisture tracking algorithm on 0.25° (c. 25 km) resolution with eight atmospheric layers on a monthly basis for the period 2003-2015. With our approach we account for multiple re-evaporation cycles of this moisture. We also calculate for each month the potential effects of forest loss on evapotranspiration. Combined, these calculations allow us to simulate the effects of land-cover changes on rainfall in downwind areas and estimate the effect on the forest. We found large regional and temporal differences in the importance how forest contribute to rainfall. The transpiration-rainfall feedback is highly important during the dry season. Between September-November, when large parts of the Amazon are at the end of the dry season, more than 50% of the rainfall is caused by the forests upstream. This means that droughts in the Amazon are alleviated by the forest. Furthermore, we found that much moisture cycles several times during its trajectory over the Amazon. After one evapotranspiration-rainfall cycle, more than 40% of the moisture is re-evaporated again. The interception-evaporation feedback is less important during droughts. Finally from our analysis, we show that the forest-rainfall feedback is

  6. A point rainfall model and rainfall intensity-duration-frequency analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Chul-Sang; Jung, Kwang-Sik [Korea University, Jochiwon(Korea); Kim, Nam-Won [Korea Institute of Construction Technology, Koyang(Korea)

    2001-12-31

    This study proposes a theoretical methodology for deriving a rainfall intensity-duration-frequency(I-D-F) curve using a simple rectangular pulses Poisson process model. As the I-D-F curve derived by considering the model structure is dependent on the rainfall model parameters estimated using the observed first and second order statistics, it becomes less sensitive to the unusual rainfall events than that derived using the annual maxima rainfall series. This study has been applied to the rainfall data at Seoul and Incheon stations to check its applicability by comparing the two I-D-F curves from the model and the data. The results obtained are as followed. (1) As the duration becomes longer, the overlap probability increases significantly. However, its contribution to the rainfall intensity decreases a little. (2) When considering the overlap of each rainfall event, especially for large duration and return period, we could see obvious increases of rainfall intensity. This result is normal as the rainfall intensity is calculated by considering both the overlap probability and return period. Also, the overlap effect for Seoul station is found much higher than that for Incheon station, which is mainly due to the different overlap probabilities calculated using different rainfall model parameter sets. (3) As the rectangular pulses Poisson processes model used in this study cannot consider the clustering characteristics of rainfall, the derived I-D-F curves show less rainfall intensities than those from the annual maxima series. However, overall pattern of both I-D-F curves are found very similar, and the difference is believed to be overcome by use of a rainfall model with the clustering consideration. (author). 14 refs., 6 tabs., 2 figs.

  7. Synoptic Analysis of Heavy Rainfall and Flood Observed in Izmir on 20 May 2015 Using Radar and Satellite Images

    Science.gov (United States)

    Avsar, Ercument

    2016-07-01

    In this study, a meteorological analysis is conducted on the sudden and heavy rainfall that occurred in Izmir on May 20, 2015. The barotropic model that is observed in upper carts is shown in detail. We can access the data of and analyze the type, severity and amount of many meteorological parameters using the meteorological radars that form a remote sensing system. The one field that uses the radars most intensively is rainfall. Images from the satellite and radar systems are used in the meteorological analysis of the heavy rainfall that occurred in Izmir on 20 May 2015, and the development of the system that led to this rainfall is shown. In this study, data received from Bornova Automatic Meteorological Observation Station (OMGI), which is under the management of Meteorology General Directorate (MGM), Izmir 2. Regional Directorate; satellite images; Radar PPI (Plan Position Indicator) and Radar MAX (Maximum Display) images are evaluated. In addition, synoptic situation, outputs of numerical estimation models, indices calculated from Skew T Log-P diagram are shown. All these results are mapped and analyzed. At the end of these analyses, it is found that this sudden rainfall had developed according to the frontal system motion. A barotropic model occurred on the day of the rainfall over the Aegean Region. As a result of the rainfall that happened in Izmir at 12.00 UTC (Universal Coordinated Time), the May month rainfall record for the last 64 years is achieved with a rainfall amount of 67.7 mm per meter square. Keywords: Izmir, barotropic model, heavy rainfall, radar, synoptic analysis

  8. Development of a Compound Distribution Markov Chain Model for Stochastic Generation of Rainfall with Long Term Persistence

    Science.gov (United States)

    Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George

    2015-04-01

    One of the overriding issues in the rainfall simulation is the underestimation of observed rainfall variability in longer timescales (e.g. monthly, annual and multi-year), which usually results into under-estimation of reservoir reliability in urban water planning. This study has developed a Compound Distribution Markov Chain (CDMC) model for stochastic generation of daily rainfall. We used two parameters of Markov Chain process (transition probabilities of wet-to-wet and dry-to-dry days) for simulating rainfall occurrence and two parameters of gamma distribution (calculated from mean and standard deviation of wet-day rainfall) for simulating wet-day rainfall amounts. While two models with deterministic parameters underestimated long term variability, our investigation found that the long term variability of rainfall in the model is predominantly governed by the long term variability of gamma parameters, rather than the variability of Markov Chain parameters. Therefore, in the third approach, we developed the CDMC model with deterministic parameters of Markov Chain process, but stochastic parameters of gamma distribution by sampling the mean and standard deviation of wet-day rainfall from their log-normal and bivariate-normal distribution. We have found that the CDMC is able to replicate both short term and long term rainfall variability, when we calibrated the model at two sites in east coast of Australia using three types of daily rainfall data - (1) dynamically downscaled, 10 km resolution gridded data produced by NSW/ACT Regional Climate Modelling project, (2) 5 km resolution gridded data by Australian Water Availability Project and (3) point scale raingauge stations data by Bureau of Meteorology, Australia. We also examined the spatial variability of parameters and their link with local orography at our field site. The suitability of the model in runoff generation and urban reservoir-water simulation will be discussed.

  9. Downscaling site rainfall from daily to 11.25-minute resolution: event, diurnal, seasonal and decadal controls on downscaling parameters

    Science.gov (United States)

    McIntyre, Neil; Shi, Shirley; Onof, Christian

    2016-04-01

    Downscaling site rainfall from daily to sub-daily resolution is often approached using the multiplicative discrete random cascade (MDRC) class of models, with mixed success. Questions in any application - for MDRCs or indeed other classes of downscaling model - is to what extent and in what way are model parameters functions of rainfall event type and/or large scale climate controls for example those linked to the El Nino Southern Oscillation (ENSO). These questions underlie the applicability of downscaling models for analysing rainfall and hydrological extremes, in particular for synthesising long-term historical or future sub-daily extremes conditional on historic or projected daily data. Coastal Queensland, Australia, is subject to combinations of multiple weather systems, including tropical cyclones, blocking systems, convective storms, frontal systems and ENSO influences. Using 100 years of fine resolution data from two gauges in central Brisbane, microcanonical MDRC models are fitted to data from 1 day to 11.25 minutes in seven cascade levels, each level dividing the time interval and its rainfall volume into two sub-intervals. Each cascade level involves estimating: the probabilities that all the rainfall observed in a time interval is concentrated in only the first of the two sub-intervals and that all the rainfall observed in a time interval is concentrated in only the second of the two sub-intervals; and also two beta distribution parameters that define the probability of a given division of the rainfall into both sub-intervals. These parameters are found to vary systematically with time of day, rainfall volume, event temporal structure, month of year, and ENSO anomaly. Reasonable downscaling performance is achieved (in terms of replicating extreme values of 11.25 minute rainfall given the observed daily data) by including the parameter dependence on the rainfall volume and event structure, although particular applications may justify development of more

  10. Preliminary study on mechanics-based rainfall kinetic energy

    Directory of Open Access Journals (Sweden)

    Yuan Jiuqin Ms.

    2014-09-01

    Full Text Available A raindrop impact power observation system was employed to observe the real-time raindrop impact power during a rainfall event and to analyze the corresponding rainfall characteristics. The experiments were conducted at different simulated rainfall intensities. As rainfall intensity increased, the observed impact power increased linearly indicating the power observation system would be satisfactory for characterizing rainfall erosivity. Momentum is the product of mass and velocity (Momentum=MV, which is related to the observed impact power value. Since there is no significant difference between momentum and impact power, observed impact power can represent momentum for different rainfall intensities. The relationship between momentum and the observed impact power provides a convenient way to calculate rainfall kinetic energy. The value of rainfall kinetic energy based on the observed impact power was higher than the classic rainfall kinetic energy. The rainfall impact power based kinetic energy and the classic rainfall kinetic energy showed linear correlation, which indicates that the raindrop impact power observation system can characterize rainfall kinetic energy. The article establishes a preliminary way to calculate rainfall kinetic energy by using the real-time observed momentum, providing a foundation for replacing the traditional methods for estimating kinetic energy of rainstorms.

  11. On Rainfall Modification by Major Urban Areas. Part 1; Observations from Space-borne Rain Radar on TRMM

    Science.gov (United States)

    Shepherd, J. Marshall; Pierce, Harold; Starr, David OC. (Technical Monitor)

    2001-01-01

    This study represents one of the first published attempts to identify rainfall modification by urban areas using satellite-based rainfall measurements. Data from the first space-based rain-radar, the Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar, are employed. Analysis of the data enables identification of rainfall patterns around Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas during the warm season. Results reveal an average increase of -28% in monthly rainfall rates within 30-60 kilometers downwind of the metropolis with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage chances are relative to an upwind CONTROL area. It was also found that maximum rainfall rates in the downwind impact area can exceed the mean value in the upwind CONTROL area by 48%-116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. These results are consistent with METROMEX studies of St. Louis almost two decades ago and more recent studies near Atlanta. Future work will investi(yate hypothesized factors causing rainfall modification by urban areas. Additional work is also needed to provide more robust validation of space-based rain estimates near major urban areas. Such research has implications for urban planning, water resource management, and understanding human impact on the environment.

  12. Statistical characterizations of rainfall structure over two tropical stations in southern India for microwave communication

    Science.gov (United States)

    Ojo, J. S.; Ajewole, M. O.; Sarkar, S. K.

    2010-08-01

    Statistical characterizations of rainfall structure over two tropical stations in southern India are reported in this paper based on the 2-year rainfall data. The statistical characterizations has been based on cumulative distribution function, exceedance of threshold values, dependence of the intensity of rainfall on the event duration, seasonal variability, and worst months concept as well as diurnal variability. These results are needed to give the detailed insights to the system designers for the development of communication gadgets needed for better service, serve as a vital tool to estimate signal outages in a year over the region and for proper planning of radio communication in the region. Finally, the study shows that the recent International Telecommunications Union Recommendations (ITU-R) value underestimated rain rate for 0.01% exceedance for the two locations.

  13. Two and a half years of country-wide rainfall maps using radio links from commercial cellular telecommunication networks

    Science.gov (United States)

    Overeem, A.; Leijnse, H.; Uijlenhoet, R.

