Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall
Haywood, Jim M.; Jones, Andy; Bellouin, Nicolas; Stephenson, David
2013-07-01
The Sahelian drought of the 1970s-1990s was one of the largest humanitarian disasters of the past 50 years, causing up to 250,000 deaths and creating 10 million refugees. It has been attributed to natural variability, over-grazing and the impact of industrial emissions of sulphur dioxide. Each mechanism can influence the Atlantic sea surface temperature gradient, which is strongly coupled to Sahelian precipitation. We suggest that sporadic volcanic eruptions in the Northern Hemisphere also strongly influence this gradient and cause Sahelian drought. Using de-trended observations from 1900 to 2010, we show that three of the four driest Sahelian summers were preceded by substantial Northern Hemisphere volcanic eruptions. We use a state-of-the-art coupled global atmosphere-ocean model to simulate both episodic volcanic eruptions and geoengineering by continuous deliberate injection into the stratosphere. In either case, large asymmetric stratospheric aerosol loadings concentrated in the Northern Hemisphere are a harbinger of Sahelian drought whereas those concentrated in the Southern Hemisphere induce a greening of the Sahel. Further studies of the detailed regional impacts on the Sahel and other vulnerable areas are required to inform policymakers in developing careful consensual global governance before any practical solar radiation management geoengineering scheme is implemented.
Sources of Sahelian-Sudan moisture: Insights from a moisture-tracing atmospheric model
Salih, Abubakr A. M.; Zhang, Qiong; Pausata, Francesco S. R.; Tjernström, Michael
2016-07-01
The summer rainfall across Sahelian-Sudan is one of the main sources of water for agriculture, human, and animal needs. However, the rainfall is characterized by large interannual variability, which has attracted extensive scientific efforts to understand it. This study attempts to identify the source regions that contribute to the Sahelian-Sudan moisture budget during July through September. We have used an atmospheric general circulation model with an embedded moisture-tracing module (Community Atmosphere Model version 3), forced by observed (1979-2013) sea-surface temperatures. The result suggests that about 40% of the moisture comes with the moisture flow associated with the seasonal migration of the Intertropical Convergence Zone (ITCZ) and originates from Guinea Coast, central Africa, and the Western Sahel. The Mediterranean Sea, Arabian Peninsula, and South Indian Ocean regions account for 10.2%, 8.1%, and 6.4%, respectively. Local evaporation and the rest of the globe supply the region with 20.3% and 13.2%, respectively. We also compared the result from this study to a previous analysis that used the Lagrangian model FLEXPART forced by ERA-Interim. The two approaches differ when comparing individual regions, but are in better agreement when neighboring regions of similar atmospheric flow features are grouped together. Interannual variability with the rainfall over the region is highly correlated with contributions from regions that are associated with the ITCZ movement, which is in turn linked to the Atlantic Multidecadal Oscillation. Our result is expected to provide insights for the effort on seasonal forecasting of the rainy season over Sahelian Sudan.
Changes in Sahelian annual vegetation growth and phenology since 1960: A modeling approach
Pierre, C.; Grippa, M.; Mougin, E.; Guichard, F.; Kergoat, L.
2016-08-01
In semi-arid areas like the Sahel, vegetation is particularly sensitive to climate variability and can play an important role in surface-atmosphere coupling. After a wet period extending from 1950 to 1970, the Sahel experienced a severe drought in the 1970s and 1980s, followed by a partial recovery of rainfall and a "re-greening" of vegetation beginning in the 1990s. This study explores how the multidecadal variability of Sahelian rainfall and particularly the drought period have affected vegetation phenology and growth since 1960. The STEP model, which is specifically designed to simulate the Sahelian annual vegetation, including the dry season processes, is run over an area extending from 13°N to 18°N and from 20°W to 20°E. Mean values, interannual variability and phenological characteristics of the Sahelian annual grasslands simulated by STEP are in good agreement with MODIS derived production and phenology over the 2001-2014 period, which demonstrates the skill of the model and allows the analysis of vegetation changes and variability over the last 50 years. It was found that droughts in the 1970s and 1980s shortened the mean vegetation cycle and reduced its amplitude and that, despite the rainfall recovery since the 1990s, the current conditions for green and dry vegetation are still below pre-drought conditions. While the decrease in vegetation production has been largely homogeneous during droughts, vegetation recovery has been heterogeneous over the Sahel since 1990, with specific changes near the western coast and at the eastern edge of the West African monsoon area. Since 1970, the Sahel also experienced an increased interannual variability in vegetation mass and phenology. In terms of phenology, region-averaged End and Length of Season are the most variable, while maximum date and Start of Season are the least variable, although the latter displays a high variability locally.
Zhang, Wenmin; Brandt, Martin; Guichard, Francoise; Tian, Qingjiu; Fensholt, Rasmus
2017-07-01
The sahelian rainfall regime is characterized by a strong spatial as well as intra- and inter-annual variability. The satellite based African Rainfall Climatology Version 2 (ARC2) daily gridded rainfall estimates with a 0.1° × 0.1° spatial resolution provides the possibility for in-depth studies of seasonal changes over a 33-year period (1983-2015). Here we analyze rainfall regime variables that require daily observations: onset, cessation, and length of the wet season; seasonal rainfall amount; number of rainy days; intensity and frequency of rainfall events; number, length, and cumulative duration of dry spells. Rain gauge stations and MSWEP (Multi-Source Weighted-Ensemble Precipitation) data were used to evaluate the agreement of rainfall variables in both space and time, and trends were analyzed. Overall, ARC2 rainfall variables reliably show the spatio-temporal dynamics of seasonal rainfall over 33 years when compared to gauge and MSWEP data. However, a higher frequency of low rainfall events (spell characteristics). Most rainfall variables (both ARC2 and gauge data) show negative anomalies (except for onset of rainy season) from 1983 until the end of the 1990s, from which anomalies become mostly positive and inter-annual variability is higher. ARC2 data show a strong increase in seasonal rainfall, wet season length (caused by both earlier onset and a late end), number of rainy days, and high rainfall events (>20 mm day-1) for the western/central Sahel over the period of analysis, whereas the opposite trend characterizes the eastern part of the Sahel.
Dynamical Models of Interactions between Herds Forage and Water Resources in Sahelian Region
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Jean Jules Tewa
2014-01-01
Full Text Available Optimal foraging is one of the capital topics nowadays in Sahelian region. The vast majority of feed consumed by ruminants in Sahelian region is still formed by natural pastures. Pastoral constraints are the high variability of available forage and drinking water in space and especially in time (highly seasonal, interannual variability and the scarcity of water resources. The mobility is the main functional and opportunistic adaptation to these constraints. Our goal in this paper is to formalize two dynamical models for interactions between a herd of domesticate animals, forage resources, and water resources inside a given Sahelian area, in order to confirm, explain, and predict by mathematical models some observations about pastoralism in Sahelian region. These models in some contexts can be similar to predator-prey models as forage and water resources can be considered as preys and herd’s animals as predators. These models exhibit very rich dynamics, since it predicts abrupt changes in consumer behaviour and disponibility of forage or water resources. The dynamics exhibits a possible coexistence between herd, resources, and water with alternative peaks in their trajectories.
Sahelian rangeland response to changes in rainfall over two decades in the Gourma region, Mali
Hiernaux, Pierre; Mougin, Eric; Diarra, Lassine; Soumaguel, Nogmana; Lavenu, François; Tracol, Yann; Diawara, Mamadou
2009-08-01
SummaryTwenty-five rangeland sites were monitored over two decades (1984-2006) first to assess the impact of the 1983-1984 droughts on fodder resources, then to better understand ecosystem functioning and dynamics. Sites are sampled along the south-north bioclimatic gradient in Gourma (Mali), within three main edaphic situations: sandy, loamy-clay and shallow soils. In addition, three levels of grazing pressure where systematically sampled within sandy soils. Located at the northern edge of the area reached by the West African monsoon, the Gourma gradient has recorded extremes in inter-annual variations of rainfall and resulting variations in vegetation growth. Following rainfall variability, inter-annual variability of herbaceous yield increases as climate gets dryer with latitudes at least on the sandy soils sites. Local redistribution of rainfall explains the high patchiness of herbaceous vegetation, especially on shallow soils. Yet spatial heterogeneity of the vegetation does not buffer between year yield variability that increases with spatial heterogeneity. At short term, livestock grazing during the wet season affects plant growth and thus yield in direction and proportions that vary with the timing and intensity of grazing. In the longer term, grazing also impinges upon species composition in many ways. Hence, long histories of heavy grazing promote either long cycle annuals refused by livestock or else short cycle good quality feed species. Primary production is maintained or even increased in the case of refusal such as Sida cordifolia, and is lessened in the case of short cycle species such as Zornia glochidiata. These behaviours explain that the yield anomalies calculated for the rangelands on sandy soils relative to the yield of site less grazed under similar climate tend to be negative in northern Sahel where the scenario of short cycle species dominates, while yield anomalies are close to nil in centre Sahel and slightly positive in South Sahel where
DEFF Research Database (Denmark)
Fensholt, Rasmus; Rasmussen, Kjeld
2011-01-01
–2007)) and SNDVI (AVHRR GIMMS NDVI dataset, 1982–2007). It is shown that the increase in SNDVI has been substantial in the Sahel-Sudanian zone over the 1982–2007 period,whereas for the period 1996–2007 the pattern of SNDVI trends is more complex. Also, trend analysis of annual rainfall from GPCP data (2...
Thirty-two Years of Sahelian Zone Growing Season Non-Stationary NDVI3g Patterns and Trends
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Assaf Anyamba
2014-04-01
Full Text Available We update the Global Inventory Modeling and Mapping Studies (GIMMS analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages with the three rainfall data sets: the Africa Rainfall Climatology from 1983 to 2012, the Variability Analyses of Surface Climate Observations Version-1.1 from 1951 to 2000, and the Nicholson ground-station precipitation rainfall data from 1981 to 1994. We use the Nicholson ground-station annual precipitation data to determine the reliability of the two continental precipitation data sets for specific locations and specific times, extrapolate these confirmed relationships over the Sahelian Zone from 1983 to 2012 with the Africa Rainfall Climatology, and then place these zonal findings within the 1951 to 2000 record of the Variability Analyses of Surface Climate Observations Version-1.1 precipitation data set. We confirm the extreme nature of the 1984–1985 Sahelian drought, a signature event that marked the minima during the 1980s desiccation period followed within ten years by near-maxima rainfall event in 1994 and positive departures is NDVI, marking beginning of predominantly wetter conditions that have persisted to 2012. We also show the NDVI3g data capture “effective” rainfall, the rainfall that is utilized by plants to grow, as compared to rainfall that evaporates or is runoff. Using our effective rainfall concept, we estimate average effective rainfall for the entire Sahelian Zone for the 1984 extreme drought was 223 mm/yr as compared to 406 mm/yr in during the 1994 wet period. We conclude that NDVI3g data can used as a proxy for analyzing and interpreting decadal-scale land surface variability and trends over semi arid-lands.
Rainfall variability modelling in Rwanda
Nduwayezu, E.; Kanevski, M.; Jaboyedoff, M.
2012-04-01
Support to climate change adaptation is a priority in many International Organisations meetings. But is the international approach for adaptation appropriate with field reality in developing countries? In Rwanda, the main problems will be heavy rain and/or long dry season. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). The spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front » mechanism. The torrential rainfall that occurs every year in Rwanda disturbs the circulation for many days, damages the houses and, more seriously, causes heavy losses of people. All districts are affected by bad weather (heavy rain) but the costs of such events are the highest in mountains districts. The objective of the current research is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. The research will include rainfalls variability mapping and probabilistic analyses of extreme events.
Frequency of extreme Sahelian storms tripled since 1982 in satellite observations.
Taylor, Christopher M; Belušić, Danijel; Guichard, Françoise; Parker, Douglas J; Vischel, Théo; Bock, Olivier; Harris, Phil P; Janicot, Serge; Klein, Cornelia; Panthou, Gérémy
2017-04-26
The hydrological cycle is expected to intensify under global warming, with studies reporting more frequent extreme rain events in many regions of the world, and predicting increases in future flood frequency. Such early, predominantly mid-latitude observations are essential because of shortcomings within climate models in their depiction of convective rainfall. A globally important group of intense storms-mesoscale convective systems (MCSs)-poses a particular challenge, because they organize dynamically on spatial scales that cannot be resolved by conventional climate models. Here, we use 35 years of satellite observations from the West African Sahel to reveal a persistent increase in the frequency of the most intense MCSs. Sahelian storms are some of the most powerful on the planet, and rain gauges in this region have recorded a rise in 'extreme' daily rainfall totals. We find that intense MCS frequency is only weakly related to the multidecadal recovery of Sahel annual rainfall, but is highly correlated with global land temperatures. Analysis of trends across Africa reveals that MCS intensification is limited to a narrow band south of the Sahara desert. During this period, wet-season Sahelian temperatures have not risen, ruling out the possibility that rainfall has intensified in response to locally warmer conditions. On the other hand, the meridional temperature gradient spanning the Sahel has increased in recent decades, consistent with anthropogenic forcing driving enhanced Saharan warming. We argue that Saharan warming intensifies convection within Sahelian MCSs through increased wind shear and changes to the Saharan air layer. The meridional gradient is projected to strengthen throughout the twenty-first century, suggesting that the Sahel will experience particularly marked increases in extreme rain. The remarkably rapid intensification of Sahelian MCSs since the 1980s sheds new light on the response of organized tropical convection to global warming, and
Frequency of extreme Sahelian storms tripled since 1982 in satellite observations
Taylor, Christopher M.; Belušić, Danijel; Guichard, Françoise; Parker, Douglas J.; Vischel, Théo; Bock, Olivier; Harris, Phil P.; Janicot, Serge; Klein, Cornelia; Panthou, Gérémy
2017-04-01
The hydrological cycle is expected to intensify under global warming, with studies reporting more frequent extreme rain events in many regions of the world, and predicting increases in future flood frequency. Such early, predominantly mid-latitude observations are essential because of shortcomings within climate models in their depiction of convective rainfall. A globally important group of intense storms—mesoscale convective systems (MCSs)—poses a particular challenge, because they organize dynamically on spatial scales that cannot be resolved by conventional climate models. Here, we use 35 years of satellite observations from the West African Sahel to reveal a persistent increase in the frequency of the most intense MCSs. Sahelian storms are some of the most powerful on the planet, and rain gauges in this region have recorded a rise in ‘extreme’ daily rainfall totals. We find that intense MCS frequency is only weakly related to the multidecadal recovery of Sahel annual rainfall, but is highly correlated with global land temperatures. Analysis of trends across Africa reveals that MCS intensification is limited to a narrow band south of the Sahara desert. During this period, wet-season Sahelian temperatures have not risen, ruling out the possibility that rainfall has intensified in response to locally warmer conditions. On the other hand, the meridional temperature gradient spanning the Sahel has increased in recent decades, consistent with anthropogenic forcing driving enhanced Saharan warming. We argue that Saharan warming intensifies convection within Sahelian MCSs through increased wind shear and changes to the Saharan air layer. The meridional gradient is projected to strengthen throughout the twenty-first century, suggesting that the Sahel will experience particularly marked increases in extreme rain. The remarkably rapid intensification of Sahelian MCSs since the 1980s sheds new light on the response of organized tropical convection to global warming
Strommer, Gabriel; Brandt, Martin; Diongue-Niang, Aida; Samimi, Cyrus
2013-04-01
In the 20th century, the West African Sahel has been a hot-spot of climatic changes. After severe drought-events in the 1970s and 1980s which were followed by a significant drop in annual precipitation, rainfall seems to increase again during the past years. Most studies are based on monthly or yearly datasets. However, many processes and events which are important for the local population depending on rainfall are not related to monthly or annual precipitation but are related to intra-annual, often daily scales. During this study, interviews with farmers and herders were conducted in the Senegalese Sahel. The results show, that wet months with unsuitably distributed precipitation can cause more harm than bringing benefits - depending on the phenological stage of the plants. Agricultural crops for example need rainfall breaks. On the other hand, natural herbaceous vegetation tolerates longer wet periods. So, a wet season can still hide dry spells that alter crops and vegetation development. Based on the results of these interviews, this study developed two indexes, one for local farmers and one for herders separately, showing if the year was favorable for them or not. The indexes integrate the length of rainy seasons, intensity and frequency of rainfall events, breaks between events and also the previous year. This way, each year is assigned to one of 5 classes. Using daily rainfall data of the Linguère weather-station (from the Senegal Meteorological Service, ANACIM), trends of the indexes from 1945 to 2002 are detected and compared to results of the interviews. Statistically relating the indexes to yearly and monthly data demonstrates, how much information can be gathered by those datasets. Furthermore, changes in intensity and frequency are related with yearly and monthly sums showing relations between daily data and annual sums. For example, a high correlation (r=0.73) between the amount of rain days (> 1 mm) and the annual rainfall is observed in Linguère.
Modelling persistence in annual Australia point rainfall
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J. P. Whiting
2003-01-01
Full Text Available Annual rainfall time series for Sydney from 1859 to 1999 is analysed. Clear evidence of nonstationarity is presented, but substantial evidence for persistence or hidden states is more elusive. A test of the hypothesis that a hidden state Markov model reduces to a mixture distribution is presented. There is strong evidence of a correlation between the annual rainfall and climate indices. Strong evidence of persistence of one of these indices, the Pacific Decadal Oscillation (PDO, is presented together with a demonstration that this is better modelled by fractional differencing than by a hidden state Markov model. It is shown that conditioning the logarithm of rainfall on PDO, the Southern Oscillation index (SOI, and their interaction provides realistic simulation of rainfall that matches observed statistics. Similar simulation models are presented for Brisbane, Melbourne and Perth. Keywords: Hydrological persistence,hidden state Markov models, fractional differencing, PDO, SOI, Australian rainfall
Intermittent rainfall in dynamic multimedia fate modeling.
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.
DEFF Research Database (Denmark)
Kaspersen, Per; Fensholt, Rasmus; Huber Gharib, Silvia
2011-01-01
Linear trend analysis and seasonal trend analysis are performed on gridded data of vegetation, rainfall, solar radiation flux, and air temperature, in order to examine the influence of the past three decades of climate variability and change on the Sahelian vegetation dynamics. Per-pixel correlat...... constraints were found to be statistically significant only for rainfall explaining only a moderate degree of observed NDVI variation, indicating that nonclimatic factors are also important for the Sahelian vegetation dynamics....
Lancelot, Renaud; Lesnoff, Matthieu
2016-01-01
Background Peste des petits ruminants (PPR) is an acute infectious viral disease affecting domestic small ruminants (sheep and goats) and some wild ruminant species in Africa, the Middle East and Asia. A global PPR control strategy based on mass vaccination—in regions where PPR is endemic—was recently designed and launched by international organizations. Sahelian Africa is one of the most challenging endemic regions for PPR control. Indeed, strong seasonal and annual variations in mating, mortality and offtake rates result in a complex population dynamics which might in turn alter the population post-vaccination immunity rate (PIR), and thus be important to consider for the implementation of vaccination campaigns. Methods In a context of preventive vaccination in epidemiological units without PPR virus transmission, we developed a predictive, dynamic model based on a seasonal matrix population model to simulate PIR dynamics. This model was mostly calibrated with demographic and epidemiological parameters estimated from a long-term follow-up survey of small ruminant herds. We used it to simulate the PIR dynamics following a single PPR vaccination campaign in a Sahelian sheep population, and to assess the effects of (i) changes in offtake rate related to the Tabaski (a Muslim feast following the lunar calendar), and (ii) the date of implementation of the vaccination campaigns. Results The persistence of PIR was not influenced by the Tabaski date. Decreasing the vaccination coverage from 100 to 80% had limited effects on PIR. However, lower vaccination coverage did not provide sufficient immunity rates (PIR < 70%). As a trade-off between model predictions and other considerations like animal physiological status, and suitability for livestock farmers, we would suggest to implement vaccination campaigns in September-October. This model is a first step towards better decision support for animal health authorities. It might be adapted to other species, livestock
Hammami, Pachka; Lancelot, Renaud; Lesnoff, Matthieu
2016-01-01
Peste des petits ruminants (PPR) is an acute infectious viral disease affecting domestic small ruminants (sheep and goats) and some wild ruminant species in Africa, the Middle East and Asia. A global PPR control strategy based on mass vaccination-in regions where PPR is endemic-was recently designed and launched by international organizations. Sahelian Africa is one of the most challenging endemic regions for PPR control. Indeed, strong seasonal and annual variations in mating, mortality and offtake rates result in a complex population dynamics which might in turn alter the population post-vaccination immunity rate (PIR), and thus be important to consider for the implementation of vaccination campaigns. In a context of preventive vaccination in epidemiological units without PPR virus transmission, we developed a predictive, dynamic model based on a seasonal matrix population model to simulate PIR dynamics. This model was mostly calibrated with demographic and epidemiological parameters estimated from a long-term follow-up survey of small ruminant herds. We used it to simulate the PIR dynamics following a single PPR vaccination campaign in a Sahelian sheep population, and to assess the effects of (i) changes in offtake rate related to the Tabaski (a Muslim feast following the lunar calendar), and (ii) the date of implementation of the vaccination campaigns. The persistence of PIR was not influenced by the Tabaski date. Decreasing the vaccination coverage from 100 to 80% had limited effects on PIR. However, lower vaccination coverage did not provide sufficient immunity rates (PIR < 70%). As a trade-off between model predictions and other considerations like animal physiological status, and suitability for livestock farmers, we would suggest to implement vaccination campaigns in September-October. This model is a first step towards better decision support for animal health authorities. It might be adapted to other species, livestock farming systems or diseases.
Modelling rainfall erosion resulting from climate change
Kinnell, Peter
2016-04-01
It is well known that soil erosion leads to agricultural productivity decline and contributes to water quality decline. The current widely used models for determining soil erosion for management purposes in agriculture focus on long term (~20 years) average annual soil loss and are not well suited to determining variations that occur over short timespans and as a result of climate change. Soil loss resulting from rainfall erosion is directly dependent on the product of runoff and sediment concentration both of which are likely to be influenced by climate change. This presentation demonstrates the capacity of models like the USLE, USLE-M and WEPP to predict variations in runoff and erosion associated with rainfall events eroding bare fallow plots in the USA with a view to modelling rainfall erosion in areas subject to climate change.
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.
Application of the rainfall infiltration breakthrough (RIB) model for ...
African Journals Online (AJOL)
Application of the rainfall infiltration breakthrough (RIB) model for groundwater ... Correlation analysis between rainfall and observed WLF data at daily scale and ... data are more realistic than those for daily data, when using long time series.
Casse, Claire; Gosset, Marielle; Vischel, Théo; Quantin, Guillaume; Alkali Tanimoun, Bachir
2016-07-01
Since 1950, the Niger River basin has gone through three main climatic periods: a wet period (1950-1960), an extended drought (1970-1980) and since 1990 a recent partial recovery of annual rainfall. Hydrological changes co-occur with these rainfall fluctuations. In most of the basin, the rainfall deficit caused an enhanced discharge deficit, but in the Sahelian region the runoff increased despite the rainfall deficit. Since 2000 the Sahelian part of the Niger has been hit by an increase of flood hazards during the so-called red flood period. In Niamey city, the highest river levels and the longest flooded period ever recorded occurred in 2003, 2010, 2012 and 2013, with heavy casualties and property damage. The reasons for these changes, and the relative role of climate versus land use-land cover (LULC) changes are still debated and are investigated in this paper. The evolution of the Niger red flood in Niamey from 1950 to 2012 is analysed based on long-term records of rainfall (three data sets based on in situ and/or satellite data) and discharge, and a hydrological model. The model is first run with the present LULC conditions in order to analyse solely the effect of rainfall variability. The impact of LULC and drainage area modification is investigated in a second step. The simulations based on the current surface conditions are able to reproduce the observed trend in the red flood occurrence and intensity since the 1980s. This has been verified with three independent rainfall data sets and implies that rainfall variability is the main driver for the red flood intensification observed over the last 30 years. The simulation results since 1953 have revealed that LULC and drainage area changes need to be invoked to explain the changes over a 60-year period.
Stochastic modelling of daily rainfall sequences
Buishand, T.A.
1977-01-01
Rainfall series of different climatic regions were analysed with the aim of generating daily rainfall sequences. A survey of the data is given in I, 1. When analysing daily rainfall sequences one must be aware of the following points:
a. Seasonality. Because of seasonal variation
Adequacy of satellite derived rainfall data for stream flow modeling
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.
A rainfall simulation model for agricultural development in Bangladesh
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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.
The Role of Orography in Determining the Sahelian Climate
Semazzi, Frederick H. M.; Sun, Liqiang
1997-05-01
model results show a weaker orographic-induced rainfall dipole pattern across West Africa in response to the 1973 SST anomaly pattern. The net result is wetter conditions along the coastal region and rainfall deficits over the Sahelian zonal strip.
Development of Rainfall Model using Meteorological Data for Hydrological Use
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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.
Users guide for distributed routing rainfall-runoff model
Dawdy, D.R.; Schaake, John C.; Alley, William M.
1978-01-01
A computer program of a watershed model for routing urban flood discharges through a branched system of pipes or natural channels using rainfall as input has been developed and documented. The model combines soil-moisture-accounting and rainfall-excess components developed by Dawdy and others (1972) with the kinematic-wave routing method presented by Leclerc and Schaake (1973). (Woodard-USGS)
Borodina, Aleksandra; Fischer, Erich M.; Knutti, Reto
2017-07-01
Model projections of regional changes in heavy rainfall are uncertain. On timescales of few decades, internal variability plays an important role and therefore poses a challenge to detect robust model response in heavy rainfall to rising temperatures. We use spatial aggregation to reduce the major role of internal variability and evaluate the heavy rainfall response to warming temperatures with observations. We show that in the regions with high rainfall intensity and for which gridded observations exist, most of the models underestimate the historical scaling of heavy rainfall and the land fraction with significant positive heavy rainfall scalings during the historical period. The historical behavior is correlated with the projected heavy rainfall intensification across models allowing to apply an observational constraint, i.e., to calibrate multimodel ensembles with observations in order to narrow the range of projections. The constraint suggests a substantially stronger intensification of future heavy rainfall than the multimodel mean.
Inverse hydrological modelling of spatio-temporal rainfall patterns
Grundmann, Jens; Hörning, Sebastian; Bárdossy, András
2016-04-01
Distributed hydrological models are commonly used for simulating the non-linear response of a watershed to rainfall events for addressing different hydrological properties of the landscape. Such models are driven by spatial rainfall patterns for consecutive time steps, which are normally generated from point measurements using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall patterns especially in data scarce regions with poorly gauged catchments or for highly dynamic, small scaled rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties are associated with poorly identified spatio-temporal rainfall distribution in distributed rainfall-runoff-modelling since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves are underestimated. For addressing these challenges a novel methodology for inverse hydrological modelling is proposed using a Markov-Chain-Monte-Carlo framework. Thereby, potential candidates of spatio-temporal rainfall patterns are generated and selected according their ability to reproduce the observed surface runoff at the catchment outlet for a given transfer function in a best way. The Methodology combines the concept of random mixing of random spatial fields with a grid-based spatial distributed rainfall runoff model. The conditional target rainfall field is obtained as a linear combination of unconditional spatial random fields. The corresponding weights of the linear combination are selected such that the spatial variability of the rainfall amounts as well as the actual observed rainfall values are reproduced. The functionality of the methodology is demonstrated on a synthetic example. Thereby, the known spatio-temporal distribution of rainfall is reproduced for a given number of point observations of rainfall and the integral catchment response at the catchment outlet for a synthetic catchment
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.
Dynamic Hydrological Modeling in Drylands with TRMM Based Rainfall
Directory of Open Access Journals (Sweden)
Elena Tarnavsky
2013-12-01
Full Text Available This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR. Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.
Institute of Scientific and Technical Information of China (English)
ZHOU Yushu; CUI Chunguang
2011-01-01
The surface rainfall processes associated with the torrential rainfall event over Hubei,China,during July 2007 were investigated using a two-dimensional cloud-resolving model.The model integrated the large-scale vertical velocity and zonal wind data from National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) for 5 days.The time and model domain mean surface rain rate was used to identify the onset,mature,and decay periods of rainfall.During the onset period,the descending motion data imposed in the lower troposphere led to a large contribution of stratiform rainfall to the model domain mean surface rainfall.The local atmospheric drying and transport of rain from convective regions mainly contributes to the stratiform rainfall.During the mature periods,the ascending motion data integrated into the model was so strong that water vapor convergence was the dominant process for both convective and stratiform rainfall.Both convective and stratiform rainfalls made important contributions to the model domain mean surface rainfall. During the decay period,descending motion data input into the model prevailed,making stratiform rainfall dominant.Stratiform rainfall was mainly caused by the water vapor convergence over raining stratiform regions.
Ruelland, D.; Ardoin-Bardin, S.; Collet, L.; Roucou, P.
2012-03-01
SummaryThis paper assesses the future variability of water resources in the short, medium and long terms over a large Sudano-Sahelian catchment in West Africa. Flow simulations were performed with a daily conceptual model. A period of nearly 50 years (1952-2000) was chosen to capture long-term hydro-climatic variability. Calibration and validation were performed on the basis of a multi-objective function that aggregates a variety of goodness-of-fit indices. The climate models HadCM3 and MPI-M under SRES-A2 were used to provide future climate scenarios over the catchment. Outputs from these models were used to generate daily rainfall and temperature series for the 21st century according to: (i) the unbias and delta methods application and (ii) spatial and temporal downscaling. A temperature-based formula was used to calculate present and future potential evapotranspiration (PET). The daily rainfall and PET series were introduced into the calibrated and validated hydrological model to simulate future discharge. The model correctly reproduces the observed discharge at the basin outlet. The Nash-Sutcliffe efficiency criterion is over 89% for both calibration and validation periods, and the volume error between simulation and observation is close to null for the overall considered period. With regard to future climate, the results show clear trends of reduced rainfall over the catchment. This rainfall deficit, together with a continuing increase in potential evapotranspiration, suggests that runoff from the basin could be substantially reduced, especially in the long term (60-65%), compared to the 1961-1990 reference period. As a result, the long-term hydrological simulations show that the catchment discharge could decrease to the same levels as those observed during the severe drought of the 1980s.
Stochastic modeling of hourly rainfall times series in Campania (Italy)
Giorgio, M.; Greco, R.
2009-04-01
Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil
A time fractional model to represent rainfall process
Directory of Open Access Journals (Sweden)
Jacques GOLDER
2014-01-01
Full Text Available This paper deals with a stochastic representation of the rainfall process. The analysis of a rainfall time series shows that cumulative representation of a rainfall time series can be modeled as a non-Gaussian random walk with a log-normal jump distribution and a time-waiting distribution following a tempered α-stable probability law. Based on the random walk model, a fractional Fokker-Planck equation (FFPE with tempered α-stable waiting times was obtained. Through the comparison of observed data and simulated results from the random walk model and FFPE model with tempered α-stable waiting times, it can be concluded that the behavior of the rainfall process is globally reproduced, and the FFPE model with tempered α-stable waiting times is more efficient in reproducing the observed behavior.
Investigation on rainfall extremes events trough a geoadditive model
Bocci, C.; Caporali, E.; Petrucci, A.; Rossi, G.
2012-04-01
Rainfall can be considered a very important variable, and rainfall extreme events analysis of great concern for the enormous impacts that they may have on everyday life particularly when related to intense rainfalls and floods, and hydraulic risk management. On the catchment area of Arno River in Tuscany, Central Italy, a geoadditive mixed model of rainfall extremes is developed. Most of the territory of Arno River has suffered in the past of many severe hydro-geological events, with high levels of risk due to the vulnerability of a unique artistic and cultural heritage. The area has a complex topography that greatly influences the precipitation regime. The dataset is composed by the time series of the annual maxima of daily rainfall recorded in about 400 rain gauges, spatially distributed over the catchment area of about 8.800 km2. The record period covers mainly the second half of 20th century. The rainfall observations are assumed to follow generalized extreme value distributions whose locations are spatially dependent and where the dependence is captured using a geoadditive model. In particular, since rainfall has a natural spatial domain and a significant spatial variability, a spatial hierarchical model for extremes is used. The spatial hierarchical models, in fact, take into account data from all locations, borrowing strength from neighbouring locations when they estimate parameters and are of great interest when small set of data is available, as in the case of rainfall extreme values. Together with rain gauges location variables further physiographic variables are investigated as explanation variables. The implemented geoadditive mixed model of spatially referenced time series of rainfall extreme values, is able to capture the spatial dynamics of the rainfall extreme phenomenon. Since the model shows evidence of a spatial trend in the rainfall extreme dynamic, the temporal dynamic and the time influence can be also taken into account. The implemented
Kessler, J.J.; Breman, H.
1991-01-01
Primary production is limited by water availability in the N Sahelian zone only; elsewhere in the region nutrient availablility is critical. Woody species influence the water balance via rainfall interception, the influence on evapotranspiration and the influence on water infiltration. The ultimate
A rainfall-based warning model for shallow landslides
Zeng, Yi-Chao; Wang, Ji-Shang; Jan, Chyan-Deng; Yin, Hsiao-Yuan; Lo, Wen-Chun
2016-04-01
According to the statistical data of past rainfall events, the climate has changed in recent decades. Rainfall patterns have presented a more concentrated, high-intensity and long-duration trend in Taiwan. The most representative event is Typhoon Morakot which induced a total of 67 enormous landslides by the extreme amount of rain during August 7 to 10 in 2009 and resulted in the heaviest casualties in southern Taiwan. In addition, the nature of vulnerability such as steep mountains and rushing rivers, fragile geology and loose surface soil results in more severe sediment-relative disasters, in which shallow landslides are widespread hazards in mountainous regions. This research aims to develop and evaluate a model for predicting shallow landslides triggered by rainfall in mountainous area. Considering the feasibility of large-scale application and practical operation, the statistical techniques is adopted to form the landslide model based on abundant historical rainfall data and landslide events. The 16 landslide inventory maps and 15 variation results by comparing satellite images taken before and after the rainfall event were interpreted and delineated since 2004 to 2011. Logit model is utilized for interpreting the relationship between rainfall characteristics and landslide events delineated from satellite. Based on the analysis results of logistic regression, the rainfall factors that are highly related to shallow landslide occurrence are selected which are 3 hours rainfall intensity I3 (mm/hr) and the effective cumulative precipitation Rt (mm) including accumulated rainfall at time t and antecedent rainfall. A landslide rainfall triggering index (LRTI) proposed for assessing the occurrence potential of shallow landslides is defined as the product of I3 and Rt. A form of probability of shallow landslide triggered threshold is proposed to offer a measure of the likelihood of landslide occurrence. Two major critical lines which represent the lower and upper
Analysis of rainfall seasonality from observations and climate models
Pascale, Salvatore; Feng, Xue; Porporato, Amilcare; Hasson, Shabeh-ul
2014-01-01
Precipitation seasonality of observational datasets and CMIP5 historical simulations are analyzed using novel quantitative measures based on information theory. Two new indicators, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere-ocean general circulation models. The RE provides a measure of how peaked the shape of the annual rainfall curve is whereas the DSI quantifies the intensity of the rainfall during the wet season. The global monsoon regions feature the largest values of the DSI. For precipitation regimes featuring one maximum in the monthly rain distribution the RE is related to the duration of the wet season. We show that the RE and the DSI are measures of rainfall seasonality fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercompari...
Distributed modelling of shallow landslides triggered by intense rainfall
Directory of Open Access Journals (Sweden)
G. B. Crosta
2003-01-01
Full Text Available Hazard assessment of shallow landslides represents an important aspect of land management in mountainous areas. Among all the methods proposed in the literature, physically based methods are the only ones that explicitly includes the dynamic factors that control landslide triggering (rainfall pattern, land-use. For this reason, they allow forecasting both the temporal and the spatial distribution of shallow landslides. Physically based methods for shallow landslides are based on the coupling of the infinite slope stability analysis with hydrological models. Three different grid-based distributed hydrological models are presented in this paper: a steady state model, a transient "piston-flow" wetting front model, and a transient diffusive model. A comparative test of these models was performed to simulate landslide occurred during a rainfall event (27–28 June 1997 that triggered hundreds of shallow landslides within Lecco province (central Southern Alps, Italy. In order to test the potential for a completely distributed model for rainfall-triggered landslides, radar detected rainfall intensity has been used. A new procedure for quantitative evaluation of distributed model performance is presented and used in this paper. The diffusive model results in the best model for the simulation of shallow landslide triggering after a rainfall event like the one that we have analysed. Finally, radar data available for the June 1997 event permitted greatly improving the simulation. In particular, radar data allowed to explain the non-uniform distribution of landslides within the study area.
Toward an environmental history of Sudano-Sahelian landscapes
DEFF Research Database (Denmark)
Wardell, David Andrew
2005-01-01
Africa; Northern Territories of the Gold Coast Colony; colonial history; environmental history; empire forestry; Sudano-Sahelian landscapes......Africa; Northern Territories of the Gold Coast Colony; colonial history; environmental history; empire forestry; Sudano-Sahelian landscapes...
Marketing of Sahelian Goats in North -Eastern Nigeria: Experience ...
African Journals Online (AJOL)
The study evaluated Sahelian goat marketing in Northeastern Nigeria, ... of the respondents as well as the channel and problem of Sahelian goat market. ... study, it was recommended that government should put in place modern goat market ...
A new approach to modeling tree rainfall interception
Xiao, Qingfu; McPherson, E. Gregory; Ustin, Susan L.; Grismer, Mark E.
2000-12-01
A three-dimensional physically based stochastic model was developed to describe canopy rainfall interception processes at desired spatial and temporal resolutions. Such model development is important to understand these processes because forest canopy interception may exceed 59% of annual precipitation in old growth trees. The model describes the interception process from a single leaf, to a branch segment, and then up to the individual tree level. It takes into account rainfall, meteorology, and canopy architecture factors as explicit variables. Leaf and stem surface roughness, architecture, and geometric shape control both leaf drip and stemflow. Model predictions were evaluated using actual interception data collected for two mature open grown trees, a 9-year-old broadleaf deciduous pear tree (Pyrus calleryana "Bradford" or Callery pear) and an 8-year-old broadleaf evergreen oak tree (Quercus suber or cork oak). When simulating 18 rainfall events for the oak tree and 16 rainfall events for the pear tree, the model over estimated interception loss by 4.5% and 3.0%, respectively, while stemflow was under estimated by 0.8% and 3.3%, and throughfall was under estimated by 3.7% for the oak tree and over estimated by 0.3% for the pear tree. A model sensitivity analysis indicates that canopy surface storage capacity had the greatest influence on interception, and interception losses were sensitive to leaf and stem surface area indices. Among rainfall factors, interception losses relative to gross precipitation were most sensitive to rainfall amount. Rainfall incident angle had a significant effect on total precipitation intercepting the projected surface area. Stemflow was sensitive to stem segment and leaf zenith angle distributions. Enhanced understanding of interception loss dynamics should lead to improved urban forest ecosystem management.
Modelling Ecuador's rainfall distribution according to geographical characteristics.
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
Evaluation of Rainfall-Runoff Models for Mediterranean Subcatchments
Cilek, A.; Berberoglu, S.; Donmez, C.
2016-06-01
The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km2. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.
EVALUATION OF RAINFALL-RUNOFF MODELS FOR MEDITERRANEAN SUBCATCHMENTS
Directory of Open Access Journals (Sweden)
A. Cilek
2016-06-01
Full Text Available The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA, a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km2. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.
Fuzzy committees of specialised rainfall-runoff models: further enhancements
Kayastha, N.; Ye, J.; Fenicia, F.; Solomatine, D.P.
2013-01-01
Often a single hydrological model cannot capture the details of a complex rainfall-runoff relationship, and a possibility here is building specialised models to be responsible for a particular aspect of this relationship and combining them forming a committee model. This study extends earlier work o
Markov modulated Poisson process models incorporating covariates for rainfall intensity.
Thayakaran, R; Ramesh, N I
2013-01-01
Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.
Directory of Open Access Journals (Sweden)
Entin Hidayah
2011-02-01
Full Text Available Disaggregation of hourly rainfall data is very important to fulfil the input of continual rainfall-runoff model, when the availability of automatic rainfall records are limited. Continual rainfall-runoff modeling requires rainfall data in form of series of hourly. Such specification can be obtained by temporal disaggregation in single site. The paper attempts to generate single-site rainfall model based upon time series (AR1 model by adjusting and establishing dummy procedure. Estimated with Bayesian Markov Chain Monte Carlo (MCMC the objective variable is hourly rainfall depth. Performance of model has been evaluated by comparison of history data and model prediction. The result shows that the model has a good performance for dry interval periods. The performance of the model good represented by smaller number of MAE by 0.21 respectively.
Applicability of open rainfall data to event-scale urban rainfall-runoff modelling
Niemi, Tero J.; Warsta, Lassi; Taka, Maija; Hickman, Brandon; Pulkkinen, Seppo; Krebs, Gerald; Moisseev, Dmitri N.; Koivusalo, Harri; Kokkonen, Teemu
2017-04-01
Rainfall-runoff simulations in urban environments require meteorological input data with high temporal and spatial resolutions. The availability of precipitation data is constantly increasing due to the shift towards more open data sharing. However, the applicability of such data for urban runoff assessments is often unknown. Here, the feasibility of Finnish Meteorological Institute's open rain gauge and open weather radar data as input sources was studied by conducting Storm Water Management Model simulations at a very small (33.5 ha) urban catchment in Helsinki, Finland. In addition to the open data sources, data were also available from two research gauges, one of them located on-site, and from a research radar. The results confirmed the importance of local precipitation measurements for urban rainfall-runoff simulations, implying the suitability of open gauge data to be largely dictated by the gauge's distance from the catchment. Performance of open radar data with 5 min and 1 km2 resolution was acceptable in terms of runoff reproduction, albeit peak flows were constantly and flow volumes often underestimated. Gauge adjustment and advection interpolation were found to improve the quality of the radar data, and at least gauge adjustment should be performed when open radar data are used. Finally, utilizing dual-polarization capabilities of radars has a potential to improve rainfall estimates for high intensity storms although more research is still needed.
Chang, Tak Kwin; Talei, Amin; Alaghmand, Sina; Ooi, Melanie Po-Leen
2017-02-01
Input selection for data-driven rainfall-runoff models is an important task as these models find the relationship between rainfall and runoff by direct mapping of inputs to output. In this study, two different input selection methods were used: cross-correlation analysis (CCA), and a combination of mutual information and cross-correlation analyses (MICCA). Selected inputs were used to develop adaptive network-based fuzzy inference system (ANFIS) in Sungai Kayu Ara basin, Selangor, Malaysia. The study catchment has 10 rainfall stations and one discharge station located at the outlet of the catchment. A total of 24 rainfall-runoff events (10-min interval) from 1996 to 2004 were selected from which 18 events were used for training and the remaining 6 were reserved for validating (testing) the models. The results of ANFIS models then were compared against the ones obtained by conceptual model HEC-HMS. The CCA and MICCA methods selected the rainfall inputs only from 2 (stations 1 and 5) and 3 (stations 1, 3, and 5) rainfall stations, respectively. ANFIS model developed based on MICCA inputs (ANFIS-MICCA) performed slightly better than the one developed based on CCA inputs (ANFIS-CCA). ANFIS-CCA and ANFIS-MICCA were able to perform comparably to HEC-HMS model where rainfall data of all 10 stations had been used; however, in peak estimation, ANFIS-MICCA was the best model. The sensitivity analysis on HEC-HMS was conducted by recalibrating the model by using the same selected rainfall stations for ANFIS. It was concluded that HEC-HMS model performance deteriorates if the number of rainfall stations reduces. In general, ANFIS was found to be a reliable alternative for HEC-HMS in cases whereby not all rainfall stations are functioning. This study showed that the selected stations have received the highest total rain and rainfall intensity (stations 3 and 5). Moreover, the contributing rainfall stations selected by CCA and MICCA were found to be located near the outlet of
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 km^{2} 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 R^{2} values increasing from 0.61 to 0.72.
Physically based modelling of rainfall-runoff processes
Diermanse, F.L.M.
2001-01-01
This PhD. research was set up to investigate the use of rainfall-runoff models for simulation of high water events in hillslope areas. First, dominant parameters for runoff production during high water events have been identified. Subsequently, the influence of antecedent conditions on runoff percen
Interpolation of daily rainfall using spatiotemporal models and clustering
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.
WRF model performance under flash-flood associated rainfall
Mejia-Estrada, Iskra; Bates, Paul; Ángel Rico-Ramírez, Miguel
2017-04-01
Understanding the natural processes that precede the occurrence of flash floods is crucial to improve the future flood projections in a changing climate. Using numerical weather prediction tools allows to determine one of the triggering conditions for these particularly dangerous events, difficult to forecast due to their short lead-time. However, simulating the spatial and temporal evolution of the rainfall that leads to a rapid rise in river levels requires determining the best model configuration without compromising the computational efficiency. The current research involves the results of the first part of a cascade modeling approach, where the Weather Research and Forecasting (WRF) model is used to simulate the heavy rainfall in the east of the UK in June 2012 when stationary thunderstorms caused 2-hour accumulated values to match those expected in the whole month of June over the city of Newcastle. The optimum model set-up was obtained after extensive testing regarding physics parameterizations, spin-up times, datasets used as initial conditions and model resolution and nesting, hence determining its sensitivity to reproduce localised events of short duration. The outputs were qualitatively and quantitatively assessed using information from the national weather radar network as well as interpolated rainfall values from gauges, respectively. Statistical and skill score values show that the model is able to produce reliable accumulated precipitation values while explicitly solving the atmospheric equations in high resolution domains as long as several hydrometeors are considered with a spin-up time that allows the model to assimilate the initial conditions without going too far back in time from the event of interest. The results from the WRF model will serve as input to run a semi-distributed hydrological model to determine the rainfall-runoff relationship within an uncertainty assessment framework that will allow evaluating the implications of assumptions at
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...
Generalized linear model for estimation of missing daily rainfall data
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.
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.
Random Modeling of Daily Rainfall and Runoff Using a Seasonal Model and Wavelet Denoising
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Chien-ming Chou
2014-01-01
Full Text Available Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.
Influence of rainfall observation network on model calibration and application
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A. Bárdossy
2008-01-01
Full Text Available The objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. A meso-scale catchment located in southwest Germany has been selected for this study. First, the semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. The performance of the hydrological model is analyzed as a function of the raingauge density. Secondly, the calibrated model is validated using interpolated precipitation from the same raingauge density used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. Lastly, the effect of missing rainfall data is investigated by using a multiple linear regression approach for filling in the missing measurements. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the above described three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need re-calibration of the model parameters, specifically model calibrated on relatively sparse precipitation information might perform well on dense precipitation information while model calibrated on dense precipitation information fails on sparse precipitation information. Also, the model calibrated with the complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as
Changes in the interannual SST-forced signals on West African rainfall. AGCM intercomparison
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Mohino, Elsa [LOCEAN/IPSL, Universite Pierre et Marie Curie, Paris Cedex 05 (France); Universidad de Sevilla, Sevilla (Spain); Rodriguez-Fonseca, Belen [Universidad Complutense de Madrid, Dpto. Geofisica y Meteorologia, Madrid (Spain); Instituto de Geociencias (CSIC-UCM), Madrid (Spain); Losada, Teresa [Universidad Complutense de Madrid, Dpto. Geofisica y Meteorologia, Madrid (Spain); Gervois, Sebastien [LOCEAN/IPSL, Universite Pierre et Marie Curie, Paris Cedex 05 (France); Janicot, Serge [LOCEAN/IPSL, IRD, Universite Pierre et Marie Curie, Paris (France); Bader, Juergen [Bjerknes Centre for Climate Research, Bergen (Norway); Ruti, Paolo [Progetto Speciale Clima Globale, Ente Nazionale per le Nuove Tecnologie, l' Energia e l' Ambiente, Rome (Italy); Chauvin, Fabrice [GAME/CNRM, Meteo-France/CNRS, Toulouse (France)
2011-11-15
Rainfall over West Africa shows strong interannual variability related to changes in Sea Surface Temperature (SST). Nevertheless, this relationship seem to be non-stationary. A particular turning point is the decade of the 1970s, which witnessed a number of changes in the climatic system, including the climate shift of the late 1970s. The first aim of this study is to explore the change in the interannual variability of West African rainfall after this shift. The analysis indicates that the dipolar features of the rainfall variability over this region, related to changes in the Atlantic SST, disappear after this period. Also, the Pacific SST variability has a higher correlation with Guinean rainfall in the recent period. The results suggest that the current relationship between the Atlantic and Pacific El Nino phenomena is the principal responsible for these changes. A fundamental goal of climate research is the development of models simulating a realistic current climate. For this reason, the second aim of this work is to test the performance of Atmospheric General Circulation models in simulating rainfall variability over West Africa. The models have been run with observed SSTs for the common period 1957-1998 as part of an intercomparison exercise. The results show that the models are able to reproduce Guinean interannual variability, which is strongly related to SST variability in the Equatorial Atlantic. Nevertheless, problems in the simulation of the Sahelian interannual variability appear: not all models are able to reproduce the observed negative link between rainfall over the Sahel and El Nino-like anomalies in the Pacific, neither the positive correlation between Mediterranean SSTs and Sahelian rainfall. (orig.)
Multivariate Logistic Model to estimate Effective Rainfall for an Event
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.
Warning Model for Shallow Landslides Induced by Extreme Rainfall
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Lien-Kwei Chien
2015-08-01
Full Text Available In this study, the geophysical properties of the landslide-prone catchment of the Gaoping River in Taiwan were investigated using zones based on landslide history in conjunction with landslide analysis using a deterministic approach based on the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability model. Typhoon Morakot in 2009 was selected as a simulation scenario to calibrate the combination of geophysical parameters in each zone before analyzing changes in the factor of safety (FS. Considering the amount of response time required for typhoons, suitable FS thresholds for landslide warnings are proposed for each town in the catchment area. Typhoon Fanapi of 2010 was used as a test scenario to verify the applicability of the FS as well as the efficacy of the cumulative rainfall thresholds derived in this study. Finally, the amount of response time provided by the FS thresholds in cases of yellow and red alerts was determined. All five of the landslide events reported by the Soil and Water Conservation Bureau were listed among the unstable sites identified in the proposed model, thereby demonstrating its effectiveness and accuracy in determining unstable areas and areas that require evacuation. These cumulative rainfall thresholds provide a valuable reference to guide disaster prevention authorities in the issuance of yellow and red alerts with the ability to reduce losses and save lives.
Modeling the Distribution of Rainfall Intensity using Hourly Data
Salisu Dan'azumi; Supiah Shamsudin; Azmi Aris
2010-01-01
Problem statement: Design of storm water best management practices to control runoff and water pollution can be achieved if a prior knowledge of the distribution of rainfall characteristics is known. Rainfall intensity, particularly in tropical climate, plays a major role in the design of runoff conveyance and erosion control systems. This study is aimed to explore the statistical distribution of rainfall intensity for Peninsular Malaysia using hourly rainfall data. Approach: Hourly rainfall ...
Modelling rainfall amounts using mixed-gamma model for Kuantan district
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.
Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony; Parana Manage, Nadeeka
2016-04-01
Stochastic simulation of rainfall is often required in the simulation of streamflow and reservoir levels for water security assessment. As reservoir water levels generally vary on monthly to multi-year timescales, it is important that these rainfall series accurately simulate the multi-year variability. However, the underestimation of multi-year variability is a well-known issue in daily rainfall simulation. Focusing on this issue, we developed a hierarchical Markov Chain (MC) model in a traditional two-part MC-Gamma Distribution modelling structure, but with a new parameterization technique. We used two parameters of first-order MC process (transition probabilities of wet-to-wet and dry-to-dry days) to simulate the wet and dry days, and two parameters of Gamma distribution (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. We found that use of deterministic Gamma parameter values results in underestimation of multi-year variability of rainfall depths. Therefore, we calculated the Gamma parameters for each month of each year from the observed data. Then, for each month, we fitted a multi-variate normal distribution to the calculated Gamma parameter values. In the model, we stochastically sampled these two Gamma parameters from the multi-variate normal distribution for each month of each year and used them to generate rainfall depth in wet days using the Gamma distribution. In another study, Mehrotra and Sharma (2007) proposed a semi-parametric Markov model. They also used a first-order MC process for rainfall occurrence simulation. But, the MC parameters were modified by using an additional factor to incorporate the multi-year variability. Generally, the additional factor is analytically derived from the rainfall over a pre-specified past periods (e.g. last 30, 180, or 360 days). They used a non-parametric kernel density process to simulate the wet day rainfall depths. In this study, we have compared the performance of our
A statistical downscaling model for summer rainfall over Pakistan
Kazmi, Dildar Hussain; Li, Jianping; Ruan, Chengqing; Zhao, Sen; Li, Yanjie
2016-10-01
A statistical approach is utilized to construct an interannual model for summer (July-August) rainfall over the western parts of South Asian Monsoon. Observed monthly rainfall data for selected stations of Pakistan for the last 55 years (1960-2014) is taken as predictand. Recommended climate indices along with the oceanic and atmospheric data on global scales, for the period April-June are employed as predictors. First 40 years data has been taken as training period and the rest as validation period. Cross-validation stepwise regression approach adopted to select the robust predictors. Upper tropospheric zonal wind at 200 hPa over the northeastern Atlantic is finally selected as the best predictor for interannual model. Besides, the next possible candidate `geopotential height at upper troposphere' is taken as the indirect predictor for being a source of energy transportation from core region (northeast Atlantic/western Europe) to the study area. The model performed well for both the training as well as validation period with correlation coefficient of 0.71 and tolerable root mean square errors. Cross-validation of the model has been processed by incorporating JRA-55 data for potential predictors in addition to NCEP and fragmentation of study period to five non-overlapping test samples. Subsequently, to verify the outcome of the model on physical grounds, observational analyses as well as the model simulations are incorporated. It is revealed that originating from the jet exit region through large vorticity gradients, zonally dominating waves may transport energy and momentum to the downstream areas of west-central Asia, that ultimately affect interannual variability of the specific rainfall. It has been detected that both the circumglobal teleconnection and Rossby wave propagation play vital roles in modulating the proposed mechanism.
Statistical Inference for Point Process Models of Rainfall
Smith, James A.; Karr, Alan F.
1985-01-01
In this paper we develop maximum likelihood procedures for parameter estimation and model selection that apply to a large class of point process models that have been used to model rainfall occurrences, including Cox processes, Neyman-Scott processes, and renewal processes. The statistical inference procedures are based on the stochastic intensity λ(t) = lims→0,s>0 (1/s)E[N(t + s) - N(t)|N(u), u process is shown to have a simple expression in terms of the stochastic intensity. The main result of this paper is a recursive procedure for computing stochastic intensities; the procedure is applicable to a broad class of point process models, including renewal Cox process with Markovian intensity processes and an important class of Neyman-Scott processes. The model selection procedure we propose, which is based on likelihood ratios, allows direct comparison of two classes of point processes to determine which provides a better model for a given data set. The estimation and model selection procedures are applied to two data sets of simulated Cox process arrivals and a data set of daily rainfall occurrences in the Potomac River basin.
Regionalized rainfall-runoff model to estimate low flow indices
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
An analogue conceptual rainfall-runoff model for educational purposes
Herrnegger, Mathew; Riedl, Michael; Schulz, Karsten
2016-04-01
Conceptual rainfall-runoff models, in which runoff processes are modelled with a series of connected linear and non-linear reservoirs, remain widely applied tools in science and practice. Additionally, the concept is appreciated in teaching due to its somewhat simplicity in explaining and exploring hydrological processes of catchments. However, when a series of reservoirs are used, the model system becomes highly parametrized and complex and the traceability of the model results becomes more difficult to explain to an audience not accustomed to numerical modelling. Since normally the simulations are performed with a not visible digital code, the results are also not easily comprehensible. This contribution therefore presents a liquid analogue model, in which a conceptual rainfall-runoff model is reproduced by a physical model. This consists of different acrylic glass containers representing different storage components within a catchment, e.g. soil water or groundwater storage. The containers are equipped and connected with pipes, in which water movement represents different flow processes, e.g. surface runoff, percolation or base flow. Water from a storage container is pumped to the upper part of the model and represents effective rainfall input. The water then flows by gravity through the different pipes and storages. Valves are used for controlling the flows within the analogue model, comparable to the parameterization procedure in numerical models. Additionally, an inexpensive microcontroller-based board and sensors are used to measure storage water levels, with online visualization of the states as time series data, building a bridge between the analogue and digital world. The ability to physically witness the different flows and water levels in the storages makes the analogue model attractive to the audience. Hands-on experiments can be performed with students, in which different scenarios or catchment types can be simulated, not only with the analogue but
Fitting rainfall interception models to forest ecosystems of Mexico
Návar, José
2017-05-01
Models that accurately predict forest interception are essential both for water balance studies and for assessing watershed responses to changes in land use and the long-term climate variability. This paper compares the performance of four rainfall interception models-the sparse Gash (1995), Rutter et al. (1975), Liu (1997) and two new models (NvMxa and NvMxb)-using data from four spatially extensive, structurally diverse forest ecosystems in Mexico. Ninety-eight case studies measuring interception in tropical dry (25), arid/semi-arid (29), temperate (26), and tropical montane cloud forests (18) were compiled and analyzed. Coefficients derived from raw data or published statistical relationships were used as model input to evaluate multi-storm forest interception at the case study scale. On average empirical data showed that, tropical montane cloud, temperate, arid/semi-arid and tropical dry forests intercepted 14%, 18%, 22% and 26% of total precipitation, respectively. The models performed well in predicting interception, with mean deviations between measured and modeled interception as a function of total precipitation (ME) generally 0.66. Model fitting precision was dependent on the forest ecosystem. Arid/semi-arid forests exhibited the smallest, while tropical montane cloud forest displayed the largest ME deviations. Improved agreement between measured and modeled data requires modification of in-storm evaporation rate in the Liu; the canopy storage in the sparse Gash model; and the throughfall coefficient in the Rutter and the NvMx models. This research concludes on recommending the wide application of rainfall interception models with some caution as they provide mixed results. The extensive forest interception data source, the fitting and testing of four models, the introduction of a new model, and the availability of coefficient values for all four forest ecosystems are an important source of information and a benchmark for future investigations in this
Modeling of the Monthly Rainfall-Runoff Process Through Regressions
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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.
Manz, Bastian Johann; Rodríguez, Juan Pablo; Maksimović, Cedo; McIntyre, Neil
2013-01-01
A key control on the response of an urban drainage model is how well the observed rainfall records represent the real rainfall variability. Particularly in urban catchments with fast response flow regimes, the selection of temporal resolution in rainfall data collection is critical. Furthermore, the impact of the rainfall variability on the model response is amplified for water quality estimates, as uncertainty in rainfall intensity affects both the rainfall-runoff and pollutant wash-off sub-models, thus compounding uncertainties. A modelling study was designed to investigate the impact of altering rainfall temporal resolution on the magnitude and behaviour of uncertainties associated with the hydrological modelling compared with water quality modelling. The case study was an 85-ha combined sewer sub-catchment in Bogotá (Colombia). Water quality estimates showed greater sensitivity to the inter-event variability in rainfall hyetograph characteristics than to changes in the rainfall input temporal resolution. Overall, uncertainties from the water quality model were two- to five-fold those of the hydrological model. However, owing to the intrinsic scarcity of observations in urban water quality modelling, total model output uncertainties, especially from the water quality model, were too large to make recommendations for particular model structures or parameter values with respect to rainfall temporal resolution.
Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization
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Chao Zhang
2015-01-01
Full Text Available Parameter optimization for the conceptual rainfall-runoff (CRR model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA global optimization method was used to calibrate the Xinanjiang (XAJ model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.
Towards a comprehensive physically-based rainfall-runoff model
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Z. Liu
2002-01-01
Full Text Available This paper introduces TOPKAPI (TOPographic Kinematic APproximation and Integration, a new physically-based distributed rainfall-runoff model deriving from the integration in space of the kinematic wave model. The TOPKAPI approach transforms the rainfall-runoff and runoff routing processes into three ‘structurally-similar’ non-linear reservoir differential equations describing different hydrological and hydraulic processes. The geometry of the catchment is described by a lattice of cells over which the equations are integrated to lead to a cascade of non-linear reservoirs. The parameter values of the TOPKAPI model are shown to be scale independent and obtainable from digital elevation maps, soil maps and vegetation or land use maps in terms of slope, soil permeability, roughness and topology. It can be shown, under simplifying assumptions, that the non-linear reservoirs aggregate into three reservoir cascades at the basin scale representing the soil, the surface and the drainage network, following the topographic and geomorphologic elements of the catchment, with parameter values which can be estimated directly from the small scale ones. The main advantage of this approach lies in its capability of being applied at increasing spatial scales without losing model and parameter physical interpretation. The model is foreseen to be suitable for land-use and climate change impact assessment; for extreme flood analysis, given the possibility of its extension to ungauged catchments; and last but not least as a promising tool for use with General Circulation Models (GCMs. To demonstrate the quality of the comprehensive distributed/lumped TOPKAPI approach, this paper presents a case study application to the Upper Reno river basin with an area of 1051 km2 based on a DEM grid scale of 200 m. In addition, a real-world case of applying the TOPKAPI model to the Arno river basin, with an area of 8135 km2 and using a DEM grid scale of 1000 m, for the
Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.
2012-12-01
Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.
Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India
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Vipul K. Dabhi
2014-01-01
Full Text Available An accurate prediction of rainfall is crucial for national economy and management of water resources. The variability of rainfall in both time and space makes the rainfall prediction a challenging task. The present work investigates the applicability of a hybrid wavelet-postfix-GP model for daily rainfall prediction of Anand region using meteorological variables. The wavelet analysis is used as a data preprocessing technique to remove the stochastic (noise component from the original time series of each meteorological variable. The Postfix-GP, a GP variant, and ANN are then employed to develop models for rainfall using newly generated subseries of meteorological variables. The developed models are then used for rainfall prediction. The out-of-sample prediction performance of Postfix-GP and ANN models is compared using statistical measures. The results are comparable and suggest that Postfix-GP could be explored as an alternative tool for rainfall prediction.
Coupling Radar Rainfall to Hydrological Models for Water Abstraction Management
Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; MacDonald, Ken
2015-04-01
The impacts of climate change and growing water use are likely to put considerable pressure on water resources and the environment. In the UK, a reform to surface water abstraction policy has recently been proposed which aims to increase the efficiency of using available water resources whilst minimising impacts on the aquatic environment. Key aspects to this reform include the consideration of dynamic rather than static abstraction licensing as well as introducing water trading concepts. Dynamic licensing will permit varying levels of abstraction dependent on environmental conditions (i.e. river flow and quality). The practical implementation of an effective dynamic abstraction strategy requires suitable flow forecasting techniques to inform abstraction asset management. Potentially the predicted availability of water resources within a catchment can be coupled to predicted demand and current storage to inform a cost effective water resource management strategy which minimises environmental impacts. The aim of this work is to use a historical analysis of UK case study catchment to compare potential water resource availability using modelled dynamic abstraction scenario informed by a flow forecasting model, against observed abstraction under a conventional abstraction regime. The work also demonstrates the impacts of modelling uncertainties on the accuracy of predicted water availability over range of forecast lead times. The study utilised a conceptual rainfall-runoff model PDM - Probability-Distributed Model developed by Centre for Ecology & Hydrology - set up in the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15 min resolution gauged flow data for calibration and validation. Data assimilation procedures are implemented to improve flow predictions using observed flow data. Uncertainties in the radar rainfall data used in the model are quantified using artificial statistical error model described by Gaussian distribution and
Leroux, L.
2015-12-01
Since the Sahelian population livelihood relies mainly on agropastoral activities, accurate information on biomass productivity dynamics and the underlying drivers are needed to manage a wide range of issues such as food security. This study aims to contribute to a better understanding of these drivers in rangeland and cropland, both at the Sahel and local scales (an agropastoral site in South-West Niger). At the Sahel scale, the MODIS Land Cover product was used to extract cropland and rangeland pixels. By analyzing MODIS NDVI trends together with TRMM3B43 annual rainfall (2000-2010), we developed a new classification scheme allowing to identify areas of persistent decline/improvement in biomass productivity and to separate rainfall-driven dynamics from other factors. The results showed an overall increase of productivity in the rangeland, and both an improvement and a degradation in the cropland. We found strong evidence that the increase in biomass productivity was generally linked to increasing rainfall, while the decrease could be attributed chiefly to other factors exclusively or to a combination of both climate- and human-induced factors (see the attached Figure). At the Niger site scale, biomass trends have been put in relation with a set of potential drivers via a RandomForest model, to define which were the explanatory factors of the observed trends. The factor set covered 5 categories: climate, natural constraints, demography, physical accessibility and land cover changes. We highlighted that tiger bushes areas were particularly prone to pressure due to overgrazing and overexploitation of wood, while positive trends were mainly observed near rivers and in fossil valleys where new agricultural practices might have been promoted. The approach developped here could help to delineate areas with decrease in crop and grassland production and thus to assess the vulnerability of the population, but also to target zones with good potential for planning long
Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo
2017-03-01
The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
2011-01-01
Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other ha...
From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution
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M. Herrnegger
2014-12-01
Full Text Available This paper presents a novel technique to calculate mean areal rainfall in a high temporal resolution of 60 min on the basis of an inverse conceptual rainfall–runoff model and runoff observations. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. The most reliable hydrological information available refers to runoff, which in the presented work is used as input for a rainfall–runoff model. Thereby a conceptual, HBV-type model is embedded in an iteration algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value that corresponds to the observation. To verify the existence, uniqueness and stability of the inverse rainfall, numerical experiments with synthetic hydrographs as inputs into the inverse model are carried out successfully. The application of the inverse model with runoff observations as driving input is performed for the Krems catchment (38.4 km2, situated in the northern Austrian Alpine foothills. Compared to station observations in the proximity of the catchment, the inverse rainfall sums and time series have a similar goodness of fit, as the independent INCA rainfall analysis of Austrian Central Institute for Meteorology and Geodynamics (ZAMG. Compared to observations, the inverse rainfall estimates show larger rainfall intensities. Numerical experiments show, that cold state conditions in the inverse model do not influence the inverse rainfall estimates, when considering an adequate spin-up time. The application of the inverse model is a feasible approach to obtain improved estimates of mean areal rainfall. These can be used to enhance interpolated rainfall fields, e.g. for the estimation of rainfall correction factors, the parameterisation of
Atmospheric nitrogen budget in Sahelian dry savannas
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C. Delon
2009-06-01
Full Text Available The atmospheric nitrogen budget depends on emission and deposition fluxes both as reduced and oxidized nitrogen compounds. In this study, a first attempt at estimating the Sahel nitrogen budget for the year 2006 is made, through measurements and simulations at three stations from the IDAF network situated in dry savanna ecosystems. Dry deposition fluxes are estimated from measurements of NO_{2}, HNO_{3} and NH_{3} gaseous concentrations, and wet deposition fluxes are calculated from NH_{4}^{+} and NO_{3}^{−} concentrations in samples of rain. Emission fluxes are estimated including biogenic emission of NO from soils (an Artificial Neural Network module has been inserted into the ISBA-SURFEX surface model, emission of NO_{x} and NH_{3} from domestic fires and biomass burning, and volatilization of NH_{3} from animal excreta.
This study uses original and unique data from remote and hardly-ever-explored regions. The monthly evolution of oxidized N compounds shows that deposition increases at the beginning of the rainy season because of large emissions of biogenic NO (pulse events. Emission of oxidized compounds is dominated by biogenic emission from soils (domestic fires and biomass burning account for 27% at the most, depending on the station, whereas emission of NH_{3} is dominated by the process of volatilization. Deposition fluxes are dominated by gaseous dry deposition processes (58% of the total, for both oxidized and reduced compounds. The average deposition flux in dry savanna ecosystems ranges from 8.6 to 10.9 kgN ha^{−1} yr^{−1}, with 30% attributed to oxidized compounds, and the other 70% attributed to NH_{x}. The average emission flux ranges from 7.8 to 9.7 kgN ha^{−1} yr^{−1}, dominated by NH_{3} volatilization (67% and biogenic emission from soils (24%. The annual budget is then
Atmospheric nitrogen budget in Sahelian dry savannas
Directory of Open Access Journals (Sweden)
C. Delon
2010-03-01
Full Text Available The atmospheric nitrogen budget depends on emission and deposition fluxes both as reduced and oxidized nitrogen compounds. In this study, a first attempt at estimating the Sahel nitrogen budget for the year 2006 is made, through measurements and simulations at three stations from the IDAF network situated in dry savanna ecosystems. Dry deposition fluxes are estimated from measurements of NO_{2}, HNO_{3} and NH_{3} gaseous concentrations and from simulated dry deposition velocities, and wet deposition fluxes are calculated from NH_{4}^{+} and NO_{3}^{−} concentrations in samples of rain. Emission fluxes are estimated including biogenic emission of NO from soils (an Artificial Neural Network module has been inserted into the ISBA-SURFEX surface model, emission of NO_{x} and NH_{3} from domestic fires and biomass burning, and volatilization of NH_{3} from animal excreta. Uncertainties are calculated for each contribution of the budget.
This study uses original and unique data from remote and hardly-ever-explored regions.The monthly evolution of oxidized N compounds shows that emission and deposition increase at the beginning of the rainy season because of large emissions of biogenic NO (pulse events. Emission of oxidized compounds is dominated by biogenic emission from soils (domestic fires and biomass burning of oxidized compounds account for 0 to 13% at the most at the annual scale, depending on the station, whereas emission of NH_{3} is dominated by the process of volatilization from soils. At the annual scale, the average gaseous dry deposition accounts for 47% of the total estimated deposition flux, for both oxidized and reduced compounds. The average estimated wet plus dry deposition flux in dry savanna ecosystems is 7.5±1.8 kgN ha^{−1} yr^{−1}, with approximately 30% attributed to oxidized compounds, and the rest attributed
Rainfall-runoff modeling for storm events in a coastal forest catchment using neural networks
Institute of Scientific and Technical Information of China (English)
WANG Yi; HE Bin
2008-01-01
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both temporal and spatial variabilities. In this article, a rainfall-runoff model using the artificial neural networks (ANN) is proposed for simulating the runoff in storm events. The study uses the data from a coastal forest catchmentlocated in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obtained by ANN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rainfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.
Duangdai, Eakkapong; Likasiri, Chulin
2017-03-01
In this work, 4 models for predicting rainfall amounts are investigated and compared using Northern Thailand's seasonal rainfall data for 1973-2008. Two models, global temperature, forest area and seasonal rainfall (TFR) and modified TFR based on a system of differential equations, give the relationships between global temperature, Northern Thailand's forest cover and seasonal rainfalls in the region. The other two models studied are time series and Autoregressive Moving Average (ARMA) models. All models are validated using the k-fold cross validation method with the resulting errors being 0.971233, 0.740891, 2.376415 and 2.430891 for time series, ARMA, TFR and modified TFR models, respectively. Under Business as Usual (BaU) scenario, seasonal rainfalls in Northern Thailand are projected through the year 2020 using all 4 models. TFR and modified TFR models are also used to further analyze how global temperature rise and government reforestation policy affect seasonal rainfalls in the region. Rainfall projections obtained via the two models are also compared with those from the International Panel on Climate Change (IPCC) under IS92a scenario. Results obtained through a mathematical model for global temperature, forest area and seasonal rainfall show that the higher the forest cover, the less fluctuation there is between rainy-season and summer rainfalls. Moreover, growth in forest cover also correlates with an increase in summer rainfalls. An investigation into the relationship between main crop productions and rainfalls in dry and rainy seasons indicates that if the rainy-season rainfall is high, that year's main-crop rice production will decrease but the second-crop rice, maize, sugarcane and soybean productions will increase in the following year.
Bell, V. A.; Carrington, D.S.; Moore, R J
2001-01-01
The purpose of the project “Comparison of Rainfall-Runoff Models for Flood Forecasting” is to provide guidance to the Environment Agency on the choice of rainfall-runoff model for use in different catchments for flood forecasting purposes. A literature review of models presented in the Part 1 Report recognised that whilst there is a plethora of “brand-name” models there is much similarity between many of them. A rather small set of model functions is common to many models and they differ in t...
Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble
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.
Tao, Wanghai; Wu, Junhu; Wang, Quanjiu
2017-03-01
Rainfall erosion is a major cause of inducing soil degradation, and rainfall patterns have a significant influence on the process of sediment yield and nutrient loss. The mathematical models developed in this study were used to simulate the sediment and nutrient loss in surface runoff. Four rainfall patterns, each with a different rainfall intensity variation, were applied during the simulated rainfall experiments. These patterns were designated as: uniform-type, increasing-type, increasing- decreasing -type and decreasing-type. The results revealed that changes in the rainfall intensity can have an appreciable impact on the process of runoff generation, but only a slight effect on the total amount of runoff generated. Variations in the rainfall intensity in a rainfall event not only had a significant effect on the process of sediment yield and nutrient loss, but also the total amount of sediment and nutrient produced, and early high rainfall intensity may lead to the most severe erosion and nutrient loss. In this study, the calculated data concur with the measured values. The model can be used to predict the process of surface runoff, sediment transport and nutrient loss associated with different rainfall patterns.
Defrance, Dimitri; Ramstein, Gilles; Charbit, Sylvie; Sultan, Benjamin; Dumas, Christophe; Vrac, Mathieu; Swingedouw, Didier; Gemenne, François; Alvarez-Solas, Jorge; Vanderlinden, Jean-Paul
2016-04-01
During the 20th century, Sahelian drought episodes like those between 1972 and 1982 showed the vulnerability of the Sahelian agro-ecosystem provoking significant intraregional southward human migrations, to or near the coast. According to the latest IPCC report, the Sahel could become increasingly impacted by climate change during the 21st century because of a lagged and shorter rainfall season having the potential to induce a drastic destabilization of the Sahelian agro-ecosystem and to heavily impact the population. Such effects could be further amplified by a net increase of the Sahelian population. Drastic climate changes over tropical areas also occurred in the past: weakening of the West African Monsoon and megadrought Sahelian episodes have been reported with a close correspondence between the large rainfall decrease and the massive freshwater discharges following ice-sheet melting or iceberg surges. During the last decades a continuous acceleration of ice-sheet mass loss has been observed and post IPCC-AR5 studies suggest the ice-sheet contribution to future sea-level rise could be revised upward due partly to the lack of an accurate representation of ice-ocean interactions. The release of freshwater discharge in response to ice-sheet instability could have large consequences on the most vulnerable regions, such as the tropical areas. To investigate the impacts of large ice-sheet instability during the 21st century, we first explore the climatic signature of Greenland or Antarctic ice-sheet collapse scenarios corresponding to 0.5 to 1.5 meter of sea-level rise, superimposed to the RCP8.5 scenario. We show that a freshwater discharge coming from Greenland melting induces a significant decrease of summer monsoon rainfall, that may lead to changes in agricultural practices. Combined with increasing demography, this has the potential to induce important human migration flows. Without adaptation measures, we estimate that tens to hundreds million people could be
Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models
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Sungwon Kim
2015-06-01
Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.
Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series
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Abdoul Aziz Diouf
2015-07-01
Full Text Available Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR and in situ biomass. A model with three variables—large seasonal integral (LINTG, length of growing season, and end of season decreasing rate—performed best (MAE = 605 kg·DM/ha; R2 = 0.68 across Sahelian ecosystems in Senegal (data for the period 1999–2013. A model with annual maximum (PEAK and start date of season showed similar performances (MAE = 625 kg·DM/ha; R2 = 0.64, allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg·DM/ha; R2 = 0.77, indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R2 = 0.51 and should improve the operational monitoring of forage resources in Sahelian rangelands.