    2016-10-01

    Although rainfall estimation employing microwave links from cellular telecommunication networks is recognized as a new promising measurement technique, its potential for long-term large-scale operational rainfall monitoring remains to be demonstrated. This study contributes to this endeavor by deriving a continuous series of rainfall maps from a large 2.5 year microwave link data set of, on average, 3383 links (2044 link paths) covering Netherlands (˜3.5 × 104 km2), a midlatitude country (˜5°E, ˜52°N) with a temperate climate. Maps are extensively verified against an independent gauge-adjusted radar rainfall data set for different temporal (15 min, 1 h, 1 day, 1 month) and spatial (0.9, 74 km2) scales. The usefulness of different steps in the rainfall retrieval algorithm, i.e., a wet-dry classification method and a filter to remove outliers, is systematically assessed. A novel dew filter is developed to correct for dew-induced wet antenna attenuation, which, although a relative underestimation of 6% to 9% is found, generally yields good results. The microwave link rainfall estimation technique performs well for the summer months (June, July, August), even outperforming interpolation of automatic rain gauge data (with a density of ˜1 gauge per 1000 km2), but large deviations are found for the winter months (December, January, February). These deviations are generally expected to be related to frozen or melting precipitation. Hence, our results show the potential of commercial microwave links for long-term large-scale operational rainfall monitoring.

  14. Humidity Profiles' Effect On The Relationship Between Ice Scattering And Rainfall In Microwave Rainfall Retrievals

    Science.gov (United States)

    Petkovic, V.; Kummerow, C. D.

    2013-12-01

    Currently, satellite microwave rainfall retrievals base their algorithm on an observed global average of the relationship between high frequency brightness temperature (Tb) depression and rainfall rate. This makes them very sensitive to differences in the ratio of ice to liquid in the cloud, resulting in regional biases of rainfall estimates. To address this problem we investigate how the environmental conditions that precede raining systems influence the ice to rainfall relationship. The vertical profile of humidity was found to be a key variable in predicting this ratio. We found that dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel, characterized by strong Tb depressions and amounts of ice aloft significantly above the globally observed average value. As a consequence, microwave retrieval algorithms misinterpret these systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit very little ice while producing high rainfall rates. These regional differences correspond well with a map of radar to radiometer biases of rainfall. Deeper understanding of the influence of environmental conditions on this ice to rain ratio provides a foundation for mapping a global ice-scattering to rainfall rate relationship that will improve satellite microwave rainfall retrievals and our understanding of cloud microphysics globally.

  15. Rainfall simulation in education

    Science.gov (United States)

    Peters, Piet; Baartman, Jantiene; Gooren, Harm; Keesstra, Saskia

    2016-04-01

    Rainfall simulation has become an important method for the assessment of soil erosion and soil hydrological processes. For students, rainfall simulation offers an year-round, attractive and active way of experiencing water erosion, while not being dependent on (outdoors) weather conditions. Moreover, using rainfall simulation devices, they can play around with different conditions, including rainfall duration, intensity, soil type, soil cover, soil and water conservation measures, etc. and evaluate their effect on erosion and sediment transport. Rainfall simulators differ in design and scale. At Wageningen University, both BSc and MSc student of the curriculum 'International Land and Water Management' work with different types of rainfall simulation devices in three courses: - A mini rainfall simulator (0.0625m2) is used in the BSc level course 'Introduction to Land Degradation and Remediation'. Groups of students take the mini rainfall simulator with them to a nearby field location and test it for different soil types, varying from clay to more sandy, slope angles and vegetation or litter cover. The groups decide among themselves which factors they want to test and they compare their results and discuss advantage and disadvantage of the mini-rainfall simulator. - A medium sized rainfall simulator (0.238 m2) is used in the MSc level course 'Sustainable Land and Water Management', which is a field practical in Eastern Spain. In this course, a group of students has to develop their own research project and design their field measurement campaign using the transportable rainfall simulator. - Wageningen University has its own large rainfall simulation laboratory, in which a 15 m2 rainfall simulation facility is available for research. In the BSc level course 'Land and Water Engineering' Student groups will build slopes in the rainfall simulator in specially prepared containers. Aim is to experience the behaviour of different soil types or slope angles when (heavy) rain

  16. Model to Estimate Monthly Time Horizons for Application of DEA in Selection of Stock Portfolio and for Maintenance of the Selected Portfolio

    Directory of Open Access Journals (Sweden)

    José Claudio Isaias

    2015-01-01

    Full Text Available In the selecting of stock portfolios, one type of analysis that has shown good results is Data Envelopment Analysis (DEA. It, however, has been shown to have gaps regarding its estimates of monthly time horizons of data collection for the selection of stock portfolios and of monthly time horizons for the maintenance of a selected portfolio. To better estimate these horizons, this study proposes a model of mathematical programming binary of minimization of square errors. This model is the paper’s main contribution. The model’s results are validated by simulating the estimated annual return indexes of a portfolio that uses both horizons estimated and of other portfolios that do not use these horizons. The simulation shows that portfolios with both horizons estimated have higher indexes, on average 6.99% per year. The hypothesis tests confirm the statistically significant superiority of the results of the proposed mathematical model’s indexes. The model’s indexes are also compared with portfolios that use just one of the horizons estimated; here the indexes of the dual-horizon portfolios outperform the single-horizon portfolios, though with a decrease in percentage of statistically significant superiority.

  17. Temporal correlation between malaria and rainfall in Sri Lanka

    Directory of Open Access Journals (Sweden)

    Galappaththy Gawrie NL

    2008-05-01

    Full Text Available Abstract Background Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex. Methods The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression. Results For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one or weak negative (at lags two to six correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography. Conclusion Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.

  18. Projected changes of rainfall seasonality and dry spells in a high greenhouse gas emissions scenario

    Science.gov (United States)

    Pascale, Salvatore; Lucarini, Valerio; Feng, Xue; Porporato, Amilcare; ul Hasson, Shabeh

    2016-02-01

    In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models contributing to Coupled Model Intercomparison Project 5 (CMIP5). We use estimates of the centroid of the monthly rainfall distribution as an index of the rainfall timing and a threshold-independent, information theory-based quantity such as relative entropy (RE) to quantify the concentration of annual rainfall and the number of dry months and to build a monsoon dimensionless seasonality index (DSI). The RE is projected to increase, with high inter-model agreement over Mediterranean-type regions—southern Europe, northern Africa and southern Australia—and areas of South and Central America, implying an increase in the number of dry days up to 1 month by the end of the twenty-first century. Positive RE changes are also projected over the monsoon regions of southern Africa and North America, South America. These trends are consistent with a shortening of the wet season associated with a more prolonged pre-monsoonal dry period. The extent of the global monsoon region, characterized by large DSI, is projected to remain substantially unaltered. Centroid analysis shows that most of CMIP5 projections suggest that the monsoonal annual rainfall distribution is expected to change from early to late in the course of the hydrological year by the end of the twenty-first century and particularly after year 2050. This trend is particularly evident over northern Africa, southern Africa and western Mexico, where more than 90 % of the models project a delay of the rainfall centroid from a few days up to 2 weeks. Over the remaining monsoonal regions, there is little inter-model agreement in terms of centroid changes.

  19. Forecasting Rainfall Time Series with stochastic output approximated by neural networks Bayesian approach

    Directory of Open Access Journals (Sweden)

    Cristian Rodriguez Rivero

    2014-07-01

    Full Text Available The annual estimate of the availability of the amount of water for the agricultural sector has become a lifetime in places where rainfall is scarce, as is the case of northwestern Argentina. This work proposes to model and simulate monthly rainfall time series from one geographical location of Catamarca, Valle El Viejo Portezuelo. In this sense, the time series prediction is mathematical and computational modelling series provided by monthly cumulative rainfall, which has stochastic output approximated by neural networks Bayesian approach. We propose to use an algorithm based on artificial neural networks (ANNs using the Bayesian inference. The result of the prediction consists of 20% of the provided data consisting of 2000 to 2010. A new analysis for modelling, simulation and computational prediction of cumulative rainfall from one geographical location is well presented. They are used as data information, only the historical time series of daily flows measured in mmH2O. Preliminary results of the annual forecast in mmH2O with a prediction horizon of one year and a half are presented, 18 months, respectively. The methodology employs artificial neural network based tools, statistical analysis and computer to complete the missing information and knowledge of the qualitative and quantitative behavior. They also show some preliminary results with different prediction horizons of the proposed filter and its comparison with the performance Gaussian process filter used in the literature.

  20. On the uncertainties associated with using gridded rainfall data as a proxy for observed

    Science.gov (United States)

    Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

    2012-05-01

    Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids - particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify

  1. On the uncertainties associated with using gridded rainfall data as a proxy for observed

    Directory of Open Access Journals (Sweden)

    C. R. Tozer

    2012-05-01

    Full Text Available Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods. This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets – the Bureau of Meteorology (BOM dataset, the Australian Water Availability Project (AWAP and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids – particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is

  2. Heavy rainfall equations for Santa Catarina, Brazil

    Directory of Open Access Journals (Sweden)

    Álvaro José Back

    2011-12-01

    Full Text Available Knowledge of intensity-duration-frequency (IDF relationships of rainfall events is extremely important to determine the dimensions of surface drainage structures and soil erosion control. The purpose of this study was to obtain IDF equations of 13 rain gauge stations in the state of Santa Catarina in Brazil: Chapecó, Urussanga, Campos Novos, Florianópolis, Lages, Caçador, Itajaí, Itá, Ponte Serrada, Porto União, Videira, Laguna and São Joaquim. The daily rainfall data charts of each station were digitized and then the annual maximum rainfall series were determined for durations ranging from 5 to 1440 min. Based on these, with the Gumbel-Chow distribution, the maximum rainfall was estimated for durations ranging from 5 min to 24 h, considering return periods of 2, 5, 10, 20, 25, 50, and 100 years,. Data agreement with the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test, at 5 % significance level. For each rain gauge station, two IDF equations of rainfall events were adjusted, one for durations from 5 to 120 min and the other from 120 to 1440 min. The results show a high variability in maximum intensity of rainfall events among the studied stations. Highest values of coefficients of variation in the annual maximum series of rainfall were observed for durations of over 600 min at the stations of the coastal region of Santa Catarina.

  3. Variance Components and Genetic Parameters Estimated for Fat and Protein Content in Individual Months of Lactation: The Case of Tsigai Sheep.

    Science.gov (United States)

    Oravcová, Marta

    2016-02-01

    The objective of this study was to assess variance components and genetic parameters for fat and protein content in Tsigai sheep using multivariate animal models in which fat and protein content in individual months of lactation were treated as different traits, and univariate models in which fat and protein content were treated as repeated measures of the same traits. Test day measurements were taken between the second and the seventh month of lactation. The fixed effects were lactation number, litter size and days in milk. The random effects were animal genetic effect and permanent environmental effect of ewe. The effect of flock-year-month of test day measurement was fitted either as a fixed (FYM) or random (fym) effect. Heritabilities for fat content were estimated between 0.06 and 0.17 (FYM fitted) and between 0.06 and 0.11 (fym fitted). Heritabilities for protein content were estimated between 0.15 and 0.23 (FYM fitted) and between 0.10 and 0.18 (fym fitted). For fat content, variance ratios of permanent environmental effect of ewe were estimated between 0.04 and 0.11 (FYM fitted) and between 0.02 and 0.06 (fym fitted). For protein content, variance ratios of permanent environmental effect of ewe were estimated between 0.13 and 0.20 (FYM fitted) and between 0.08 and 0.12 (fym fitted). The proportion of phenotypic variance explained by fym effect ranged from 0.39 to 0.43 for fat content and from 0.25 to 0.36 for protein content. Genetic correlations between individual months of lactation ranged from 0.74 to 0.99 (fat content) and from 0.64 to 0.99 (protein content). Fat content heritabilities estimated with univariate animal models roughly corresponded with heritability estimates from multivariate models: 0.13 (FYM fitted) and 0.07 (fym fitted). Protein content heritabilities estimated with univariate animal models also corresponded with heritability estimates from multivariate models: 0.18 (FYM fitted) and 0.13 (fym fitted).