The sensitivity of catchment runoff models to rainfall data at different spatial scales
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V. A. Bell
2000-01-01
Full Text Available The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911 but significantly better during convective rain (R2 of 0.953 compared to 0.909. The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed
Does better rainfall interpolation improve hydrological model performance?
Bàrdossy, Andràs; Kilsby, Chris; Lewis, Elisabeth
2017-04-01
High spatial variability of precipitation is one of the main sources of uncertainty in rainfall/runoff modelling. Spatially distributed models require detailed space time information on precipitation as input. In the past decades a lot of effort was spent on improving precipitation interpolation using point observations. Different geostatistical methods like Ordinary Kriging, External Drift Kriging or Copula based interpolation can be used to find the best estimators for unsampled locations. The purpose of this work is to investigate to what extents more sophisticated precipitation estimation methods can improve model performance. For this purpose the Wye catchment in Wales was selected. The physically-based spatially-distributed hydrological model SHETRAN is used to describe the hydrological processes in the catchment. 31 raingauges with 1 hourly temporal resolution are available for a time period of 6 years. In order to avoid the effect of model uncertainty model parameters were not altered in this study. Instead 100 random subsets consisting of 14 stations each were selected. For each of the configurations precipitation was interpolated for each time step using nearest neighbor (NN), inverse distance (ID) and Ordinary Kriging (OK). The variogram was obtained using the temporal correlation of the time series measured at different locations. The interpolated data were used as input for the spatially distributed model. Performance was evaluated for daily mean discharges using the Nash-Sutcliffe coefficient, temporal correlations, flow volumes and flow duration curves. The results show that the simplest NN and the sophisticated OK performances are practically equally good, while ID performed worse. NN was often better for high flows. The reason for this is that NN does not reduce the variance, while OK and ID yield smooth precipitation fields. The study points out the importance of precipitation variability and suggests the use of conditional spatial simulation as
Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches.
Eisenberg, Marisa C; Kujbida, Gregory; Tuite, Ashleigh R; Fisman, David N; Tien, Joseph H
2013-12-01
Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.
Modeling rainfall-runoff processes using smoothed particle hydrodynamics with mass-varied particles
Chang, Tsang-Jung; Chang, Yu-Sheng; Chang, Kao-Hua
2016-12-01
In this study, a novel treatment of adopting mass-varied particles in smoothed particle hydrodynamics (SPH) is proposed to solve the shallow water equations (SWEs) and model the rainfall-runoff process. Since SWEs have depth-averaged or cross-section-averaged features, there is no sufficient dimension to add rainfall particles. Thus, SPH-SWE methods have focused on modeling discharge flows in open channels or floodplains without rainfall. With the proposed treatment, the application of SPH-SWEs can be extended to rainfall-runoff processes in watersheds. First, the numerical procedures associated with using mass-varied particles in SPH-SWEs are introduced and derived. Then, numerical validations are conducted for three benchmark problems, including uniform rainfall over a 1D flat sloping channel, nonuniform rain falling over a 1D three-slope channel with different rainfall durations, and uniform rainfall over a 2D plot with complex topography. The simulated results indicate that the proposed treatment can avoid the necessity of a source term function of mass variation, and no additional particles are needed for the increase of mass. Rainfall-runoff processes can be well captured in the presence of hydraulic jumps, dry/wet bed flows, and supercritical/subcritical/transcritical flows. The proposed treatment using mass-varied particles was proven robust and reliable for modeling rainfall-runoff processes. It can provide a new alternative for investigating practical hydrological problems.
An artificial neural network model for rainfall forecasting in Bangkok, Thailand
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N. Q. Hung
2009-08-01
Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.
An artificial neural network model for rainfall forecasting in Bangkok, Thailand
Directory of Open Access Journals (Sweden)
N. Q. Hung
2008-01-01
Full Text Available The present study developed an artificial neural network (ANN model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997–1999. Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.
Neural network emulation of a rainfall-runoff model
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R. J. Abrahart
2007-02-01
Full Text Available The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling.
Pal, Debdatta; Mitra, Subrata Kumar
2016-10-01
This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.
Application of the rainfall infiltration breakthrough (RIB) model for ...
African Journals Online (AJOL)
2012-05-23
May 23, 2012 ... Scenarios using the data from Oudebosch with different rainfall and groundwater abstraction inputs ... A manual entitled 'Preparation of a manual on Quantitative ..... The definition of the symbols used in the RIB programme.
A regression-kriging model for estimation of rainfall in the Laohahe basin
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.
Sultan, B.; Roudier, P.; Quirion, P.; Alhassane, A.; Muller, B.; Dingkuhn, M.; Ciais, P.; Guimberteau, M.; Traore, S.; Baron, C.
2013-03-01
Sub-Saharan West Africa is a vulnerable region where a better quantification and understanding of the impact of climate change on crop yields is urgently needed. Here, we have applied the process-based crop model SARRA-H calibrated and validated over multi-year field trials and surveys at eight contrasting sites in terms of climate and agricultural practices in Senegal, Mali, Burkina Faso and Niger. The model gives a reasonable correlation with observed yields of sorghum and millet under a range of cultivars and traditional crop management practices. We applied the model to more than 7000 simulations of yields of sorghum and millet for 35 stations across West Africa and under very different future climate conditions. We took into account 35 possible climate scenarios by combining precipitation anomalies from -20% to 20% and temperature anomalies from +0 to +6 °C. We found that most of the 35 scenarios (31/35) showed a negative impact on yields, up to -41% for +6 °C/ - 20% rainfall. Moreover, the potential future climate impacts on yields are very different from those recorded in the recent past. This is because of the increasingly adverse role of higher temperatures in reducing crop yields, irrespective of rainfall changes. When warming exceeds +2 °C, negative impacts caused by temperature rise cannot be counteracted by any rainfall change. The probability of a yield reduction appears to be greater in the Sudanian region (southern Senegal, Mali, Burkina Faso, northern Togo and Benin), because of an exacerbated sensitivity to temperature changes compared to the Sahelian region (Niger, Mali, northern parts of Senegal and Burkina Faso), where crop yields are more sensitive to rainfall change. Finally, our simulations show that the photoperiod-sensitive traditional cultivars of millet and sorghum used by local farmers for centuries seem more resilient to future climate conditions than modern cultivars bred for their high yield potential (-28% versus -40% for
A combined Pòlya process and mixture distribution approach to rainfall modelling
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E. Todini
1997-01-01
Full Text Available A new probabilistic interpretation of at site rainfall sequences is introduced for the development of a stochastic model of rain. The model, is divided into two sub models; the first one describing the total number of rainfall spells within a window of time is described by a Pòlya process in order to reproduce better the variable probability of occurrence of rainfall during storm events (due to the presence of different numbers of rainfall cells; the second sub model, conditional on the first one, describes the total quantity of rainfall in the time window, given a number of rainfall spells. The probabilistic rainfall model, which has shown interesting properties in reproducing the probability distribution of observed data at time scales ranging from one hour to twenty-four hours, may be the basis for a number of applications which include the development of a conditional stochastic generator of rain, within the frame of real-time flood forecasting, and the derivation of a probabilistic distribution of rainfall extremes at the various time scales.
A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations
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
Bitew, Menberu M.; Gebremichael, Mekonnen
2011-06-01
The goal of this study is to evaluate the accuracy of four global high-resolution satellite rainfall products (CMORPH, TMPA 3B42RT, TMPA 3B42, and PERSIANN) through the hydrologic simulation of a 1656 km2 mountainous watershed in the fully distributed MIKE SHE hydrologic model. This study shows that there are significant biases in the satellite rainfall estimates and large variations in rainfall amounts, leading to large variations in hydrologic simulations. The rainfall algorithms that use primarily microwave data (CMORPH and TMPA 3B42RT) show consistent and better performance in streamflow simulation (bias in the order of -53% to -3%, Nash-Sutcliffe efficiency (NSE) from 0.34 to 0.65); the rainfall algorithm that uses primarily infrared data (PERSIANN) shows lower performance (bias from -82% to -3%, Nash-Sutcliffe efficiency from -0.39 to 0.43); and the rainfall algorithm that merges the satellite data with rain gage data (TMPA 3B42) shows inconsistencies and the lowest performance (bias from -86% to 0.43%, Nash-Sutcliffe efficiency from -0.50 to 0.27). A dilemma between calibrating the hydrologic model with rain gage data and calibrating it with the corresponding satellite rainfall data is presented. Calibrating the model with corresponding satellite rainfall data increases the performance of satellite streamflow simulation compared to the model calibrated with rain gage data, but decreases the performance of satellite evapotranspiration simulation.
Scaling Properties of Rainfall-Induced Landslides Predicted by a Physically Based Model
Alvioli, M; Rossi, M
2013-01-01
Natural landslides exhibit scaling properties, including the frequency of the size of the landslides, and the rainfall conditions responsible for landslides. Reasons for the scaling behavior of landslides are poorly known, and only a few attempts were made to describe the empirical evidences of the self-similar scaling behavior of landslides with physically based models. We investigate the possibility of using the TRIGRS code, a consolidated, physically motivated, numerical model to describe the stability conditions of natural slopes forced by rainfall, to determine the frequency of the area of the unstable slopes and the rainfall intensity-duration (I-D) conditions that result in landslides in a region.We apply TRIGRS in a portion of the Upper Tiber River Basin, Central Italy. The spatially distributed model predicts the stability conditions of individual grid cells, given the terrain and rainfall conditions. We run TRIGRS using multiple rainfall histories, and we compare the results to empirical evidences o...
A space-time hybrid hourly rainfall model for derived flood frequency analysis
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U. Haberlandt
2008-12-01
Full Text Available For derived flood frequency analysis based on hydrological modelling long continuous precipitation time series with high temporal resolution are needed. Often, the observation network with recording rainfall gauges is poor, especially regarding the limited length of the available rainfall time series. Stochastic precipitation synthesis is a good alternative either to extend or to regionalise rainfall series to provide adequate input for long-term rainfall-runoff modelling with subsequent estimation of design floods. Here, a new two step procedure for stochastic synthesis of continuous hourly space-time rainfall is proposed and tested for the extension of short observed precipitation time series.
First, a single-site alternating renewal model is presented to simulate independent hourly precipitation time series for several locations. The alternating renewal model describes wet spell durations, dry spell durations and wet spell intensities using univariate frequency distributions separately for two seasons. The dependence between wet spell intensity and duration is accounted for by 2-copulas. For disaggregation of the wet spells into hourly intensities a predefined profile is used. In the second step a multi-site resampling procedure is applied on the synthetic point rainfall event series to reproduce the spatial dependence structure of rainfall. Resampling is carried out successively on all synthetic event series using simulated annealing with an objective function considering three bivariate spatial rainfall characteristics. In a case study synthetic precipitation is generated for some locations with short observation records in two mesoscale catchments of the Bode river basin located in northern Germany. The synthetic rainfall data are then applied for derived flood frequency analysis using the hydrological model HEC-HMS. The results show good performance in reproducing average and extreme rainfall characteristics as well as in
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.
Green, Daniel; Pattison, Ian; Yu, Dapeng
2016-04-01
Surface water (pluvial) flooding occurs when rainwater from intense precipitation events is unable to infiltrate into the subsurface or drain via natural or artificial drainage channels. Surface water flooding poses a serious hazard to urban areas across the world, with the UK's perceived risk appearing to have increased in recent years due to surface water flood events seeming more severe and frequent. Surface water flood risk currently accounts for 1/3 of all UK flood risk, with approximately two million people living in urban areas at risk of a 1 in 200-year flood event. Research often focuses upon using numerical modelling techniques to understand the extent, depth and severity of actual or hypothetical flood scenarios. Although much research has been conducted using numerical modelling, field data available for model calibration and validation is limited due to the complexities associated with data collection in surface water flood conditions. Ultimately, the data which numerical models are based upon is often erroneous and inconclusive. Physical models offer a novel, alternative and innovative environment to collect data within, creating a controlled, closed system where independent variables can be altered independently to investigate cause and effect relationships. A physical modelling environment provides a suitable platform to investigate rainfall-runoff processes occurring within an urban catchment. Despite this, physical modelling approaches are seldom used in surface water flooding research. Scaled laboratory experiments using a 9m2, two-tiered 1:100 physical model consisting of: (i) a low-cost rainfall simulator component able to simulate consistent, uniformly distributed (>75% CUC) rainfall events of varying intensity, and; (ii) a fully interchangeable, modular plot surface have been conducted to investigate and quantify the influence of a number of terrestrial and meteorological factors on overland flow and rainfall-runoff patterns within a modelled
On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution
Directory of Open Access Journals (Sweden)
G. Bruni
2014-06-01
Full Text Available Cities are increasingly vulnerable to floods generated by intense rainfall, because of their high degree of imperviousness, implementation of infrastructures, and changes in precipitation patterns due to climate change. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction. In this paper, a detailed study of the sensitivity of urban hydrological response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar for four rainstorms were used as input into a detailed hydrodynamic sewer model for an urban catchment in Rotterdam, the Netherlands. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size, catchment sampling number (rainfall resolution vs. catchment size, runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively. Results show catchment smearing effect for rainfall resolution approaching half the catchment size, i.e. for catchments sampling numbers greater than 0.5 averaged rainfall volumes decrease about 20%. Moreover, deviations in maximum water depths, form 10 to 30% depending on the storm, occur for rainfall resolution close to storm size, describing storm smearing effect due to rainfall coarsening. Model results also show the sensitivity of modelled runoff peaks and maximum water depths to the resolution of the runoff areas and sewer density respectively. Sensitivity to temporal resolution of rainfall input seems low compared to spatial resolution, for the storms analysed in this study. Findings are in agreement with previous studies on natural catchments
A Monte-Carlo Bayesian framework for urban rainfall error modelling
Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian
2016-04-01
Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data
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.
RAINFALL-RUNOFF MODELING IN THE TURKEY RIVER USING NUMERICAL AND REGRESSION METHODS
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J. Behmanesh
2015-01-01
Full Text Available Modeling rainfall-runoff relationships in a watershed have an important role in water resources engineering. Researchers have used numerical models for modeling rainfall-runoff process in the watershed because of non-linear nature of rainfall-runoff relationship, vast data requirement and physical models hardness. The main object of this research was to model the rainfall-runoff relationship at the Turkey River in Mississippi. In this research, two numerical models including ANN and ANFIS were used to model the rainfall-runoff process and the best model was chosen. Also, by using SPSS software, the regression equations were developed and then the best equation was selected from regression analysis. The obtained results from the numerical and regression modeling were compared each other. The comparison showed that the model obtained from ANFIS modeling was better than the model obtained from regression modeling. The results also stated that the Turkey river flow rate had a logical relationship with one and two days ago flow rate and one, two and three days ago rainfall values.
RAINFALL-RUNOFF MODELING IN THE TURKEY RIVER USING NUMERICAL AND REGRESSION METHODS
Directory of Open Access Journals (Sweden)
J. Behmanesh
2015-03-01
Full Text Available Modeling rainfall-runoff relationships in a watershed have an important role in water resources engineering. Researchers have used numerical models for modeling rainfall-runoff process in the watershed because of non-linear nature of rainfall-runoff relationship, vast data requirement and physical models hardness. The main object of this research was to model the rainfall-runoff relationship at the Turkey River in Mississippi. In this research, two numerical models including ANN and ANFIS were used to model the rainfall-runoff process and the best model was chosen. Also, by using SPSS software, the regression equations were developed and then the best equation was selected from regression analysis. The obtained results from the numerical and regression modeling were compared each other. The comparison showed that the model obtained from ANFIS modeling was better than the model obtained from regression modeling. The results also stated that the Turkey river flow rate had a logical relationship with one and two days ago flow rate and one, two and three days ago rainfall values.
MODELLING OF SHORT DURATION RAINFALL (SDR INTENSITY EQUATIONS FOR ERZURUM
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Serkan ŞENOCAK
2007-01-01
Full Text Available The scope of this study is to develop a rainfall intensity-duration-frequency (IDF equation for some return periods at Erzurum rainfall station. The maximum annual rainfall values for 5, 10, 15, 30 and 60 minutes are statistically analyzed for the period 1956 – 2004 by using some statistical distributions such as the Generalized Extreme Values (GEV, Gumbel, Normal, Two-parameter Lognormal, Three-parameter Lognormal, Gamma, Pearson type III and Log-Pearson type III distributions. ?2 goodness-of-fit test was used to choose the best statistical distribution among all distributions. IDF equation constants and coefficients of correlation (R for each emprical functions are calculated using nonlinear estimation method for each return periods (T = 2, 5, 10, 25, 50, 75 and 100 years. The most suitable IDF equation is observed that ( B max i (t = A/ t + C , except for T=100 years, because of the highest coefficients of correlation.
Application of vector autoregressive model for rainfall and groundwater level analysis
Keng, Chai Yoke; Shan, Fam Pei; Shimizu, Kunio; Imoto, Tomoaki; Lateh, Habibah; Peng, Koay Swee
2017-08-01
Groundwater is a crucial water supply for industrial, agricultural and residential use, hence it is important to understand groundwater system. Groundwater is a dynamic natural resource and can be recharged. The amount of recharge depends on the rate and duration of rainfall, as rainfall comprises an important component of the water cycle and is the prime source of groundwater recharge. This study applies Vector Autoregressive (VAR) model in the analysis of rainfall and groundwater level. The study area that is focused in the study is along the East-West Highway, Gerik-Jeli, Malaysia. The VAR model with optimum lag length 8, VAR(8) is selected to model the rainfall and groundwater level in the study area. Result of Granger causality test shows significant influence of rainfall to groundwater level. Impulse Response Function reveals that changes in rainfall significantly affect changes in groundwater level after some time lags. Moreover, Variance Decomposition reported that rainfall contributed to the forecast of the groundwater level. The VAR(8) model is validated by comparing the actual value with the in-sample forecasted value and the result is satisfied with all forecasted groundwater level values lies inside the confidence interval which indicate that the model is reliable. Furthermore, the closeness of both actual and forecasted groundwater level time series plots implies the high degree of accurateness of the estimated model.
Evaluating regional climate models for simulating sub-daily rainfall extremes
Cortés-Hernández, Virginia Edith; Zheng, Feifei; Evans, Jason; Lambert, Martin; Sharma, Ashish; Westra, Seth
2016-09-01
Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model "gets the right answers for the right reasons". This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM—the Weather Research Forecasting model—over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement.
Investigation of building energy autonomy in the sahelian environment
Coulibaly, O.; Ouedraogo, A.; Kuznik, F.; Baillis, D.; Koulidiati, J.
2012-02-01
In this study, the energy generation of a set of photovoltaic panels is compared with the energy load of a building in order to analyse its autonomy in the sahelian environment when taking into account, the orientation, the insulation and the energy transfer optimisation of its windows. The Type 56 TRNSYS multizone building model is utilized for the energy load simulation and the Type 94 model of the same code enables the coupling of photovoltaic (PV) panels with the building. Without insulation, the PV energy generation represents 73.52 and 111.79% of the building electric energy load, respectively for poly-crystalline and mono-crystalline panels. For the same PV characteristics and when we insulate the roof and the floor, the energy generation increases to represent successively 121.09 and 184.13%. In the meantime, for building without insulation and with insulate the roof, the floor and 2 cm insulated walls, the energy consumption ratios decrease respectively from 201.13 to 105.20 kWh/m2/year. The investigations finally show that it is even possible to generate excess energy (positive energy building) and reduce the number and incident surface area of the PV panels if we conjugate the previous model with building passive architectural design mode (orientation, solar protection ...).
A dependence modelling study of extreme rainfall in Madeira Island
Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra
2016-08-01
The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.
Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model
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).
Vos, de N.J.; Rientjes, T.H.M.
2005-01-01
The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of
Intercontinental Transport and Climatic Impact of Saharan and Sahelian Dust
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N'Datchoh Evelyne Touré
2012-01-01
Full Text Available The Sahara and Sahel regions of Africa are important sources of dust particles into the atmosphere. Dust particles from these regions are transported over the Atlantic Ocean to the Eastern American Coasts. This transportation shows temporal and spatial variability and often reaches its peak during the boreal summer (June-July-August. The regional climate model (RegCM 4.0, containing a module of dust emission, transport, and deposition processes, is used in this study. Saharan and Sahelian dusts emissions, transports, and climatic impact on precipitations during the spring (March-April-May and summer (June-July-August were studied using this model. The results showed that the simulation were coherent with observations made by the MISR satellite and the AERONET ground stations, within the domain of Africa (Banizoumba, Cinzana, and M’Bour and Ragged-point (Barbados Islands. The transport of dust particles was predominantly from North-East to South-West over the studied period (2005–2010. The seasonality of dust plumes’ trajectories was influenced by the altitudes reached by dusts in the troposphere. The impact of dusts on climate consisted of a cooling effect both during the boreal summer and spring over West Africa (except Southern-Guinea and Northern-Liberia, Central Africa, South-America, and Caribbean where increased precipitations were observed.
Real Time Updating in Distributed Urban Rainfall Runoff Modelling
DEFF Research Database (Denmark)
Borup, Morten; Madsen, Henrik
When it rains on urban areas the rainfall runoff is transported out of the city via the drainage system. Frequently, the drainage system cannot handle all the rain water, which results in problems like flooding or overflows into natural water bodies. To reduce these problems the systems...
Energy Technology Data Exchange (ETDEWEB)
Diro, G.T. [The Abdus salam International Centre for Theoretical Physics, Earth System Physics section, Trieste (Italy); University of Reading, Department of Meteorology, Reading (United Kingdom); Grimes, D.I.F.; Black, E. [University of Reading, Department of Meteorology, Reading (United Kingdom)
2011-07-15
In this study, the oceanic regions that are associated with anomalous Ethiopian summer rains were identified and the teleconnection mechanisms that give rise to these associations have been investigated. Because of the complexities of rainfall climate in the horn of Africa, Ethiopia has been subdivided into six homogeneous rainfall zones and the influence of SST anomalies was analysed separately for each zone. The investigation made use of composite analysis and modelling experiments. Two sets of composites of atmospheric fields were generated, one based on excess/deficit rainfall anomalies and the other based on warm/cold SST anomalies in specific oceanic regions. The aim of the composite analysis was to determine the link between SST and rainfall in terms of large scale features. The modelling experiments were intended to explore the causality of these linkage. The results show that the equatorial Pacific, the midlatitude northwest Pacific and the Gulf of Guinea all exert an influence on the summer rainfall in various part of the country. The results demonstrate that different mechanisms linked to sea surface temperature control variations in rainfall in different parts of Ethiopia. This has important consequences for seasonal forecasting models which are based on statistical correlations between SST and seasonal rainfall totals. It is clear that such statistical models should take account of the local variations in teleconnections. (orig.)
Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall
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.
parameterrization of microphysical and dynamical processes of rainfall in thunderstorm cloud model
Directory of Open Access Journals (Sweden)
S. J.
2002-12-01
Full Text Available In this research parameterization of the precipitation process in Ogura & Takahashi (O-T thunderstorm model was improved in microphysical processes, specially in the autoconversion process to form raindrops, in the glaciation process and in the terminal velocities of rain and hail. The rainfall intensity became much heavier with Kesslers parameterization, the second peak of the rainfall intensity disappeared with Biggs freezing probability, and the rainfall intensity became much heavier and sharper with Lin et als terminal velocities of rain and hail than in the O-T original model. Finally, the derived rainfall pattern based on the improved model has much similarities to the observation data. This paper expresses the basic research for studying the physical treatment in clouds. The modified O-T model has different applications in analyzing radar observation data, estimate the potential of soil erosion, parameteriztion of shower in mesoscale numerical weather prediction and eta.
ENSO teleconnections with Australian rainfall in coupled model simulations of the last millennium
Brown, Josephine R.; Hope, Pandora; Gergis, Joelle; Henley, Benjamin J.
2016-07-01
El Niño-Southern Oscillation is the major source of interannual rainfall variability in the Australian region, with the strongest influence over eastern Australia. The strength of this regional ENSO-rainfall teleconnection varies in the observational record. Climate model simulations of the "last millennium" (850-1850 C.E.) can be used to quantify the natural variability of the relationship between ENSO and Australian rainfall on decadal and longer time scales, providing a baseline for evaluating future projections. In this study, historical and last millennium (LM) simulations from six models were obtained from the Coupled Model Intercomparison Project Phase 5 and Palaeoclimate Modelling Intercomparison Project Phase 3. All models reproduce the observed negative correlation between September to February (SONDJF) eastern Australian rainfall and the NINO3.4 index, with varying skill. In the LM simulations, all models produce decadal-scale cooling over eastern Australia in response to volcanic forcing, as well as a long-term cooling trend. Rainfall variability over the same region is not strongly driven by external forcing, with each model simulating rainfall anomalies of different phase and magnitude. SONDJF eastern Australian rainfall is strongly correlated with ENSO in the LM simulations for all models, although some models simulate periods when the teleconnection weakens substantially for several decades. Changes in ENSO variance play a role in modulating the teleconnection strength, but are not the only factor. The long-term average spatial pattern of the ENSO-Australian rainfall teleconnection is similar in the LM and historical simulations, although the spatial pattern varies over time in the LM simulations.
Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.
2016-12-01
One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.
Investigating and predicting landslides using a rainfall-runoff model in Southern Norway
Kråbøl, Eline Haga
2016-01-01
Landslides are amongst the most destructive natural hazards, causing damage to infrastructures, such as roads, railways and houses, and can, in a worst-case scenario, take lives. By studying the effect and response of rainfall using the temporal and spatial distribution of the storage and discharge, a better understanding of landslide processes and a more detailed prediction can be possible. This study employs a parameter-parsimonious rainfall-runoff model, the Distance Distribution model (DD...
Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue
2016-01-01
This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...
Characteristics of rainfall events in regional climate model simulations for the Czech Republic
Svoboda, Vojtěch; Hanel, Martin; Máca, Petr; Kyselý, Jan
2017-02-01
Characteristics of rainfall events in an ensemble of 23 regional climate model (RCM) simulations are evaluated against observed data in the Czech Republic for the period 1981-2000. Individual rainfall events are identified using the concept of minimum inter-event time (MIT) and only heavy events (15 % of events with the largest event depths) during the warm season (May-September) are considered. Inasmuch as an RCM grid box represents a spatial average, the effects of areal averaging of rainfall data on characteristics of events are investigated using the observed data. Rainfall events from the RCM simulations are then compared to those from the at-site and area-average observations. Simulated number of heavy events and seasonal total precipitation due to heavy events are on average represented relatively well despite the higher spatial variation compared to observations. RCM-simulated event depths are comparable to the area-average observations, while event durations are overestimated and other characteristics related to rainfall intensity are significantly underestimated. The differences between RCM-simulated and at-site observed rainfall event characteristics are in general dominated by the biases of the climate models rather than the areal-averaging effect. Most of the rainfall event characteristics in the majority of the RCM simulations show a similar altitude-dependence pattern as in the observed data. The number of heavy events and seasonal total precipitation due to heavy events increase with altitude, and this dependence is captured better by the RCM simulations with higher spatial resolution.
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D. E. Robertson
2013-05-01
Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.
Degefu, Mekonnen Adnew; Rowell, David P.; Bewket, Woldeamlak
2017-04-01
Rainfall variability in Ethiopia has significant effects on rainfed agriculture and hydropower, so understanding its association with slowly varying global sea surface temperatures (SSTs) is potentially important for prediction purposes. We provide an overview of the seasonality and spatial variability of these teleconnections across Ethiopia. A quasi-objective method is employed to define coherent seasons and regions of SST-rainfall teleconnections for Ethiopia. We identify three seasons (March-May, MAM; July-September, JAS; and October-November, ON), which are similar to those defined by climatological rainfall totals. We also identify three new regions (Central and western Ethiopia, CW-Ethiopia; Southern Ethiopia, S-Ethiopia; and Northeast Ethiopia, NE-Ethiopia) that are complementary to those previously defined here based on distinct SST-rainfall teleconnections that are useful when predicting interannual anomalies. JAS rainfall over CW-Ethiopia is negatively associated with SSTs over the equatorial east Pacific and Indian Ocean. New regional detail is added to that previously found for the whole of East Africa, in particular that ON rainfall over S-Ethiopia is positively associated with equatorial east Pacific SSTs and with the Indian Ocean Dipole (IOD). Also, SST-to-rainfall correlations for other season-regions, and specifically for MAM in all regions, are found to be negligible. The representation of these teleconnections in the HadGEM2 and HadGEM3-GA3.0 coupled climate models shows mixed skill. Both models poorly represent the statistically significant teleconnections, except that HadGEM2 and the low resolution (N96) version of HadGEM3-GA3.0 better represent the association between the IOD and S-Ethiopian ON rainfall. Additionally, both models are able to represent the lack of SST-rainfall correlation in other seasons and other parts of Ethiopia.
Degefu, Mekonnen Adnew; Rowell, David P.; Bewket, Woldeamlak
2016-06-01
Rainfall variability in Ethiopia has significant effects on rainfed agriculture and hydropower, so understanding its association with slowly varying global sea surface temperatures (SSTs) is potentially important for prediction purposes. We provide an overview of the seasonality and spatial variability of these teleconnections across Ethiopia. A quasi-objective method is employed to define coherent seasons and regions of SST-rainfall teleconnections for Ethiopia. We identify three seasons (March-May, MAM; July-September, JAS; and October-November, ON), which are similar to those defined by climatological rainfall totals. We also identify three new regions (Central and western Ethiopia, CW-Ethiopia; Southern Ethiopia, S-Ethiopia; and Northeast Ethiopia, NE-Ethiopia) that are complementary to those previously defined here based on distinct SST-rainfall teleconnections that are useful when predicting interannual anomalies. JAS rainfall over CW-Ethiopia is negatively associated with SSTs over the equatorial east Pacific and Indian Ocean. New regional detail is added to that previously found for the whole of East Africa, in particular that ON rainfall over S-Ethiopia is positively associated with equatorial east Pacific SSTs and with the Indian Ocean Dipole (IOD). Also, SST-to-rainfall correlations for other season-regions, and specifically for MAM in all regions, are found to be negligible. The representation of these teleconnections in the HadGEM2 and HadGEM3-GA3.0 coupled climate models shows mixed skill. Both models poorly represent the statistically significant teleconnections, except that HadGEM2 and the low resolution (N96) version of HadGEM3-GA3.0 better represent the association between the IOD and S-Ethiopian ON rainfall. Additionally, both models are able to represent the lack of SST-rainfall correlation in other seasons and other parts of Ethiopia.
Directory of Open Access Journals (Sweden)
D. E. Robertson
2013-09-01
Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short
Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models
Directory of Open Access Journals (Sweden)
A. Menon
2013-01-01
Full Text Available The possibility of an impact of global warming on the Indian monsoon is of critical importance for the large population of this region. Future projections within the Coupled Model Intercomparison Project Phase 3 (CMIP-3 showed a wide range of trends with varying magnitude and sign across models. Here the Indian summer monsoon rainfall is evaluated in 20 CMIP-5 models for the period 1850 to 2100. In the new generation of climate models a consistent increase in seasonal mean rainfall during the summer monsoon periods arises. All models simulate stronger seasonal mean rainfall in the future compared to the historic period under the strongest warming scenario RCP-8.5. Increase in seasonal mean rainfall is the largest for the RCP-8.5 scenario compared to other RCPs. The interannual variability of the Indian monsoon rainfall also shows a consistent positive trend under unabated global warming. Since both the long-term increase in monsoon rainfall as well as the increase in interannual variability in the future is robust across a wide range of models, some confidence can be attributed to these projected trends.
Modelling rainfall interception in unlogged and logged forest areas of Central Kalimantan, Indonesia
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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.
Savage, W.Z.; Godt, J.W.; Baum, R.L.; ,
2003-01-01
We describe a model for regional initiation of shallow landslides based on an approximate analytic solution to Richards equation combined with an infinite-slope calculation. The model applied over digital topography computes pressure heads and factors of safety as functions of depth for geographic information system (GIS) grid cells at any time during and after rainfall events. An example is presented that simulates the progressive development of shallow landslides on steep slopes during a rainfall event. This example shows how this modeling provides insights into transient rainfall-caused processes that trigger shallow slope instability and consequent regionally distributed debris-flow events. Specifically, we infer that the spatial pattern of instability is primarily controlled by topography, while intensity and duration of rainfall, and the subsequent pore-pressure responses control the temporal pattern of instability. ?? 2003 Millpress.
Kigobe, M.; McIntyre, N.; Wheater, H. S.
2009-04-01
Interest in the application of climate and hydrological models in the Nile basin has risen in the recent past; however, the first drawback for most efforts has been the estimation of historic precipitation patterns. In this study we have applied stochastic models to infill and extend observed data sets to generate inputs for hydrological modelling. Several stochastic climate models within the Generalised Linear Modelling (GLM) framework have been applied to reproduce spatial and temporal patterns of precipitation in the Kyoga basin. A logistic regression model (describing rainfall occurrence) and a gamma distribution (describing rainfall amounts) are used to model rainfall patterns. The parameters of the models are functions of spatial and temporal covariates, and are fitted to the observed rainfall data using log-likelihood methods. Using the fitted model, multi-site rainfall sequences over the Kyoga basin are generated stochastically as a function of the dominant seasonal, climatic and geographic controls. The rainfall sequences generated are then used to drive a semi distributed hydrological model using the Soil Water and Assessment Tool (SWAT). The sensitivity of runoff to uncertainty associated with missing precipitation records is thus tested. In an application to the Lake Kyoga catchment, the performance of the hydrological model highly depends on the spatial representation of the input precipitation patterns, model parameterisation and the performance of the GLM stochastic models used to generate the input rainfall. The results obtained so far disclose that stochastic models can be developed for several climatic regions within the Kyoga basin; and, given identification of a stochastic rainfall model; input uncertainty due to precipitation can be usefully quantified. The ways forward for rainfall modelling and hydrological simulation in Uganda and the Upper Nile are discussed. Key Words: Precipitation, Generalised Linear Models, Input Uncertainty, Soil Water
Akinsanola, A. A.; Ajayi, V. O.; Adejare, A. T.; Adeyeri, O. E.; Gbode, I. E.; Ogunjobi, K. O.; Nikulin, G.; Abolude, A. T.
2017-03-01
This study presents evaluation of the ability of Rossby Centre Regional Climate Model (RCA4) driven by nine global circulation models (GCMs), to skilfully reproduce the key features of rainfall climatology over West Africa for the period of 1980-2005. The seasonal climatology and annual cycle of the RCA4 simulations were assessed over three homogenous subregions of West Africa (Guinea coast, Savannah, and Sahel) and evaluated using observed precipitation data from the Global Precipitation Climatology Project (GPCP). Furthermore, the model output was evaluated using a wide range of statistical measures. The interseasonal and interannual variability of the RCA4 were further assessed over the subregions and the whole of the West Africa domain. Results indicate that the RCA4 captures the spatial and interseasonal rainfall pattern adequately but exhibits a weak performance over the Guinea coast. Findings from the interannual rainfall variability indicate that the model performance is better over the larger West Africa domain than the subregions. The largest difference across the RCA4 simulated annual rainfall was found in the Sahel. Result from the Mann-Kendall test showed no significant trend for the 1980-2005 period in annual rainfall either in GPCP observation data or in the model simulations over West Africa. In many aspects, the RCA4 simulation driven by the HadGEM2-ES perform best over the region. The use of the multimodel ensemble mean has resulted to the improved representation of rainfall characteristics over the study domain.
A space-time stochastic model of rainfall for satellite remote-sensing studies
Bell, Thomas L.
1987-01-01
A model of the spatial and temporal distribution of rainfall is described that produces random spatial rainfall patterns with these characteristics: (1) the model is defined on a grid with each grid point representing the average rain rate over the surrounding grid box, (2) rain occurs at any one grid point, on average, a specified percentage of the time and has a lognormal probability distribution, (3) spatial correlation of the rainfall can be arbitrarily prescribed, and (4) time stepping is carried out so that large-scale features persist longer than small-scale features. Rain is generated in the model from the portion of a correlated Gaussian random field that exceeds a threshold. The portion of the field above the threshold is rescaled to have a lognormal probability distribution. Sample output of the model designed to mimic radar observations of rainfall during the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), is shown. The model is intended for use in evaluating sampling strategies for satellite remote-sensing of rainfall and for development of algorithms for converting radiant intensity received by an instrument from its field of view into rainfall amount.
Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series
DEFF Research Database (Denmark)
Diouf, Abdoul Aziz; Brandt, Martin Stefan; Verger, Aleixandre
2015-01-01
Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data....... This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables—large seasonal integral (LINTG), length of growing season...
Scaling statistics in a critical, nonlinear physical model of tropical oceanic rainfall
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K. M. Nordstrom
2003-01-01
Full Text Available Over the last two decades, concepts of scale invariance have come to the fore in both modeling and data analysis in hydrological precipitation research. With the advent of the use of the multiplicative random cascade model, these concepts have become increasingly more important. However, unifying this statistical view of the phenomenon with the physics of rainfall has proven to be a rather nontrivial task. In this paper, we present a simple model, developed entirely from qualitative physical arguments, without invoking any statistical assumptions, to represent tropical atmospheric convection over the ocean. The model is analyzed numerically. It shows that the data from the model rainfall look very spiky, as if generated from a random field model. They look qualitatively similar to real rainfall data sets from Global Atmospheric Research Program (GARP Atlantic Tropical Experiment (GATE. A critical point is found in a model parameter corresponding to the Convective Inhibition (CIN, at which rainfall changes abruptly from non-zero to a uniform zero value over the entire domain. Near the critical value of this parameter, the model rainfall field exhibits multifractal scaling determined from a fractional wetted area analysis and a moment scaling analysis. It therefore must exhibit long-range spatial correlations at this point, a situation qualitatively similar to that shown by multiplicative random cascade models and GATE rainfall data sets analyzed previously (Over and Gupta, 1994; Over, 1995. However, the scaling exponents associated with the model data are different from those estimated with real data. This comparison identifies a new theoretical framework for testing diverse physical hypotheses governing rainfall based in empirically observed scaling statistics.
Scaling statistics in a critical, nonlinear physical model of tropical oceanic rainfall
Nordstrom, K. M.; Gupta, V. K.
Over the last two decades, concepts of scale invariance have come to the fore in both modeling and data analysis in hydrological precipitation research. With the advent of the use of the multiplicative random cascade model, these concepts have become increasingly more important. However, unifying this statistical view of the phenomenon with the physics of rainfall has proven to be a rather nontrivial task. In this paper, we present a simple model, developed entirely from qualitative physical arguments, without invoking any statistical assumptions, to represent tropical atmospheric convection over the ocean. The model is analyzed numerically. It shows that the data from the model rainfall look very spiky, as if generated from a random field model. They look qualitatively similar to real rainfall data sets from Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE). A critical point is found in a model parameter corresponding to the Convective Inhibition (CIN), at which rainfall changes abruptly from non-zero to a uniform zero value over the entire domain. Near the critical value of this parameter, the model rainfall field exhibits multifractal scaling determined from a fractional wetted area analysis and a moment scaling analysis. It therefore must exhibit long-range spatial correlations at this point, a situation qualitatively similar to that shown by multiplicative random cascade models and GATE rainfall data sets analyzed previously (Over and Gupta, 1994; Over, 1995). However, the scaling exponents associated with the model data are different from those estimated with real data. This comparison identifies a new theoretical framework for testing diverse physical hypotheses governing rainfall based in empirically observed scaling statistics.
Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.
Komasi, Mehdi; Sharghi, Soroush
2016-01-01
Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.
van der Heijden, Sven; Callau Poduje, Ana; Müller, Hannes; Shehu, Bora; Haberlandt, Uwe; Lorenz, Manuel; Wagner, Sven; Kunstmann, Harald; Müller, Thomas; Mosthaf, Tobias; Bárdossy, András
2015-04-01
For the design and operation of urban drainage systems with numerical simulation models, long, continuous precipitation time series with high temporal resolution are necessary. Suitable observed time series are rare. As a result, intelligent design concepts often use uncertain or unsuitable precipitation data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic precipitation data for urban drainage modelling are advanced, tested, and compared. The presented study compares four different approaches of precipitation models regarding their ability to reproduce rainfall and runoff characteristics. These include one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model, and one disaggregation approach based on daily precipitation measurements. All four models produce long precipitation time series with a temporal resolution of five minutes. The synthetic time series are first compared to observed rainfall reference time series. Comparison criteria include event based statistics like mean dry spell and wet spell duration, wet spell amount and intensity, long term means of precipitation sum and number of events, and extreme value distributions for different durations. Then they are compared regarding simulated discharge characteristics using an urban hydrological model on a fictitious sewage network. First results show a principal suitability of all rainfall models but with different strengths and weaknesses regarding the different rainfall and runoff characteristics considered.
Event-based rainfall-runoff modelling of the Kelantan River Basin
Basarudin, Z.; Adnan, N. A.; Latif, A. R. A.; Tahir, W.; Syafiqah, N.
2014-02-01
Flood is one of the most common natural disasters in Malaysia. According to hydrologists there are many causes that contribute to flood events. The two most dominant factors are the meteorology factor (i.e climate change) and change in land use. These two factors contributed to floods in recent decade especially in the monsoonal catchment such as Malaysia. This paper intends to quantify the influence of rainfall during extreme rainfall events on the hydrological model in the Kelantan River catchment. Therefore, two dynamic inputs were used in the study: rainfall and river discharge. The extreme flood events in 2008 and 2004 were compared based on rainfall data for both years. The events were modeled via a semi-distributed HEC-HMS hydrological model. Land use change was not incorporated in the study because the study only tries to quantify rainfall changes during these two events to simulate the discharge and runoff value. Therefore, the land use data representing the year 2004 were used as inputs in the 2008 runoff model. The study managed to demonstrate that rainfall change has a significant impact to determine the peak discharge and runoff depth for the study area.
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X. Chen
2013-09-01
Full Text Available A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.
Raia, S; Rossi, M; Baum, R L; Godt, J W; Guzzetti, F
2013-01-01
Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the TRIG...
Assessment of runoff contributing catchment areas in rainfall runoff modelling
DEFF Research Database (Denmark)
Thorndahl, Søren; Johansen, C.; Schaarup-Jensen, Kjeld
2006-01-01
recommended literature values for residential areas. It is proven by comparing rainfall-runoff measurements from four different residential catchments that the literature values of the hydrological reduction factor are over-estimated for this type of catchment. In addition, different catchment descriptions...... are presented in order to investigate how the hydrological reduction factor depends on the level of detail regarding the catchment description. When applying a total survey of the catchment area, including all possible impervious surfaces, a hydrological reduction factor of approximately 0.5 for residential...
Assessment of Runoff Contributing Catchment Areas in Rainfall Runoff Modelling
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Johansen, C.; Schaarup-Jensen, Kjeld
2005-01-01
recommended literary values for residential areas. It is proven by comparing rainfall-runoff measurements from four different residential catchments that the literary values of the hydrological reduction factor are over-estimated for this type of catchments. In addition, different catchment descriptions...... are presented in order to investigate how the hydrological reduction factor depends on the level of detail regarding the catchment description. When applying a total survey of the catchment area, including all possible impervious surfaces, a hydrological reduction factor of approximately 0.5 for residential...
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V. Maggioni
2012-10-01
Full Text Available The contribution of rainfall forcing errors relative to model (structural and parameter uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM, forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty or by adding randomly generated noise (representing model structure and parameter uncertainty to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.
Photosynthesis and carbon balance of a Sahelian fallow savanna
Hanan, N.P.; Kabat, P.; Dolman, A.J.; Elbers, J.A.
1998-01-01
Eddy-covariance measurements of CO2 exchange above a Sahelian savanna consisting of small shrubs over a near-continuous herb layer were made during the HAPEX-Sahel experiment in Niger, West Africa. The measurements were made near-continuously during an 8-week period, covering the main part of the ra
Chan, S. C.; Kendon, E. J.; Roberts, N. M.; Fowler, H. J.; Blenkinsop, S.
2016-09-01
Flash flooding is often caused by sub-hourly rainfall extremes. Here, we examine southern UK sub-hourly 10 min rainfall from Met Office state-of-the-art convective-permitting model simulations for the present and future climate. Observational studies have shown that the duration of rainfall can decrease with temperature in summer in some regions. The duration decrease coincides with an intensification of sub-hourly rainfall extremes. This suggests that rainfall duration and sub-hourly rainfall intensity may change in future under climate change with important implications for future changes in flash flooding risk. The simulations show clear intensification of sub-hourly rainfall, but we fail to detect any decrease in rainfall duration. In fact, model results suggest the opposite with a slight (probably insignificant) lengthening of both extreme and non-extreme rainfall events in the future. The lengthening is driven by rainfall intensification without clear changes in the shape of the event profile. Other metrics are also examined, including the relationship between intense 10 min rainfall and temperature, and return levels changes; all are consistent with results found for hourly rainfall. No evaluation of model performance at the sub-hourly timescale is possible, highlighting the need for high-quality sub-hourly observations. Such sub-hourly observations will advance our understanding of the future risks of flash flooding.
Seasonal forecasting of Bangladesh summer monsoon rainfall using simple multiple regression model
Indian Academy of Sciences (India)
Md Mizanur Rahman; M Rafiuddin; Md Mahbub Alam
2013-04-01
In this paper, the development of a statistical forecasting method for summer monsoon rainfall over Bangladesh is described. Predictors for Bangladesh summer monsoon (June–September) rainfall were identified from the large scale ocean–atmospheric circulation variables (i.e., sea-surface temperature, surface air temperature and sea level pressure). The predictors exhibited a significant relationship with Bangladesh summer monsoon rainfall during the period 1961–2007. After carrying out a detailed analysis of various global climate datasets; three predictors were selected. The model performance was evaluated during the period 1977–2007. The model showed better performance in their hindcast seasonal monsoon rainfall over Bangladesh. The RMSE and Heidke skill score for 31 years was 8.13 and 0.37, respectively, and the correlation between the predicted and observed rainfall was 0.74. The BIAS of the forecasts (% of long period average, LPA) was −0.85 and Hit score was 58%. The experimental forecasts for the year 2008 summer monsoon rainfall based on the model were also found to be in good agreement with the observation.
Global Warming Induced Changes in Rainfall Characteristics in IPCC AR5 Models
Lau, William K. M.; Wu, Jenny, H.-T.; Kim, Kyu-Myong
2012-01-01
Changes in rainfall characteristic induced by global warming are examined from outputs of IPCC AR5 models. Different scenarios of climate warming including a high emissions scenario (RCP 8.5), a medium mitigation scenario (RCP 4.5), and 1% per year CO2 increase are compared to 20th century simulations (historical). Results show that even though the spatial distribution of monthly rainfall anomalies vary greatly among models, the ensemble mean from a sizable sample (about 10) of AR5 models show a robust signal attributable to GHG warming featuring a shift in the global rainfall probability distribution function (PDF) with significant increase (>100%) in very heavy rain, reduction (10-20% ) in moderate rain and increase in light to very light rains. Changes in extreme rainfall as a function of seasons and latitudes are also examined, and are similar to the non-seasonal stratified data, but with more specific spatial dependence. These results are consistent from TRMM and GPCP rainfall observations suggesting that extreme rainfall events are occurring more frequently with wet areas getting wetter and dry-area-getting drier in a GHG induced warmer climate.
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.
Modelling and Simulation of Seasonal Rainfall Using the Principle of Maximum Entropy
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Jonathan Borwein
2014-02-01
Full Text Available We use the principle of maximum entropy to propose a parsimonious model for the generation of simulated rainfall during the wettest three-month season at a typical location on the east coast of Australia. The model uses a checkerboard copula of maximum entropy to model the joint probability distribution for total seasonal rainfall and a set of two-parameter gamma distributions to model each of the marginal monthly rainfall totals. The model allows us to match the grade correlation coefficients for the checkerboard copula to the observed Spearman rank correlation coefficients for the monthly rainfalls and, hence, provides a model that correctly describes the mean and variance for each of the monthly totals and also for the overall seasonal total. Thus, we avoid the need for a posteriori adjustment of simulated monthly totals in order to correctly simulate the observed seasonal statistics. Detailed results are presented for the modelling and simulation of seasonal rainfall in the town of Kempsey on the mid-north coast of New South Wales. Empirical evidence from extensive simulations is used to validate this application of the model. A similar analysis for Sydney is also described.
Rainfall Runoff Modelling for Cedar Creek using HEC-HMS model
Pathak, P.; Kalra, A.
2015-12-01
Rainfall-runoff modelling studies are carried out for the purpose of basin and river management. Different models have been effectively used to examine relationships between rainfall and runoff. Cedar Creek Watershed Basin, the largest tributary of St. Josephs River, located in northeastern Indiana, was selected as a study area. The HEC-HMS model developed by US Army Corps of Engineers was used for the hydrological modelling. The national elevation and national hydrography data was obtained from United States Geological Survey National Map Viewer and the SSURGO soil data was obtained from United States Department of Agriculture. The watershed received hypothetical uniform rainfall for a duration of 13 hours. The Soil Conservation Service Curve Number and Unit Hydrograph methods were used for simulating surface runoff. The simulation provided hydrological details about the quantity and variability of runoff in the watershed. The runoff for different curve numbers was computed for the same basin and rainfall, and it was found that outflow peaked at an earlier time with a higher value for higher curve numbers than for smaller curve numbers. It was also noticed that the impact on outflow values nearly doubled with an increase of curve number of 10 for each subbasin in the watershed. The results from the current analysis may aid water managers in effectively managing the water resources within the basin. 1 Graduate Student, Department of Civil and Environmental Engineering, Southern Illinois University Carbondale, Carbondale, Illinois, 62901-6603 2 Development Review Division, Clark County Public Works, 500 S. Grand Central Parkway, Las Vegas, NV 89155, USA
Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa
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
Rainfall Climatology of the US Based on a Multifractal Storm Model
Lepore, C.; Molini, A.; Veneziano, D.; Yoon, S.
2012-12-01
Whether the multifractal properties of rainfall are impacted by climatology and therefore deviate from universality is a vexing question in both hydrology and the climate sciences and a crucial issue for rainfall downscaling applications. In a recent paper, Veneziano and Lepore (The Scaling of Temporal Rainfall, WRR, 2012) suggested a rainfall model with alternating storms and dry inter-storm periods and beta-lognormal multifractal rainfall intensity inside the storms. The parameters of the model are the rate of storm arrivals λ , the mean value mD and coefficient of variation VD of storm duration, the mean rainfall intensity inside the storms mI, and the multifractal parameters Cβ (lacunarity), CLN (intermittency), and dmax (outer limit of the scaling range). We use this model and 200 hourly rainfall records from NOAA to describe the variability of intense rainfall over the continental US. The records are selected based on length (at least 25 years) and data quality (quantization, fraction of unavailable values, periods when rainfall is reported as aggregated total depth…). We conclude that CLN and dmax display large systematic variations in space and with season. In particular, CLN decreases as latitude increases, from 0.20-0.25 along the Gulf of Mexico to about 0.12 in New England and 0.08 in the Northwest. This spatial variation is captured in approximation by partitioning the continental US into 11 climatic regions. Seasonal analysis shows that in most regions CLN is highest in the summer and lowest in the winter, following similar variations in the frequency and intensity of convective rainfall. An exception is the Northwest region, where CLN is almost constant throughout the year. The outer scale dmax is negatively correlated with CLN and follows opposite trends. The lacunarity parameter Cβ is lowest (around 0.04) in the Northeast and highest (around 0.07) in Florida and the Midwestern region. Lacunarity tends to be higher in the spring and summer
Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India
Indian Academy of Sciences (India)
Nachiketa Acharya; S C Kar; Makarand A Kulkarni; U C Mohanty; L N Sahoo
2011-10-01
The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.
Changes in lakes water volume and runoff over ungauged Sahelian watersheds
Gal, L.; Grippa, M.; Hiernaux, P.; Peugeot, C.; Mougin, E.; Kergoat, L.
2016-09-01
A large part of the Sahel consists of endorheic hydrological systems, where reservoirs and lakes capture surface runoff during the rainy season, making water available during the dry season. Monitoring and understanding the dynamics of these lakes and their relationships to the ecohydrological evolution of the region is important to assess past, present and future changes of water resources in the Sahel. Yet, most of Sahelian watersheds are still ungauged or poorly gauged, which hinders the assessment of the water flows feeding the lakes and the overall runoff over their watershed. In this paper, a methodology is developed to estimate water inflow to lakes for ungauged watersheds. It is tested for the Agoufou lake in the Gourma region in Mali, for which in situ water height measurements and surface areas estimations by remote sensing are simultaneously available. A Height-Volume-Area (HVA) model is developed to relate water volume to water height and lake surface area. This model is combined to daily evaporation and precipitation to estimate water inflow to the lake, which approximates runoff over the whole watershed. The ratio between annual water inflow and precipitation increases over the last sixty years as a result of a significant increase in runoff coefficient over the Agoufou watershed. The method is then extended to derive water inflow to three other Sahelian lakes in Mauritania and Niger. No in situ measurements are available and lake surface areas estimation by remote sensing is the only source of information. Dry season surface area changes and estimated evaporation are used to select a suited VA relationship for each case. It is found that the ratio between annual water inflow and precipitation has also increased in the last 60 years over these watersheds, although trends at the Mauritanian site are not statistically significant. The remote sensing approach developed in this study can be easily applied to recent sensors such as Sentinel-2 or Landsat-8
A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate
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.
Duc, Hiep Nguyen; Rivett, Kelly; MacSween, Katrina; Le-Anh, Linh
2017-01-01
Rainfall in New South Wales (NSW), located in the southeast of the Australian continent, is known to be influenced by four major climate drivers: the El Niño/Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD). Many studies have shown the influences of ENSO, IPO modulation, SAM and IOD on rainfall in Australia and on southeast Australia in particular. However, only limited work has been undertaken using a multiple regression framework to examine the extent of the combined effect of these climate drivers on rainfall. This paper analysed the role of these combined climate drivers and their interaction on the rainfall in NSW using Bayesian Model Averaging (BMA) to account for model uncertainty by considering each of the linear models across the whole model space which is equal to the set of all possible combinations of predictors to find the model posterior probabilities and their expected predictor coefficients. Using BMA for linear regression models, we are able to corroborate and confirm the results from many previous studies. In addition, the method gives the ranking order of importance and the probability of the association of each of the climate drivers and their interaction on the rainfall at a site. The ability to quantify the relative contribution of the climate drivers offers the key to understand the complex interaction of drivers on rainfall, or lack of rainfall in a region, such as the three big droughts in southeastern Australia which have been the subject of discussion and debate recently on their causes.
Williams, C.; Kniveton, D.; Layberry, R.
2009-04-01
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 subcontinent 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 is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.
[Local sensitivity and its stationarity analysis for urban rainfall runoff modelling].
Lin, Jie; Huang, Jin-Liang; Du, Peng-Fei; Tu, Zhen-Shun; Li, Qing-Sheng
2010-09-01
Sensitivity analysis of urban-runoff simulation is a crucial procedure for parameter identification and uncertainty analysis. Local sensitivity analysis using Morris screening method was carried out for urban rainfall runoff modelling based on Storm Water Management Model (SWMM). The results showed that Area, % Imperv and Dstore-Imperv are the most sensitive parameters for both total runoff volume and peak flow. Concerning total runoff volume, the sensitive indices of Area, % Imperv and Dstore-Imperv were 0.46-1.0, 0.61-1.0, -0.050(-) - 5.9, respectively; while with respect to peak runoff, they were 0.48-0.89, 0.59-0.83, 0(-) -9.6, respectively. In comparison, the most sensitive indices (Morris) for all parameters with regard to total runoff volume and peak flow appeared in the rainfall event with least rainfall; and less sensitive indices happened in the rainfall events with heavier rainfall. Furthermore, there is considerable variability in sensitive indices for each rainfall event. % Zero-Imperv's coefficient variations have the largest values among all parameters for total runoff volume and peak flow, namely 221.24% and 228.10%. On the contrary, the coefficient variations of conductivity among all parameters for both total runoff volume and peak flow are the smallest, namely 0.
The role of observation uncertainty in the calibration of hydrologic rainfall-runoff models
Directory of Open Access Journals (Sweden)
T. Ghizzoni
2007-06-01
Full Text Available Hydrologic rainfall-runoff models are usually calibrated with reference to a limited number of recorded flood events, for which rainfall and runoff measurements are available. In this framework, model's parameters consistency depends on the number of both events and hydrograph points used for calibration, and on measurements reliability. Recently, to make users aware of application limits, major attention has been devoted to the estimation of uncertainty in hydrologic modelling. Here a simple numerical experiment is proposed, that allows the analysis of uncertainty in hydrologic rainfall-runoff modelling associated to both quantity and quality of available data.
A distributed rainfall-runoff model based on geomorphologic concepts has been used. The experiment involves the analysis of an ensemble of model runs, and its overall set up holds if the model is to be applied in different catchments and climates, or even if a different hydrologic model is used. With reference to a set of 100 synthetic rainfall events characterized by a given rainfall volume, the effect of uncertainty in parameters calibration is studied. An artificial truth – perfect observation – is created by using the model in a known configuration. An external source of uncertainty is introduced by assuming realistic, i.e. uncertain, discharge observations to calibrate the model. The range of parameters' values able to "reproduce" the observation is studied. Finally, the model uncertainty is evaluated and discussed. The experiment gives useful indications about the number of both events and data points needed for a careful and stable calibration of a specific model, applied in a given climate and catchment. Moreover, an insight on the expected and maximum error in flood peak discharge simulations is given: errors ranging up to 40% are to be expected if parameters are calibrated on insufficient data sets.
Areal rainfall estimation using moving cars - computer experiments including hydrological modeling
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.
Generation of future high-resolution rainfall time series with a disaggregation model
Müller, Hannes; Haberlandt, Uwe
2017-04-01
High-resolution rainfall data are needed in many fields of hydrology and water resources management. For analyzes of future rainfall condition climate scenarios exist with hourly values of rainfall. However, the direct usage of these data is associated with uncertainties which can be indicated by comparisons of observations and C20 control runs. An alternative is the derivation of changes of rainfall behavior over the time from climate simulations. Conclusions about future rainfall conditions can be drawn by adding these changes to observed time series. A multiplicative cascade model is used in this investigation for the disaggregation of daily rainfall amounts to hourly values. Model parameters can be estimated by REMO rainfall time series (UBA-, BfG- and ENS-realization), based on ECHAM5. Parameter estimation is carried out for C20 period as well as near term and long term future (2021-2050 and 2071-2100). Change factors for both future periods are derived by parameter comparisons and added to the parameters estimated from observed time series. This enables the generation of hourly rainfall time series from observed daily values with respect to future changes. The investigation is carried out for rain gauges in Lower Saxony. Generated Time series are analyzed regarding statistical characteristics, e.g. extreme values, event-based (wet spell duration and amounts, dry spell duration, …) and continuum characteristics (average intensity, fraction of dry intervals,…). The generation of the time series is validated by comparing the changes in the statistical characteristics from the REMO data and from the disaggregated data.
A spatial and nonstationary model for the frequency of extreme rainfall events
DEFF Research Database (Denmark)
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan;
2013-01-01
Changes in the properties of extreme rainfall events have been observed worldwide. In relation to the discussion of ongoing climatic changes, it is of high importance to attribute these changes to known sources of climate variability. Focusing on spatial and temporal changes in the frequency...... of extreme rainfall events, a statistical model is tested for this purpose. The model is built on the theory of generalized linear models and uses Poisson regression solved by generalized estimation equations. Spatial and temporal explanatory variables can be included simultaneously, and their relative...... importance can be assessed. Additionally, the model allows for a spatial correlation between the measurements. Data from a Danish rain gauge network are used as a case study for model evaluation. Focusing on 10 min and 24 h rainfall extremes, it was found that regional variation in the mean annual...
Simulation of the Indian summer monsoon onset-phase rainfall using a regional model
Srinivas, C. V.
2015-09-11
This study examines the ability of the Advanced Research WRF (ARW) regional model to simulate Indian summer monsoon (ISM) rainfall climatology in different climate zones during the monsoon onset phase in the decade 2000–2009. The initial and boundary conditions for ARW are provided from the NCEP/NCAR Reanalysis Project (NNRP) global reanalysis. Seasonal onset-phase rainfall is compared with corresponding values from 0.25° IMD (India Meteorological Department) rainfall and NNRP precipitation data over seven climate zones (perhumid, humid, dry/moist, subhumid, dry/moist, semiarid and arid) of India to see whether dynamical downscaling using a regional model yields advantages over just using large-scale model predictions. Results show that the model could simulate the onset phase in terms of progression and distribution of rainfall in most zones (except over the northeast) with good correlations and low error metrics. The observed mean onset dates and their variability over different zones are well reproduced by the regional model over most climate zones. It has been found that the ARW performed similarly to the reanalysis in most zones and improves the onset time by 1 to 3 days in zones 4 and 7, in which the NNRP shows a delayed onset compared to the actual IMD onset times. The variations in the onset-phase rainfall during the below-normal onset (June negative) and above-normal onset (June positive) phases are well simulated. The slight underestimation of onset-phase rainfall in the northeast zone could be due to failure in resolving the wide extent of topographic variations and the associated multiscale interactions in that zone. Spatial comparisons showed improvement of pentad rainfall in both space and quantity in ARW simulations over NNRP data, as evident from a wider eastward distribution of pentad rainfall over the Western Ghats, central and eastern India, as in IMD observations. While NNRP under-represented the high pentad rainfall over northeast, east and
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
Institute of Scientific and Technical Information of China (English)
Hong-jun BAO; Li-li WANG; Zhi-jia LI; Lin-na ZHAO; Guo-ping ZHANG
2010-01-01
A grid-based distributed hydrological model, the Block-wise use of TOPMODEL (BTOPMC), which was developed from the original TOPMODEL, was used for hydrological daily rainfall-runoff simulation. In the BTOPMC model, the runoff is explicitly calculated on a cell-by-cell basis, and the Muskingum-Cunge flow concentration method is used. In order to test the model's applicability, the BTOPMC model and the Xin'anjiang model were applied to the simulation of a humid watershed and a semi-humid to semi-arid watershed in China. The model parameters were optimized with the Shuffle Complex Evolution (SCE-UA) method. Results show that both models can effectively simulate the daily hydrograph in humid watersheds, but that the BTOPMC model performs poorly in semi-humid to semi-arid watersheds. The excess-infiltration mechanism should be incorporated into the BTOPMC model to broaden the model's applicability.
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
Directory of Open Access Journals (Sweden)
A. N. N. Chedop
2016-01-01
Full Text Available This paper presents a field study of indicators of the performance of absorption refrigerating systems in the Sudano-Sahelian region of Cameroon. A modeling of various components of the system is performed in order to choose the technology best adapted for the region. Based on the characteristics from the manufacturers, four absorption chillers were studied; namely: EAW Schüco LB 15 and 30; Rotartica Solar 045 and Sonnenklima Suninverse. The model of Kühn and Ziegler and meteorological data of the city of Maroua ((14.33 °E, 10.58 °N were used to assess the coefficient of the performance (COP of these machines. Results showed that the Sonnenklima Suninverse technology is best suited for the climatic conditions of the Sudano-Sahelian region with COP values between 1.5 and 3.5.
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
Directory of Open Access Journals (Sweden)
A. El-Shafie
2011-07-01
Full Text Available Rainfall is considered as one of the major component of the hydrological process, it takes significant part of evaluating drought and flooding events. Therefore, it is important to have accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting task such as Multi-Layer Perceptron Neural Networks (MLP-NN. In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series.
Two different static neural networks and one dynamic neural network namely; Multi-Layer Peceptron Neural network (MLP-NN, Radial Basis Function Neural Network (RBFNN and Input Delay Neural Network (IDNN, respectively, have been examined in this study. Those models had been developed for two time horizon in monthly and weekly rainfall basis forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008 on weekly basis and 22 yr (1987–2008 for monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural network. Results showed that MLP-NN neural network model able to follow the similar trend of the actual rainfall, yet it still relatively poor. RBFNN model achieved better accuracy over the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model outperformed during training and testing stage which prove a consistent level of accuracy with seen and unseen data. Furthermore, the IDNN significantly enhance the forecasting accuracy if compared with the other static neural network model as they could memorize the
A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process
Nourani, Vahid; Komasi, Mehdi
2013-05-01
This paper demonstrates the potential use of Artificial Intelligence (AI) techniques for predicting daily runoff at multiple gauging stations. Uncertainty and complexity of the rainfall-runoff process due to its variability in space and time in one hand and lack of historical data on the other hand, cause difficulties in the spatiotemporal modeling of the process. In this paper, an Integrated Geomorphological Adaptive Neuro-Fuzzy Inference System (IGANFIS) model conjugated with C-means clustering algorithm was used for rainfall-runoff modeling at multiple stations of the Eel River watershed, California. The proposed model could be used for predicting runoff in the stations with lack of data or any sub-basin within the watershed because of employing the spatial and temporal variables of the sub-basins as the model inputs. This ability of the integrated model for spatiotemporal modeling of the process was examined through the cross validation technique for a station. In this way, different ANFIS structures were trained using Sugeno algorithm in order to estimate daily discharge values at different stations. In order to improve the model efficiency, the input data were then classified into some clusters by the means of fuzzy C-means (FCMs) method. The goodness-of-fit measures support the gainful use of the IGANFIS and FCM methods in spatiotemporal modeling of hydrological processes.
van der Merwe, M. R.; Du Preez, M.
2012-12-01
Cholera has become endemic in coastal and inland areas within the tropics as well as areas outside of the tropics in Africa. Climate conditions and weather patterns differ between areas reporting cholera cases in Africa. Some areas experience two rainfall seasons compared to areas with only one rainfall season in a year. Further, climate variability or ENSO events affect local weather conditions differently. La Niña, i.e. cold events lead to higher than normal rainfall in areas in southern Africa compared to areas close to the equator in eastern Africa which report less than normal rainfall. Time series analysis of cholera cases and rainfall data at different spatial resolutions highlight the overlap of the rainfall season with the reporting of cholera cases. Cholera cases are also reported in between rainy seasons in different areas but the incidence is significantly less compared to the rainy season. An increase in the intensity of outbreaks is also noted during the rainy season following a drier than normal 'dry' season. This necessitates the understanding of the reasons for the observed correlation between rainfall season and cholera outbreaks in order to develop a prediction model which can accurately predict the likelihood of an outbreak. Due to the complexities associated with accurately predicting weather data more than seven days ahead of time it is necessary to identify global drivers with a lagged effect on local rainfall patterns. Climate variability, i.e. ENSO is investigated at different temporal scales; spatial locations and time lags. Sea surface temperature anomalies (SSTa) measured closed to the equator and in the southern parts of the Indian Ocean are more closely associated with rainfall anomalies at specific time lags in equatorial, East African, south East African and central African areas compared to SSTa measured in different regions in the Pacific Ocean. An explanatory prediction model is developed for conditions in Mozambique (coastal
The ensemble particle filter (EnPF) in rainfall-runoff models
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
2009-01-01
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
Exploring the potential of multivariate depth-damage and rainfall-damage models
DEFF Research Database (Denmark)
van Ootegem, Luc; van Herck, K.; Creten, T.
2017-01-01
In Europe, floods are among the natural catastrophes that cause the largest economic damage. This article explores the potential of two distinct types of multivariate flood damage models: ‘depth-damage’ models and ‘rainfall-damage’ models. We use survey data of 346 Flemish households that were vi...
The ensemble particle filter (EnPF) in rainfall-runoff models
Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.
2009-01-01
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the
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.
Zaller, Johann; Simmer, Laura; Tabi Tataw, James; Formayer, Herbert; Hösch, Johannes; Baumgarten, Andreas
2013-04-01
Climate change scenarios for eastern Austria predict a seasonal shift in precipitation patterns with fewer but heavier rainfall events and longer drought periods during the growing season and more precipitation during winter. This is expected to alter arthropods living in natural and agricultural ecosystems with consequences for several ecosystem functions and services. In order to better understand the effects of future rainfall patterns on aboveground arthropods inhabiting an agroecosystem, we conducted an experiment where we simulated rainfall patterns in model arable systems with three different soil types. Experiments were conducted in winter wheat cultivated in a lysimeter facility near Vienna, Austria, where three different soil types (calcaric phaeozem, calcic chernozem and gleyic phaeozem) were subjected to long-term current vs. predicted rainfall patterns according to regionalized climate change projections for 2071-2100. Aboveground arthropods were assessed by suction sampling in April, May and June 2012. We found significant differences in mean total arthropod abundances between the sampling dates with 20 ± 2 m-2, 90 ± 20 m-2 and 289 ± 54 m-2 in April, May and June, respectively. Across all three sampling dates, future rainfall patterns significantly reduced the abundance of Araneae (-43%), Auchenorrhyncha (-39%), Coleoptera (-48%), Carabidae (-41%), Chrysomelidae (-64%), Collembola (-58%), Diptera (-75%) and Neuroptera (-73%). Generally, different soil types had no effect on the abundance of arthropods. The diversity of arthropod communities was unaffected by rainfall patterns or soil types. Correlation analyses of arthropod abundances with crop biomass, weed density and abundance suggest that rainfall effects indirectly affected arthropods via changes on crops and weeds. In conclusion, these results show that future rainfall patterns will have detrimental effects on the abundance of a variety of aboveground arthropods in winter wheat with potential
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
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A. El-Shafie
2012-04-01
Full Text Available Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN. In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series.
Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN, radial basis function neural network (RBFNN and input delay neural network (IDNN, respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008 on a weekly basis and 22 yr (1987–2008 on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia
El-Shafie, A.; Noureldin, A.; Taha, M.; Hussain, A.; Mukhlisin, M.
2012-04-01
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997-2008) on a weekly basis and 22 yr (1987-2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.
Zhang, Yongqiang; Vaze, Jai; Chiew, Francis H. S.; Teng, Jin; Li, Ming
2014-09-01
Understanding a catchment's behaviours in terms of its underlying hydrological signatures is a fundamental task in surface water hydrology. It can help in water resource management, catchment classification, and prediction of runoff time series. This study investigated three approaches for predicting six hydrological signatures in southeastern Australia. These approaches were (1) spatial interpolation with three weighting schemes, (2) index model that estimates hydrological signatures using catchment characteristics, and (3) classical rainfall-runoff modelling. The six hydrological signatures fell into two categories: (1) long-term aggregated signatures - annual runoff coefficient, mean of log-transformed daily runoff, and zero flow ratio, and (2) signatures obtained from daily flow metrics - concavity index, seasonality ratio of runoff, and standard deviation of log-transformed daily flow. A total of 228 unregulated catchments were selected, with half the catchments randomly selected as gauged (or donors) for model building and the rest considered as ungauged (or receivers) to evaluate performance of the three approaches. The results showed that for two long-term aggregated signatures - the log-transformed daily runoff and runoff coefficient, the index model and rainfall-runoff modelling performed similarly, and were better than the spatial interpolation methods. For the zero flow ratio, the index model was best and the rainfall-runoff modelling performed worst. The other three signatures, derived from daily flow metrics and considered to be salient flow characteristics, were best predicted by the spatial interpolation methods of inverse distance weighting (IDW) and kriging. Comparison of flow duration curves predicted by the three approaches showed that the IDW method was best. The results found here provide guidelines for choosing the most appropriate approach for predicting hydrological behaviours at large scales.
Calibrating a Rainfall-Runoff and Routing Model for the Continental United States
Jankowfsky, S.; Li, S.; Assteerawatt, A.; Tillmanns, S.; Hilberts, A.
2014-12-01
Catastrophe risk models are widely used in the insurance industry to estimate the cost of risk. The models consist of hazard models linked to vulnerability and financial loss models. In flood risk models, the hazard model generates inundation maps. In order to develop country wide inundation maps for different return periods a rainfall-runoff and routing model is run using stochastic rainfall data. The simulated discharge and runoff is then input to a two dimensional inundation model, which produces the flood maps. In order to get realistic flood maps, the rainfall-runoff and routing models have to be calibrated with observed discharge data. The rainfall-runoff model applied here is a semi-distributed model based on the Topmodel (Beven and Kirkby, 1979) approach which includes additional snowmelt and evapotranspiration models. The routing model is based on the Muskingum-Cunge (Cunge, 1969) approach and includes the simulation of lakes and reservoirs using the linear reservoir approach. Both models were calibrated using the multiobjective NSGA-II (Deb et al., 2002) genetic algorithm with NLDAS forcing data and around 4500 USGS discharge gauges for the period from 1979-2013. Additional gauges having no data after 1979 were calibrated using CPC rainfall data. The model performed well in wetter regions and shows the difficulty of simulating areas with sinks such as karstic areas or dry areas. Beven, K., Kirkby, M., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24 (1), 43-69. Cunge, J.A., 1969. On the subject of a flood propagation computation method (Muskingum method), J. Hydr. Research, 7(2), 205-230. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on evolutionary computation, 6(2), 182-197.
Optimal parameters for the Green-Ampt infiltration model under rainfall conditions
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Chen Li
2015-06-01
Full Text Available The Green-Ampt (GA model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations has not been studied extensively. We compared calculated rainfall infiltration results for various soils obtained using existing GA parameterizations with those obtained by solving the Richards equation for variably saturated flow. Results provided an overview of GA model performance evaluated by means of a root-meansquare- error-based objective function across a large region in GA parameter space as compared to the Richards equation, which showed a need for seeking optimal GA parameters. Subsequent analysis enabled the identification of optimal GA parameters that provided a close fit with the Richards equation. The optimal parameters were found to substantially outperform the standard theoretical parameters, thus improving the utility and accuracy of the GA model for infiltration simulations under rainfall conditions. A sensitivity analyses indicated that the optimal parameters may change for some rainfall scenarios, but are relatively stable for high-intensity rainfall events.
Williams, C.; Kniveton, D.; Layberry, R.
2007-12-01
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. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, 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 a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.
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Ibrahim Suliman Hanaish
2011-01-01
Full Text Available Three versions of Bartlett Lewis rectangular pulse rainfall models, namely, the Original Bartlett Lewis (OBL, Modified Bartlett Lewis (MBL, and 2N-cell-type Bartlett Lewis model (BL2n, are considered. These models are fitted to the hourly rainfall data from 1970 to 2008 obtained from Petaling Jaya rain gauge station, located in Peninsular Malaysia. The generalized method of moments is used to estimate the model parameters. Under this method, minimization of two different objective functions which involve different weight functions, one weight is inversely proportional to the variance and another one is inversely proportional to the mean squared, is carried out using Nelder-Mead optimization technique. For the purpose of comparison of the performance of the three different models, the results found for the months of July and November are used for illustration. This performance is assessed based on the goodness of fit of the models. In addition, the sensitivity of the parameter estimates to the choice of the objective function is also investigated. It is found that BL2n slightly outperforms OBL. However, the best model is the Modified Bartlett Lewis MBL, particularly when the objective function considered involves weight which is inversely proportional to the variance.
Capabilities of stochastic rainfall models as data providers for urban hydrology
Haberlandt, Uwe
2017-04-01
For planning of urban drainage systems using hydrological models, long, continuous precipitation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative. This contribution compares three precipitation models regarding their suitability to provide 5 minute continuous rainfall time series for a) sizing of drainage networks for urban flood protection and b) dimensioning of combined sewage systems for pollution reduction. The rainfall models are a parametric stochastic model (Haberlandt et al., 2008), a non-parametric probabilistic approach (Bárdossy, 1998) and a stochastic downscaling of dynamically simulated rainfall (Berg et al., 2013); all models are operated both as single site and multi-site generators. The models are applied with regionalised parameters assuming that there is no station at the target location. Rainfall and discharge characteristics are utilised for evaluation of the model performance. The simulation results are compared against results obtained from reference rainfall stations not used for parameter estimation. The rainfall simulations are carried out for the federal states of Baden-Württemberg and Lower Saxony in Germany and the discharge simulations for the drainage networks of the cities of Hamburg, Brunswick and Freiburg. Altogether, the results show comparable simulation performance for the three models, good capabilities for single site simulations but low skills for multi-site simulations. Remarkably, there is no significant difference in simulation performance comparing the tasks flood protection with pollution reduction, so the models are finally able to simulate both the extremes and the long term characteristics of rainfall equally well. Bárdossy, A., 1998. Generating precipitation time series using simulated annealing. Wat. Resour. Res., 34(7): 1737-1744. Berg, P., Wagner, S., Kunstmann, H., Schädler, G
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.