  4. Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

    Science.gov (United States)

    Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

    2010-01-01

    It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with

  5. Technical Report Series on Global Modeling and Data Assimilation. Volume 12; Comparison of Satellite Global Rainfall Algorithms

    Science.gov (United States)

    Suarez, Max J. (Editor); Chang, Alfred T. C.; Chiu, Long S.

    1997-01-01

    Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates. Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

  6. Topographic relationships for design rainfalls over Australia

    Science.gov (United States)

    Johnson, F.; Hutchinson, M. F.; The, C.; Beesley, C.; Green, J.

    2016-02-01

    Design rainfall statistics are the primary inputs used to assess flood risk across river catchments. These statistics normally take the form of Intensity-Duration-Frequency (IDF) curves that are derived from extreme value probability distributions fitted to observed daily, and sub-daily, rainfall data. The design rainfall relationships are often required for catchments where there are limited rainfall records, particularly catchments in remote areas with high topographic relief and hence some form of interpolation is required to provide estimates in these areas. This paper assesses the topographic dependence of rainfall extremes by using elevation-dependent thin plate smoothing splines to interpolate the mean annual maximum rainfall, for periods from one to seven days, across Australia. The analyses confirm the important impact of topography in explaining the spatial patterns of these extreme rainfall statistics. Continent-wide residual and cross validation statistics are used to demonstrate the 100-fold impact of elevation in relation to horizontal coordinates in explaining the spatial patterns, consistent with previous rainfall scaling studies and observational evidence. The impact of the complexity of the fitted spline surfaces, as defined by the number of knots, and the impact of applying variance stabilising transformations to the data, were also assessed. It was found that a relatively large number of 3570 knots, suitably chosen from 8619 gauge locations, was required to minimise the summary error statistics. Square root and log data transformations were found to deliver marginally superior continent-wide cross validation statistics, in comparison to applying no data transformation, but detailed assessments of residuals in complex high rainfall regions with high topographic relief showed that no data transformation gave superior performance in these regions. These results are consistent with the understanding that in areas with modest topographic relief, as

  7. Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

    Science.gov (United States)

    Hussain, Yawar; Satgé, Frédéric; Hussain, Muhammad Babar; Martinez-Carvajal, Hernan; Bonnet, Marie-Paule; Cárdenas-Soto, Martin; Roig, Henrique Llacer; Akhter, Gulraiz

    2017-01-01

    The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

  8. On the relationship of coastal tropical rainfall and the large-scale atmosphere

    CERN Document Server

    Bergemann, Martin; Lane, Todd P

    2015-01-01

    Rainfall in coastal areas of the tropics is often shaped by the presence of circulations directly associated with the topography, such as land-sea and/or mountain-valley breezes. In many regions the coastally-affected rainfall consitutes more than half of the overall rainfall received. Weather and climate models with parametrized convection produce large errors in rainfall in tropical coastal regions, most commonly underestimating rainfall over land and overestimating it over the ocean. Building on an algorithm to objectively identify rainfall that is associated with land-sea interaction we investigate whether the relationship between rainfall in coastal regions and the large-scale atmosphere differs from that over the open ocean or over inland areas. We combine 3-hourly satellite estimates of rainfall with estimates of the large-scale atmospheric state from reanalyses. We find that when grouped by rainfall intensity, medium-intensity coastal rainfall in the tropics occurs in more stable conditions and drier ...

  9. How Sensitive is Large-scale Flood Inundation to Rainfall Variability?: Water Balance Analysis Based on Basin-wide Rainfall-Runoff-Inundation Simulation

    Science.gov (United States)

    Sayama, T.; Tatebe, Y.; Tanaka, S.

    2013-12-01

    The 2011 large-scale flood over the Chao Phraya River (CPR) basin resulted in the worst economic flood damage to Thailand. The flooding was induced mainly by unprecedented rainfall from five typhoons and tropical depressions between May and October. The total rainfall in the six months during the monsoon season was approximately 1,400 mm, while the average monsoon-season rainfall in this region is about 1,000 mm, and previous large-scale floods were caused by a total rainfall of approximately 1,200 mm. The interpretation of the additional 200 mm of rainfall compared to past events can greatly affect the understanding of the 2011 flood disaster. Up until now, the magnitude of the flood hazard itself has received little attention due to the seemingly insignificant rainfall variability. Instead, the increase of societal vulnerability, such as accumulation of assets in flood-prone areas, has been more highlighted. Nevertheless, without understanding the impact of the rainfall variability on flood runoff and inundation, essential characteristics of the flood disaster may be misinterpreted. In this study, we focused on the hydrologic characteristics of the flood based on 52 year-long inundation simulation. We applied a 2D Rainfall-Runoff-Inundation (RRI) model to the entire CPR basin. After the model validation with river discharges and water levels, remote sensing inundation extents, and peak inundation water depths for 2011, we conducted water balance analysis from the simulation results to investigate the relationship among rainfall, runoff and inundation volumes. The simulation, by taking two major dams into account, found that 131 mm (9%) of the total rainfall (1,400 mm) may have flooded at the peak. The estimated sensitivity of flood inundation to rainfall (dF/dP) was 0.25. This suggests that the additional 200 mm of rainfall may have resulted in a 50 mm, or 8.2 billion m3, increase in flood inundation volume. It accounts for more than 60 % of the total storage

  10. Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

    Indian Academy of Sciences (India)

    Azadeh Ahmadi; Ali Moridi; Elham Kakaei Lafdani; Ghasem Kianpisheh

    2014-10-01

    Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

  11. Investigating the mechanisms of diurnal rainfall variability over Peninsular Malaysia using the non-hydrostatic regional climate model

    Science.gov (United States)

    Jamaluddin, Ahmad Fairudz; Tangang, Fredolin; Chung, Jing Xiang; Juneng, Liew; Sasaki, Hidetaka; Takayabu, Izuru

    2017-07-01

    This study aims to provide a basis for understanding the mechanisms of diurnal rainfall variability over Peninsular Malaysia by utilising the Non-Hydrostatic Regional Climate Model (NHRCM). The present day climate simulations at 5 km resolution over a period of 20 years, from 1st December 1989 to 31st January 2010 were conducted using the six-hourly Japanese re-analysis 55 years (JRA-55) data and monthly Centennial in situ Observation Based Estimates (COBE) of sea surface temperature as lateral and lower boundary conditions. Despite some biases, the NHRCM performed reasonably well in simulating diurnal rainfall cycles over Peninsular Malaysia. During inter-monsoon periods, the availability of atmospheric moisture played a major role in modulating afternoon rainfall maxima over the foothills of the Titiwangsa mountain range (FT sub-region). During the southwest monsoon, a lack of atmospheric moisture inhibits the occurrence of convective rainfall over the FT sub-region. The NHRCM was also able to simulate the suppression of the diurnal rainfall cycle over the east coast of Peninsular Malaysia (EC sub-region) and afternoon rainfall maximum over the Peninsular Malaysia inland-valley (IN sub-region) area during the northeast monsoon. Over the EC sub-region, daytime radiational warming of the top of clouds enhanced atmospheric stability, thus reducing afternoon rainfall. On the other hand, night-time radiational cooling from cloud tops decreases atmospheric stability and increases nocturnal rainfall. In the early morning, the rainfall maximum was confined to the EC sub-region due to the retardation of the north-easterly monsoonal wind by the land breeze and orographic blocking. However, in the afternoon, superimposition of the sea breeze on the north-easterly monsoonal wind strengthened the north-easterly wind, thus causing the zone of convection to expand further inland.

  12. Maximum daily rainfall in South Korea

    Indian Academy of Sciences (India)

    Saralees Nadarajah; Dongseok Choi

    2007-08-01

    Annual maxima of daily rainfall for the years 1961–2001 are modeled for five locations in South Korea (chosen to give a good geographical representation of the country). The generalized extreme value distribution is fitted to data from each location to describe the extremes of rainfall and to predict its future behavior. We find evidence to suggest that the Gumbel distribution provides the most reasonable model for four of the five locations considered. We explore the possibility of trends in the data but find no evidence suggesting trends. We derive estimates of 10, 50, 100, 1000, 5000, 10,000, 50,000 and 100,000 year return levels for daily rainfall and describe how they vary with the locations. This paper provides the first application of extreme value distributions to rainfall data from South Korea.

  13. Observed daily large-scale rainfall patterns during BOBMEX-1999

    Indian Academy of Sciences (India)

    A K Mitra; M Das Gupta; R K Paliwal; S V Singh

    2003-06-01

    A daily rainfall dataset and the corresponding rainfall maps have been produced by objective analysis of rainfall data. The satellite estimate of rainfall and the raingauge values are merged to form the final analysis. Associated with epochs of monsoon these rainfall maps are able to show the rainfall activities over India and the Bay of Bengal region during the BOBMEX period. The intra-seasonal variations of rainfall during BOBMEX are also seen using these data. This dataset over the oceanic region compares well with other available popular datasets like GPCP and CMAP. Over land this dataset brings out the features of monsoon in more detail due to the availability of more local raingauge stations.

  14. A multiplier-based method of generating stochastic areal rainfall from point rainfalls

    Science.gov (United States)

    Ndiritu, J. G.

    Catchment modelling for water resources assessment is still mainly based on rain gauge measurements as these are more easily available and cover longer periods than radar and satellite-based measurements. Rain gauges however measure the rain falling on an extremely small proportion of the catchment and the areal rainfall obtained from these point measurements are consequently substantially uncertain. These uncertainties in areal rainfall estimation are generally ignored and the need to assess their impact on catchment modelling and water resources assessment is therefore imperative. A method that stochastically generates daily areal rainfall from point rainfall using multiplicative perturbations as a means of dealing with these uncertainties is developed and tested on the Berg catchment in the Western Cape of South Africa. The differences in areal rainfall obtained by alternately omitting some of the rain gauges are used to obtain a population of plausible multiplicative perturbations. Upper bounds on the applicable perturbations are set to prevent the generation of unrealistically large rainfall and to obtain unbiased stochastic rainfall. The perturbations within the set bounds are then fitted into probability density functions to stochastically generate the perturbations to impose on areal rainfall. By using 100 randomly-initialized calibrations of the AWBM catchment model and Sequent Peak Analysis, the effects of incorporating areal rainfall uncertainties on storage-yield-reliability analysis are assessed. Incorporating rainfall uncertainty is found to reduce the required storage by up to 20%. Rainfall uncertainty also increases flow-duration variability considerably and reduces the median flow-duration values by an average of about 20%.