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.
Introducing a rainfall compound distribution model based on weather patterns sub-sampling
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F. Garavaglia
2010-06-01
Full Text Available This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis.
First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France.
The distribution of the multi-exponential weather patterns (MEWP is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period.
Influence of dry-season vegetation variability on Sahelian dust during 2002-2015
Kergoat, L.; Guichard, F.; Pierre, C.; Vassal, C.
2017-05-01
The drivers of dust emission interannual variability in North Africa, the largest dust source on Earth, are still debated. Early studies outlined the role of previous-season rainfall and vegetation growth, while some recent studies emphasize the role of wind variability. Here we use a newly developed estimation of dry-season nonphotosynthetic vegetation cover in the Sahel based on Moderate Resolution Imaging Spectroradiometer (MODIS) short-wave infrared bands over the 2002-2015 period. The vegetation growth anomalies caused by variability of rainfall in June-September translate to anomalies of dry vegetation cover that persist throughout the dry season until May. These vegetation anomalies explain 43% (50%) of the year-to-year variance in Sahelian-mean dry-season aerosol optical depth (AOD) as derived from MODIS Deep Blue (Sun photometers). Similar explained variance is found with 10 m wind speed and dust uplift potential. The central Sahel proves more important than the western Sahel for dry-season AOD variability.
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M. N. Ahasan
2013-01-01
Full Text Available Simulation of high impact rainfall events over southeastern hilly region of Bangladesh has been carried out using Fifth-Generation PSU/NCAR Mesoscale Model (MM5 conducting two historical rainfall events, namely, 21 June, 2004 and 11 July, 2004. These extraordinary rainfall events were localized over the Rangamati region and recorded 304 mm and 337 mm rainfall on 21 June, 2004 and 11 July, 2004, respectively, over Rangamati within a span of 24 h. The model performance was evaluated by examining the different predicted and derived parameters. It is found that the seasonal monsoon trough has northerly position compared to normal and pass through Bangladesh extending up to northeast India for both cases. The heat low was found to be intense (996 hPa with strong north-south pressure gradient (12–15 hPa. The analysis of the geopotential height field at 200 hPa shows that the Tibetan high is shifted towards south by 7-8° latitudes with axis along 22–25°N for both cases. The analysis of the wind field shows that the areas of high impact rainfall exhibit strong convergence of low level monsoon circulation (~19–58 knots. The strong southwesterlies were found to exist up to 500 hPa level in both cases. The lower troposphere (925–500 hPa was characterized by the strong vertical wind shear (~9–18 ms−1 and high relative vorticity (~20–40 × 10−5 s−1. The analysis also shows that the areas of high impact rainfall events and neighbourhoods are characterized by strong low level convergence and upper level divergence. The strong southwesterly flow causes transportation of large amount of moisture from the Bay of Bengal towards Bangladesh, especially over the areas of Rangamati and neighbourhoods. The high percentage of relative humidity extends up to the upper troposphere along a narrow vertical column. Model produced details structure of the spatial patterns of rainfall over Bangladesh reasonably well though there are some
Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph
2017-04-01
Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative
Yang, Ting; Wang, Quanjiu; Wu, Laosheng; Zhao, Guangxu; Liu, Yanli; Zhang, Pengyu
2016-07-01
Nutrients transport is a main source of water pollution. Several models describing transport of soil nutrients such as potassium, phosphate and nitrate in runoff water have been developed. The objectives of this research were to describe the nutrients transport processes by considering the effect of rainfall detachment, and to evaluate the factors that have greatest influence on nutrients transport into runoff. In this study, an existing mass-conservation equation and rainfall detachment process were combined and augmented to predict runoff of nutrients in surface water in a Loess Plateau soil in Northwestern Yangling, China. The mixing depth is a function of time as a result of rainfall impact, not a constant as described in previous models. The new model was tested using two different sub-models of complete-mixing and incomplete-mixing. The complete-mixing model is more popular to use for its simplicity. It captured the runoff trends of those high adsorption nutrients, and of nutrients transport along steep slopes. While the incomplete-mixing model predicted well for the highest observed concentrations of the test nutrients. Parameters inversely estimated by the models were applied to simulate nutrients transport, results suggested that both models can be adopted to describe nutrients transport in runoff under the impact of rainfall.
Statistical analysis of error propagation from radar rainfall to hydrological models
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D. Zhu
2013-04-01
Full Text Available This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2 in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.
Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows
Talebi, A.; Uijlenhoet, R.; Troch, P.A.
2008-01-01
Recently, D'Odorico and Fagherazzi (2003) proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003). Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building bl
Optimal parameters for the Green-Ampt infiltration model under rainfall conditions
Chen, Li; Xiang, Long; Young, Michael H.; Yin, Jun; Yu, Zhongbo; van Genuchten, Martinus Th.
2015-01-01
The Green-Ampt (GA) model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations) has not been studied extensively. We compared calcu
DEFF Research Database (Denmark)
Stisen, Simon; Sandholt, Inge
2010-01-01
The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage...... and distributed nature of satellite-based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main...
Rainfall-Runoff Modelling using Modified NRCS-CN,RS and GIS -A Case Study
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P.Sundara Kumar
2016-03-01
Full Text Available Study of rainfall and runoff for any area and modeling it, is one of the important aspects for planning and development of water resources. The development of water resources and its effective management plays a vital role in development of any country more particularly in India, which is an agricultural based economy. Hence it is intended to develop a model of Rainfall and runoff to a river basin and also apply the methodology to Sarada River Basin which has drainage area of 1252.99 Sq.km. The basin is situated in Vishakhapatnam district of Andhra Pradesh, India. The rainfall and runoff data has been collected from the gauging stations of the basin apart from rainfall data from nearby stations. MNRCS-CN method has been adopted to calculate runoff. Various hydrological parameters like soil information, rainfall, land use and land cover (LU/LC were considered to use in MNRCS-CN method. The depth of runoff has been computed for different land use patterns using, IRS-P4- LISS IV data for the study area. Based on the analysis, land use/land cover pattern of Sarada River Basin has been prepared. The land use/land cover patterns were also visually interpreted and digitized using ERDAS IMAGINE software. The raster data was processed in ERDAS and geo-referenced and various maps viz. LU/LC maps, drainage map, contour map, DEM (Digital elevation model have been generated apart from rainfall potential map using GIS tool. The estimated runoff using MNRCS-CN model has been simulated and compared with that of actual runoff. The performance of the model is found to be good for the data considered. The coefficient of determination R2 value for the observed runoff and that of the computed runoff is found to be more than 0.72 for the selected watershed basin
Lin, Kun-Hsiang; Tseng, Hung-Wei; Kuo, Chen-Min; Yang, Tao-Chang; Yu, Pao-Shan
2016-04-01
Typhoons with heavy rainfall and strong wind often cause severe floods and losses in Taiwan, which motivates the development of rainfall forecasting models as part of an early warning system. Thus, this study aims to develop rainfall forecasting models based on two machine learning methods, support vector machines (SVMs) and random forests (RFs), and investigate the performances of the models with different predictor sets for searching the optimal predictor set in forecasting. Four predictor sets were used: (1) antecedent rainfalls, (2) antecedent rainfalls and typhoon characteristics, (3) antecedent rainfalls and meteorological factors, and (4) antecedent rainfalls, typhoon characteristics and meteorological factors to construct for 1- to 6-hour ahead rainfall forecasting. An application to three rainfall stations in Yilan River basin, northeastern Taiwan, was conducted. Firstly, the performance of the SVMs-based forecasting model with predictor set #1 was analyzed. The results show that the accuracy of the models for 2- to 6-hour ahead forecasting decrease rapidly as compared to the accuracy of the model for 1-hour ahead forecasting which is acceptable. For improving the model performance, each predictor set was further examined in the SVMs-based forecasting model. The results reveal that the SVMs-based model using predictor set #4 as input variables performs better than the other sets and a significant improvement of model performance is found especially for the long lead time forecasting. Lastly, the performance of the SVMs-based model using predictor set #4 as input variables was compared with the performance of the RFs-based model using predictor set #4 as input variables. It is found that the RFs-based model is superior to the SVMs-based model in hourly typhoon rainfall forecasting. Keywords: hourly typhoon rainfall forecasting, predictor selection, support vector machines, random forests
Evaluation of a conceptual rainfall forecasting model from observed and simulated rain events
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L. Dolciné
1998-01-01
Full Text Available Very short-term rainfall forecasting models designed for runoff analysis of catchments, particularly those subject to flash-floods, typically include one or more variables deduced from weather radars. Useful variables for defining the state and evolution of a rain system include rainfall rate, vertically integrated rainwater content and advection velocity. The forecast model proposed in this work complements recent dynamical formulations by focusing on a formulation incorporating these variables using volumetric radar data to define the model state variables, determining the rainfall source term directly from multi-scan radar data, explicitly accounting for orographic enhancement, and explicitly incorporating the dynamical model components in an advection-diffusion scheme. An evaluation of this model is presented for four rain events collected in the South of France and in the North-East of Italy. Model forecasts are compared with two simple methods: persistence and extrapolation. An additional analysis is performed using an existing mono-dimensional microphysical meteorological model to produce simulated rain events and provide initialization data. Forecasted rainfall produced by the proposed model and the extrapolation method are compared to the simulated events. The results show that the forecast model performance is influenced by rainfall temporal variability and performance is better for less variable rain events. The comparison with the extrapolation method shows that the proposed model performs better than extrapolation in the initial period of the forecast lead-time. It is shown that the performance of the proposed model over the extrapolation method depends essentially on the additional vertical information available from voluminal radar.
Development of Rainfall-Discharge Model for Future NPP candidate Site
Energy Technology Data Exchange (ETDEWEB)
An, Ji-hong; Yee, Eric [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)
2015-10-15
By this study, most suitable model for future nuclear power plant site in Yeongdeok to be used to predict peak amount of riverine flooding was developed by examining historical rainfall and discharge data from the nearest gage station which is Jodong water level gage station in Taehwa basin. Sitting a nuclear power plant (NPP) requires safety analyses that include the effects of extreme events such as flooding or earthquake. In light of South Korean government's 15-year power supply plan that calls for the construction of new nuclear power station in Yeongdeok, it becomes more important to site new station in a safe area from flooding. Because flooding or flooding related accidents mostly happen due to extremely intense rainfall, it is necessary to find out the relationship between rainfall and run-off by setting up feasible model to figure out the peak flow of the river around nuclear related facilities.
Directory of Open Access Journals (Sweden)
S. Raia
2014-03-01
Full Text Available Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. These models extend spatially the static stability models adopted in geotechnical engineering, and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the operation of the existing models lays in the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of rainfall-induced shallow landslides. For this purpose, we have modified the transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS code. The new code (TRIGRS-P adopts a probabilistic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs
Directory of Open Access Journals (Sweden)
S. Raia
2013-02-01
Full Text Available Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS code. The new code (TRIGRS-P adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying
Raia, S.; Alvioli, M.; Rossi, M.; Baum, R.L.; Godt, J.W.; Guzzetti, F.
2013-01-01
Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters
Uncertainity and equifinality driven by rainfall in the APEX model
Uncertainty is an inherent part of complex environmental models. Uncertainty in model inputs, model parameterization, and model structure can propagate non-linearly to the model outputs. Evaluating, quantifying, and reporting uncertainty is crucial when model results are used as basis for managerial...
Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara
2015-09-01
Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the
Extreme Rainfall Analysis using Bayesian Hierarchical Modeling in the Willamette River Basin, Oregon
Love, C. A.; Skahill, B. E.; AghaKouchak, A.; Karlovits, G. S.; England, J. F.; Duren, A. M.
2016-12-01
We present preliminary results of ongoing research directed at evaluating the worth of including various covariate data to support extreme rainfall analysis in the Willamette River basin using Bayesian hierarchical modeling (BHM). We also compare the BHM derived extreme rainfall estimates with their respective counterparts obtained from a traditional regional frequency analysis (RFA) using the same set of rain gage extreme rainfall data. The U.S. Army Corps of Engineers (USACE) Portland District operates thirteen dams in the 11,478 square mile Willamette River basin (WRB) located in northwestern Oregon, a major tributary of the Columbia River whose 187 miles long main stem, the Willamette River, flows northward between the Coastal and Cascade Ranges. The WRB contains approximately two-thirds of Oregon's population and 20 of the 25 most populous cities in the state. Extreme rainfall estimates are required to support risk-informed hydrologic analyses for these projects as part of the USACE Dam Safety Program. We analyze daily annual rainfall maxima data for the WRB utilizing the spatial BHM R package "spatial.gev.bma", which has been shown to be efficient in developing coherent maps of extreme rainfall by return level. Our intent is to profile for the USACE an alternate methodology to a RFA which was developed in 2008 due to the lack of an official NOAA Atlas 14 update for the state of Oregon. Unlike RFA, the advantage of a BHM-based analysis of hydrometeorological extremes is its ability to account for non-stationarity while providing robust estimates of uncertainty. BHM also allows for the inclusion of geographical and climatological factors which we show for the WRB influence regional rainfall extremes. Moreover, the Bayesian framework permits one to combine additional data types into the analysis; for example, information derived via elicitation and causal information expansion data, both being additional opportunities for future related research.
Climatology of observed rainfall in Southeast France at the Regional Climate Model scales
Froidurot, Stéphanie; Molinié, Gilles; Diedhiou, Arona
2016-04-01
In order to provide convenient data to assess rainfall simulated by Regional Climate Models, a spatial database (hereafter called K-REF) has been designed. This database is used to examine climatological features of rainfall in Southeast France, a study region characterized by two mountain ranges of comparable altitude (the Cévennes and the Alps foothill) on both sides of the Rhône valley. Hourly records from 1993 to 2013 have been interpolated to a 0.1° × 0.1° latitude-longitude regular grid and accumulated over 3-h periods in K-REF. The assessment of K-REF relatively to the SAFRAN daily rainfall reanalysis indicates consistent patterns and magnitudes between the two datasets even though K-REF fields are smoother. A multi-scale analysis of the occurrence and non-zero intensity of rainfall is performed and shows that the maps of the 50th and 95th percentiles of 3- and 24-h rain intensity highlight different patterns. The maxima of the 50th and 95th percentiles are located over plain and mountainous areas respectively. Moreover, the location of these maxima is not the same for the 3- and 24-h intensities. To understand these differences between median and intense rainfall on the one hand and between the 3- and 24-h rainfall on the other hand, we analyze the statistical distributions and the space-time structure of occurrence and intensity of the 3-h rainfall in two classes of days, defined as median and intense. This analysis illustrates the influence of two factors on the triggering and the intensity of rain in the region: the solar cycle and the orography. The orographic forcing appears to be quite different for the two ranges of the domain and is much more pronounced over the Cévennes.
Directory of Open Access Journals (Sweden)
Johann G. Zaller
2014-10-01
Full Text Available Climate change scenarios for Central Europe predict less frequent but heavier rainfalls and longer drought periods during the growing season. This is expected to alter arthropods in agroecosystems that are important as biocontrol agents, herbivores or food for predators (e.g. farmland birds. In a lysimeter facility (totally 18 3-m2-plots, we experimentally tested the effects of long-term past vs. prognosticated future rainfall variations (15% increased rainfall per event, 25% more dry days according to regionalized climate change models from the Intergovernmental Panel on Climate Change (IPCC on aboveground arthropods in winter wheat (Triticum aestivum L. cultivated at three different soil types (calcaric phaeozem, calcic chernozem and gleyic phaeozem. Soil types were established 17 years and rainfall treatments one month before arthropod sampling; treatments were fully crossed and replicated three times. Aboveground arthropods were assessed by suction sampling, their mean abundances (± SD differed between April, May and June with 20 ± 3 m-2, 90 ± 35 m-2 and 289 ± 93 individuals m-2, respectively. Averaged across sampling dates, future rainfall reduced the abundance of spiders (Araneae, -47%, cicadas and leafhoppers (Auchenorrhyncha, -39%, beetles (Coleoptera, -52%, ground beetles (Carabidae, -41%, leaf beetles (Chrysomelidae, -64%, spring tails (Collembola, -58%, flies (Diptera, -73% and lacewings (Neuroptera, -73% but increased the abundance of snails (Gastropoda, +69%. Across sampling dates, soil types had no effects on arthropod abundances. Arthropod diversity was neither affected by rainfall nor soil types. Arthropod abundance was positively correlated with weed biomass for almost all taxa; abundance of Hemiptera and of total arthropods was positively correlated with weed density. These detrimental effects of future rainfall varieties on arthropod taxa in wheat fields can potentially alter arthropod-associated agroecosystem services.
Directory of Open Access Journals (Sweden)
Xiuli Sang
2012-01-01
Full Text Available We constructed a similarity model (based on Euclidean distance between rainfall and runoff to study time-correlated characteristics of rainfall-runoff similar patterns in the upstream Red River Basin and presented a detailed evaluation of the time correlation of rainfall-runoff similarity. The rainfall-runoff similarity was used to determine the optimum similarity. The results showed that a time-correlated model was found to be capable of predicting the rainfall-runoff similarity in the upstream Red River Basin in a satisfactory way. Both noised and denoised time series by thresholding the wavelet coefficients were applied to verify the accuracy of model. And the corresponding optimum similar sets obtained as the equation solution conditions showed an interesting and stable trend. On the whole, the annual mean similarity presented a gradually rising trend, for quantitatively estimating comprehensive influence of climate change and of human activities on rainfall-runoff similarity.
Comparing rainfall variability, model complexity and hydrological response at the intra-event scale
Cristiano, Elena; ten Veldhuis, Marie-claire; Ochoa-Rodriguez, Susana; van de Giesen, Nick
2017-04-01
The high variability in space and time of rainfall is one of the main aspects that influence hydrological response and generation of pluvial flooding. This phenomenon has a bigger impact in urban areas, where response is usually faster and flow peaks are typically higher, due to the high degree of imperviousness. Previous researchers have investigated sensitivity of urban hydrodynamic models to rainfall space-time resolution as well as interactions with model structure and resolution. They showed that finding a proper match between rainfall resolution and model complexity is important and that sensitivity increases for smaller urban catchment scales. Results also showed high variability in hydrological response sensitivity, the origins of which remain poorly understood. In this work, we investigate the interaction between rainfall input variability and model structure and scale at high resolution, i.e. 1-15 minutes in time and 100m to 3 km in space. Apart from studying summary statistics such as relative peak flow errors and coefficient of determination, we look into characteristics of response hydrographs to find explanations for response variability in relation to catchment properties as well storm event characteristics (e.g. storm scale and movement, single-peak versus multi-peak events). The aim is to identify general relations between storm temporal and spatial scale and catchment scale in explaining variability of hydrological response. Analyses are conducted for the Cranbrook catchment (London, UK), using 3 hydrodynamic models set up in InfoWorks ICM: a low resolution semi-distributed (SD1) model, a high resolution semi-distributed (SD2) model and a fully distributed (FD) model. These models represent the spatial variability of the land in different ways: semi-distributed models divide the surface in subcatchments, each of them modelled in a lumped way (51 subcatchment for the S model and 4409 subcatchments for the SD model), while the fully distributed
Incorporating rainfall uncertainty in a SWAT model: the river Zenne basin (Belgium) case study
Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy
2013-04-01
The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique
Kedia, Sumita; Cherian, Ribu; Islam, Sahidul; Das, Subrata Kumar; Kaginalkar, Akshara
2016-12-01
A regional climate model, WRFChem has been utilized to simulate aerosol and rainfall distribution over India during July 2010 which was a normal monsoon year. Two identical simulations, one includes aerosol feedback via their direct and indirect effects and other one without any aerosol effect, are structured to understand the impact of aerosol net (direct + indirect) effect on rainfall pattern over India. Model results are accompanied by satellite and ground based observations to examine the robustness of the model simulations. It is shown that the model can reproduce the spatial and temporal characteristics of meteorological parameters, rainfall distribution, aerosol optical depth and single scattering albedo reasonably well. Model simulated spatial distribution and magnitude of aerosol optical depth over India are realistic, particularly over northwest India, where mineral dust is a major contributor to the total aerosol loading and over Indo-Gangetic Plain region (IGP) where AOD remains high throughout the year. Net (shortwave + longwave) atmospheric heating rate is the highest (> 0.27 K day - 1) over east IGP due to abundant dust and anthropogenic aerosols while it is the lowest over peninsular India and over the Thar desert (< 0.03 K day - 1) which can be attributed to less aerosol concentration and longwave cooling, respectively. It is shown that, inclusion of aerosol direct and indirect effects have strong influence ( ± 20%) on rainfall magnitude and its distribution over Indian subcontinent during monsoon.
Directory of Open Access Journals (Sweden)
Y. Xuan
2009-03-01
Full Text Available Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1 The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2 The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3 the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
A Canonical Response in Rainfall Characteristics to Global Warming: Projections by IPCC CMIP5 Models
Lau, William K. M.; Wu, H. T.; Kim, K. M.
2012-01-01
Changes in rainfall characteristics induced by global warming are examined based on probability distribution function (PDF) analysis, from outputs of 14 IPCC (Intergovernmental Panel on Climate Change), CMIP (5th Coupled Model Intercomparison Project) models under various scenarios of increased CO2 emissions. Results show that collectively CMIP5 models project a robust and consistent global and regional rainfall response to CO2 warming. Globally, the models show a 1-3% increase in rainfall per degree rise in temperature, with a canonical response featuring large increase (100-250 %) in frequency of occurrence of very heavy rain, a reduction (5-10%) of moderate rain, and an increase (10-15%) of light rain events. Regionally, even though details vary among models, a majority of the models (>10 out of 14) project a consistent large scale response with more heavy rain events in climatologically wet regions, most pronounced in the Pacific ITCZ and the Asian monsoon. Moderate rain events are found to decrease over extensive regions of the subtropical and extratropical oceans, but increases over the extratropical land regions, and the Southern Oceans. The spatial distribution of light rain resembles that of moderate rain, but mostly with opposite polarity. The majority of the models also show increase in the number of dry events (absence or only trace amount of rain) over subtropical and tropical land regions in both hemispheres. These results suggest that rainfall characteristics are changing and that increased extreme rainfall events and droughts occurrences are connected, as a consequent of a global adjustment of the large scale circulation to global warming.
Williams, C. J. R.; Kniveton, D. R.; Layberry, R.
2009-04-01
It is increasingly accepted that 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 subcontinent 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 Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, 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. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will
Conditioning rainfall-runoff model parameters to reduce prediction uncertainty in ungauged basins
Visessri, S.; McIntyre, N.; Maksimovic, C.
2012-12-01
Conditioning rainfall-runoff model parameters in ungauged catchments in Thailand presents problems common to ungauged basins involving data availability, data quality, and rainfall-runoff model suitability, which all contribute to prediction uncertainty. This paper attempts to improve the estimation of streamflow in ungauged basins and reduce associated uncertainties using the approaches of conditioning the prior parameter space. 35 catchments from the upper Ping River basin, Thailand are selected as a case study. The catchments have a range of attributes e.g. catchment sizes 20-6350 km2, elevations 632-1529 m above sea level. and annual rainfall 846-1447 mm/year. For each catchment, three indices - rainfall-runoff elasticity, base flow index and runoff coefficient - are calculated using the observed rainfall-runoff data and regression equations relating these indices to the catchment attributes are identified. Uncertainty in expected indices is defined by the regression error distribution, approximated by a Gaussian model. The IHACRES model is applied for simulating streamflow. The IHACRES parameters are randomly sampled from their presumed prior parameter space. For each sampled parameter set, the streamflow and hence the three indices are modelled. The parameter sets are conditioned on the probability distributions of the regionalised indices, allowing ensemble predictions to be made. The objective function, NSE, calculated for daily and weekly time steps from the water years 1995-2000, is used to assess model performance. Ability to capture observed streamflow and the precision of the estimate is evaluated using reliability and sharpness measures. Similarity in modelled and expected indices contributes to good objective function values. Using only the regionalised runoff coefficient to condition the model yields better NSE values compared to using either only the rainfall-runoff elasticity or only the base flow index. Conditioning on the runoff coefficient
Multiobjective training of artificial neural networks for rainfall-runoff modeling
De Vos, N.J.; Rientjes, T.H.M.
2008-01-01
This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for thi
Estimation of evapotranspiration for a small catchment as an input for rainfall-runoff model
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).
Baudena, M.|info:eu-repo/dai/nl/340303867; Boni, G.; Ferraris, L.; von Hardenberg, J.; Provenzale, A.
2007-01-01
Vegetation in arid and semi-arid regions is affected by intermittent water availability. We discuss a simple stochastic model describing the coupled dynamics of soil moisture and vegetation, and study the effects of rainfall intermittency. Soil moisture dynamics is described by a ecohydrological box
Multiobjective training of artificial neural networks for rainfall-runoff modeling
De Vos, N.J.; Rientjes, T.H.M.
2008-01-01
This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for
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.
Multi-model forecast skill for mid-summer rainfall over southern Africa
CSIR Research Space (South Africa)
Landman, WA
2012-02-01
Full Text Available . Multi-model forecasts are obtained by: i) downscaling each model’s 850 hPa geopotential height field forecast using canonical correlation analysis (CCA) and then simply averaging the rainfall forecasts; and ii) by combining the three models’ 850 h...
Langousis, Andreas; Mamalakis, Antonios; Deidda, Roberto; Marrocu, Marino
2016-01-01
To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris () developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.
Integer Valued Autoregressive Models for Tipping Bucket Rainfall Measurements
DEFF Research Database (Denmark)
Thyregod, Peter; Carstensen, Niels Jacob; Madsen, Henrik
1999-01-01
A new method for modelling the dynamics of rain sampled by a tipping bucket rain gauge is proposed. The considered models belong to the class of integer valued autoregressive processes. The models take the autocorelation and discrete nature of the data into account. A first order, a second order...... and a threshold model are presented together with methods to estimate the parameters of each model. The models are demonstrated to provide a good description of dt from actual rain events requiring only two to four parameters....
Modelling the embedded rainfall process using tipping bucket data
DEFF Research Database (Denmark)
Thyregod, Peter; Arnbjerg-Nielsen, Karsten; Madsen, Henrik;
1998-01-01
A new method for modelling the dynamics of rain measurement processes is suggested. The method takes the discrete nature and autocorrelation of measurements from the tipping bucket rain gauge into consideration. The considered model is a state space model with a Poisson marginal distribution....... In the model there is only one parameter, a thinning parameter. The model is tested on 39 rain events. The estimated value for the various rain events is reflecting a subjective classification of rain events into frontal and convective rain. Finally, it is demonstrated how the model can be used for simulation...
Cowden, Joshua R.; Watkins, David W., Jr.; Mihelcic, James R.
2008-10-01
SummarySeveral parsimonious stochastic rainfall models are developed and compared for application to domestic rainwater harvesting (DRWH) assessment in West Africa. Worldwide, improved water access rates are lowest for Sub-Saharan Africa, including the West African region, and these low rates have important implications on the health and economy of the region. Domestic rainwater harvesting (DRWH) is proposed as a potential mechanism for water supply enhancement, especially for the poor urban households in the region, which is essential for development planning and poverty alleviation initiatives. The stochastic rainfall models examined are Markov models and LARS-WG, selected due to availability and ease of use for water planners in the developing world. A first-order Markov occurrence model with a mixed exponential amount model is selected as the best option for unconditioned Markov models. However, there is no clear advantage in selecting Markov models over the LARS-WG model for DRWH in West Africa, with each model having distinct strengths and weaknesses. A multi-model approach is used in assessing DRWH in the region to illustrate the variability associated with the rainfall models. It is clear DRWH can be successfully used as a water enhancement mechanism in West Africa for certain times of the year. A 200 L drum storage capacity could potentially optimize these simple, small roof area systems for many locations in the region.
DEFF Research Database (Denmark)
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
DEFF Research Database (Denmark)
Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
DEFF Research Database (Denmark)
Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas
2012-01-01
Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....
Notes of Numerical Simulation of Summer Rainfall in China with a Regional Climate Model REMO
Institute of Scientific and Technical Information of China (English)
CUI Xuefeng; HUANG Gang; CHEN Wen
2008-01-01
Regional climate models are major tools for regional climate simulation and their output are mostly used for climate impact studies. Notes are reported from a series of numerical simulations of summer rainfall in China with a regional climate model. Domain sizes and running modes are major foci. The results reveal that the model in forecast mode driven by "perfect" boundaries could reasonably represent the inter-annual differences: heavy rainfall along the Yangtze River in 1998 and dry conditions in 1997. Model simulation in climate mode differs to a greater extent from observation than that in forecast mode. This may be due to the fact that in climate mode it departs further from the driving fields and relies more on internal model dynamical processes. A smaller domain in climate mode outperforms a larger one. Further development of model parameterizations including dynamic vegetation are encouraged in future studies.
Hydrological Modelling Using a Rainfall Simulator over an Experimental Hillslope Plot
Directory of Open Access Journals (Sweden)
Arpit Chouksey
2017-03-01
Full Text Available Hydrological processes are complex to compute in hilly areas when compared to plain areas. The governing processes behind runoff generation on hillslopes are subsurface storm flow, saturation excess flow, overland flow, return flow and pipe storage. The simulations of the above processes in the soil matrix require detailed hillslope hydrological modelling. In the present study, a hillslope experimental plot has been designed to study the runoff generation processes on the plot scale. The setup is designed keeping in view the natural hillslope conditions prevailing in the Northwestern Himalayas, India where high intensity rainfall events occur frequently. A rainfall simulator was installed over the experimental hillslope plot to generate rainfall with an intensity of 100 mm/h, which represents the dominating rainfall intensity range in the region. Soil moisture sensors were also installed at variable depths from 100 to 1000 mm at different locations of the plot to observe the soil moisture regime. From the experimental observations it was found that once the soil is saturated, it remains at field capacity for the next 24–36 h. Such antecedent moisture conditions are most favorable for the generation of rapid stormflow from hillslopes. A dye infiltration test was performed on the undisturbed soil column to observe the macropore fraction variability over the vegetated hillslopes. The estimated macropore fractions are used as essential input for the hillslope hydrological model. The main objective of the present study was to develop and test a method for estimating runoff responses from natural rainfall over hillslopes of the Northwestern Himalayas using a portable rainfall simulator. Using the experimental data and the developed conceptual model, the overland flow and the subsurface flow through a macropore-dominated area have been estimated/analyzed. The surface and subsurface runoff estimated using the developed hillslope hydrological model
Assessment of rainfall-runoff modelling for climate change mitigation
Otieno, Hesbon; Han, Dawei; Woods, Ross
2015-04-01
Sustainable water resources management requires reliable methods for quantification of hydrological variables. This is a big challenge in developing countries, due to the problem of inadequate data as a result of sparse gauge networks. Successive occurrence of both abundance and shortage of water can arise in a catchment within the same year, with deficit situations becoming an increasingly occurring phenomenon in Kenya. This work compares the performance of two models in the Tana River catchment in Kenya, in generation of synthetic flow data. One of the models is the simpler USGS Thornthwaite monthly water balance model that uses a monthly time step and has three parameters. In order to explore alternative modelling schemes, the more complex Pitman model with 19 parameters was also applied in the catchment. It is uncertain whether the complex model (Pitman) will do better than the simple model, because a model with a large number of parameters may do well in the current system but poorly in future. To check this we have used old data (1970-1985) to calibrate the models and to validate with recent data (after 1985) to see which model is robust over time. This study is relevant and useful to water resources managers in scenario analysis for water resources management, planning and development in African countries with similar climates and catchment conditions.
A gridded hourly rainfall dataset for the UK applied to a national physically-based modelling system
Lewis, Elizabeth; Blenkinsop, Stephen; Quinn, Niall; Freer, Jim; Coxon, Gemma; Woods, Ross; Bates, Paul; Fowler, Hayley
2016-04-01
An hourly gridded rainfall product has great potential for use in many hydrological applications that require high temporal resolution meteorological data. One important example of this is flood risk management, with flooding in the UK highly dependent on sub-daily rainfall intensities amongst other factors. Knowledge of sub-daily rainfall intensities is therefore critical to designing hydraulic structures or flood defences to appropriate levels of service. Sub-daily rainfall rates are also essential inputs for flood forecasting, allowing for estimates of peak flows and stage for flood warning and response. In addition, an hourly gridded rainfall dataset has significant potential for practical applications such as better representation of extremes and pluvial flash flooding, validation of high resolution climate models and improving the representation of sub-daily rainfall in weather generators. A new 1km gridded hourly rainfall dataset for the UK has been created by disaggregating the daily Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset using comprehensively quality-controlled hourly rain gauge data from over 1300 observation stations across the country. Quality control measures include identification of frequent tips, daily accumulations and dry spells, comparison of daily totals against the CEH-GEAR daily dataset, and nearest neighbour checks. The quality control procedure was validated against historic extreme rainfall events and the UKCP09 5km daily rainfall dataset. General use of the dataset has been demonstrated by testing the sensitivity of a physically-based hydrological modelling system for Great Britain to the distribution and rates of rainfall and potential evapotranspiration. Of the sensitivity tests undertaken, the largest improvements in model performance were seen when an hourly gridded rainfall dataset was combined with potential evapotranspiration disaggregated to hourly intervals, with 61% of catchments showing an increase in NSE between
Kobayashi, Kenichiro; Otsuka, Shigenori; Apip; Saito, Kazuo
2016-08-01
This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall-runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s-1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.
Regionalization parameters of conceptual rainfall-runoff model
Osuch, M.
2003-04-01
Main goal of this study was to develop techniques for the a priori estimation parameters of hydrological model. Conceptual hydrological model CLIRUN was applied to around 50 catchment in Poland. The size of catchments range from 1 000 to 100 000 km2. The model was calibrated for a number of gauged catchments with different catchment characteristics. The parameters of model were related to different climatic and physical catchment characteristics (topography, land use, vegetation and soil type). The relationships were tested by comparing observed and simulated runoff series from the gauged catchment that were not used in the calibration. The model performance using regional parameters was promising for most of the calibration and validation catchments.
Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries
Pattnayak, K. C.; Kar, S. C.; Dalal, Mamta; Pattnayak, R. K.
2017-05-01
Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) comprising Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand brings together 21% of the world population. Thus the impact of climate change in this region is a major concern for all. To study the climate change, fifth phase of Climate Model Inter-comparison Project (CMIP5) models have been used to project the climate for the 21st century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 over the BIMSTEC countries for the period 1901 to 2100 (initial 105 years are historical period and the later 95 years are projected period). Climate change in the projected period has been examined with respect to the historical period. In order to validate the models, the mean annual rainfall has been compared with observations from multiple sources and temperature has been compared with the data from Climatic Research Unit (CRU) during the historical period. Comparison reveals that ensemble mean of the models is able to represent the observed spatial distribution of rainfall and temperature over the BIMSTEC countries. Therefore, data from these models may be used to study the future changes in the 21st century. Four out of six models show that the rainfall over India, Thailand and Myanmar has decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka show an increasing trend in both the RCP scenarios. In case of temperature, all the models show an increasing trend over all the BIMSTEC countries in both the scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. The rate of increase/decrease in rainfall and temperature are relatively more in RCP8.5 than RCP4.5 over all these countries. Inter-model comparison show that there are uncertainties within the CMIP5 model projections. More similar studies are required to be done for better understanding the model uncertainties in climate projections over this region.
Short time step continuous rainfall modeling and simulation of extreme events
Callau Poduje, A. C.; Haberlandt, U.
2017-09-01
The design, planning, operation and overall assessment of urban drainage systems require long and continuous rain series in a high temporal resolution. Unfortunately, the availability of this data is usually short. Nevertheless a precipitation model could be used to tackle this shortcoming; therefore it is in the aim of this study to present a stochastic point precipitation model to reproduce average rainfall event properties along with extreme values. For this purpose a model is proposed to generate long synthetic series of rainfall for a temporal resolution of 5 min. It is based on an alternating renewal framework and events are characterized by variables describing durations, amounts and peaks. A group of 24 stations located in the north of Germany is used to set up and test the model. The adequate modeling of joint behaviour of rainfall amount and duration is found to be essential for reproducing the observed properties, especially for the extreme events. Copulas are advantageous tools for modeling these variables jointly; however caution must be taken in the selection of the proper copula. The inclusion of seasonality and small events is as well tested and found to be useful. The model is directly validated by generating long synthetic time series and comparing them with observed ones. An indirect validation is as well performed based on a fictional urban hydrological system. The proposed model is capable of reproducing seasonal behaviour and main characteristics of the rainfall events including extremes along with urban flooding and overflow behaviour. Overall the performance of the model is acceptable compared to the design practice. The proposed model is simple to interpret, fast to implement and to transfer to other regions, whilst showing acceptable results.
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.
Manivasagam, V. S.; Nagarajan, R.
2017-03-01
Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June-October) and rabi (October-February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976-2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th-39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd-39th SMWs) can suffer around 30-40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16-22 October) to the 40th SMW (1-7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd-39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th-53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with
Model simulations of rainfall over southern Africa and its eastern ...
African Journals Online (AJOL)
2016-01-01
Jan 1, 2016 ... 2015). Two different types of CCAM simulations are ana- lysed here. Firstly, an ...... Sweden: observation versus model simulation. Tellus A 63 http:// ... for Atmospheric Sciences, September 2011, Hartebeeshoek. NESBITT SW ...
Hillslope soil erosion and runoff model for natural rainfall events
Institute of Scientific and Technical Information of China (English)
Zhanyu Zhang; Guohua Zhang; Changqing Zuo; Xiaoyu Pi
2008-01-01
By using the momentum theorem and water balance principle, basic equations of slope runoff were derived, soil erosion by raindrop splash and runoff were discussed and a model was established for decribing hillslope soil erosion processes. The numerical solution of the model was obtained by adopting the Preissmann format and considering the common solution-determining conditions, from which not only the runoff and soil erosion but also their processes can be described. The model was validated by ten groups of observation data of Soil Conservation Ecological Science and Technology Demonstration Park of Jiangxi Province. Comparisons show that the maximum relative error between simulation and experimental data is about 10.98% for total runoff and 15% for total erosion, 5.2% for runoff process and 6.1% for erosion process, indicating that the model is conceptually realistic and reliable and offers a feasible approach for further studies on the soil erosion process.
Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.
2011-12-01
In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C
Directory of Open Access Journals (Sweden)
Adi Nugroho
2014-11-01
Full Text Available Agricultural and plantation activities in Indonesia, especially in Semarang, Central Java, Indonesia rely on water supply from the rainfall. The rainfall in the future is basically influenced by rainfall patterns, humidity and temperature in the past. In this case, Vector Autoregression (VAR multivariate model is applied to forecast the rainfall in the future, in which all along Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG generally uses ARIMA model (Autoregressive Integrated Moving Average to carry out the same thing. The study applied the data, comprising the data of rainfall, humidity and temperature taken on a monthly basis during 2001-2013 periods from 5 measurement stations. Plotting of rainfall forecast result with VAR method is portrayed in the form of isohyet contour map to see the correlation between rainfall and coordinates of the area of the rainfall. The forecast result shows that VAR method is quite accurate to use for rainfall forecast in the study area as well as better than ARIMA method to forecast the same thing as having smaller Mean Absolute Error (MAE and Mean Absolute Percentage Error(MAPE.
DEFF Research Database (Denmark)
Sunyer Pinya, Maria Antonia; Madsen, H.; Rosbjerg, Dan;
Changes in rainfall extremes under climate change conditions are subject to numerous uncertainties. One of the most important uncertainties arises from the inherent uncertainty in climate models. In recent years, many efforts have been made in creating large multi-model ensembles of both Regional...... Climate Models (RCMs) and General Circulation Models (GCMs). These multi-model ensembles provide the information needed to estimate probabilistic climate change projections. Several probabilistic methods have been suggested. One common assumption in most of these methods is that the climate models...... of accounting for the climate model interdependency when estimating the uncertainty of climate change projections....
Towards a generic rainfall-runoff model for green roofs.
Kasmin, H; Stovin, V R; Hathway, E A
2010-01-01
A simple conceptual model for green roof hydrological processes is shown to reproduce monitored data, both during a storm event, and over a longer continuous simulation period. The model comprises a substrate moisture storage component and a transient storage component. Storage within the substrate represents the roof's overall stormwater retention capacity (or initial losses). Following a storm event the retention capacity is restored by evapotranspiration (ET). However, standard methods for quantifying ET do not exist. Monthly ET values are identified using four different approaches: analysis of storm event antecedent dry weather period and initial losses data; calibration of the ET parameter in a continuous simulation model; use of the Thornthwaite ET formula; and direct laboratory measurement of evaporation. There appears to be potential to adapt the Thornthwaite ET formula to provide monthly ET estimates from local temperature data. The development of a standardized laboratory test for ET will enable differences resulting from substrate characteristics to be quantified.
Calibrating max-stable models of rainfall extremes at multiple timescales
Le, Phuong Dong; Leonard, Michael; Westra, Seth
2016-04-01
Understanding the probabilistic behaviour of extreme rainfall events is critical for estimating the risk of flooding, leading to better design of infrastructure and management of flood events. The majority of engineering design is based on estimates of the probability of extreme rainfall known as the Intensity-Frequency-Duration relationship (IDF). IDF curves are estimated at each rain gauge and are subsequently interpolated for application to ungauged locations. The pointwise nature of IDF estimates leads to difficulties, especially at sub-daily timescales, due to the sparseness of sub-daily extreme rainfall data. As a result there is greater uncertainty and potential for bias when estimating sub-daily extreme rainfall. By using a model that incorporates dependence between spatial extremes as well as across multiple timescales, there is considerable potential to improve estimates of extreme rainfall. The aim of this research is to develop max-stable models of extreme rainfall that have both spatial dependence as well as dependence across timescales. Max-stable processes are a direct extension of the univariate generalized extreme value (GEV) model into the spatial domain. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, Koutsoyiannis et al. (1998) proposed a mathematical framework which expresses the parameters as a function of timescale. This parameterization is important because it allows data to be incorporated from daily rainfall stations to improve estimates at sub-daily timescales. The approach therefore addresses the issue of sparseness for sub-daily stations by exploiting the denser network of daily stations. A case study in the Hawkesbury-Nepean catchment near Sydney is used, having 82 daily gauges (>50 years) and 13 sub-daily gauges (>24 years) over a region of 300 km x 300 km area. The max
Assessing spatio-temporal variability of rainfall using a simple physically based statistical model
Hutchinson, M. F.; Xu, T.; Kesteven, J.
2010-12-01
Reliable assessment of spatio-temporal variability of observed rainfall is difficult in the current climate because of the complex spatial variability displayed by daily and shorter time scale rainfall data. As demonstrated in a recent analysis of Canadian daily precipitation data by Hutchinson et al. (2009), direct interpolation of short time scale precipitation data is a poor way to address spatial patterns of rainfall extremes. Addressing the behaviour of projected future precipitation extremes is made even more difficult by the limited temporal and spatial resolution of precipitation as simulated by global climate models. The “uniform drizzle” that tends to be produced by these models makes the assessment of even straightforward statistics, such as daily rainfall occurrence, problematic. Putting aside significant inter-model variability, the more reliable outputs of global models include mean fluxes, such as monthly rainfall amounts, and associated insight into the nature of the modelled precipitation in relation to forcing synoptic systems. The truncated power of normal distribution, as described by Hutchinson (1995), offers a relatively simple way to make progress. Two of the three model parameters are simply calibrated in terms of monthly mean fluxes and the model is able to accurately describe precipitation extremes. These model parameters can also be robustly determined from serially incomplete data. It can be argued that the model has a broad physical process basis by modelling rainfall as an event that occurs as an appropriate threshold is exceeded. This analysis extends the approach of Stidd (1954, 1973) who suggested the cube root as a universal normalising power. We show that the power parameter, once robustly calibrated, displays a broadly spatially varying distribution of around 0.5. This corresponds well with the two dimensional synoptic convergence that is required to produce precipitation. The power parameter appears to be related to the
Wu, Chunhung; Huang, Jyuntai
2017-04-01
Most of the landslide cases in Taiwan were triggered by rainfall or earthquake events. The heavy rainfall in the typhoon seasons, from June to October, causes the landslide hazard more serious. Renai Towhship is of the most large landslide cases after 2009 Typhoon Morakot (from Aug. 5 to Aug. 10, 2009) in Taiwan. Around 2,744 landslides cases with the total landslide area of 21.5 km2 (landslide ratio =1.8%), including 26 large landslide cases, induced after 2009 Typhoon Morakot in Renai Towhship. The area of each large landslides case is more than 0.1 km2, and the area of the largest case is around 0.96 km2. 58% of large landslide cases locate in the area with metamorphosed sandstone. The mean slope of 26 large landslide cases ranges from 15 degree to 56 degree, and the accumulated rainfall during 2009 Typhoon Morakot ranges from 530 mm to 937 mm. Three methods, including frequency ratio method (abbreviated as FR), weights of evidence method (abbreviated as WOE), and logistic regression method (abbreviated as LR), are used in this study to establish the landslides susceptibility in the Renai Township, Nantou County, Taiwan. Eight landslide related-factors, including elevation, slope, aspect, geology, land use, distance to drainage, distance to fault, accumulation rainfall during 2009 Typhoon Morakot, are used to establish the landslide susceptibility models in this study. The landslide inventory after 2009 Typhoon Morakot is also used to test the model performance in this study. The mean accumulated rainfall in Renai Township during 2009 typhoon Morakot was around 735 mm with the maximum 1-hr, 3-hrs, and 6-hrs rainfall intensity of 44 mm/1-hr, 106 mm/3-hrs and 204 mm/6-hrs, respectively. The range of original susceptibility values established by three methods are 4.0 to 20.9 for FR, -33.8 to -16.1 for WOE, and -41.7 to 5.7 for LR, and the mean landslide susceptibility value are 8.0, -24.6 and 0.38, respectively. The AUC values are 0.815 for FR, 0.816 for WOE, and 0
Directory of Open Access Journals (Sweden)
D. Mellor
2000-01-01
Full Text Available Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes. Keywords: SHETRAN, ARNO, HYREX, rainfall-runoff model, Brue, real-time flow forecasting
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...
Fouladi Osgouei, Hojjatollah; Zarghami, Mahdi; Ashouri, Hamed
2016-04-01
The availability of spatial, high-resolution rainfall data is one of the most essential needs in the study of water resources. These data are extremely valuable in providing flood awareness for dense urban and industrial areas. The first part of this paper applies an optimization-based method to the calibration of radar data based on ground rainfall gauges. Then, the climatological Z-R relationship for the Sahand radar, located in the East Azarbaijan province of Iran, with the help of three adjacent rainfall stations, is obtained. The new climatological Z-R relationship with a power-law form shows acceptable statistical performance, making it suitable for radar-rainfall estimation by the Sahand radar outputs. The second part of the study develops a new heterogeneous random-cascade model for spatially disaggregating the rainfall data resulting from the power-law model. This model is applied to the radar-rainfall image data to disaggregate rainfall data with coverage area of 512 × 512 km2 to a resolution of 32 × 32 km2. Results show that the proposed model has a good ability to disaggregate rainfall data, which may lead to improvement in precipitation forecasting, and ultimately better water-resources management in this arid region, including Urmia Lake.
Fouladi Osgouei, Hojjatollah; Zarghami, Mahdi; Ashouri, Hamed
2017-07-01
The availability of spatial, high-resolution rainfall data is one of the most essential needs in the study of water resources. These data are extremely valuable in providing flood awareness for dense urban and industrial areas. The first part of this paper applies an optimization-based method to the calibration of radar data based on ground rainfall gauges. Then, the climatological Z-R relationship for the Sahand radar, located in the East Azarbaijan province of Iran, with the help of three adjacent rainfall stations, is obtained. The new climatological Z-R relationship with a power-law form shows acceptable statistical performance, making it suitable for radar-rainfall estimation by the Sahand radar outputs. The second part of the study develops a new heterogeneous random-cascade model for spatially disaggregating the rainfall data resulting from the power-law model. This model is applied to the radar-rainfall image data to disaggregate rainfall data with coverage area of 512 × 512 km2 to a resolution of 32 × 32 km2. Results show that the proposed model has a good ability to disaggregate rainfall data, which may lead to improvement in precipitation forecasting, and ultimately better water-resources management in this arid region, including Urmia Lake.
文, 勇起; BUN, Yuki
2013-01-01
In recent years, many flood damage and drought attributed to urbanization has occurred. At present infiltration facility is suggested for the solution of these problems. Based on this background, the purpose of this study is investigation of quantification of flood control and water utilization effect of rainfall infiltration facility by using water balance analysis model. Key Words : flood control, water utilization , rainfall infiltration facility
Satellite-based Flood Modeling Using TRMM-based Rainfall Products
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Greg Easson
2007-12-01
Full Text Available Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASAÃ¢Â€Â™s Tropical Rainfall Measuring Mission (TRMM Multi-satellitePrecipitation Analysis (TMPA product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.
A Modeling Study of Diurnal Rainfall Variations during the 21-Day Period of TOGA COARE
Institute of Scientific and Technical Information of China (English)
GAO Shouting; CUI Xiaopeng; Xiaofan LI
2009-01-01
The surface rainfall processes and diurnal variations associated with tropical oceanic convection are examined by analyzing a surface rainfall equation and thermal budget based on hourly zonal-mean data from a series of two-dimensional cloud-resolving simulations.The model is integrated for 21 days with imposed large-scale vertical velocity,zonal wind,and horizontal advection obtained from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) in the control experiment.Diurnal analysis shows that the infrared radiative cooling after sunset,as well as the advective cooling associated with imposed large-scale ascending motion,destabilize the atmosphere and release convective available potential energy to energize nocturnal convective development.Substantial local atmospheric drying is associated with the nocturnal rainfall peak in early morning,which is a result of the large condensation and deposition rates in the vapor budget.Sensitivity experiments show that diurnal variations of radiation and large-scale forcing can produce a nocturnal rainfall peak through infrared and advective cooling,respectively.
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.
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.
Neural network modelling of rainfall interception in four different forest stands
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Ibrahim Yurtseven
2013-12-01
Full Text Available The objective of this study is to reveal whether it is possible to predict rainfall, throughfall and stemflow in forest ecosystems with less effort, using several measurements of rainfall interception (hereafter interception and an artificial neural network based linear regression model (ANN model. To this end, the Kerpe Research Forest in the province of Kocaeli, which houses stands of mixed deciduous-broadleaf forest (Castanea sativa Mill., Fagus orientalis Lipsky, Quercus spp., black pine (Pinus nigra Arnold, maritime pine (Pinus pinaster Aiton and Monterey pine (Pinus radiata D. Don, was selected study site. Four different forest stands were observed for a period of two years, during which rainfall, throughfall and stemflow measurements were conducted. These measurements were separately calculated for each individual stand, based on interception values and the use of stemflow data in strict accordance with the rainfall data, and the measured throughfall interception values were compared with values estimated by the ANN model. In this comparison, 70% of the total data was used for testing, and 30% was used for estimation and performance evaluation. No significant differences were found between values predicted with the help of the model and the measured values. In other words, interception values predicted by the ANN models were parallel with the measured values. In this study, the most success was achieved with the models of the Monterey pine stand (r2 = 0.9968; Mean Squared Error MSE = 0.16 and the mixed deciduous forest stand (r2 = 0.9964; MSE = 0.08, followed by models of the maritime pine stand (r2 = 0.9405; MSE = 1.27 and the black pine stand (r2 = 0.843, MSE = 17.36.
Neural network modelling of rainfall interception in four different forest stands
Directory of Open Access Journals (Sweden)
İbrahim Yurtseven
2013-11-01
Full Text Available The objective of this study is to reveal whether it is possible to predict rainfall, through fall and stem flow in forest ecosystems with less effort, using several measurements of rainfall interception (hereafter ‘interception’ and an artificial neural network based linear regression model (ANN model. To this end, the Kerpe Research Forest in the province of Kocaeli, which houses stands of mixed deciduous-broadleaf forest (Castanea sativa Mill., Fagusorientalis Lipsky, Quercus spp., black pine (Pinus nigra Arnold, maritime pine (Pinus pinaster Aiton and Monterey pine (Pinus radiata D. Don, was selected study site. Four different forest stands were observed for a period of two years, during which rainfall, throughfall and stemflow measurements were conducted. These measurements were separately calculated for each individual stand, based on interception values and the use of stemflow data in strict accordance with the rainfall data, and the measured throughfall interceptionvalues were compared with values estimated by the ANN model.In this comparison, 70% of the total data was used for testing, and 30% was used for estimation and performance evaluation. No significant differences were found between values predicted with the help of the model and the measured values. In other words, interception values predicted by the ANN models were parallel with the measured values. In this study, the most success was achieved with the models of the Monterey pine stand (r2 = 0.9968; Mean Squared Error MSE = 0.16 and the mixed deciduous forest stand (r2 = 0.9964; MSE = 0.08, followed by models of the maritime pine stand (r2 = 0.9405; MSE = 1.27 and the black pine stand (r2 = 0.843, MSE = 17.36.
Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model
Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.
2017-09-01
The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.
Analysis of a temperature- and rainfall-dependent model for malaria transmission dynamics.
Okuneye, Kamaldeen; Gumel, Abba B
2017-05-01
A new non-autonomous model is designed and used to assess the impact of variability in temperature and rainfall on the transmission dynamics of malaria in a population. In addition to adding age-structure in the host population and the dynamics of immature malaria mosquitoes, a notable feature of the new model is that recovered individuals do not revert to wholly-susceptible class (that is, recovered individuals enjoy reduced susceptibility to new malaria infection). In the absence of disease-induced mortality, the disease-free solution of the model is shown to be globally-asymptotically stable when the associated reproduction ratio is less than unity. The model has at least one positive periodic solution when the reproduction ratio exceeds unity (and the disease persists in the community in this case). Detailed uncertainty and sensitivity analysis, using mean monthly temperature and rainfall data from KwaZulu-Natal province of South Africa, shows that the top three parameters of the model that have the most influence on the disease transmission dynamics are the mosquito carrying capacity, transmission probability per contact for susceptible mosquitoes and human recovery rate. Numerical simulations of the model show that, for the KwaZulu-Natal province, malaria burden increases with increasing mean monthly temperature and rainfall in the ranges ([17-25]°C and [32-110] mm), respectively (and decreases with decreasing mean monthly temperature and rainfall values). In particular, transmission is maximized for mean monthly temperature and rainfall in the ranges [21-25]°C and [95-125] mm. This occurs for a six-month period in KwaZulu-Natal (hence, this study suggests that anti-malaria control efforts should be intensified during this period). It is shown, for the fixed mean monthly temperature of KwaZulu-Natal, that malaria burden decreases whenever the amount of rainfall exceeds a certain threshold value. It is further shown (through sensitivity analysis and
Urban flood return period assessment through rainfall-flood response modelling
Murla Tuyls, Damian; Thorndahl, Søren
2017-04-01
Intense rainfall can often cause severe floods, especially in urbanized areas, where population density or large impermeable areas are found. In this context, floods can generate a direct impact in a social-environmental-economic viewpoint. Traditionally, in design of Urban Drainage Systems (UDS), correlation between return period (RP) of a given rainfall and RP of its consequent flood has been assumed to be linear (e.g. DS/EN752 (2008)). However, this is not always the case. Complex UDS, where diverse hydraulic infrastructures are often found, increase the heterogeneity of system response, which may cause an alteration of the mentioned correlation. Consequently, reliability on future urban planning, design and resilience against floods may be also affected by this misassumption. In this study, an assessment of surface flood RP across rainfall RP has been carried out at Lystrup, a urbanized catchment area of 440ha and 10.400inhab. located in Jutland (Denmark), which has received the impact of several pluvial flooding in the last recent years. A historical rainfall dataset from the last 35 years from two different rain gauges located at 2 and 10 km from the study area has been provided by the Danish Wastewater Pollution Committee and the Danish Meteorological Institute (DMI). The most extreme 25 rainfall events have been selected through a two-step multi-criteria procedure, ensuring an adequate variability of rainfall, from extreme high peak storms with a short duration to moderate rainfall with longer duration. In addition, a coupled 1D/2D surface and network UDS model of the catchment area developed in an integrated MIKE URBAN and MIKE Flood model (DHI 2014), considering both permeable and impermeable areas, in combination with a DTM (2x2m res.) has been used to study and assess in detail flood RP. Results show an ambiguous relation between rainfall RP and flood response. Local flood levels, flood area and volume RP estimates should therefore not be neglected in
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.
Alvioli, M.; Baum, R.L.
2016-01-01
We describe a parallel implementation of TRIGRS, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model for the timing and distribution of rainfall-induced shallow landslides. We have parallelized the four time-demanding execution modes of TRIGRS, namely both the saturated and unsaturated model with finite and infinite soil depth options, within the Message Passing Interface framework. In addition to new features of the code, we outline details of the parallel implementation and show the performance gain with respect to the serial code. Results are obtained both on commercial hardware and on a high-performance multi-node machine, showing the different limits of applicability of the new code. We also discuss the implications for the application of the model on large-scale areas and as a tool for real-time landslide hazard monitoring.
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Abdouramane Gado Djibo
2015-09-01
Full Text Available Since the 90s, several studies were conducted to evaluate the predictability of the Sahelian rainy season and propose seasonal rainfall forecasts to help stakeholders to take the adequate decisions to adapt with the predicted situation. Unfortunately, two decades later, the forecasting skills remains low and forecasts have a limited value for decision making while the population is still suffering from rainfall interannual variability: this shows the limit of commonly used predictors and forecast approaches for this region. Thus, this paper developed and tested new predictors and new approaches to predict the upcoming seasonal rainfall amount over the Sirba watershed. Predictors selected through a linear correlation analysis were further processed using combined linear methods to identify those having high predictive power. Seasonal rainfall was forecasted using a set of linear and non-linear models. An average lag time up to eight months was obtained for all models. It is found that the combined linear methods performed better than non-linear, possibly because non-linear models require larger and better datasets for calibration. The R2, Nash and Hit rate score are respectively 0.53, 0.52, and 68% for the combined linear approach; and 0.46, 0.45, 61% for non-linear principal component analysis.
Small-scale Rainfall Challenges Tested with Semi-distributed and Distributed Hydrological Models
Ichiba, Abdellah; Tchiguirinskaia, Ioulia; Gires, Auguste; Schertzer, Daniel; Bompard, Philippe
2016-04-01
Nowadays, there is a growing interest on small-scale rainfall information, provided by weather radars, to be used in urban water management and decision-making. Indeed, it helps to better understand the essential interactions between natural and man-made urban environments, both being complex systems. However the integration of this information in hydrological models remains a big challenge. In fact, urban water managers often rely on lumped or semi-distributed models with much coarser data resolution. The scope of this work is to investigate the sensitivity of two hydrological models to small-scale rainfall, and their potential improvements to integrate wholly the small-scale information. The case study selected to perform this study is a small urban catchment (245 ha), located at Val-de-Marne county (southeast of Paris, France). Investigations were conducted using either CANOE model, a semi-distributed conceptual model that is widely used in France for urban modeling, or a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech (www hmco-dev.enpc.fr/Tools-Training/Tools/Multi-Hydro.php). Initially, in CANOE model the catchment was divided into 9 sub-catchments with size ranging from 1ha to 76ha. A refinement process was conduced in the framework of this investigation in order to improve the model resolution by considering higher number of smaller sub-catchments. The new configuration consists of 44 sub-catchments with size ranging from 1ha-14ha. The Multi-Hydro modeling approach consists on rasterizing the catchment information to a regular spatial grid of a resolution chosen by the user. Each pixel is then affected by specific information, e.g., a unique land type per pixel, for which hydrological and physical properties are set. First of all, both models were validated with respect to real flow measurements using three types of rainfall data: (1) point measurement data coming form the Sucy-en-Brie rain gauge; (2) Meteo
Kashani, Mahsa H.; Ghorbani, Mohammad Ali; Dinpashoh, Yagob; Shahmorad, Sedaghat
2016-09-01
Rainfall-runoff simulation is an important task in water resources management. In this study, an integrated Volterra model with artificial neural networks (IVANN) was presented to simulate the rainfall-runoff process. The proposed integrated model includes the semi-distributed forms of the Volterra and ANN models which can explore spatial variation in rainfall-runoff process without requiring physical characteristic parameters of the catchments, while taking advantage of the potential of Volterra and ANNs models in nonlinear mapping. The IVANN model was developed using hourly rainfall and runoff data pertaining to thirteen storms to study short-term responses of a forest catchment in northern Iran; and its performance was compared with that of semi-distributed integrated ANN (IANN) model and lumped Volterra model. The Volterra model was applied as a nonlinear model (second-order Volterra (SOV) model) and solved using the ordinary least square (OLS) method. The models performance were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge and error of time for peak to arrive. Results showed that the IVANN model performs well than the other semi-distributed and lumped models to simulate the rainfall-runoff process. Comparing to the integrated models, the lumped SOV model has lower precision to simulate the rainfall-runoff process.
A statistical forecast model for Tropical Cyclone Rainfall and flood events for the Hudson River
Cioffi, Francesco; Conticello, Federico; Hall, Thimoty; Lall, Upmanu; Orton, Philip
2014-05-01
Tropical Cyclones (TCs) lead to potentially severe coastal flooding through wind surge and also through rainfall-runoff processes. There is growing interest in modeling these processes simultaneously. Here, a statistical approach that can facilitate this process is presented with an application to the Hudson River Basin that is associated with the New York City metropolitan area. Three submodels are used in sequence. The first submodel is a stochastic model of the complete life cycle of North Atlantic (NA) tropical cyclones developed by Hall and Yonekura (2011). It uses archived data of TCs throughout the North Atlantic to estimate landfall rates at high geographic resolution as a function of the ENSO state and of sea surface temperature (SST). The second submodel translates the attributes of a tropical cyclone simulated by the first model to rainfall intensity at selected stations within the watershed of Hudson River. Two different approaches are used and compared: artificial neural network (ANN) and k-nearest neighbor (KNN). Finally, the third submodel transforms, once again, by using an ANN approach and KNN, the rainfall intensities, calculated for the ensemble of the stations, to the streamflows at specific points of the tributaries of the Hudson River. These streamflows are to be used as inputs in a hydrodynamic model that includes storm surge surge dynamics for the simulation of coastal flooding along the Hudson River. Calibration and validation of the model is carried out by using, selected tropical cyclone data since 1950, and hourly station rainfall and streamflow recorded for such extreme events. Four stream gauges (Troy dam, Mohawk River at Cohoes, Mohawk River diversion at Crescent Dam, Hudson River above lock one nr Waterford), a gauge from a tributary in the lower Hudson River, and over 20 rain gauges are used. The performance of the proposed model as tool for storm events is then analyzed and discussed.
Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model
Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.
2017-09-01
The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.
Comparison of TS and ANN Models with the Results of Emission Scenarios in Rainfall Prediction
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S. Babaei Hessar
2016-02-01
Full Text Available Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010 of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN and Time Seri (TS methods were compared with the result of the Emission Scenarios (A2 & B1. HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029. The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2 in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1 was suitable for Tabriz and Khoy stations .In the Urmia station MA(1 was
Prediction of Rainfall-Induced Landslides in Tegucigalpa, Honduras, Using a Hydro-Geotechnical Model
Garcia Urquia, Elias; Axelsson, K.
2010-05-01
Central America is constantly being affected by natural hazards. Among these events are hurricanes and earthquakes, capable of triggering landslides that can alter the natural landscape, destroy infrastructure and cause the death of people in the most important settlements of the region. Hurricane Mitch in October of 1998 was of particular interest for the region, since it provoked hundreds of rainfall-induced landslides, mainly in 4 different countries. Studies carried out after Hurricane Mitch have allowed researchers to identify the factors that contribute to slope instability in many vulnerable areas. As Tegucigalpa, Honduras was partially destroyed due to the various landslide and flooding events triggered by this devastating hurricane, various research teams have deepened in their investigations and have proposed measures to mitigate the effects of similar future incidents. A model coupling an infinite-slope analysis and a simple groundwater flow approach can serve as a basis to predict the occurrence of landslides in Tegucigalpa, Honduras as a function of topographic, hydrological and soil variables. A safety map showing the rainfall-triggered landslide risk zones for Tegucigalpa, Honduras is to be created. As opposed to previous safety maps in which only steady-state conditions are studied, this analysis is extended and different steady-state and quasi-dynamic scenarios are considered for comparison. For the purpose of the latter settings, a hydrological analysis that determines the rainfall extreme values and their return periods in Tegucigalpa will account for the influence of rainfall on the groundwater flow and strength of soils. It is known that the spatial distribution of various factors that contribute to the risk of landslides (i.e. soil thickness, conductivity and strength properties; rainfall intensity and duration; root strength; subsurface flow orientation) is hard to determine. However, an effort is done to derive correlations for these
Analysis of flash flood-triggering rainfall for a process-oriented hydrological model
Garambois, P. A.; Larnier, K.; Roux, H.; Labat, D.; Dartus, D.
2014-02-01
We propose an extended study of recent flood-triggering storms and resulting hydrological responses for catchments in the Pyrenean foothills up to the Aude region. For hydrometeorological sciences, it appears relevant to characterize flash floods and the storm that triggered them over various temporal and spatial scales. There are very few studies of extreme storm-caused floods in the literature covering the Mediterranean and highlighting, for example, the quickness and seasonality of this natural phenomenon. The present analysis is based on statistics that clarify the dependence between the spatial and temporal distributions of rainfall at catchment scale, catchment morphology and runoff response. Given the specific space and time scales of rainfall cell development, we show that the combined use of radar and a rain gauge network appears pertinent. Rainfall depth and intensity are found to be lower for catchments in the Pyrenean foothills than for the nearby Corbières or Montagne Noire regions. We highlight various hydrological behaviours and show that an increase in initial soil saturation tends to foster quicker catchment flood response times, of around 3 to 10 h. The hydrometeorological data set characterized in this paper constitutes a wealth of information to constrain a physics-based distributed model for regionalization purposes in the case of flash floods. Moreover, the use of diagnostic indices for rainfall distribution over catchment drainage networks highlights a unimodal trend in spatial temporal storm distributions for the entire flood dataset. Finally, it appears that floods in mountainous Pyrenean catchments are generally triggered by rainfall near the catchment outlet, where the topography is lower.
Litta, A. J.; Chakrapani, B.; Mohankumar, K.
2007-07-01
Heavy rainfall events become significant in human affairs when they are combined with hydrological elements. The problem of forecasting heavy precipitation is especially difficult since it involves making a quantitative precipitation forecast, a problem well recognized as challenging. Chennai (13.04°N and 80.17°E) faced incessant and heavy rain about 27 cm in 24 hours up to 8.30 a.m on 27th October 2005 completely threw life out of gear. This torrential rain caused by deep depression which lay 150km east of Chennai city in Bay of Bengal intensified and moved west north-west direction and crossed north Tamil Nadu and south Andhra Pradesh coast on 28th morning. In the present study, we investigate the predictability of the MM5 mesoscale model using different cumulus parameterization schemes for the heavy rainfall event over Chennai. MM5 Version 3.7 (PSU/NCAR) is run with two-way triply nested grids using Lambert Conformal Coordinates (LCC) with a nest ratio of 3:1 and 23 vertical layers. Grid sizes of 45, 15 and 5 km are used for domains 1, 2 and 3 respectively. The cumulus parameterization schemes used in this study are Anthes-Kuo scheme (AK), the Betts-Miller scheme (BM), the Grell scheme (GR) and the Kain-Fritsch scheme (KF). The present study shows that the prediction of heavy rainfall is sensitive to cumulus parameterization schemes. In the time series of rainfall, Grell scheme is in good agreement with observation. The ideal combination of the nesting domains, horizontal resolution and cloud parameterization is able to simulate the heavy rainfall event both qualitatively and quantitatively.
Projections of Rainfall and Temperature from CMIP5 Models over BIMSTEC Countries
Pattnayak, K. C.; Kar, S. C.; Ragi, A. R.
2014-12-01
Rainfall and surface temperature are the most important climatic variables in the context of climate change. Thus, these variables simulated from fifth phase of the Climate Model Inter-comparison Project (CMIP5) models have been compared against Climatic Research Unit (CRU) observed data and projected for the twenty first century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. Results for the seven countries under Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) such as Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand have been examined. Six CMIP5 models namely GFDL-CM3, GFDL-ESM2M, GFDL-ESM2G, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES have been chosen for this study. The study period has been considered is from 1861 to 2100. From this period, initial 145 years i.e. 1861 to 2005 is reference or historical period and the later 95 years i.e. 2005 to 2100 is projected period. The climate change in the projected period has been examined with respect to the reference period. In order to validate the models, the mean annual rainfall and temperature has been compared with CRU over the reference period 1901 to 2005. Comparison reveals that most of the models are able to capture the spatial distribution of rainfall and temperature over most of the regions of BIMSTEC countries. Therefore these model data can be used to study the future changes in the 21st Century. Four out six models shows that the rainfall over Central and North India, Thailand and eastern part of Myanmar shows decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka shows an increasing trend in both RCP 4.5 and 8.5 scenarios. In case of temperature, all of the models show an increasing trend over all the BIMSTEC countries in both scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. Annual cycles of rainfall and temperature over Bangladesh, Myanmar and Thailand
CO 2 flux and photosynthesis of a Sahelian savanna during HAPEX-Sahel
Hanan, N. P.; Elbers, J. A.; Kabat, P.; Dolman, A. J.; de Bruin, H. A. R.
1996-05-01
Eddy-covariance measurements of CO 2 exchange above a Sahelian savanna consisting of small shrubs over a continuous herb layer were made during the HAPEX-Sahel experiment in Niger, West Africa. The measurements were made near-continuously during an 8 week period, covering the main part of the rainy season and three weeks at the beginning of the dry season. In this paper the measurements are corrected for in-canopy storage of CO 2 and the nightime measurements used to derive respiration functions for the soil, roots and aerial plant material. Photosynthetic CO 2 uptake was calculated and the measurements compared to simulations using a biochemical photosynthesis model in a simple (“big-leaf”) implementation, with average stomatal conductance given as an independent input. This model was able to reproduce the measurements (RMS error 1.95 μmol m -2 s -1) with only minor parameter adjustment.
Physically based modelling of sediment generation and transport under a large rainfall simulator
Adams, Russell; Elliott, Sandy
2006-07-01
A series of large rainfall simulator experiments was conducted in 2002 and 2003 on a small plot located in an experimental catchment in the North Island of New Zealand. These experiments measured both runoff and sediment transport under carefully controlled conditions. A physically based hydrological modelling system (SHETRAN) was then applied to reproduce the observed hydrographs and sedigraphs. SHETRAN uses physically based equations to represent flow and sediment transport, and two erodibility coefficients to model detachment of soil particles by raindrop erosion and overland flow erosion. The rate of raindrop erosion also depended on the amount of bare ground under the simulator; this was estimated before each experiment. These erodibility coefficients were calibrated systematically for summer and winter experiments separately, and lower values were obtained for the summer experiments. Earlier studies using small rainfall simulators in the vicinity of the plot also found the soil to be less erodible in summer and autumn. Limited validation of model parameters was carried out using results from a series of autumn experiments. The modelled suspended sediment load was also sensitive to parameters controlling the generation of runoff from the rainfall simulator plot; therefore, we found that accurate runoff predictions were important for the sediment predictions, especially from the experiments where the pasture cover was good and overland flow erosion was the dominant mechanism. The rainfall simulator experiments showed that the mass of suspended sediment increased post-grazing, and according to the model this was due to raindrop detachment. The results indicated that grazing cattle or sheep on steeply sloping hill-country paddocks should be carefully managed, especially in winter, to limit the transport of suspended sediment into watercourses.
Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability
Singh, U. K.; Singh, G. P.; Singh, Vikas
2015-04-01
The performance of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (coupled and uncoupled both) in simulating the seasonal summer (June-August) monsoon rainfall variability over Asia (especially over India and East Asia) has been evaluated in detail using hind-cast data (3 months advance) generated from APCC which provides the regional climate information product services based on multi-model ensemble dynamical seasonal prediction systems. The skill of each global climate model over Asia was tested separately in detail for the period of 21 years (1983-2003), and simulated Asian summer monsoon rainfall (ASMR) has been verified using various statistical measures for Indian and East Asian land masses separately. The analysis found a large variation in spatial ASMR simulated with uncoupled model compared to coupled models (like Predictive Ocean Atmosphere Model for Australia, National Centers for Environmental Prediction and Japan Meteorological Agency). The simulated ASMR in coupled model was closer to Climate Prediction Centre Merged Analysis of Precipitation (CMAP) compared to uncoupled models although the amount of ASMR was underestimated in both models. Analysis also found a high spread in simulated ASMR among the ensemble members (suggesting that the model's performance is highly dependent on its initial conditions). The correlation analysis between sea surface temperature (SST) and ASMR shows that that the coupled models are strongly associated with ASMR compared to the uncoupled models (suggesting that air-sea interaction is well cared in coupled models). The analysis of rainfall using various statistical measures suggests that the multi-model ensemble (MME) performed better compared to individual model and also separate study indicate that Indian and East Asian land masses are more useful compared to Asia monsoon rainfall as a whole. The results of various statistical measures like skill of multi-model ensemble, large spread
Assessing the performance of the independence method in modeling spatial extreme rainfall
Zheng, Feifei; Thibaud, Emeric; Leonard, Michael; Westra, Seth
2015-09-01
Spatial statistical methods are often employed to improve precision when estimating marginal distributions of extreme rainfall. Methods such as max-stable and copula models parameterize the spatial dependence and provide a continuous spatial representation. Alternatively, the independence method can be used to estimate marginal parameters without the need for parameterizing the spatial dependence, and this method has been under-utilized in hydrologic applications. This paper investigates the effectiveness of the independence method for marginal parameter estimation of spatially dependent extremes. Its performance is compared with three spatial dependence models (max-stable Brown-Resnick, max-stable Schlather, and Gaussian copula) by means of a simulation study. The independence method is statistically robust in estimating parameters and their associated confidence intervals for spatial extremes with various underlying dependence structures. The spatial dependence models perform comparably with the independence method when the spatial dependence structure is correctly specified; otherwise they exhibit considerably worse performance. We conclude that the independence method is more appealing for modeling the marginal distributions of spatial extremes (e.g., regional estimation of trends in rainfall extremes) due to its greater robustness and simplicity. The four statistical methods are illustrated using a spatial data set comprising 69 subdaily rainfall series from the Greater Sydney region, Australia.
Why Is Rainfall Error Analysis Requisite for Data Assimilation and Climate Modeling?
Hou, Arthur Y.; Zhang, Sara Q.
2004-01-01
Given the large temporal and spatial variability of precipitation processes, errors in rainfall observations are difficult to quantify yet crucial to making effective use of rainfall data for improving atmospheric analysis, weather forecasting, and climate modeling. We highlight the need for developing a quantitative understanding of systematic and random errors in precipitation observations by examining explicit examples of how each type of errors can affect forecasts and analyses in global data assimilation. We characterize the error information needed from the precipitation measurement community and how it may be used to improve data usage within the general framework of analysis techniques, as well as accuracy requirements from the perspective of climate modeling and global data assimilation.
Directory of Open Access Journals (Sweden)
P. Cowpertwait
2012-09-01
Full Text Available A spatial-temporal point process model of rainfall is fitted to data taken from three homogeneous regions in the Basque Country, Spain. The model is the superposition of two spatial-temporal Neyman-Scott processes, in which rain cells are modelled as discs with radii that follow exponential distributions. In addition, the model includes a parameter for the radius of storm discs, so that rain only occurs when both a cell and a storm disc overlap a point. The model is fitted to data for each month, taken from each of the three homogeneous regions, using a modified method of moments procedure that ensures a smooth seasonal variation in the parameter estimates.