  15. A method for combining passive microwave and infrared rainfall observations

    Science.gov (United States)

    Kummerow, Christian; Giglio, Louis

    1995-01-01

    Because passive microwave instruments are confined to polar-orbiting satellites, rainfall estimates must interpolate across long time periods, during which no measurements are available. In this paper the authors discuss a technique that allows one to partially overcome the sampling limitations by using frequent infrared observations from geosynchronous platforms. To accomplish this, the technique compares all coincident microwave and infrared observations. From each coincident pair, the infrared temperature threshold is selected that corresponds to an area equal to the raining area observed in the microwave image. The mean conditional rainfall rate as determined from the microwave image is then assigned to pixels in the infrared image that are colder than the selected threshold. The calibration is also applied to a fixed threshold of 235 K for comparison with established infrared techniques. Once a calibration is determined, it is applied to all infrared images. Monthly accumulations for both methods are then obtained by summing rainfall from all available infrared images. Two examples are used to evaluate the performance of the technique. The first consists of a one-month period (February 1988) over Darwin, Australia, where good validation data are available from radar and rain gauges. For this case it was found that the technique approximately doubled the rain inferred by the microwave method alone and produced exceptional agreement with the validation data. The second example involved comparisons with atoll rain gauges in the western Pacific for June 1989. Results here are overshadowed by the fact that the hourly infrared estimates from established techniques, by themselves, produced very good correlations with the rain gauges. The calibration technique was not able to improve upon these results.

  16. Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications

    Directory of Open Access Journals (Sweden)

    Sagar Ratna Bajracharya

    2015-01-01

    Full Text Available Estimation of the flow generated in the Brahmaputra river basin is important for establishing an effective flood prediction and warning services as well as for water resources assessment and management. But this is a data scarce region with few and unevenly distributed hydrometeorological stations. Five high-resolution satellite rainfall products (CPC RFE2.0, RFE2.0-Modified, CMORPH, GSMaP, and TRMM 3B42 were evaluated at different spatial and temporal resolutions (daily, dekadal, monthly, and seasonal with observed rain gauge data from 2004 to 2006 to determine their ability to fill the data gap and suitability for use in hydrological and water resources management applications. Grid-to-grid (G-G and catchment-to-catchment (C-C comparisons were performed using the verification methods developed by the International Precipitation Working Group (IPWG. Comparing different products, RFE2.0-Modified, TRMM 3B42, and CMORPH performed best; they all detected heavy, moderate, and low rainfall but still significantly underestimated magnitude of rainfall, particularly in orographically influenced areas. Overall, RFE2.0-Modified performed best showing a high correlation coefficient with observed data and low mean absolute error, root mean square error, and multiple bias and is reasonably good at detecting the occurrence of rainfall. TRMM 3B42 showed the second best performance. The study demonstrates that there is a potential use of satellite rainfall in a data scarce region.

  17. Rainfall measurement using radio links from cellular communication networks

    NARCIS (Netherlands)

    Leijnse, H.; Uijlenhoet, R.; Stricker, J.N.M.

    2007-01-01

    We investigate the potential of radio links such as employed by commercial cellular communication companies to monitor path-averaged rainfall. We present an analysis of data collected using two 38-GHz links during eight rainfall events over a 2-month period (October¿November 2003) during mostly stra

  18. Impacts of Two-Type ENSO on Rainfall over Taiwan

    OpenAIRE

    Chen-Chih Lin; Yi-Jiun Liou; Shih-Jen Huang

    2015-01-01

    Impacts of two-type ENSO (El Niño/Southern Oscillation), canonical ENSO and ENSO Modoki, on rainfall over Taiwan are investigated by the monthly mean rainfall data accessed from Taiwan Central Weather Bureau. The periods of the two-type ENSO are distinguished by Niño 3.4 index and ENSO Modoki index (EMI). The rainfall data in variously geographical regions are analyzed with the values of Niño 3.4 and EMI by correlation method. Results show that the seasonal rainfalls over Taiwan are different...

  19. Impacts of Two-Type ENSO on Rainfall over Taiwan

    OpenAIRE

    Chen-Chih Lin; Yi-Jiun Liou; Shih-Jen Huang

    2015-01-01

    Impacts of two-type ENSO (El Niño/Southern Oscillation), canonical ENSO and ENSO Modoki, on rainfall over Taiwan are investigated by the monthly mean rainfall data accessed from Taiwan Central Weather Bureau. The periods of the two-type ENSO are distinguished by Niño 3.4 index and ENSO Modoki index (EMI). The rainfall data in variously geographical regions are analyzed with the values of Niño 3.4 and EMI by correlation method. Results show that the seasonal rainfalls over Taiwan are different...

  20. A rainfall simulation model for agricultural development in Bangladesh

    Directory of Open Access Journals (Sweden)

    M. Sayedur Rahman

    2000-01-01

    Full Text Available A rainfall simulation model based on a first-order Markov chain has been developed to simulate the annual variation in rainfall amount that is observed in Bangladesh. The model has been tested in the Barind Tract of Bangladesh. Few significant differences were found between the actual and simulated seasonal, annual and average monthly. The distribution of number of success is asymptotic normal distribution. When actual and simulated daily rainfall data were used to drive a crop simulation model, there was no significant difference of rice yield response. The results suggest that the rainfall simulation model perform adequately for many applications.

  1. Application of GRACE to the assessment of model-based estimates of monthly Greenland Ice Sheet mass balance (2003-2012)

    Science.gov (United States)

    Schlegel, Nicole-Jeanne; Wiese, David N.; Larour, Eric Y.; Watkins, Michael M.; Box, Jason E.; Fettweis, Xavier; van den Broeke, Michiel R.

    2016-09-01

    Quantifying the Greenland Ice Sheet's future contribution to sea level rise is a challenging task that requires accurate estimates of ice sheet sensitivity to climate change. Forward ice sheet models are promising tools for estimating future ice sheet behavior, yet confidence is low because evaluation of historical simulations is challenging due to the scarcity of continental-wide data for model evaluation. Recent advancements in processing of Gravity Recovery and Climate Experiment (GRACE) data using Bayesian-constrained mass concentration ("mascon") functions have led to improvements in spatial resolution and noise reduction of monthly global gravity fields. Specifically, the Jet Propulsion Laboratory's JPL RL05M GRACE mascon solution (GRACE_JPL) offers an opportunity for the assessment of model-based estimates of ice sheet mass balance (MB) at ˜ 300 km spatial scales. Here, we quantify the differences between Greenland monthly observed MB (GRACE_JPL) and that estimated by state-of-the-art, high-resolution models, with respect to GRACE_JPL and model uncertainties. To simulate the years 2003-2012, we force the Ice Sheet System Model (ISSM) with anomalies from three different surface mass balance (SMB) products derived from regional climate models. Resulting MB is compared against GRACE_JPL within individual mascons. Overall, we find agreement in the northeast and southwest where MB is assumed to be primarily controlled by SMB. In the interior, we find a discrepancy in trend, which we presume to be related to millennial-scale dynamic thickening not considered by our model. In the northwest, seasonal amplitudes agree, but modeled mass trends are muted relative to GRACE_JPL. Here, discrepancies are likely controlled by temporal variability in ice discharge and other related processes not represented by our model simulations, i.e., hydrological processes and ice-ocean interaction. In the southeast, GRACE_JPL exhibits larger seasonal amplitude than predicted by the

  2. Linking ENSO and heavy rainfall events over Coastal British Columbia through a weather pattern classification

    Directory of Open Access Journals (Sweden)

    P. Brigode

    2012-10-01

    Full Text Available Classifications of atmospheric weather patterns (WPs are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia Coastal region (Canada and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March, which is the period of heavy precipitation events in Coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño Southern Oscillations significantly influence the frequency of occurrence of two Coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, MEWP heavy rainfall estimations do not show significant evolution of heavy rainfall behaviour between Niño and Niña winters. However, the WP approach captures the

  3. Linking ENSO and heavy rainfall events over coastal British Columbia through a weather pattern classification

    Directory of Open Access Journals (Sweden)

    P. Brigode

    2013-04-01

    Full Text Available Classifications of atmospheric weather patterns (WPs are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia (BC coastal region (Canada and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March, which is the period of heavy precipitation events in coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples (i.e. sub-samples constituted by days having similar atmospheric circulation patterns and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño-Southern Oscillations (ENSO significantly influence the frequency of occurrence of two coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, heavy rainfall estimations do not show significant evolution of heavy

  4. Linking ENSO and heavy rainfall events over coastal British Columbia through a weather pattern classification

    Science.gov (United States)

    Brigode, P.; Mićović, Z.; Bernardara, P.; Paquet, E.; Garavaglia, F.; Gailhard, J.; Ribstein, P.

    2013-04-01

    Classifications of atmospheric weather patterns (WPs) are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia (BC) coastal region (Canada) and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March), which is the period of heavy precipitation events in coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples (i.e. sub-samples constituted by days having similar atmospheric circulation patterns) and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño-Southern Oscillations (ENSO) significantly influence the frequency of occurrence of two coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, heavy rainfall estimations do not show significant evolution of heavy rainfall

  5. Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change.

    Science.gov (United States)

    van der Pol, T D; van Ierland, E C; Gabbert, S; Weikard, H-P; Hendrix, E M T

    2015-05-01

    Stormwater drainage and other water systems are vulnerable to changes in rainfall and runoff and need to be adapted to climate change. This paper studies impacts of rainfall variability and changing return periods of rainfall extremes on cost-effective adaptation of water systems to climate change given a predefined system performance target, for example a flood risk standard. Rainfall variability causes system performance estimates to be volatile. These estimates may be used to recurrently evaluate system performance. This paper presents a model for this setting, and develops a solution method to identify cost-effective investments in stormwater drainage adaptations. Runoff and water levels are simulated with rainfall from stationary rainfall distributions, and time series of annual rainfall maxima are simulated for a climate scenario. Cost-effective investment strategies are determined by dynamic programming. The method is applied to study the choice of volume for a storage basin in a Dutch polder. We find that 'white noise', i.e. trend-free variability of rainfall, might cause earlier re-investment than expected under projected changes in rainfall. The risk of early re-investment may be reduced by increasing initial investment. This can be cost-effective if the investment involves fixed costs. Increasing initial investments, therefore, not only increases water system robustness to structural changes in rainfall, but could also offer insurance against additional costs that would occur if system performance is underestimated and re-investment becomes inevitable.

  6. Comparação de distribuições de probabilidade e estimativa da precipitação provável para região de Barbacena, MG Comparasion of probability distribution models and estimative of the probable rainfall for the Barbacena County, MG

    Directory of Open Access Journals (Sweden)

    Bruno Teixeira Ribeiro

    2007-10-01

    Full Text Available Estudos probabilísticos envolvendo variáveis climáticas são de extrema importância para as atividades da agropecuária, construção civil, turismo, transporte, dentre outros. Visando contribuir para o planejamento da agricultura irrigada, este trabalho teve como objetivos comparar distribuições de probabilidade ajustadas às séries históricas decendiais e mensais, e estimar as precipitações prováveis para o município de Barbacena, MG. Foram estudados os meses de dezembro, janeiro e fevereiro, no período de 1942 a 2003, constituindo-se séries históricas com 62 anos de observações. As lâminas diárias foram totalizadas em períodos mensais e decendiais, sendo aplicadas as distribuições log-Normal 2 parâmetros, log-Normal 3 parâmetros e Gama. Para avaliar a adequabilidade das distribuições, nos períodos estudados, utilizou-se o teste de Qui-quadrado (chi2, ao nível de 5% de significância. As precipitações prováveis foram estimadas para cada período estudado utilizando a distribuição que apresentou o menor valor de chi2, nos níveis de probabilidade de excedência de 75, 90 e 98%. A distribuição Gama foi a que melhor se ajustou aos dados. O estudo de precipitações prováveis é uma boa ferramenta no auxílio da tomada de decisão quanto ao planejamento e uso da irrigação.Probabilistic studies involving climatic variables are of extreme importance for farming activities, construction, tourism, among others. Seeking to contribute for the planning of irrigate agriculture, this work had as objectives to compare adjusted probability distribution models to the monthly and decennial historical series and to estimate the probable rainfall for the Barbacena County, Minas Gerais State, Brazil. Rainfall data of December, January and February, from 1942 to 2003, were studied, constituting historical series with 62 years of observations. Daily rainfall depths were added for 10 and 30 days, applying Gama, log-Normal 2 and

  7. Pattern-oriented memory interpolation of sparse historical rainfall records

    Science.gov (United States)

    Matos, J. P.; Cohen Liechti, T.; Portela, M. M.; Schleiss, A. J.

    2014-03-01

    The pattern-oriented memory (POM) is a novel historical rainfall interpolation method that explicitly takes into account the time dimension in order to interpolate areal rainfall maps. The method is based on the idea that rainfall patterns exist and can be identified over a certain area by means of non-linear regressions. Having been previously benchmarked with a vast array of interpolation methods using proxy satellite data under different time and space availabilities, in the scope of the present contribution POM is applied to rain gauge data in order to produce areal rainfall maps. Tested over the Zambezi River Basin for the period from 1979 to 1997 (accurate satellite rainfall estimates based on spaceborne instruments are not available for dates prior to 1998), the novel pattern-oriented memory historical interpolation method has revealed itself as a better alternative than Kriging or Inverse Distance Weighing in the light of a Monte Carlo cross-validation procedure. Superior in most metrics to the other tested interpolation methods, in terms of the Pearson correlation coefficient and bias the accuracy of POM's historical interpolation results are even comparable with that of recent satellite rainfall products. The new method holds the possibility of calculating detailed and performing daily areal rainfall estimates, even in the case of sparse rain gauging grids. Besides their performance, the similarity to satellite rainfall estimates inherent to POM interpolations can contribute to substantially extend the length of the rainfall series used in hydrological models and water availability studies in remote areas.

  8. Weather radar rainfall data in urban hydrology

    Science.gov (United States)

    Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick; Ellerbæk Nielsen, Jesper; ten Veldhuis, Marie-Claire; Arnbjerg-Nielsen, Karsten; Rasmussen, Michael R.; Molnar, Peter

    2017-03-01

    Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.

  9. Mallows statistic in the selection of models to predict the monthly and annual average rainfall in Rio Grande do Sul, Brazil. = Estatística de Mallows na seleção de modelos de predição da precipitação média mensal e anual no Rio Grande do Sul.

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    Claudia Fernanda Almeida Teixeira

    2013-08-01

    Full Text Available The Mallows Cp statistic can be used in the selection of the best subsets in hydrological modeling, especially in cases where many variables are used. Besause there are, in many cases, the interest in estimating the monthly and annual average rainfall based on geographic coordinates of latitude and longitude, and altitude. Consequently, the aim of this study was to verify the information gain when applied to statistical Cp Mallows in the selection of the best subsets of multiple linear regression to predict the precipitation of some municipalities in the state of Rio Grande do Sul. Daily precipitation data from 26 meteorological stations, in addition to seven others, used to validation of the proposed linear models, belonging to seven mesoregions of Rio Grande do Sul were collected and analyzed. After the formation of the series, precipitation values were adjusted from linear models, using multiple linear regression in which the dependent variable was the precipitation and independent variables, the geographic coordinates of latitude and longitude, and altitude. The Cp statistic was used in the selection of sets and, subsequently applied statistical indexes mean square error, standard error of prediction bias factor wereused to obtain the accuracy factor for comparison between observed versus predicted precipitation. From the results obtained itcan be concluded that, from the point of view of parsimony, the statistic proposed by Mallows proved adequate in the selectionof models for prediction of monthly and annual rainfall of the stations analyzed. = A estatística Cp de Mallows pode ser utilizada na seleção de melhores subconjuntos na modelagem hidrológica,principalmente nos casos em que são utilizadas muitas variáveis. Com base no fato de que há, em muitos casos, o interesse em estimar a precipitação média mensal e anual baseada nas coordenadas geográficas latitude e longitude, e altitude, objetivouse com este trabalho verificar o

  10. Performance of two predictive uncertainty estimation approaches for conceptual Rainfall-Runoff Model: Bayesian Joint Inference and Hydrologic Uncertainty Post-processing

    Science.gov (United States)

    Hernández-López, Mario R.; Romero-Cuéllar, Jonathan; Camilo Múnera-Estrada, Juan; Coccia, Gabriele; Francés, Félix

    2017-04-01

    It is noticeably important to emphasize the role of uncertainty particularly when the model forecasts are used to support decision-making and water management. This research compares two approaches for the evaluation of the predictive uncertainty in hydrological modeling. First approach is the Bayesian Joint Inference of hydrological and error models. Second approach is carried out through the Model Conditional Processor using the Truncated Normal Distribution in the transformed space. This comparison is focused on the predictive distribution reliability. The case study is applied to two basins included in the Model Parameter Estimation Experiment (MOPEX). These two basins, which have different hydrological complexity, are the French Broad River (North Carolina) and the Guadalupe River (Texas). The results indicate that generally, both approaches are able to provide similar predictive performances. However, the differences between them can arise in basins with complex hydrology (e.g. ephemeral basins). This is because obtained results with Bayesian Joint Inference are strongly dependent on the suitability of the hypothesized error model. Similarly, the results in the case of the Model Conditional Processor are mainly influenced by the selected model of tails or even by the selected full probability distribution model of the data in the real space, and by the definition of the Truncated Normal Distribution in the transformed space. In summary, the different hypotheses that the modeler choose on each of the two approaches are the main cause of the different results. This research also explores a proper combination of both methodologies which could be useful to achieve less biased hydrological parameter estimation. For this approach, firstly the predictive distribution is obtained through the Model Conditional Processor. Secondly, this predictive distribution is used to derive the corresponding additive error model which is employed for the hydrological parameter

  11. Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo Estimates of rainfall erosivity in São Paulo state by an artificial neural network

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    Michel Castro Moreira

    2006-12-01

    Full Text Available O conhecimento do valor da erosividade da chuva (R de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID. Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.Knowledge on rainfall erosivity (R of particular sites is fundamental for soil loss estimation by the Universal Soil Loss Equation (USLE and therefore highly important in conservation planning. In order to obtain the R value estimates for places where it is unknown, an artificial neural network (ANN was developed for the state of São Paulo, and its accuracy compared with the Inverse Distance Weighted (IDW interpolation method. The developed ANN presented a smaller mean relative error in the R estimation and a confidence index classified as "excellent", better than the IDW. ANN can therefore be used to estimate R values for soil use planning, management and conservation in São Paulo state.

  12. Passive microwave rainfall retrieval: A mathematical approach via sparse learning

    Science.gov (United States)

    Ebtehaj, M.; Lerman, G.; Foufoula-Georgiou, E.

    2013-12-01

    Detection and estimation of surface rainfall from spaceborne radiometric imaging is a challenging problem. The main challenges arise due to the nonlinear relationship of surface rainfall with its microwave multispectral signatures, the presence of noise, insufficient spatial resolution in observations, and the mixture of the earth surface and atmospheric radiations. A mathematical approach is presented for the detection and retrieval of surface rainfall from radiometric observations via supervised learning. In other words, we use a priori known libraries of high-resolution rainfall observations (e.g., obtained by an active radar) and their coincident spectral signatures (i.e., obtained by a radiometer) to design a mathematical model for rainfall retrieval. This model views the rainfall retrieval as a nonlinear inverse problem and relies on sparsity-promoting Bayesian inversion techniques. In this approach, we assume that small neighborhoods of the rainfall fields and their spectral signatures live on manifolds with similar local geometry and encode those neighborhoods in two joint libraries, the so-called rainfall and spectral dictionaries. We model rainfall passive microwave images by sparse linear combinations of the atoms of the spectral dictionary and then use the same representation coefficients to retrieve surface rain rates from the corresponding rainfall dictionary. The proposed methodology is examined by the use of spectral and rainfall dictionaries provided by the microwave imager (TMI) and precipitation radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. Pros and cons of the presented approach are studied by extensive comparisons with the current operational rainfall algorithm of the TRMM satellite. Future extensions are also highlighted for potential application in the era of the Global Precipitation Measurement (GPM) mission. Comparing the retrieved rain rates for Hurricane Danielle 08/29/2010 (UTC 09:48:00). (Top panel) PR-2A

  13. Rainfall deficit and excess rainfall during vegetation of early potatoes varieties in central-eastern Poland (1971-2005

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    Elżbieta Radzka

    2015-06-01

    Full Text Available The study was based on data collected from nine stations of the Institute of Meteoro­logy and Water Management in central-eastern Poland (1971-2005 concerning monthly precipitation total and mean monthly air temperature during the vegetation period of early potatoes (April-July. Optimal precipitation for early potato was calculated according to the Klatt indexes for medium cohesive and light soils in the successive months of the vegetation period. Rainfall deficit and excess rainfall were determined based on differences between monthly precipitation totals recorded in the years of the study and values considered to be optimal. It was found that the frequency of rainfall deficit during vegetation of early potato in each analysed location both for medium cohesive soil and for light soil exceeded the frequency of its excess. The greatest mean monthly rainfall deficit from the multiannual period in the vegetation season of early potato in all the analysed locations and for both soil types was recorded in June, while excess rainfall was observed in July. Lower values of standard deviation for rainfall deficit were calculated in the case of light soil than medium cohesive soil, while an opposite dependence was recorded for excess rainfall. The risk for early potato plantations on light soil was connected with frequent extreme deficits. They were observed most often in the south-eastern part of the study area, while they were rarest in the belt from Pułtusk towards Szepietowo. Values of the slope of the trend lines were low for all the weather stations and most of them were statistically non-significant. However, all values concerning rainfall deficit were negative, which indicates its slight increase from year to year. A significant trend for changes in rainfall deficit was observed only in Włodawa and Siedlce, while for excess rainfall it was found in Szepietowo and Białowieża.