Daily temperature data from twenty three sites are used to fit a stochastic temperature model. A principal component analysis of the maximum daily temperatures across the sites indicates that 92% of the variance is explained by the first component, implying that this component can be used to account for spatial variation. A harmonic equation with autoregressive error terms is fitted to the first principal component. The temperature model is obtained by regressing the maximum daily temperature on the first principal component, an indicator variable for the region, and altitude. This, together with scaling and a regression model of temperature range, enables hourly temperatures to be predicted. Rainfall is included as an explanatory variable but has only a marginal influence when predicting temperatures.
A distributed model (TETIS; Francés et al., 2007 is calibrated for a selected catchment. Five hundred years of data are simulated using the rainfall and temperature models and used as input to the calibrated TETIS model to obtain simulated discharges to compare with observed discharges. Kolmogorov-Smirnov tests indicate that there is no significant difference in the distributions of observed and simulated maximum flows at the same sites, thus supporting the use of the
The 26 July 2005 heavy rainfall event over Mumbai: numerical modeling aspects
Sahany, Sandeep; Venugopal, V.; Nanjundiah, Ravi S.
2010-12-01
The performance of the Advanced Regional Prediction System (ARPS) in simulating an extreme rainfall event is evaluated, and subsequently the physical mechanisms leading to its initiation and sustenance are explored. As a case study, the heavy precipitation event that led to 65 cm of rainfall accumulation in a span of around 6 h (1430 LT-2030 LT) over Santacruz (Mumbai, India), on 26 July, 2005, is selected. Three sets of numerical experiments have been conducted. The first set of experiments (EXP1) consisted of a four-member ensemble, and was carried out in an idealized mode with a model grid spacing of 1 km. In spite of the idealized framework, signatures of heavy rainfall were seen in two of the ensemble members. The second set (EXP2) consisted of a five-member ensemble, with a four-level one-way nested integration and grid spacing of 54, 18, 6 and 1 km. The model was able to simulate a realistic spatial structure with the 54, 18, and 6 km grids; however, with the 1 km grid, the simulations were dominated by the prescribed boundary conditions. The third and final set of experiments (EXP3) consisted of a five-member ensemble, with a four-level one-way nesting and grid spacing of 54, 18, 6, and 2 km. The Scaled Lagged Average Forecasting (SLAF) methodology was employed to construct the ensemble members. The model simulations in this case were closer to observations, as compared to EXP2. Specifically, among all experiments, the timing of maximum rainfall, the abrupt increase in rainfall intensities, which was a major feature of this event, and the rainfall intensities simulated in EXP3 (at 6 km resolution) were closest to observations. Analysis of the physical mechanisms causing the initiation and sustenance of the event reveals some interesting aspects. Deep convection was found to be initiated by mid-tropospheric convergence that extended to lower levels during the later stage. In addition, there was a high negative vertical gradient of equivalent potential
Top-down methodology for rainfall-runoff modelling and evaluation of hydrological extremes
Willems, Patrick
2014-05-01
A top-down methodology is presented for implementation and calibration of a lumped conceptual catchment rainfall-runoff model that aims to produce high model performance (depending on the quality and availability of data) in terms of rainfall-runoff discharges for the full range from low to high discharges, including the peak and low flow extremes. The model is to be used to support water engineering applications, which most often deal with high and low flows as well as cumulative runoff volumes. With this application in mind, the paper wants to contribute to the above-mentioned problems and advancements on model evaluation, model-structure selection, the overparameterization problem and the long time the modeller needs to invest or the difficulties one encounters when building and calibrating a lumped conceptual model for a river catchment. The methodology is an empirical and step-wise technique that includes examination of the various model components step by step through a data-based analysis of response characteristics. The approach starts from a generalized lumped conceptual model structure. In this structure, only the general components of a lumped conceptual model, such as the existence of storage and routing elements, and their inter-links, are pre-defined. The detailed specifications on model equations and parameters are supported by advanced time series analysis of the empirical response between the rainfall and evapotranspiration inputs and the river flow output. Subresponses are separated and submodel components and related subsets of parameters are calibrated as independently as possible. At the same time, the model-structure identification process aims to reach parsimonious submodel-structures, and accounts for the serial dependency of runoff values, which typically is higher for low flows than for high flows. It also accounts for the heteroscedasticity and dependency of model residuals when evaluating the model performance. It is shown that this step
Rainfall runoff modelling of the Upper Ganga and Brahmaputra basins using PERSiST.
Futter, M N; Whitehead, P G; Sarkar, S; Rodda, H; Crossman, J
2015-06-01
There are ongoing discussions about the appropriate level of complexity and sources of uncertainty in rainfall runoff models. Simulations for operational hydrology, flood forecasting or nutrient transport all warrant different levels of complexity in the modelling approach. More complex model structures are appropriate for simulations of land-cover dependent nutrient transport while more parsimonious model structures may be adequate for runoff simulation. The appropriate level of complexity is also dependent on data availability. Here, we use PERSiST; a simple, semi-distributed dynamic rainfall-runoff modelling toolkit to simulate flows in the Upper Ganges and Brahmaputra rivers. We present two sets of simulations driven by single time series of daily precipitation and temperature using simple (A) and complex (B) model structures based on uniform and hydrochemically relevant land covers respectively. Models were compared based on ensembles of Bayesian Information Criterion (BIC) statistics. Equifinality was observed for parameters but not for model structures. Model performance was better for the more complex (B) structural representations than for parsimonious model structures. The results show that structural uncertainty is more important than parameter uncertainty. The ensembles of BIC statistics suggested that neither structural representation was preferable in a statistical sense. Simulations presented here confirm that relatively simple models with limited data requirements can be used to credibly simulate flows and water balance components needed for nutrient flux modelling in large, data-poor basins.
Multi-Site Calibration of Linear Reservoir Based Geomorphologic Rainfall-Runoff Models
Directory of Open Access Journals (Sweden)
Bahram Saeidifarzad
2014-09-01
Full Text Available Multi-site optimization of two adapted event-based geomorphologic rainfall-runoff models was presented using Non-dominated Sorting Genetic Algorithm (NSGA-II method for the South Fork Eel River watershed, California. The first model was developed based on Unequal Cascade of Reservoirs (UECR and the second model was presented as a modified version of Geomorphological Unit Hydrograph based on Nash’s model (GUHN. Two calibration strategies were considered as semi-lumped and semi-distributed for imposing (or unimposing the geomorphology relations in the models. The results of models were compared with Nash’s model. Obtained results using the observed data of two stations in the multi-site optimization framework showed reasonable efficiency values in both the calibration and the verification steps. The outcomes also showed that semi-distributed calibration of the modified GUHN model slightly outperformed other models in both upstream and downstream stations during calibration. Both calibration strategies for the developed UECR model during the verification phase showed slightly better performance in the downstream station, but in the upstream station, the modified GUHN model in the semi-lumped strategy slightly outperformed the other models. The semi-lumped calibration strategy could lead to logical lag time parameters related to the basin geomorphology and may be more suitable for data-based statistical analyses of the rainfall-runoff process.
Directory of Open Access Journals (Sweden)
Ly, S.
2013-01-01
Full Text Available Watershed management and hydrological modeling require data related to the very important matter of precipitation, often measured using raingages or weather stations. Hydrological models often require a preliminary spatial interpolation as part of the modeling process. The success of spatial interpolation varies according to the type of model chosen, its mode of geographical management and the resolution used. The quality of a result is determined by the quality of the continuous spatial rainfall, which ensues from the interpolation method used. The objective of this article is to review the existing methods for interpolation of rainfall data that are usually required in hydrological modeling. We review the basis for the application of certain common methods and geostatistical approaches used in interpolation of rainfall. Previous studies have highlighted the need for new research to investigate ways of improving the quality of rainfall data and ultimately, the quality of hydrological modeling.
Physically-based Flood Modeling Driven by Radar Rainfall in the Upper Guadalupe River Basin, Texas
Sharif, H. O.; Chintalapudi, S.; El Hassan, A.
2011-12-01
The upstream portion of the Guadalupe River Basin (Upper Guadalupe River Basin) is prone to frequent flooding due to its physiographic properties (thin soils, exposed bedrock, and sparse vegetation). The Upper Guadalupe River watershed above Comfort, Texas drains an area of 2,170 square kilometers. This watershed is located at the central part of the Texas Hill Country. This study presents hydrologic analysis of the June 2002, November-2004, and August-2007 flood events that occurred in Upper Guadalupe River Basin. The physically based, distributed-parameter Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model was used to simulate the above flooding events. The first event was used in model while the other two were used for validation. GSSHA model was driven by both rain gauge and Multi-sensor Precipitation Estimator (MPE) rainfall inputs. Differences in simulation results were compared in terms of the hydrographs at different locations in the basin as well as the spatial distribution of hydrologic processes. GSSHA simulations driven by MPE rainfall match very well the USGS observed hydrograph. GSSHA simulation driven by rain gauge rainfall for June-2002 storm event underestimated the peak flow.
Modeling the effects of aerosols to increase rainfall in regions with shortage
Shukla, J. B.; Sundar, Shyam; Misra, A. K.; Naresh, Ram
2013-05-01
It is well known that the emissions of hot gases from various power stations and other industrial sources in the regional atmosphere cause decrease in rainfall around these complexes. To overcome this shortage, one method is to introduce artificially conducive aerosol particles in the atmosphere using aeroplane to increase rainfall. To prove the feasibility of this idea, in this paper, a nonlinear mathematical model is proposed involving five dependent variables, namely, the volume density of water vapour, number densities of cloud droplets and raindrops, and the concentrations of small and large size conducive aerosol particles. It is assumed that two types of aerosol particles are introduced in the regional atmosphere, one of them is of small size CCN type which is conducive to increase cloud droplets from vapour phase, while the other is of large size and is conducive to transform the cloud droplets to raindrops. The model is analyzed using stability theory of differential equations and computer simulation. The model analysis shows that due to the introduction of conducive aerosol particles in the regional atmosphere, the rainfall increases as compared to the case when no aerosols are introduced in the atmosphere of the region under consideration. The computer simulation confirms the analytical results.
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.
Zhao, Yong; Zhang, Huqiang
2016-05-01
Based on the historical and RCP8.5 experiments from 25 Coupled Model Intercomparison Project phase 5 (CMIP5) models, the impacts of sea surface temperature (SST) warming in the tropical Indian Ocean (IO) on the projected change in summer rainfall over Central Asia (CA) are investigated. The analysis is designed to answer three questions: (1) Can CMIP5 models reproduce the observed influence of the IO sea surface temperatures (SSTs) on the CA rainfall variations and the associated dynamical processes? (2) How well do the models agree on their projected rainfall changes over CA under warmed climate? (3) How much of the uncertainty in such rainfall projections is due to different impacts of IO SSTs in these models? The historical experiments show that in most models summer rainfall over CA are positively correlated to the SSTs in the IO. Furthermore, for models with higher rainfall-SSTs correlations, the dynamical processes accountable for such impacts are much closer to what have been revealed in observational data: warmer SSTs tend to favor the development of anti-cyclonic circulation patterns at low troposphere over north and northwest of the Arabian Sea and the Bay of Bengal. These anomalous circulation patterns correspond to significantly enhanced southerly flow which carries warm and moisture air mass from the IO region up to the northeast. At the same time, there is a cyclonic flow over the central and eastern part of the CA which further brings the tropical moisture into the CA and provides essential moist conditions for its rainfall generation. In the second half of twenty-first century, although all the 25 models simulate warmed SSTs, significant uncertainty exists in their projected rainfall changes over CA: half of them suggest summer rainfall increases, but the other half project rainfall decreases. However, when we select seven models out of the 25 based on their skills in capturing the dynamical processes as observed, then the model projected changes
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.
Prasad Ghimire, Chandra; Adrian Bruijnzeel, L.; Lubczynski, Maciek W.; Ravelona, Maafaka; Zwartendijk, Bob W.; van Meerveld, H. J. (Ilja)
2017-02-01
Secondary forests occupy a larger area than old-growth rain forests in many tropical regions but their hydrological functioning is still poorly understood. In particular, little is known about the various components of evapotranspiration in these possibly vigorously regenerating forests. This paper reports on a comparison of measured and modeled canopy interception losses (I) from a semi-mature (ca. 20 years) and a young (5-7 years) secondary forest in the lower montane rain forest zone of eastern Madagascar. Measurements of gross rainfall (P), throughfall (Tf), and stemflow (Sf) were made in both forests for one year (October 2014-September 2015) and the revised analytical model of Gash et al. (1995) was tested for the first time in a tropical secondary forest setting. Overall measured Tf, Sf and derived I in the semi-mature forest were 71.0%, 1.7% and 27.3% of incident P, respectively. Corresponding values for the young forest were 75.8%, 6.2% and 18.0%. The high Sf for the young forest reflects the strongly upward thrusting habit of the branches of the dominant species (Psiadia altissima), which favours funneling of P. The value of I for the semi-mature forest is similar to values reported for old-growth tropical lower montane rain forests elsewhere but I for the younger forest is higher than reported for similarly aged tropical lowland forests. These findings can be explained largely by the prevailing low rainfall intensities and the frequent occurrence of small rainfall events. The revised analytical model was able to reproduce measured cumulative I at the two sites accurately and succeeded in capturing the variability in I associated with the seasonal variability in rainfall intensity, provided that Tf-based values for the average wet-canopy evaporation rates were used instead of values derived with the Penman-Monteith equation.
Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model
Berezowski, T.; Nossent, J.; Chormański, J.; Batelaan, O.
2015-04-01
As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction - SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.
A. N. N. Chedop; N. Djongyang; R.Tchinda; Zaatri, A.; J. D. Kana
2016-01-01
This paper presents a field study of indicators of the performance of absorption refrigerating systems in the Sudano-Sahelian region of Cameroon. A modeling of various components of the system is performed in order to choose the technology best adapted for the region. Based on the characteristics from the manufacturers, four absorption chillers were studied; namely: EAW Schüco LB 15 and 30; Rotartica Solar 045 and Sonnenklima Suninverse. The model of Kühn and Ziegler and meteorological data o...
HYDROSCAPE: A SCAlable and ParallelizablE Rainfall Runoff Model for Hydrological Applications
Piccolroaz, S.; Di Lazzaro, M.; Zarlenga, A.; Majone, B.; Bellin, A.; Fiori, A.
2015-12-01
In this work we present HYDROSCAPE, an innovative streamflow routing method based on the travel time approach, and modeled through a fine-scale geomorphological description of hydrological flow paths. The model is designed aimed at being easily coupled with weather forecast or climate models providing the hydrological forcing, and at the same time preserving the geomorphological dispersion of the river network, which is kept unchanged independently on the grid size of rainfall input. This makes HYDROSCAPE particularly suitable for multi-scale applications, ranging from medium size catchments up to the continental scale, and to investigate the effects of extreme rainfall events that require an accurate description of basin response timing. Key feature of the model is its computational efficiency, which allows performing a large number of simulations for sensitivity/uncertainty analyses in a Monte Carlo framework. Further, the model is highly parsimonious, involving the calibration of only three parameters: one defining the residence time of hillslope response, one for channel velocity, and a multiplicative factor accounting for uncertainties in the identification of the potential maximum soil moisture retention in the SCS-CN method. HYDROSCAPE is designed with a simple and flexible modular structure, which makes it particularly prone to massive parallelization, customization according to the specific user needs and preferences (e.g., rainfall-runoff model), and continuous development and improvement. Finally, the possibility to specify the desired computational time step and evaluate streamflow at any location in the domain, makes HYDROSCAPE an attractive tool for many hydrological applications, and a valuable alternative to more complex and highly parametrized large scale hydrological models. Together with model development and features, we present an application to the Upper Tiber River basin (Italy), providing a practical example of model performance and
First soil moisture values from SMOS over a Sahelian region.
Gruhier, Claire; Kerr, Yann; de Rosnay, Patricia; Pellarin, Thierry; Grippa, Manuela
2010-05-01
Soil moisture is a crucial variable which influences the land surface processes. Numerous studies shown microwaves at low frequency are particularly performed to access to soil moisture values. SMOS (Soil Moisture and Ocean Salinity), launched the November 2th 2009, is the first space mission dedicated to soil moisture observations. Before SMOS, several soil moisture products were provided, based on active or passive microwaves measurements. Gruhier et al. (2010) analyse five of them over a Sahelian area. The results show that the range of volumetric soil moisture values obtained over Sahel is drastically different depending on the remote sensing approach used to produce soil moisture estimates. Although microwave bands currently available are not optimal, some products are in very good agreement with ground data. The main goal of this study is to introduce the first soil moisture maps from SMOS over West Africa. A first analyse of values over a Sahelian region is investigated. The study area is located in Gourma region in Mali. This site has been instrumented in the context of the AMMA project (African Monsoon Multidisciplinary Analysis) and was specifically designed to address the validation of remotely sensed soil moisture. SMOS soil moisture values was analysed with ground knowledge and placed in the context of previous soil moisture products. The high sensitivity of the L-band used by SMOS should provide very accurate soil moisture values.
Estimation of Model and Parameter Uncertainty For A Distributed Rainfall-runoff Model
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.
Modeling on runoff concentration caused by rainfall on hillslopes and application in Maoping slope
Institute of Scientific and Technical Information of China (English)
LIU Qingquan; LI Jiachun
2006-01-01
Based on the fact that the concentration flowlines of overland flow depend on the surface landform of hillslope, a kinematic wave model was developed for simulating runoff generation and flow concentration caused by rainfall on hillslopes. The model-simulated results agree well with the experimental observations. Applying the model to the practical case of Maoping slope, we obtained the characteristics of runoff generation and infiltration on the slope. Especially, the simulated results adequately reflected the confluent pattern of surface runoff, which offers a scientific foundation for designing the drainage engineering on the Maoping slope.
A distributed model for slope stability analysis using radar detected rainfall intensity
Leoni, L.; Rossi, G.; Catani, F.
2009-04-01
The term shallow landslides is widely used in literature to describe a slope movement of limited size that mainly develops in soils up to a maximum of a few meters. Shallow landslides are usually triggered by heavy rainfall because, as the water starts to infiltrate in the soil, the pore-water pressure increases so that the shear strength of the soil is reduced leading to slope failure. We have developed a distributed hydrological-geotechnical model for the forecasting of the temporal and spatial distribution of shallow landslides to be used as a warning system for civil protection purpose. The model uses radar detected rainfall intensity as the input for the hydrological simulation of the infiltration. Using the rainfall pattern detected by the radar is in fact possible to dynamically control the redistribution of groundwater pressure associated with transient infiltration of rain so as to infer the slope stability of the studied area. The model deals with both saturated and unsaturated conditions taking into account the effect of soil suction when the soil is not completely saturated. Two pilot sites have been chosen to develop and test this model: the Armea basin (Liguria, Italy) and the Ischia Island (Campania, Italy). In recent years several severe rainstorms have occurred in both these areas. In at least two cases these have triggered numerous shallow landslides that have caused victims and damaged roads, buildings and agricultural activities. In its current stage, the basic basin-scale model applied for predicting the probable location of shallow landslides involves several stand-alone components. The solution suggested by Iverson for the Richards equation is used to estimate the transient groundwater pressure head distribution according to radar detected rainfall intensity. A soil depth prediction scheme and a limit-equilibrium infinite slope stability algorithm are used to calculate the distributed factor of safety (FS) at different depths and to record
DEFF Research Database (Denmark)
Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik
2012-01-01
We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain...... satisfactory predictions for the smaller catchment but rather large uncertainties for the bigger catchment where the applied storage cascade seems too simple. Radar rainfall introduces more uncertainty into the flow forecast model estimation. However, the radar rainfall forecasts also result in a slightly...
A coupled atmosphere and multi-layer land surface model for improving heavy rainfall simulation
Directory of Open Access Journals (Sweden)
M. Haggag
2008-04-01
Full Text Available A multi-layer land surface model (SOLVEG is dynamically coupled to the non-hydrostatic atmospheric model (MM5 in order to represent better spatial variations and changes in land surface characteristics compared with the land surface parameterization schemes included in the MM5. In this coupling, calculations of the atmosphere and land surface models are carried out as independent tasks of different processors; a model coupler controls these calculations and data exchanges among models using Message Passing Interface (MPI. This coupled model is applied to the record-breaking heavy rain events occurred in Kyushu Island, the southernmost of Japan's main islands, from 20 July to 25 July in 2006. The test computations are conducted by using both the developed coupled model and the original land surface parameterization of MM5. The result of these computations shows that SOLVEG reproduce higher ground temperature than land surface parameterization schemes in the MM5. This result indicates the feedback of land surface processes between MM5 and SOLVEG plays an important role in the computation. The most pronounced difference is in the rainfall simulation that shows the importance of coupling SOLVEG and MM5. The coupled model accurately reproduces the heavy rainfall events observed in Kyushu Island compared to the original MM5 from both the spatial and temporal point of view. This paper clearly shows that realistic simulation of rainfall event strongly depends on land-surface processes interacting with cloud development that depends on surface heat and moisture fluxes, which in turn are mainly determined by land surface vegetation and soil moisture storage. Soil temperature/moisture changes significantly affect the localized precipitation and modest improvement in the land surface representation can enhance the heavy rain simulation. MM5-SOLVEG coupling shows a clear image of land surface-atmosphere interactions and the dynamic feedback on
The Role of Spatio-Temporal Resolution of Rainfall Inputs on a Landscape Evolution Model
Skinner, C. J.; Coulthard, T. J.
2015-12-01
Landscape Evolution Models are important experimental tools for understanding the long-term development of landscapes. Designed to simulate timescales ranging from decades to millennia, they are usually driven by precipitation inputs that are lumped, both spatially across the drainage basin, and temporally to daily, monthly, or even annual rates. This is based on an assumption that the spatial and temporal heterogeneity of the rainfall will equalise over the long timescales simulated. However, recent studies (Coulthard et al., 2012) have shown that such models are sensitive to event magnitudes, with exponential increases in sediment yields generated by linear increases in flood event size at a basin scale. This suggests that there may be a sensitivity to the spatial and temporal scales of rainfall used to drive such models. This study uses the CAESAR-Lisflood Landscape Evolution Model to investigate the impact of spatial and temporal resolution of rainfall input on model outputs. The sediment response to a range of temporal (15 min to daily) and spatial (5 km to 50km) resolutions over three different drainage basin sizes was observed. The results showed the model was sensitive to both, generating up to 100% differences in modelled sediment yields with smaller spatial and temporal resolution precipitation. Larger drainage basins also showed a greater sensitivity to both spatial and temporal resolution. Furthermore, analysis of the distribution of erosion and deposition patterns suggested that small temporal and spatial resolution inputs increased erosion in drainage basin headwaters and deposition in the valley floors. Both of these findings may have implications for existing models and approaches for simulating landscape development.
How would peak rainfall intensity affect runoff predictions using conceptual water balance models?
Yu, B.
2015-06-01
Most hydrological models use continuous daily precipitation and potential evapotranspiration for streamflow estimation. With the projected increase in mean surface temperature, hydrological processes are set to intensify irrespective of the underlying changes to the mean precipitation. The effect of an increase in rainfall intensity on the long-term water balance is, however, not adequately accounted for in the commonly used hydrological models. This study follows from a previous comparative analysis of a non-stationary daily series of stream flow of a forested watershed (River Rimbaud) in the French Alps (area = 1.478 km2) (1966-2006). Non-stationarity in the recorded stream flow occurred as a result of a severe wild fire in 1990. Two daily models (AWBM and SimHyd) were initially calibrated for each of three distinct phases in relation to the well documented land disturbance. At the daily and monthly time scales, both models performed satisfactorily with the Nash-Sutcliffe coefficient of efficiency (NSE) varying from 0.77 to 0.92. When aggregated to the annual time scale, both models underestimated the flow by about 22% with a reduced NSE at about 0.71. Exploratory data analysis was undertaken to relate daily peak hourly rainfall intensity to the discrepancy between the observed and modelled daily runoff amount. Preliminary results show that the effect of peak hourly rainfall intensity on runoff prediction is insignificant, and model performance is unlikely to improve when peak daily precipitation is included. Trend analysis indicated that the large decrease of precipitation when daily precipitation amount exceeded 10-20 mm may have contributed greatly to the decrease in stream flow of this forested watershed.
Hydrological Modeling of Rainfall-Watershed-Bioretention System with EPA SWMM
gülbaz, sezar; melek kazezyılmaz-alhan, cevza
2016-04-01
Water resources should be protected for the sustainability of water supply and water quality. Human activities such as high urbanization with lack of infrastructure system and uncontrolled agricultural facilities adversely affect the water resources. Therefore, recent techniques should be investigated in detail to avoid present and future problems like flood, drought and water pollution. Low Impact Development-Best Management Practice (LID-BMP) is such a technique to manage storm water runoff and quality. There are several LID storm water BMPs such as bioretention facilities, rain gardens, storm water wetlands, vegetated rooftops, rain barrels, vegetative swales and permeable pavements. Bioretention is a type of Low Impact Developments (LIDs) implemented to diminish adverse effects of urbanization by reducing peak flows over the surface and improving surface water quality simultaneously. Different soil types in different ratios are considered in bioretention design which affects the performance of bioretention systems. Therefore, in this study, a hydrologic model for bioretention is developed by using Environmental Protection Agency Storm Water Management Model (EPA SWMM). Part of the input data is supplied to the hydrologic model by experimental setup called Rainfall-Watershed-Bioretention (RWB). RWB System is developed to investigate the relation among rainfall, watershed and bioretention. This setup consists of three main parts which are artificial rainfall system, drainage area and four bioretention columns with different soil mixture. EPA SWMM is a dynamic simulation model for the surface runoff which develops on a watershed during a rainfall event. The model is commonly used to plan, analyze, and control storm water runoff, to design drainage system components and to evaluate watershed management of both urban and rural areas. Furthermore, EPA SWMM is a well-known program to model LID-Bioretention in the literature. Therefore, EPA SWMM is employed in drainage
Multi-criteria validation of artificial neural network rainfall-runoff modeling
Directory of Open Access Journals (Sweden)
R. Modarres
2008-12-01
Full Text Available In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4 is the best ANN for the basin comparing with other MLP networks and empirical regression model, but the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low-medium and high flows. Although the MLP4 network gives the best performance among ANNs for low-medium and high flows based on different statistics but the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low-medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.
rainfall runoff model for cala noff model for calabar metropolis u ...
African Journals Online (AJOL)
eobe
4 DEPARTMENT OF CIVIL ENGINEERING, R. E-mail addresses ... have the highest average rainfall. However, due ...... [10] Nigerian Meteorological Agency, 2010. [11] Darayatne ... and Environment Research, Queensland, Australia,. 6-8 July ...
Breinl, Korbinian; Di Baldassarre, Giuliano; Girons Lopez, Marc
2017-04-01
We assess uncertainties of multi-site rainfall generation across spatial scales and different climatic conditions. Many research subjects in earth sciences such as floods, droughts or water balance simulations require the generation of long rainfall time series. In large study areas the simulation at multiple sites becomes indispensable to account for the spatial rainfall variability, but becomes more complex compared to a single site due to the intermittent nature of rainfall. Weather generators can be used for extrapolating rainfall time series, and various models have been presented in the literature. Even though the large majority of multi-site rainfall generators is based on similar methods, such as resampling techniques or Markovian processes, they often become too complex. We think that this complexity has been a limit for the application of such tools. Furthermore, the majority of multi-site rainfall generators found in the literature are either not publicly available or intended for being applied at small geographical scales, often only in temperate climates. Here we present a revised, and now publicly available, version of a multi-site rainfall generation code first applied in 2014 in Austria and France, which we call TripleM (Multisite Markov Model). We test this fast and robust code with daily rainfall observations from the United States, in a subtropical, tropical and temperate climate, using rain gauge networks with a maximum site distance above 1,000km, thereby generating one million years of synthetic time series. The modelling of these one million years takes one night on a recent desktop computer. In this research, we first start the simulations with a small station network of three sites and progressively increase the number of sites and the spatial extent, and analyze the changing uncertainties for multiple statistical metrics such as dry and wet spells, rainfall autocorrelation, lagged cross correlations and the inter-annual rainfall
Licznar, Paweł; Łomotowski, Janusz; Rupp, David E.
2011-03-01
Six variations of multiplicative random cascade models for generating fine-resolution (i.e., 5-minute interval) rainfall time series were evaluated for rainfall in Wroclaw, Poland. Of these variations, one included a new beta-normal generator for a microcanonical cascade. This newly proposed model successfully reproduces the statistical behavior of local 5-minute rainfalls, in terms of intermittency as well as variability. In contrast, both the canonical cascade models with either constant or time-scaled parameters and a microcanonical cascade model with a beta generator substantially underestimate 5-minute maximum rainfall intensities. The canonical models also fail to properly reproduce the intermittency of the rainfall process across a range of timescales. New observations are also made concerning the histograms of the breakdown coefficients (BDC). The tendency of the BDC histograms to have values exactly equal to 0.5 is identified and explained by the quality of pluviograph records. Moreover, the hierarchical evolution of BDC histograms from beta-like for long time steps to beta-normal histograms for short time steps is observed for the first time. The potential advantage is shown of synthetic high resolution rainfall time series generated by the revised microcanonical model for use in hydrology, especially hydrodynamic modelling of urban drainage networks.
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.
Ouyang, Wei; Guo, Bobo; Hao, Fanghua; Huang, Haobo; Li, Junqi; Gong, Yongwei
2012-12-30
Managing storm rainfall runoff is paramount in semi-arid regions with urban development. In Beijing, pollution prevention in urban storm runoff and storm water utilization has been identified as the primary strategy for urban water management. In this paper, we sampled runoff during storm rainfall events and analyzed the concentration of chemical oxygen demand (COD), total suspended solids (TSS) and total phosphorus (TP) in the runoff. Furthermore, the first flush effect of storm rainfall from diverse underlying surfaces was also analyzed. With the Storm Water Management Model (SWMM), the different impervious rates of underlying surfaces during the storm runoff process were expressed. The removal rates of three typical pollutants and their interactions with precipitation and underlying surfaces were identified. From these rates, the scenarios regarding the urban storm runoff pollution loading from different designs of underlying previous rates were assessed with the SWMM. First flush effect analysis showed that the first 20% of the storm runoff should be discarded, which can help in utilizing the storm water resource. The results of this study suggest that the SWMM can express in detail the storm water pollution patterns from diverse underlying surfaces in Beijing, which significantly affected water quality. The scenario analysis demonstrated that impervious rate adjustment has the potential to reduce runoff peak and decrease pollution loading.
Geospatial Modeling for Investigating Spatial Pattern and Change Trend of Temperature and Rainfall
Directory of Open Access Journals (Sweden)
Md. Abu Syed
2016-04-01
Full Text Available Bangladesh has been experiencing increased temperature and change in precipitation regime, which might adversely affect the important ecosystems in the country differentially. The river flows and groundwater recharge over space and time are determined by changes in temperature, evaporation and crucially precipitation. These again have a spatio-temporal dimension. This geospatial modeling research aimed at investigating spatial patterns and changing trends of temperature and rainfall within the geographical boundary of Bangladesh. This would facilitate better understanding the change pattern and their probable impacts on the ecosystem. The southeastern region, which is one of the most important forest ecosystem zones in the country, is experiencing early onset and withdrawal of rain but increasing trends in total rainfall except in the Monsoon season. This means that the region is experiencing a lower number of rainy days. However, total rainfall has not changed significantly. The differential between maximum and minimum showed an increasing trend. This changing pattern in average max and min temperature along with precipitation might cause a situation in which the species that are growing now may shift to suitable habitats elsewhere in the future. Consequently, the biodiversity, watersheds and fisheries, productivity of land, agriculture and food security in the region will be affected by these observed changes in climate.
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.
Mechanism of ENSO influence on the South Asian monsoon rainfall in global model simulations
Joshi, Sneh; Kar, Sarat C.
2017-02-01
Coupled ocean atmosphere global climate models are increasingly being used for seasonal scale simulation of the South Asian monsoon. In these models, sea surface temperatures (SSTs) evolve as coupled air-sea interaction process. However, sensitivity experiments with various SST forcing can only be done in an atmosphere-only model. In this study, the Global Forecast System (GFS) model at T126 horizontal resolution has been used to examine the mechanism of El Niño-Southern Oscillation (ENSO) forcing on the monsoon circulation and rainfall. The model has been integrated (ensemble) with observed, climatological and ENSO SST forcing to document the mechanism on how the South Asian monsoon responds to basin-wide SST variations in the Indian and Pacific Oceans. The model simulations indicate that the internal variability gets modulated by the SSTs with warming in the Pacific enhancing the ensemble spread over the monsoon region as compared to cooling conditions. Anomalous easterly wind anomalies cover the Indian region both at 850 and 200 hPa levels during El Niño years. The locations and intensity of Walker and Hadley circulations are altered due to ENSO SST forcing. These lead to reduction of monsoon rainfall over most parts of India during El Niño events compared to La Niña conditions. However, internally generated variability is a major source of uncertainty in the model-simulated climate.
A GIS framework for the stochastic distributed modelling of rainfall induced shallow landslides
Raia, S.; Rossi, M.; Marchesini, I.; Baum, R. L.; Godt, J. W.; Guzzetti, F.
2011-12-01
Deterministic distributed models to forecast shallow landslides spatially extend site-specific slope stability and infiltration models. A problem with using existing deterministic models to forecast shallow landslides is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes, particularly over large areas. An additional problem is the operational difficulty in performing the simulations. This is because of the amount and diversity of the topographic, geological, hydrological, and rainfall data required by the numerical models. To overcome these problems, we propose a stochastic approach to the distributed modelling of shallow rainfall-induced landslides in a GIS environment. For this purpose, we developed a new stochastic version of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis code (TRIGRS). The new code (TRIGRS-S) uses Gaussian and uniform probability distributions to describe the mechanical and hydrological properties of the slope materials. A Monte Carlo approach is used to investigate the variability of the model parameters. To help in the preparation of the model input data, and in the execution of the simulations, we implemented the TRIGRS-S code in the GRASS GIS environment. Statistical analysis of the results is performed in R, a programming language and software environment for statistical computing and plotting. TRIGRS-S was tested in a 3-km2 area north of Seattle, USA, and in a 13-km2 area south of Perugia, Italy. Adoption of the stochastic framework in the two study areas has resulted in improved spatial forecasts of shallow landslides, when compared to the deterministic forecasts. We attribute the difference to the natural variability of the mechanical and hydrological properties of the slope materials, and to the uncertainty associated with the simplified slope- stability and infiltration models. We expect the stochastic approach, code TRIGRS
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
Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.
2017-02-01
Weather forecasting is an important issue in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. India experiences the precious Southwest monsoon season for four months from June to September. The present paper describes an empirical study for modeling and forecasting the time series of Southwest monsoon rainfall patterns in the North-East India. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The study has shown that the SARIMA (0, 1, 1) (1, 0, 1)4 model is appropriate for analyzing and forecasting the future rainfall patterns. The Analysis of Means (ANOM) is a useful alternative to the analysis of variance (ANOVA) for comparing the group of treatments to study the variations and critical comparisons of rainfall patterns in different months of the season.
Sikorska, A. E.; Scheidegger, A.; Banasik, K.; Rieckermann, J.
2012-04-01
Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
Lehmann, Peter; von Ruette, Jonas; Fan, Linfeng; Or, Dani
2014-05-01
Rapid debris flows initiated by rainfall induced shallow landslides present a highly destructive natural hazard in steep terrain. The impact and run-out paths of debris flows depend on the volume, composition and initiation zone of released material and are requirements to make accurate debris flow predictions and hazard maps. For that purpose we couple the mechanistic 'Catchment-scale Hydro-mechanical Landslide Triggering (CHLT)' model to compute timing, location, and landslide volume with simple approaches to estimate debris flow runout distances. The runout models were tested using two landslide inventories obtained in the Swiss Alps following prolonged rainfall events. The predicted runout distances were in good agreement with observations, confirming the utility of such simple models for landscape scale estimates. In a next step debris flow paths were computed for landslides predicted with the CHLT model for a certain range of soil properties to explore its effect on runout distances. This combined approach offers a more complete spatial picture of shallow landslide and subsequent debris flow hazards. The additional information provided by CHLT model concerning location, shape, soil type and water content of the released mass may also be incorporated into more advanced models of runout to improve predictability and impact of such abruptly-released mass.
A Lagrangian model for soil water dynamics during rainfall-driven conditions
Zehe, Erwin; Jackisch, Conrad
2016-09-01
Within this study we propose a stochastic approach to simulate soil water dynamics in the unsaturated zone by using a non-linear, space domain random walk of water particles. Soil water is represented by particles of constant mass, which travel according to the Itô form of the Fokker-Planck equation. The model concept builds on established soil physics by estimating the drift velocity and the diffusion term based on the soil water characteristics. A naive random walk, which assumes all water particles to move at the same drift velocity and diffusivity, overestimated depletion of soil moisture gradients compared to a Richards solver. This is because soil water and hence the corresponding water particles in smaller pore size fractions are, due to the non-linear decrease in soil hydraulic conductivity with decreasing soil moisture, much less mobile. After accounting for this subscale variability in particle mobility, the particle model and a Richards solver performed highly similarly during simulated wetting and drying circles in three distinctly different soils. Both models were in very good accordance during rainfall-driven conditions, regardless of the intensity and type of the rainfall forcing and the shape of the initial state. Within subsequent drying cycles the particle model was typically slightly slower in depleting soil moisture gradients than the Richards model. Within a real-world benchmark, the particle model and the Richards solver showed the same deficiencies in matching observed reactions of topsoil moisture to a natural rainfall event. The particle model performance, however, clearly improved after a straightforward implementation of rapid non-equilibrium infiltration, which treats event water as different types of particles, which travel initially in the largest pore fraction at maximum velocity and experience a slow diffusive mixing with the pre-event water particles. The proposed Lagrangian approach is hence a promising, easy
Ghumman, Abul Razzaq; Al-Salamah, Ibrahim Saleh; AlSaleem, Saleem Saleh; Haider, Husnain
2017-02-01
Geomorphological instantaneous unit hydrograph (GIUH) usually uses geomorphologic parameters of catchment estimated from digital elevation model (DEM) for rainfall-runoff modeling of ungauged watersheds with limited data. Higher resolutions (e.g., 5 or 10 m) of DEM play an important role in the accuracy of rainfall-runoff models; however, such resolutions are expansive to obtain and require much greater efforts and time for preparation of inputs. In this research, a modeling framework is developed to evaluate the impact of lower resolutions (i.e., 30 and 90 m) of DEM on the accuracy of Clark GIUH model. Observed rainfall-runoff data of a 202-km(2) catchment in a semiarid region was used to develop direct runoff hydrographs for nine rainfall events. Geographical information system was used to process both the DEMs. Model accuracy and errors were estimated by comparing the model results with the observed data. The study found (i) high model efficiencies greater than 90% for both the resolutions, and (ii) that the efficiency of Clark GIUH model does not significantly increase by enhancing the resolution of the DEM from 90 to 30 m. Thus, it is feasible to use lower resolutions (i.e., 90 m) of DEM in the estimation of peak runoff in ungauged catchments with relatively less efforts. Through sensitivity analysis (Monte Carlo simulations), the kinematic wave parameter and stream length ratio are found to be the most significant parameters in velocity and peak flow estimations, respectively; thus, they need to be carefully estimated for calculation of direct runoff in ungauged watersheds using Clark GIUH model.