  14. How important is tropospheric humidity for coastal rainfall in the tropics?

    Science.gov (United States)

    Bergemann, Martin; Jakob, Christian

    2016-06-01

    Climate models show considerable rainfall biases in coastal tropical areas, where approximately 33% of the overall rainfall received is associated with coastal land-sea interaction. Building on an algorithm to objectively identify rainfall that is associated with land-sea interaction we investigate whether the relationship between rainfall in coastal regions and atmospheric humidity differs from that over the open ocean or over inland areas. We combine 3-hourly satellite estimates of rainfall with humidity estimates from reanalyses and investigate if coastal rainfall reveals the well-known relationship between area-averaged precipitation and column-integrated moisture. We find that rainfall that is associated with coastal land-sea effects occurs under much drier midtropospheric conditions than that over the ocean and does not exhibit a pronounced critical value of humidity. In addition, the dependence of the amount of rainfall on midtropospheric moisture is significantly weaker when the rainfall is coastally influenced.

  15. Assessment of satellite rainfall products over the Andean plateau

    Science.gov (United States)

    Satgé, Frédéric; Bonnet, Marie-Paule; Gosset, Marielle; Molina, Jorge; Hernan Yuque Lima, Wilson; Pillco Zolá, Ramiro; Timouk, Franck; Garnier, Jérémie

    2016-01-01

    Nine satellite rainfall estimations (SREs) were evaluated for the first time over the South American Andean plateau watershed by comparison with rain gauge data acquired between 2005 and 2007. The comparisons were carried out at the annual, monthly and daily time steps. All SREs reproduce the salient pattern of the annual rain field, with a marked north-south gradient and a lighter east-west gradient. However, the intensity of the gradient differs among SREs: it is well marked in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 (TMPA-3B42), Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Global Satellite Mapping of Precipitation (GSMaP) products, and it is smoothed out in the Climate prediction center MORPHing (CMORPH) products. Another interesting difference among products is the contrast in rainfall amounts between the water surfaces (Lake Titicaca) and the surrounding land. Some products (TMPA-3B42, PERSIANN and GSMaP) show a contradictory rainfall deficit over Lake Titicaca, which may be due to the emissivity contrast between the lake and the surrounding lands and warm rain cloud processes. An analysis differentiating coastal Lake Titicaca from inland pixels confirmed this trend. The raw or Real Time (RT) products have strong biases over the study region. These biases are strongly positive for PERSIANN (above 90%), moderately positive for TMPA-3B42 (28%), strongly negative for CMORPH (- 42%) and moderately negative for GSMaP (- 18%). The biases are associated with a deformation of the rain rate frequency distribution: GSMaP underestimates the proportion of rainfall events for all rain rates; CMORPH overestimates the proportion of rain rates below 2 mm day- 1; and the other products tend to overestimate the proportion of moderate to high rain rates. These biases are greatly reduced by the gauge adjustment in the TMPA-3B42, PERSIANN and CMORPH products, whereas a

  16. Regional regression equations for the estimation of selected monthly low-flow duration and frequency statistics at ungaged sites on streams in New Jersey

    Science.gov (United States)

    Watson, Kara M.; McHugh, Amy R.

    2014-01-01

    Regional regression equations were developed for estimating monthly flow-duration and monthly low-flow frequency statistics for ungaged streams in Coastal Plain and non-coastal regions of New Jersey for baseline and current land- and water-use conditions. The equations were developed to estimate 87 different streamflow statistics, which include the monthly 99-, 90-, 85-, 75-, 50-, and 25-percentile flow-durations of the minimum 1-day daily flow; the August–September 99-, 90-, and 75-percentile minimum 1-day daily flow; and the monthly 7-day, 10-year (M7D10Y) low-flow frequency. These 87 streamflow statistics were computed for 41 continuous-record streamflow-gaging stations (streamgages) with 20 or more years of record and 167 low-flow partial-record stations in New Jersey with 10 or more streamflow measurements. The regression analyses used to develop equations to estimate selected streamflow statistics were performed by testing the relation between flow-duration statistics and low-flow frequency statistics for 32 basin characteristics (physical characteristics, land use, surficial geology, and climate) at the 41 streamgages and 167 low-flow partial-record stations. The regression analyses determined drainage area, soil permeability, average April precipitation, average June precipitation, and percent storage (water bodies and wetlands) were the significant explanatory variables for estimating the selected flow-duration and low-flow frequency statistics. Streamflow estimates were computed for two land- and water-use conditions in New Jersey—land- and water-use during the baseline period of record (defined as the years a streamgage had little to no change in development and water use) and current land- and water-use conditions (1989–2008)—for each selected station using data collected through water year 2008. The baseline period of record is representative of a period when the basin was unaffected by change in development. The current period is

  17. Space-time organization of debris flows-triggering rainfall: effects on the identification of the rainfall threshold relationships

    Science.gov (United States)

    Borga, Marco; Nikolopoulos, Efthymios; Creutin, Jean Dominique; Marra, Francesco

    2015-04-01

    . Crema, L. Marchi, F. Marra, F. Guzzetti, M. Borga, 2014: Impact of uncertainty in rainfall estimation on the identification of rainfall thresholds for debris flow occurrence. Geomorphology, 221 (2014), 286-297. DOI: 10.1016/j.geomorph.2014.06.015 Nikolopoulos, E.I., F. Marra, J.D. Creutin, M. Borga, 2015: Estimation of debris flow triggering rainfall: influence of rain gauge density and interpolation methods. Geomorphology, conditionally accepted.

  18. Main diurnal cycle pattern of rainfall in East Java

    Science.gov (United States)

    Rais, Achmad Fahruddin; Yunita, Rezky

    2017-08-01

    The diurnal cycle pattern of rainfall was indicated as an intense feature in East Java. The research of diurnal cycle generally was only based on satellite estimation which had limitations in accuracy and temporal resolution. The hourly rainfall data of Climate Prediction Center Morphing Technique (CMORPH) and gauge were blended using the best correction method between transformation distribution (DT) and quantile mapping (QM) to increase the accuracy. We used spatiotemporal composite to analyse the concentration patterns of maximum rainfall and principal component analysis (PCA) to identify the spatial and temporal dominant patterns of diurnal rainfall. QM was corrected CMORPH data since it was best method. The eastern region of East Java had a rainfall peak at 14 local time (LT) and the western region had a rainfall peak at 16 LT.

  19. Modelos matemáticos para predição da chuva de projeto para regiões do Estado de Minas Gerais Mathematical models for the estimation of rainfall in selected regions of Minas Gerais State, Brazil

    Directory of Open Access Journals (Sweden)

    Carlos R. de Mello

    2003-04-01

    Full Text Available O uso de modelos matemáticos para predição da chuva é uma forma prática e precisa para determinação do valor a ser aplicado em projetos, sendo útil para localidades desprovidas de informações pluviométricas. Objetivou-se ajustar o método de Bell, que possui características de regionalização para a chuva de projeto, com base em equações de chuvas intensas e modelos de probabilidade de Gumbel de estações meteorológicas do Estado de Minas Gerais ajustando, também, um modelo para cada região do estado. Avaliaram-se os modelos considerando-se o coeficiente de determinação e os erros médios em relação aos dados originais. Para validação, trabalhou-se com três estações meteorológicas da região Norte não usadas para ajuste do respectivo modelo. Foram analisadas três metodologias para estimativa da chuva intensa padrão (h(60,2, que pondera o método usado, ressaltando-se a média aritmética, a média ponderada pelo inverso do quadrado da distância e a predição geoestatística (krigagem. Observou-se que os modelos possuem bons indicadores estatísticos e a validação produziu erros baixos, mostrando que os modelos podem ser aplicados, especialmente se a krigagem for usada para estimativa do parâmetro h(60,2.The use of mathematical models for predicting rainfall is a practical and accurate way of determining this parameter to be applied to regions which do not have any precipitation data. Based on the intense rainfall equations and Gumbel's probability model for maximum daily precipitation of meteorological stations in Minas Gerais State, the objective of this work was to adjust the Bell's Method, with regional features, for rainfall, adjusting one model for each region. The regional parameters were estimated by non-linear multiple regression, using Gauss-Newton's method. The goodness of the models was evaluated by the coefficient of determination and mean errors of prediction as compared to the original data

  20. Sources of Uncertainty in Rainfall Maps from Cellular Communication Networks

    Science.gov (United States)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2015-04-01

    Accurate measurements of rainfall are important in many hydrological applications, for instance, flash-flood early-warning systems, hydraulic structures design, agriculture, weather forecasting, and climate modelling. Rainfall intensities can be retrieved from (commercial) microwave link networks. Whenever possible, link networks measure and store the decrease in power of the electromagnetic signal at regular intervals. The decrease in power is largely due to the attenuation by raindrops along the link paths. Such an alternative technique fulfills the continuous strive for measurements of rainfall in time and space at higher resolutions, especially in places where traditional rain gauge networks are scarce or poorly maintained. Rainfall maps from microwave link networks have recently been introduced at country-wide scales. Despite their potential in rainfall estimation at high spatiotemporal resolutions, the uncertainties present in rainfall maps from link networks are not yet fully comprehended. The aim of this work is to identify and quantify the sources of uncertainty present in interpolated rainfall maps from link rainfall depths. In order to disentangle these sources of uncertainty, we classified them into two categories: (1) those associated with the individual microwave link measurements, i.e., the physics involved in the measurements such as wet antenna attenuation, sampling interval of measurements, wet/dry period classification, drop size distribution (DSD), and multi-path propagation; (2) those associated with mapping, i.e., the combined effect of the interpolation methodology, the spatial density of the network, and the availability of link measurements. We computed ~ 3500 rainfall maps from real and simulated link rainfall depths for 12 days for the land surface of The Netherlands. These rainfall maps were compared against quality-controlled gauge-adjusted radar rainfall fields (assumed to be the ground truth). Thus, we were able to not only identify

  1. Evaluating rainfall kinetic energy - intensity relationships with observed disdrometric data

    Science.gov (United States)

    Angulo-Martinez, Marta; Begueria, Santiago; Latorre, Borja

    2016-04-01

    Rainfall kinetic energy is required for determining erosivity, the ability of rainfall to detach soil particles and initiate erosion. Its determination relay on the use of disdrometers, i.e. devices capable of measuring the drop size distribution and velocity of falling raindrops. In the absence of such devices, rainfall kinetic energy is usually estimated with empirical expressions relating rainfall energy and intensity. We evaluated the performance of 14 rainfall energy equations in estimating one-minute rainfall energy and event total energy, in comparison with observed data from 821 rainfall episodes (more than 100 thousand one-minute observations) by means of an optical disdrometer. In addition, two sources of bias when using such relationships were evaluated: i) the influence of using theoretical terminal raindrop fall velocities instead of measured values; and ii) the influence of time aggregation (rainfall intensity data every 5-, 10-, 15-, 30-, and 60-minutes). Empirical relationships did a relatively good job when complete events were considered (R2 > 0.82), but offered poorer results for within-event (one-minute resolution) variation. Also, systematic biases where large for many equations. When raindrop size distribution was known, estimating the terminal fall velocities by empirical laws produced good results even at fine time resolution. The influence of time aggregation was very high in the estimated kinetic energy, although linear scaling may allow empirical correction. This results stress the importance of considering all these effects when rainfall energy needs to be estimated from more standard precipitation records. , and recommends the use of disdrometer data to locally determine rainfall kinetic energy.

  2. Seasonal UK hydrological forecasts using rainfall forecasts - what level of skill?

    Science.gov (United States)

    Bell, Victoria; Davies, Helen; Kay, Alison; Scaife, Adam

    2017-04-01

    Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe, including the UK have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. The Hydrological Outlook UK (HOUK: www.hydoutuk.net) is the first operational hydrological forecast system for the UK that delivers monthly outlooks of the water situation for both river flow and groundwater levels. The output from the HOUK are publicly available and used each month by government agencies, practitioners and academics alongside other sources of information such as flood warnings and meteorological forecasts. The HOUK brings together information on current and forecast weather conditions, and river flows, and uses several modelling approaches to explore possible future hydrological conditions. One of the techniques combines ensembles of monthly-resolution seasonal rainfall forecasts provided by the Met Office GloSea5 forecast system with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The approach combines a high resolution, spatially distributed hydrological initial condition (HIC) provided by a hydrological model (Grid-to-Grid) driven by weather observations up to the forecast time origin. Considerable efforts have been made to accommodate the temporal and spatial resolution of the GloSea5 rainfall forecasts (monthly time-step and national-scale) in a spatially distributed forecasting system, leading to the development of a monthly resolution water balance model (WBM) to forecast regional mean river flows for the next 1 and 3 months ahead. The work presented here provides the first assessment of the skill in the HOUK national-scale flow forecasts using an ensemble of rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009). The skill in the combined modelling system has been assessed for different seasons and regions of Britain, and compared to what might be achieved using

  3. Estimating Monthly Solar Radiation in South-Central Chile Estimación de Radiación Solar Mensual en la Zona Centro Sur de Chile

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    José Álvarez

    2011-12-01

    Full Text Available Solar radiation is a key component in process-based models. The amount of this energy depends on the location, time of the year, and atmospheric conditions. Several equations and models have been developed for different conditions using historical data from weather station networks or satellite measurements. However, solar radiation estimates are too local since they rely on weather stations or have a resolution that is too coarse when working with satellites. In this study, we estimated monthly global solar radiation for the south-central region of Chile using the r.sun model and validated it with observations from automatic weather stations. We analyzed the performance of global radiation results with the Hargreaves-Samani (HS and Bristow-Campbell (BC models. Estimates from a calibrated rsun model accounted for 89% of the variance (r² = 0.89 in monthly mean values for 15 locations in the research area. The model performed very well for a wide area and conditions in Chile when we compared it with the HS and BC models. Our estimates of global solar radiation using the rsun model could be improved through calibration of ground measurements and more precise cloudiness estimates as they become available. With additional procedures, the rsun model could be used to provide spatial estimates of daily, weekly, monthly, and yearly solar radiation.La radiación solar es un componente clave en los modelos basados en procesos. La cantidad de esta energía depende de la ubicación, época del año, y también de las condiciones atmosféricas. Varias ecuaciones y modelos han sido desarrollados para diferentes condiciones, utilizando datos históricos de las redes de estaciones meteorológicas o de las mediciones por satélite. Sin embargo, las estimaciones de la radiación solar son demasiado locales con estaciones meteorológicas, o con una resolución muy gruesa cuando se trabaja con satélites. En el presente estudio se estimó radiación solar global

  4. Assessment of Temperature and Elevation Controls on Spatial Variability of Rainfall in Iran

    OpenAIRE

    Majid Javari

    2017-01-01

    With rainfall changes, hydrological process variability increases. This study predicts the potential effects of temperature and topography characteristics on rainfall spatial variability. Temperature and topography were considered as two effective factors that may influence monthly rainfall. This study uses rainfall and temperature data from 174 synoptic and climatic stations and 39,055 rain, elevation and temperature points extracted by ArcGIS10.3 over the 40 years (1975–2014). In this study...

  5. Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia

    OpenAIRE

    Maryam Montazerolghaem; Willem Vervoort; Budiman Minasny; Alex McBratney

    2016-01-01

    Knowledge of temporal and spatial variability of climate and rainfall can improve agriculture production and can help to manage risks caused by climate variability. Available high-quality monthly rainfall data from the Australian Bureau of Meteorology for 1907–2011 was used to investigate the leading seasonal mode of the long-term rainfall variability over south-eastern and eastern Australia. Spatio-temporal variations of seasonal rainfall and their connection to oceanic-atmospheric predictor...

  6. Estimation of Genetic Parameters for First Lactation Monthly Test-day Milk Yields using Random Regression Test Day Model in Karan Fries Cattle

    Science.gov (United States)

    Singh, Ajay; Singh, Avtar; Singh, Manvendra; Prakash, Ved; Ambhore, G. S.; Sahoo, S. K.; Dash, Soumya

    2016-01-01

    A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields. PMID:26954137

  7. Projected changes of rainfall seasonality and dry spells in a high concentration pathway 21st century scenario

    CERN Document Server

    Pascale, Salvatore; Feng, Xue; Porporato, Amilcare; Hasson, Shabeh ul

    2014-01-01

    In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models participating to Coupled Model Intercomparison Project 5. We use estimates of the centroid of the monthly rainfall distribution as an index of the rainfall timing and a threshold-independent, information theory-based quantity such as relative entropy (RE) to quantify the concentration of annual rainfall and the number of dry months and to build a monsoon dimensionless seasonality index (DSI). The RE is projected to increase, with high inter-model agreement over Mediterranean-type regions (southern Europe, northern Africa and southern Australia) and areas of South and Central America, implying an increase in the number of dry days up to one month by the end of the twenty-first century. Positive RE changes are also projected over the monsoon regions of southern Africa and North America,...

  8. Incorporation of an evolutionary algorithm to estimate transfer-functions for a parameter regionalization scheme of a rainfall-runoff model

    Science.gov (United States)

    Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

    2016-04-01

    This contribution presents a framework, which enables the use of an Evolutionary Algorithm (EA) for the calibration and regionalization of the hydrological model COSEROreg. COSEROreg uses an updated version of the HBV-type model COSERO (Kling et al. 2014) for the modelling of hydrological processes and is embedded in a parameter regionalization scheme based on Samaniego et al. (2010). The latter uses subscale-information to estimate model via a-priori chosen transfer functions (often derived from pedotransfer functions). However, the transferability of the regionalization scheme to different model-concepts and the integration of new forms of subscale information is not straightforward. (i) The usefulness of (new) single sub-scale information layers is unknown beforehand. (ii) Additionally, the establishment of functional relationships between these (possibly meaningless) sub-scale information layers and the distributed model parameters remain a central challenge in the implementation of a regionalization procedure. The proposed method theoretically provides a framework to overcome this challenge. The implementation of the EA encompasses the following procedure: First, a formal grammar is specified (Ryan et al., 1998). The construction of the grammar thereby defines the set of possible transfer functions and also allows to incorporate hydrological domain knowledge into the search itself. The EA iterates over the given space by combining parameterized basic functions (e.g. linear- or exponential functions) and sub-scale information layers into transfer functions, which are then used in COSEROreg. However, a pre-selection model is applied beforehand to sort out unfeasible proposals by the EA and to reduce the necessary model runs. A second optimization routine is used to optimize the parameters of the transfer functions proposed by the EA. This concept, namely using two nested optimization loops, is inspired by the idea of Lamarckian Evolution and Baldwin Effect

  9. Spatial Variability of Rainfall

    DEFF Research Database (Denmark)

    Jensen, N.E.; Pedersen, Lisbeth

    2005-01-01

    As a part of a Local Area Weather Radar (LAWR) calibration exercise 15 km south of Århus, Denmark, the variability in accumulated rainfall within a single radar pixel (500 by 500 m) was measured using nine high-resolution rain gauges. The measured values indicate up to a 100% variation between...

  10. The Wageningen Rainfall Simulator

    NARCIS (Netherlands)

    Lassu, Tamas; Seeger, K.M.; Peters, P.D.; Keesstra, S.D.

    2015-01-01

    The set-up and characterisation of an indoor nozzle-type rainfall simulator (RS) at Wageningen University, the Netherlands, are presented. It is equipped with four Lechler nozzles (two nr. 460·788 and two nr. 461·008). The tilting irrigation plot is 6 m long and 2·5 m wide. An electrical pump

  11. Extreme Rainfall Impacts in Fractured Permeable Catchments

    Science.gov (United States)

    Ireson, A. M.; Butler, A. P.

    2009-12-01

    Serious groundwater flooding events have occurred on Chalk catchments in both the UK and north west Europe in the last decade, causing substantial amounts of disruption and economic damage. These fractured, permeable catchments are characterized by low surface runoff, high baseflow indices and strongly attenuated streamflow hydrographs. They have a general resilience to drought and pluvial/fluvial flooding. The small pore size of the Chalk matrix (~ 1 µm) exerts a high suction, such that dynamic storage is primarily due to the fractures, and amounts to ~ 1% of the total volume. As a result, under sustained rainfall the water table can rise up to exceptional levels leading to surface water emergence from springs and valleys. Floodwater may slowly drain with the topography, or, in localized depressions, it may simply pond until the groundwater levels decline. In winter 2000/1, a sequence of individually unexceptional rainfall events over several months led to large scale flooding in the Pang catchment, Berkshire, UK. By contrast, an extreme rainfall event on 20th July 2007 in the same catchment caused a very rapid response at the water table, but due to the antecedent conditions did not lead to flooding. The objective of this study is to quantify how the water table in a fractured permeable catchment responds to different types of rainfall, and the implications of this for groundwater flooding. We make use of measurements from the Pang catchment, including: rainfall (tipping bucket gauges); actual evaporation (eddy flux correlation); soil water content (profile probes and neutron probes); near surface matric potential (tensiometers and equitensiometers); deep (>10m) matric potential (deep jacking tensiometers); and water table elevation (piezometers). Conventional treatment of recharge in Chalk aquifers considers a fixed bypass component of rainfall, normally 15%, to account for the role of the fractures. However, interpretation of the field data suggest three modes

  12. Intermittent rainfall in dynamic multimedia fate modeling.

    Science.gov (United States)

    Hertwich, E G

    2001-03-01

    It has been shown that steady-state multimedia models (level III fugacity models) lead to a substantial underestimate of air concentrations for chemicals with a low Henry's law constant (H multimedia models are used to estimate the spatial range or inhalation exposure. A dynamic model of pollutant fate is developed for conditions of intermittent rainfall to calculate the time profile of pollutant concentrations in different environmental compartments. The model utilizes a new, mathematically efficient approach to dynamic multimedia fate modeling that is based on the convolution of solutions to the initial conditions problem. For the first time, this approach is applied to intermittent conditions. The investigation indicates that the time-averaged pollutant concentrations under intermittent rainfall can be approximated by the appropriately weighted average of steady-state concentrations under conditions with and without rainfall.

  13. RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Celso A. G. Santos

    2016-01-01

    Full Text Available The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009 from a raingauge and also using daily rainfall data (1998–2013 from the Tropical Rainfall Measuring Mission (TRMM. The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256– 512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

  14. The relationship between the Guinea Highlands and the West African offshore rainfall maximum

    Science.gov (United States)

    Hamilton, H. L.; Young, G. S.; Evans, J. L.; Fuentes, J. D.; Núñez Ocasio, K. M.

    2017-01-01

    Satellite rainfall estimates reveal a consistent rainfall maximum off the West African coast during the monsoon season. An analysis of 16 years of rainfall in the monsoon season is conducted to explore the drivers of such copious amounts of rainfall. Composites of daily rainfall and midlevel meridional winds centered on the days with maximum rainfall show that the day with the heaviest rainfall follows the strongest midlevel northerlies but coincides with peak low-level moisture convergence. Rain type composites show that convective rain dominates the study region. The dominant contribution to the offshore rainfall maximum is convective development driven by the enhancement of upslope winds near the Guinea Highlands. The enhancement in the upslope flow is closely related to African easterly waves propagating off the continent that generate low-level cyclonic vorticity and convergence. Numerical simulations reproduce the observed rainfall maximum and indicate that it weakens if the African topography is reduced.

  15. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    Science.gov (United States)

    Overeem, Aart; Uijlenhoet, Remko; Leijnse, Hidde

    2016-04-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. We present a rainfall retrieval algorithm, which is employed to obtain rainfall maps from microwave links in a cellular communication network. We compare these rainfall maps to gauge-adjusted radar rainfall maps. The microwave link data set, as well as the developed code, a package in the open source scripting language "R", are freely available at GitHub (https://github.com/overeem11/RAINLINK). The purpose of this presentation is to promote rainfall mapping utilizing microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

  16. Estimating Green Water Footprints in a Temperate Environment

    Directory of Open Access Journals (Sweden)

    Tim Hess

    2010-07-01

    Full Text Available The “green” water footprint (GWF of a product is often considered less important than the “blue” water footprint (BWF as “green” water generally has a low, or even negligible, opportunity cost. However, when considering food, fibre and tree products, is not only a useful indicator of the total appropriation of a natural resource, but from a methodological perspective, blue water footprints are frequently estimated as the residual after green water is subtracted from total crop water use. In most published studies, green water use (ETgreen has been estimated from the FAO CROPWAT model using the USDA method for effective rainfall. In this study, four methods for the estimation of the ETgreen of pasture were compared. Two were based on effective rainfall estimated from monthly rainfall and potential evapotranspiration, and two were based on a simulated water balance using long-term daily, or average monthly, weather data from 11 stations in England. The results show that the effective rainfall methods significantly underestimate the annual ETgreen in all cases, as they do not adequately account for the depletion of stored soil water during the summer. A simplified model, based on annual rainfall and reference evapotranspiration (ETo has been tested and used to map the average annual ETgreen of pasture in England.

  17. RAINFALL-RUNOFF MODELING IN THE TURKEY RIVER USING ...

    African Journals Online (AJOL)

    2015-01-15

    Jan 15, 2015 ... Modeling rainfall-runoff relationships in a watershed have an important role in water .... Initial estimations will improve following the development of the model. .... Resources Research Nordic Hydrology, 33 (5), 2002,33 1-346.

  18. Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

    NARCIS (Netherlands)

    Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

    2016-01-01

    Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km2) 15 min rainfall maps can

  19. How important is tropospheric humidity for coastal rainfall in the tropics?

    CERN Document Server

    Bergemann, Martin

    2016-01-01

    Recent research has community have shown that tropical convection and rainfall is sensitive to mid-tropospheric humidity. Therefore it has been suggested to improve the representation of moist convection by making cumulus parameterizations more sensitive to mid-tropospheric moisture. Climate models show considerable rainfall biases in coastal tropical areas, where approx. 33 % of the overall rainfall received is associated with coastal land-sea interaction. Building on an algorithm to objectively identify rainfall that is associated with land-sea interaction we investigate whether the relationship between rainfall in coastal regions and atmospheric humidity differs from that over the open ocean or over inland areas. We combine 3-hourly satellite estimates of rainfall with humidity estimates from reanalyses and investigate if coastal rainfall reveals the well known relationship between area-averaged precipitation and column integrated moisture. We find that rainfall that is associated with coastal land-sea eff...

  20. The Winter Rainfall of Malaysia

    National Research Council Canada - National Science Library

    Chen, Tsing-Chang; Tsay, Jenq-Dar; Yen, Ming-Cheng; Matsumoto, Jun

    2013-01-01

    .... The major cause of the rainfall maximum of Peninsular Malaysia is cold surge vortices (CSVs) and heavy rainfall/flood (HRF) events propagating from the Philippine area and Borneo. In contrast, the major cause of the rainfall maximum of Borneo is these rain-producing disturbances trapped in Borneo. Disturbances of the former group are formed by the cold sur...

  1. Development of Rainfall Model using Meteorological Data for Hydrological Use

    Directory of Open Access Journals (Sweden)

    Mohd Adib Mohammad Razi

    2013-11-01

    Full Text Available Abstract At present, research on forecasting unpredictable weather such as heavy rainfall is one of the most important challenges for equipped meteorological center. In addition, the incidence of significant weather events is estimated to rise in the near future due to climate change, and this situation inspires more studies to be done. This study introduces a rainfall model that has been developed using selected rainfall parameters with the aim to recognize rainfall depth in a catchment area. This study proposes a rainfall model that utilizes the amount of rainfall, temperature, humidity and pressure records taken from selected stations in Peninsular Malaysia and they are analyzed using SPSS multiple regression model. Seven meteorological stations are selected for data collection from 1997 until 2007 in Peninsular Malaysia which are Senai, Kuantan, Melaka, Subang, Ipoh, Bayan Lepas, and Chuping. Multiple Regression analysis in Statistical Package for Social Science (SPSS software has been used to analyze a set of eleven years (1997 – 2007 meteorological data. Senai rainfall model gives an accurate result compared to observation rainfall data and this model were validating with data from Kota Tinggi station. The analysis shows that the selected meteorological parameters influence the rainfall development. As a result, the rainfall model developed for Senai proves that it can be used in Kota Tinggi catchment area within the limit boundaries, as the two stations are close from one another. Then, the amounts of rainfall at the Senai and Kota Tinggi stations are compared and the calibration analysis shows that the proposed rainfall model can be used in both areas.

  2. General Formula for Estimation of Monthly Mean Global Solar Radiation in Different Climates on the South and North Coasts of Iran

    Directory of Open Access Journals (Sweden)

    Ali A. Sabziparvar

    2006-12-01

    Full Text Available Using sunshine duration, cloud cover, relative humidity, average of maximum temperature, and ground albedo as the input of several radiation models, the monthly average daily solar radiation on horizontal surface in various coastal cities of the South (25.23∘ N and the North (38.42∘ N of Iran are estimated. Several radiation models are tested and further are revised by taking into consideration the effects of relative humidity, ground albedo, and Sun-Earth distance. Model validation is performed by using up to 13 years (1988–2000 of daily solar observations. Errors are calculated using MBE, MABE, MPE, and RMSE statistical criteria (see nomenclature and further a general formula which estimates the global radiation in different climates of coastal regions is suggested. The proposed method shows a good agreement (less than 7% deviation with the long-term pyranometric data. In comparison with other works done so far, the suggested method performs a higher degree of accuracy for those of two regions. The model results can be extended to other locations in coastal regions where solar data are not available.

  3. Uncertainty evaluation of design rainfall for urban flood risk analysis.

    Science.gov (United States)

    Fontanazza, C M; Freni, G; La Loggia, G; Notaro, V

    2011-01-01

    A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aims to assess the uncertainty of such an approach and for suggesting improvements in the definition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copula method was applied to rainfall features (total depth, duration and maximum intensity) to generate synthetic rainfalls that are more consistent with historical events. The procedure was applied to a real case study, and the results were compared with those obtained by simulating other typical synthetic rainfall events linked to intensity-duration-frequency (IDF) curves. The copula-based multi-variate analysis is more robust and adapts well to experimental flood locations even if it is more complex and time-consuming. This study demonstrates that statistical correlations amongst rainfall frequency, duration, volume and peak intensity can partially explain the weak reliability of flood-frequency analyses based on synthetic rainfall events.

  4. Estimation of monthly global solar irradiation using the Hargreaves-Samani model and an artificial neural network for the state of Alagoas in northeastern Brazil

    Science.gov (United States)

    Lyra, Gustavo Bastos; Zanetti, Sidney Sára; Santos, Anderson Amorim Rocha; de Souza, José Leonaldo; Lyra, Guilherme Bastos; Oliveira-Júnior, José Francisco; Lemes, Marco Antônio Maringolo

    2016-08-01

    The monthly global solar irradiation ( H g) was estimated using the Hargreaves-Samani model (HS) with three different approaches for determining the k r coefficient and using an artificial neural network (ANN). The data consisted of long-term climate series measured at eight conventional meteorological stations in the state of Alagoas and its borders in northeastern Brazil. The approaches to determine the k r coefficient of the HS model included (i) the method proposed by Hargreaves (1994) (0.190 and 0.162 for coastal and interior regions, respectively), (ii) a method analogous to the previous except with altitude correction, and (iii) k r fitted with local climatic data. A new spatial interpolation method is also proposed to determine k r as a function of geographical coordinates and altitude. The fitted local values of k r (0.168-0.179 and 0.189-0.231 for interior and coastal stations, respectively) exhibited a strong dependence ( r 2 = 0.81) on latitude, longitude, and altitude. The estimates of H g obtained with the HS model using fitted local values of k r and those using the ANN were similar (determination coefficient - r 2 = 0.75 and Willmontt agreement coefficient - d = 0.93) and better than those from the HS model using an altitude-corrected k r ( r 2 = 0.68 and d = 0.90) or the values proposed by Hargreaves (1994) ( r 2 = 0.57 and d = 0.85). The estimates of H g were less accurate and precise for the coastal stations, where cloudiness and humidity are high and the thermal amplitude is small.

  5. Trend analysis of rainfall time series for Sindh river basin in India

    Science.gov (United States)

    Gajbhiye, Sarita; Meshram, Chandrashekhar; Mirabbasi, Rasoul; Sharma, S. K.

    2016-08-01

    The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002.

  6. Simulating Spatial Distribution of Canopy Rainfall Interception of Forests in China

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The canopy rainfall interception modei linked to environmental conditions and biological features is established on the basis of stationary observation and measurements in China. Upscaling from site observation to regional Ievel estimation of canopy rainfall interception has been made. The potential interception value of forests during the rainfall season in China according to rainfall records of May, July and September in the year 1982, has been simulated and mapped under the GIS software package Idris...

  7. Variation in rainfall interception along a forest succession gradient

    Science.gov (United States)

    Zimmermann, Beate