Cocke, Steven; Larow, T. E.; Shin, D. W.
2007-02-01
Seasonal rainfall predictions over the southeast United States using the recently developed Florida State University (FSU) nested regional spectral model are presented. The regional model is nested within the FSU coupled model, which includes a version of the Max Plank Institute Hamburg Ocean Primitive Equation model. The southeast U.S. winter has a rather strong climatic signal due to teleconnections with tropical Pacific sea surface temperatures and thus provides a good test case scenario for a modeling study. Simulations were done for 12 boreal winter seasons, from 1986 to 1997. Both the regional and global models captured the basic large-scale patterns of precipitation reasonably well when compared to observed station data. The regional model was able to predict the anomaly pattern somewhat better than the global model. The regional model was particularly more skillful at predicting the frequency of significant rainfall events, in part because of the ability to produce heavier rainfall events.
Assessing rainfall triggered landslide hazards through physically based models under uncertainty
Balin, D.; Metzger, R.; Fallot, J. M.; Reynard, E.
2009-04-01
Hazard and risk assessment require, besides good data, good simulation capabilities to allow prediction of events and their consequences. The present study introduces a landslide hazards assessment strategy based on the coupling of hydrological physically based models with slope stability models that should be able to cope with uncertainty of input data and model parameters. The hydrological model used is based on the Water balance Simulation Model, WASIM-ETH (Schulla et al., 1997), a fully distributed hydrological model that has been successfully used previously in the alpine regions to simulate runoff, snowmelt, glacier melt, and soil erosion and impact of climate change on these. The study region is the Vallon de Nant catchment (10km2) in the Swiss Alps. A sound sensitivity analysis will be conducted in order to choose the discretization threshold derived from a Laser DEM model, to which the hydrological model yields the best compromise between performance and time computation. The hydrological model will be further coupled with slope stability methods (that use the topographic index and the soil moisture such as derived from the hydrological model) to simulate the spatial distribution of the initiation areas of different geomorphic processes such as debris flows and rainfall triggered landslides. To calibrate the WASIM-ETH model, the Monte Carlo Markov Chain Bayesian approach is privileged (Balin, 2004, Schaefli et al., 2006). The model is used in a single and a multi-objective frame to simulate discharge and soil moisture with uncertainty at representative locations. This information is further used to assess the potential initial areas for rainfall triggered landslides and to study the impact of uncertain input data, model parameters and simulated responses (discharge and soil moisture) on the modelling of geomorphological processes.
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A. J. Pitman
2012-11-01
Full Text Available The impact of historical land use induced land cover change (LULCC on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO_{2} from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO_{2}, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO_{2} except for one model where the CO_{2}-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.
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.
Continuum modeling and limit equilibrium analysis of slope movement due to rainfall infiltration
Borja, Ronaldo; White, Joshua; Wu, Wei
2010-05-01
Hydrologically-driven landslides and debris flows are highly destructive events that threaten lives and critical infrastructure worldwide. Despite decades of extensive slope stability model development, the fundamental controls connecting the hydrologic and geotechnical processes that trigger slope failure are not well quantified. We use a fully coupled, physics-based finite element model to address this shortcoming. We develop and test a 3D continuum slope-deformation model that couples solid-deformation with fluid-flow processes in variably saturated soils, and assess the capability of the coupled model to predict stresses and deformation necessary to trigger slope failure. We then compare the continuum model with traditional limit equilibrium solutions based on the modified Bishop method of slices to assess the stability of the slope as a function of rainfall infiltration using a scalar stability indicator called factor of safety. For this assessment, we use extensive measurements from a densely instrumented mountain slope (The Coos Bay Experimental Catchment) where a large, rainfall-triggered slope failure occurred. The use of sophisticated, fully coupled numerical simulations combined with comprehensive field-measurements provides an unprecedented opportunity to advance the state of understanding of landslide failure processes and effective mitigation measures.
Prediction of monsoon rainfall with a nested grid mesoscale limited area model
Indian Academy of Sciences (India)
S K Roy Bhowmik
2003-12-01
At the India Meteorological Department (IMD), New Delhi, a 12-level limited area model with 100km horizontal resolution has been in use for weather forecasting. The present study uses this model together with a higher horizontal resolution (50 km) and vertical resolution (16-levels) model to examine the impact of increased resolution to simulate mesoscale features of rainfall during monsoon disturbances. The model was run for 22 days in the month of August 1997 and one week in September 1997 during three monsoon depressions and one cyclonic storm in the Bay of Bengal. The model results are compared with observations. The study shows that the model can capture mesoscale convective organization associated with monsoon depression.
Decadal prediction of Sahel rainfall using dynamics-based indices
Otero, Noelia; Mohino, Elsa; Gaetani, Marco
2016-12-01
At decadal time scales, the capability of state-of-the-art atmosphere-ocean coupled climate models in predicting the precipitation in Sahel is assessed. A set of 14 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is selected and two experiments are analysed, namely initialized decadal hindcasts and forced historical simulations. Considering the strong linkage of the atmospheric circulation signatures over West Africa with the rainfall variability, this study aims to investigate the potential of using wind fields for decadal predictions. Namely, a West African monsoon index (WAMI) is defined, based on the coherence of low (925 hPa) and high (200 hPa) troposphere wind fields, which accounts for the intensity of the monsoonal circulation. A combined empirical orthogonal functions analysis is applied to explore the wind fields' covariance modes, and a set of indices is defined on the basis of the identified patterns. The WAMI predictive skill is assessed by comparing WAMI from coupled models with WAMI from reanalysis products and with a standardized precipitation index (SPI) from observations. Results suggest that the predictive skill is highly model dependent and it is strongly related to the WAMI definition. In addition, hindcasts are more skilful than historical simulations in both deterministic and probability forecasts, which suggests an added value of initialization for decadal predictability. Moreover, coupled models are more skilful in predicting the observed SPI than the WAMI obtained from reanalysis. WAMI performance is also compared with decadal predictions from CMIP5 models based on a Sahelian precipitation index, and an improvement in predictive skill is observed in some models when WAMI is used. Therefore, we conclude that dynamics-based indices are potentially more effective for decadal prediction of precipitation in Sahel than precipitation-based indices for those models in which Sahel rainfall variability is not well
von Ruette, Jonas; Papritz, Andreas; Lehmann, Peter; Rickli, Christian; Or, Dani
2011-10-01
Statistical models that exploit the correlation between landslide occurrence and geomorphic properties are often used to map the spatial occurrence of shallow landslides triggered by heavy rainfalls. In many landslide susceptibility studies, the true predictive power of the statistical model remains unknown because the predictions are not validated with independent data from other events or areas. This study validates statistical susceptibility predictions with independent test data. The spatial incidence of landslides, triggered by an extreme rainfall in a study area, was modeled by logistic regression. The fitted model was then used to generate susceptibility maps for another three study areas, for which event-based landslide inventories were also available. All the study areas lie in the northern foothills of the Swiss Alps. The landslides had been triggered by heavy rainfall either in 2002 or 2005. The validation was designed such that the first validation study area shared the geomorphology and the second the triggering rainfall event with the calibration study area. For the third validation study area, both geomorphology and rainfall were different. All explanatory variables were extracted for the logistic regression analysis from high-resolution digital elevation and surface models (2.5 m grid). The model fitted to the calibration data comprised four explanatory variables: (i) slope angle (effect of gravitational driving forces), (ii) vegetation type (grassland and forest; root reinforcement), (iii) planform curvature (convergent water flow paths), and (iv) contributing area (potential supply of water). The area under the Receiver Operating Characteristic (ROC) curve ( AUC) was used to quantify the predictive performance of the logistic regression model. The AUC values were computed for the susceptibility maps of the three validation study areas (validation AUC), the fitted susceptibility map of the calibration study area (apparent AUC: 0.80) and another
Changes in densities of Sahelian bird species in response to recent ...
African Journals Online (AJOL)
Changes in densities of Sahelian bird species in response to recent habitat ... pronounced, due to anthropogenic effects and (potentially) climate change. ... We predicted declines in the abundance of woodland bird species with deforestation.
Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model
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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.
Scavenging of ultrafine particles by rainfall at a boreal site: observations and model estimations
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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.
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M. Sudha
2015-12-01
Full Text Available Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP and Adaptive Neuro Fuzzy Inference System (ANFIS was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA, Structural Learning Algorithm on Vague Environment (SLAVE and Particle Swarm OPtimization (PSO. The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %.
Loss Modeling with a Data-Driven Approach in Event-Based Rainfall-Runoff Analysis
Chua, L. H. C.
2012-04-01
Mathematical models require the estimation of rainfall abstractions for accurate predictions of runoff. Although loss models such as the constant loss and exponential loss models are commonly used, these methods are based on simplified assumptions of the physical process. A new approach based on the data driven paradigm to estimate rainfall abstractions is proposed in this paper. The proposed data driven model, based on the artificial neural network (ANN) does not make any assumptions on the loss behavior. The estimated discharge from a physically-based model, obtained from the kinematic wave (KW) model assuming zero losses, was used as the only input to the ANN. The output is the measured discharge. Thus, the ANN functions as a black-box loss model. Two sets of data were analyzed for this study. The first dataset consists of rainfall and runoff data, measured from an artificial catchment (area = 25 m2) comprising two overland planes (slope = 11%), 25m long, transversely inclined towards a rectangular channel (slope = 2%) which conveyed the flow, recorded using calibrated weigh tanks, to the outlet. Two rain gauges, each placed 6.25 m from either ends of the channel, were used to record rainfall. Data for six storm events over the period between October 2002 and December 2002 were analyzed. The second dataset was obtained from the Upper Bukit Timah catchment (area = 6.4 km2) instrumented with two rain gauges and a flow measuring station. A total of six events recorded between November 1987 and July 1988 were selected for this study. The runoff predicted by the ANN was compared with the measured runoff. In addition, results from KW models developed for both the catchments were used as a benchmark. The KW models were calibrated assuming the loss rate for an average event for each of the datasets. The results from both the ANN and KW models agreed well with the runoff measured from the artificial catchment. The KW model is expected to perform well since the catchment
Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows
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A. Talebi
2008-07-01
Full Text Available Recently, D'Odorico and Fagherazzi (2003 proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003. Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building blocks of the model are: an infinite-slope stability analysis; a steady-state kinematic wave model (KW of hollow groundwater hydrology; and a statistical model relating intensity, duration, and frequency of extreme precipitation. Here we extend the work of D'Odorico and Fagherazzi (2003 by incorporating a more realistic description of hollow hydrology (hillslope storage Boussinesq model, HSB such that this model can also be applied to more gentle slopes and hollows with different plan shapes. We show that results obtained using the KW and HSB models are significantly different as in the KW model the diffusion term is ignored. We generalize our results by examining the stability of several hollow types with different plan shapes (different convergence degree. For each hollow type, the minimum value of the landslide-triggering saturated depth corresponding to the triggering precipitation (critical recharge rate is computed for steep and gentle hollows. Long term analysis of shallow landslides by the presented model illustrates that all hollows show a quite different behavior from the stability view point. In hollows with more convergence, landslide occurrence is limited by the supply of deposits (supply limited regime or rainfall events (event limited regime while hollows with low convergence degree are unconditionally stable regardless of the soil thickness or rainfall intensity. Overall, our results show that in addition to the effect of slope angle, plan shape (convergence degree also controls the subsurface flow and this process affects the probability distribution of landslide occurrence in different hollows. Finally, we conclude that
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.
Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox
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Cécile Dardel
2014-04-01
Full Text Available Rain Use Efficiency (RUE, defined as Aboveground Net Primary Production (ANPP divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region. The debate is fueled by methodological issues, especially when using satellite remote sensing data to estimate ANPP, and by differences in the ecological interpretation. An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall (“ANPP residuals”. In this paper, we use long-term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data (GIMMS-3g Normalized Difference Vegetation Index to estimate ANPP, RUE, and the ANPP residuals, over the period 1984–2010. The residuals as well as RUE do not reveal any trend over time over the Gourma region, implying that vegetation is resilient over that period, when data are aggregated at the Gourma scale. We find no conflict between field-derived and satellite-derived results in terms of trends. The nature (linearity of the ANPP/rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation. However, at odds with a stable RUE, an increased run-off coefficient has been observed in the area over the same period, pointing towards land degradation. The divergence of these two indicators of ecosystem resilience (stable RUE and land degradation (increasing run-off coefficient is referred to as the “second Sahelian paradox”. When shallow soils and deep soils are examined separately, high resilience is diagnosed on the deep soil sites. However, some of the shallow soils show signs of degradation, being characterized by decreasing vegetation cover and increasing run-off coefficient. Such results show that contrasted changes may co-exist within a region where a
Williams, C.; Kniveton, D.; Layberry, R.
2009-04-01
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 subcontinent 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 is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, high resolution satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA) are used as a basis for undertaking model experiments using a state-of-the-art regional climate model. The MIRA dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the regional climate model's domain size are briefly presented, before a comparison of simulated daily rainfall from the model with the satellite-derived dataset. Secondly, simulations of current climate and rainfall extremes from the model are compared to the MIRA dataset at daily timescales. Finally, the results from the idealised SST experiments are presented, suggesting highly nonlinear associations between rainfall extremes
Olivares, Lucio; Picarelli, Luciano; Savastano, Vincenzo; Damiano, Emilia; Greco, Roberto; Guida, Andrea
2010-05-01
A significant part of Italian mountainous areas are covered by pyroclastic deposits resting at slope angles higher than 40-50°. The stability of these steep slopes in loose or poorly cemented pyroclastic materials is essentially guaranteed by the positive effects of matrix suction on shear strength until an increase in saturation (and hence a decrease in suction) is induced by seepage initiated by different processes. The Cervinara flowslide (Campania, Italy) is a typical case where rainfall infiltration increased saturation and hence led to failure of shallow layered pyroclastic deposits. This case study is examined by means of a numerical model calibrated through back-analysis of flume tests, which link instability to rainwater infiltration. The complexity of infiltration process on unsaturated layered slope requires the set up of a numerical model. The model includes a 3D volume finite algorithm (I-MOD3D) developed in VBA application for ARCOBJECTTM/ARCGIS 9.2TM to automate the mesh-generation starting from a Digital Terrain Model allowing the analysis of slope response at catchment scale. Model calibration was carried out using either data from laboratory tests on natural soil samples or from infiltration tests on layered slope model. Model validation was carried out through back-analysis of in situ suction measurements using initial and boundary conditions derived from field monitoring. Comparison between the results of slope model infiltration tests, numerical simulations and in situ measurements showed that the developed numerical model represents reliable tool for predicting slope response to rainfall infiltration for shallow layered pyroclastic deposits.
The role of aerosols to increase rainfall in the regions with less intensity rain: A modeling study
Directory of Open Access Journals (Sweden)
Shyam Sundar
2013-03-01
Full Text Available In this paper, we established an ecological type three-dimensional nonlinear mathematical model to study the effect of aerosol particles in increasing rainfall in the regions of less intensity rain. The phenomenon of nonlinearity is based on the concepts of ecology related to growth rate, death rate and interaction process (Smith, 1974. It is assumed that clouds are formed in the atmosphere but are not able to develop uninterrupted rainfall. The rainfall can be enhanced by introducing aerosol particles conducive to raindrops formation from cloud droplets. It is shown that the intensity of rainfall increases as the concentration of externally introduced aerosols and the density of cloud droplets increases. The numerical simulation has also been performed tosupport analytical results.
Sahelian rangeland response to changes in rainfall over two decades in the Gourma region, Mali
Hiernaux, P.; Mougin, Eric; Diarra, L.; Soumaguel, Nogmana; Lavenu, François; Y. Tracol; Diawara, Mamadou
2009-01-01
Twenty-five rangeland sites were monitored over two decades (1984-2006) first to assess the impact of the 1983-1984 droughts on fodder resources, then to better understand ecosystem functioning and dynamics. Sites are sampled along the south-north bioclimatic gradient in Gourma (Mali), within three main edaphic situations: sandy, loamy-clay and shallow soils. In addition, three levels of grazing pressure where systematically sampled within sandy soils. Located at the northern edge of the area...
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Emmanuel Isabelle
2016-01-01
Full Text Available The influence of rainfall spatial variability on hydrographs modelling at catchment outlet remains an open scientific debate. [1] have proposed rainfall variability indexes aiming at summarising the influence of rainfall spatial organisation on hydrographs features. This preliminary work was based on a large simulated database. The present article shows how the proposed indexes may be used in a real case study to discriminate rainfall events for which information on spatial rainfall organization is crucial for hydrograph modelling, and therefore to better illustrate the added value of high resolution rainfall information as input of hydrological models. The presented case study is located in the Cevennes Region in south-eastern France. The tested flow events are split into two subsets according to the values of the rainfall variability indexes. The comparison between modelled and measured hydrographs is then performed separately for each subset. The results obtained suggest that, on average, modelling results taking into account high resolution rainfall data are significantly improved for the subset for which the influence of rainfall variability is expected to be significant according to the indexes values. Although limited to a relatively small number of hydrographs, this case study can be viewed as a first confirmation of the pertinence of the rainfall variability indexes proposed in [1] to investigate the influence of rainfall spatial variability on the shape of hydrographs at catchment outlet.
Institute of Scientific and Technical Information of China (English)
LIU Hong-Bo
2012-01-01
In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation reproduces reasonably well the evolution of the rainfall during the study period＇s three successive rainy phases, especially the frequent heavy rainfall events occurring in the Huai River Basin. The model captures the major rainfall peak observed by the monitoring stations in the morning. Another peak appears later than that shown by the observations. In addition, the simulation realistically captures not only the evolution of the low-level winds but also the characteristics of their diurnal variation. The strong southwesterly （low-level jet, LLJ） wind speed increases beginning in the early evening and reaches a peak in the morning; it then gradually decreases until the afternoon. The intense LLJ forms a strong convergent circulation pattern in the early morning along the Yangtze-Huai River Basin. This pattern partly explains the rainfall peak observed at this time. This study furnishes a basis for the further analysis of the mechanisms of evolution of the LLJ and for the further study of the interactions between the LLJ and rainfall.
A comparison of model ensembles for attributing 2012 West African rainfall
Parker, Hannah R.; Lott, Fraser C.; Cornforth, Rosalind J.; Mitchell, Daniel M.; Sparrow, Sarah; Wallom, David
2017-01-01
In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from weather@home with a regional version of HadAM3P. These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles. However, the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the magnitude in the atmosphere-only model ensembles due to larger ensemble sizes from single models with more constrained simulations. Certainty is greatly decreased when considering a CMIP5 ensemble that can represent the relevant teleconnections due to a decrease in ensemble members. An increase in probability of high precipitation in HadGEM3-A using the observed trend in sea surface temperatures (SSTs) for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect.
Dakhlaoui, H.; Ruelland, D.; Tramblay, Y.; Bargaoui, Z.
2017-07-01
To evaluate the impact of climate change on water resources at the catchment scale, not only future projections of climate are necessary but also robust rainfall-runoff models that must be fairly reliable under changing climate conditions. The aim of this study was thus to assess the robustness of three conceptual rainfall-runoff models (GR4j, HBV and IHACRES) on five basins in northern Tunisia under long-term climate variability, in the light of available future climate scenarios for this region. The robustness of the models was evaluated using a differential split sample test based on a climate classification of the observation period that simultaneously accounted for precipitation and temperature conditions. The study catchments include the main hydrographical basins in northern Tunisia, which produce most of the surface water resources in the country. A 30-year period (1970-2000) was used to capture a wide range of hydro-climatic conditions. The calibration was based on the Kling-Gupta Efficiency (KGE) criterion, while model transferability was evaluated based on the Nash-Sutcliffe efficiency criterion and volume error. The three hydrological models were shown to behave similarly under climate variability. The models simulated the runoff pattern better when transferred to wetter and colder conditions than to drier and warmer ones. It was shown that their robustness became unacceptable when climate conditions involved a decrease of more than 25% in annual precipitation and an increase of more than +1.75 °C in annual mean temperatures. The reduction in model robustness may be partly due to the climate dependence of some parameters. When compared to precipitation and temperature projections in the region, the limits of transferability obtained in this study are generally respected for short and middle term. For long term projections under the most pessimistic emission gas scenarios, the limits of transferability are generally not respected, which may hamper the
Forecasting Rainfall Induced Landslide using High Resolution DEM and Simple Water Budget Model
Luzon, P. K. D.; Lagmay, A. M. F. A.
2014-12-01
Philippines is hit by an average of 20 typhoons per year bringing large amount of rainfall. Monsoon carrying rain coming from the southwest of the country also contributes to the annual total rainfall that causes different hazards. Such is shallow landslide mainly triggered by high saturation of soil due to continuous downpour which could take up from hours to days. Recent event like this happened in Zambales province September of 2013 where torrential rain occurred for 24 hours amounting to half a month of rain. Rainfall intensity measured by the nearest weather station averaged to 21 mm/hr from 10 pm of 22 until 10 am the following day. The monsoon rains was intensified by the presence of Typhoon Usagi positioned north and heading northwest of the country. A number of landslides due to this happened in 3 different municipalities; Subic, San Marcelino and Castillejos. The disaster have taken 30 lives from the province. Monitoring these areas for the entire country is but a big challenge in all aspect of disaster preparedness and management. The approach of this paper is utilizing the available forecast of rainfall amount to monitor highly hazardous area during the rainy seasons and forecasting possible landslide that could happen. A simple water budget model following the equation Perct=Pt-R/Ot-∆STt-AETt (where as the terms are Percolation, Runoff, Change in Storage, and Actual Evapotraspiration) was implemented in quantifying all the water budget component. Computations are in Python scripted grid system utilizing the widely used GIS forms for easy transfer of data and faster calculation. Results of successive runs will let percolation and change in water storage as indicators of possible landslide.. This approach needs three primary sets of data; weather data, topographic data, and soil parameters. This research uses 5 m resolution DEM (IfSAR) to define the topography. Soil parameters are from fieldworks conducted. Weather data are from the Philippine
Prediction of Experimental Rainfall-Eroded Soil Area Based on S-Shaped Growth Curve Model Framework
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Wen Nie
2015-07-01
Full Text Available Rainfall-induced soil erosion of a mountain area plays a significant role in supplying sediment and shaping the landscape. The related area of soil erosion, as an index of the changed landscape, is easier to calculate visually using some popular imaging tools. By image analysis, our work shows that the changing of the soil erosion area admits the structure of an S-growth curve. Therefore, we propose to establish an S-curve model, based on incremental learning, to predict the soil erosion area. In the process of incremental learning, we dynamically update the accumulative rainfall and rainfall intensity to train the parameters of our S-curve model. In order to verify our prediction model, the index of area is utilized to express the output of eroded soil in a series of experiments. The results show that the proposed S-growth curve model can be used to estimate the growth of the soil erosion area (average relative error 3%–9.7% according to variable soil material and rainfall intensity. The original S-growth curve model can calculate the erosion areas of just one soil material and one rainfall condition whose average relative error is 7.5%–12.2%; compared to the simple time series analysis-moving average method (average relative error 5.7%–12.1%, our proposed S-growth curve model can reveal the physical mechanism and evolution of the research object.
Serrat-Capdevila, A.; Abitew, T. A.; Roy, T.; van Griensven, A.; Valdes, J. B.; Bauwens, W.
2015-12-01
Hydrometeorological monitoring networks are often limited for basins located in the developing world such as the transboundary Mara Basin. The advent of earth observing systems have brought satellite rainfall and evapotranspiration products, which can be used to force hydrological models in data scarce basins. The objective of this study is to develop improved hydrologic simulations using distributed satellite rainfall products (CMORPH and TMPA) with a bias-correction, and compare the performance with different input data and models. The bias correction approach for the satellite-products (CMORPH and TMPA) involves the use of a distributed reference dataset (CHIRPS) and historical ground gauge records. We have applied the bias-corrected satellite products to force the Soil and Water Assessment Tool (SWAT) model for the Mara Basin. Firstly, we calibrate the SWAT parameters related to ET simulation using ET from remote sensing. Then, the SWAT parameters that control surface processes are calibrated using the available limited flow. From the analysis, we noted that not only the bias-corrected satellite rainfall but also augmenting limited flow data with monthly remote sensing ET improves the model simulation skill and reduces the parameter uncertainty to some extent. We have planned to compare these results from a lumped model forced by the same input satellite rainfall. This will shed light on the potential of satellite rainfall and remote sensing ET along with in situ data for hydrological processes modeling and the inherent uncertainty in a data scarce basin.
Serinaldi, F.
2010-12-01
Discrete multiplicative random cascade (MRC) models were extensively studied and applied to disaggregate rainfall data, thanks to their formal simplicity and the small number of involved parameters. Focusing on temporal disaggregation, the rationale of these models is based on multiplying the value assumed by a physical attribute (e.g., rainfall intensity) at a given time scale L, by a suitable number b of random weights, to obtain b attribute values corresponding to statistically plausible observations at a smaller L/b time resolution. In the original formulation of the MRC models, the random weights were assumed to be independent and identically distributed. However, for several studies this hypothesis did not appear to be realistic for the observed rainfall series as the distribution of the weights was shown to depend on the space-time scale and rainfall intensity. Since these findings contrast with the scale invariance assumption behind the MRC models and impact on the applicability of these models, it is worth studying their nature. This study explores the possible presence of dependence of the parameters of two discrete MRC models on rainfall intensity and time scale, by analyzing point rainfall series with 5-min time resolution. Taking into account a discrete microcanonical (MC) model based on beta distribution and a discrete canonical beta-logstable (BLS), the analysis points out that the relations between the parameters and rainfall intensity across the time scales are detectable and can be modeled by a set of simple functions accounting for the parameter-rainfall intensity relationship, and another set describing the link between the parameters and the time scale. Therefore, MC and BLS models were modified to explicitly account for these relationships and compared with the continuous in scale universal multifractal (CUM) model, which is used as a physically based benchmark model. Monte Carlo simulations point out that the dependence of MC and BLS
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F. Serinaldi
2010-12-01
Full Text Available Discrete multiplicative random cascade (MRC models were extensively studied and applied to disaggregate rainfall data, thanks to their formal simplicity and the small number of involved parameters. Focusing on temporal disaggregation, the rationale of these models is based on multiplying the value assumed by a physical attribute (e.g., rainfall intensity at a given time scale L, by a suitable number b of random weights, to obtain b attribute values corresponding to statistically plausible observations at a smaller L/b time resolution. In the original formulation of the MRC models, the random weights were assumed to be independent and identically distributed. However, for several studies this hypothesis did not appear to be realistic for the observed rainfall series as the distribution of the weights was shown to depend on the space-time scale and rainfall intensity. Since these findings contrast with the scale invariance assumption behind the MRC models and impact on the applicability of these models, it is worth studying their nature. This study explores the possible presence of dependence of the parameters of two discrete MRC models on rainfall intensity and time scale, by analyzing point rainfall series with 5-min time resolution. Taking into account a discrete microcanonical (MC model based on beta distribution and a discrete canonical beta-logstable (BLS, the analysis points out that the relations between the parameters and rainfall intensity across the time scales are detectable and can be modeled by a set of simple functions accounting for the parameter-rainfall intensity relationship, and another set describing the link between the parameters and the time scale. Therefore, MC and BLS models were modified to explicitly account for these relationships and compared with the continuous in scale universal multifractal (CUM model, which is used as a physically based benchmark model. Monte Carlo simulations point out
Radar rainfall estimates in an alpine environment using inverse hydrological modelling
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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.
Boyko, Oleksiy; Zheleznyak, Mark
2015-04-01
The original numerical code TOPKAPI-IMMS of the distributed rainfall-runoff model TOPKAPI ( Todini et al, 1996-2014) is developed and implemented in Ukraine. The parallel version of the code has been developed recently to be used on multiprocessors systems - multicore/processors PC and clusters. Algorithm is based on binary-tree decomposition of the watershed for the balancing of the amount of computation for all processors/cores. Message passing interface (MPI) protocol is used as a parallel computing framework. The numerical efficiency of the parallelization algorithms is demonstrated for the case studies for the flood predictions of the mountain watersheds of the Ukrainian Carpathian regions. The modeling results is compared with the predictions based on the lumped parameters models.
Salciarini, D.; Godt, J.W.; Savage, W.Z.; Conversini, P.; Baum, R.L.; Michael, J.A.
2006-01-01
We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.
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.
Detailed Flood Modeling and Hazard Assessment from Storm Tides, Rainfall and Sea Level Rise
Orton, P. M.; Hall, T. M.; Georgas, N.; Conticello, F.; Cioffi, F.; Lall, U.; Vinogradov, S. V.; Blumberg, A. F.
2014-12-01
A flood hazard assessment has been conducted for the Hudson River from New York City to Troy at the head of tide, using a three-dimensional hydrodynamic model and merging hydrologic inputs and storm tides from tropical and extra-tropical cyclones, as well as spring freshet floods. Our recent work showed that neglecting freshwater flows leads to underestimation of peak water levels at up-river sites and neglecting stratification (typical with two-dimensional modeling) leads to underestimation all along the Hudson. The hazard assessment framework utilizes a representative climatology of over 1000 synthetic tropical cyclones (TCs) derived from a statistical-stochastic TC model, and historical extra-tropical cyclones and freshets from 1950-present. Hydrodynamic modeling is applied with seasonal variations in mean sea level and ocean and estuary stratification. The model is the Stevens ECOM model and is separately used for operational ocean forecasts on the NYHOPS domain (http://stevens.edu/NYHOPS). For the synthetic TCs, an Artificial Neural Network/ Bayesian multivariate approach is used for rainfall-driven freshwater inputs to the Hudson, translating the TC attributes (e.g. track, SST, wind speed) directly into tributary stream flows (see separate presentation by Cioffi for details). Rainfall intensity has been rising in recent decades in this region, and here we will also examine the sensitivity of Hudson flooding to future climate warming-driven increases in storm precipitation. The hazard assessment is being repeated for several values of sea level, as projected for future decades by the New York City Panel on Climate Change. Recent studies have given widely varying estimates of the present-day 100-year flood at New York City, from 2.0 m to 3.5 m, and special emphasis will be placed on quantifying our study's uncertainty.
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A. E. Sikorska
2011-12-01
Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced by 150% with Bayesian updating, using only a few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
Modeling sugarcane ripening as a function of accumulated rainfall in Southern Brazil
Cardozo, Nilceu P.; Sentelhas, Paulo C.; Panosso, Alan R.; Palhares, Antonio L.; Ide, Bernardo Y.
2015-12-01
The effect of weather variables on sugarcane ripening is a process still not completely understood, despite its huge impact on the quality of raw material for the sugar energy industry. The aim of the present study was to evaluate the influence of weather variables on sugarcane ripening in southern Brazil, propose empirical models for estimating total recoverable sugar (TRS) content, and evaluate the performance of these models with experimental and commercial independent data from different regions. A field experiment was carried out in Piracicaba, in the state of São Paulo, Brazil, considering eight sugarcane cultivars planted monthly, from March to October 2002. In 2003, at the harvest, 12 months later, samples were collected to evaluate TRS (kg t-1). TRS and weather variables (air temperature, solar radiation, relative humidity, and rainfall) were analyzed using descriptive and multivariate statistical analysis to understand their interactions. From these correlations, variables were selected to generate empirical models for estimating TRS, according to the cultivar groups and their ripening characteristics (early, mid, and late). These models were evaluated by residual analysis and regression analysis with independent experimental data from two other locations in the same years and with independent commercial data from six different locations from 2005 to 2010. The best performances were found with exponential models which considered cumulative rainfall during the 120 days before harvest as an independent variable ( R 2 adj ranging from 0.92 to 0.95). Independent evaluations revealed that our models were capable of estimating TRS with reasonable to high precision ( R 2 adj ranging from 0.66 to 0.99) and accuracy ( D index ranging from 0.90 to 0.99), and with low mean absolute percentage errors (MAPE ≤ 5 %), even in regions with different climatic conditions.
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A. E. Sikorska
2012-04-01
Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
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José L. Valencia
2015-11-01
Full Text Available Rainfall, one of the most important climate variables, is commonly studied due to its great heterogeneity, which occasionally causes negative economic, social, and environmental consequences. Modeling the spatial distributions of rainfall patterns over watersheds has become a major challenge for water resources management. Multifractal analysis can be used to reproduce the scale invariance and intermittency of rainfall processes. To identify which factors are the most influential on the variability of multifractal parameters and, consequently, on the spatial distribution of rainfall patterns for different time scales in this study, universal multifractal (UM analysis—C1, α, and γs UM parameters—was combined with non-parametric statistical techniques that allow spatial-temporal comparisons of distributions by gradients. The proposed combined approach was applied to a daily rainfall dataset of 132 time-series from 1931 to 2009, homogeneously spatially-distributed across a 25 km × 25 km grid covering the Ebro River Basin. A homogeneous increase in C1 over the watershed and a decrease in α mainly in the western regions, were detected, suggesting an increase in the frequency of dry periods at different scales and an increase in the occurrence of rainfall process variability over the last decades.
Tian, F.; Sivapalan, M.; Li, H.; Hu, H.
2007-12-01
The importance of diagnostic analysis of hydrological models is increasingly recognized by the scientific community (M. Sivapalan, et al., 2003; H. V. Gupta, et al., 2007). Model diagnosis refers to model structures and parameters being identified not only by statistical comparison of system state variables and outputs but also by process understanding in a specific watershed. Process understanding can be gained by the analysis of observational data and model results at the specific watershed as well as through regionalization. Although remote sensing technology can provide valuable data about the inputs, state variables, and outputs of the hydrological system, observational rainfall-runoff data still constitute the most accurate, reliable, direct, and thus a basic component of hydrology related database. One critical question in model diagnostic analysis is, therefore, what signature characteristic can we extract from rainfall and runoff data. To this date only a few studies have focused on this question, such as Merz et al. (2006) and Lana-Renault et al. (2007), still none of these studies related event analysis with model diagnosis in an explicit, rigorous, and systematic manner. Our work focuses on the identification of the dominant runoff generation mechanisms from event analysis of rainfall-runoff data, including correlation analysis and analysis of timing pattern. The correlation analysis involves the identification of the complex relationship among rainfall depth, intensity, runoff coefficient, and antecedent conditions, and the timing pattern analysis aims to identify the clustering pattern of runoff events in relation to the patterns of rainfall events. Our diagnostic analysis illustrates the changing pattern of runoff generation mechanisms in the DMIP2 test watersheds located in Oklahoma region, which is also well recognized by numerical simulations based on TsingHua Representative Elementary Watershed (THREW) model. The result suggests the usefulness of
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper develops a new inter-basin water transfer-supply and risk assessment model with consideration of rainfall forecast information. Firstly, based on the current state of reservoir and rainfall forecast information from the global forecast system (GFS), the actual diversion amount can be determined according to the inter-basin water transfer rules with the decision tree method; secondly, the reservoir supply operation system is used to distribute water resource of the inter-basin water transfer reservoir; finally, the integrated risk assessment model is built by selecting the reliability of water transfer, the reliability (water shortage risk), the resiliency and the vulnerability of water supply as risk analysis indexes. The case study shows that the inter-basin water transfer-supply model with rainfall forecast information considered can reduce the comprehensive risk and improve the utilization efficiency of water resource, as compared with conventional and optimal water distribution models.
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M. Coustau
2012-04-01
Full Text Available Rainfall-runoff models are crucial tools for the statistical prediction of flash floods and real-time forecasting. This paper focuses on a karstic basin in the South of France and proposes a distributed parsimonious event-based rainfall-runoff model, coherent with the poor knowledge of both evaporative and underground fluxes. The model combines a SCS runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The efficiency of the model is discussed not only to satisfactorily simulate floods but also to get powerful relationships between the initial condition of the model and various predictors of the initial wetness state of the basin, such as the base flow, the Hu2 index from the Meteo-France SIM model and the piezometric levels of the aquifer. The advantage of using meteorological radar rainfall in flood modelling is also assessed. Model calibration proved to be satisfactory by using an hourly time step with Nash criterion values, ranging between 0.66 and 0.94 for eighteen of the twenty-one selected events. The radar rainfall inputs significantly improved the simulations or the assessment of the initial condition of the model for 5 events at the beginning of autumn, mostly in September–October (mean improvement of Nash is 0.09; correction in the initial condition ranges from −205 to 124 mm, but were less efficient for the events at the end of autumn. In this period, the weak vertical extension of the precipitation system and the low altitude of the 0 °C isotherm could affect the efficiency of radar measurements due to the distance between the basin and the radar (~60 km. The model initial condition S is correlated with the three tested predictors (R^{2} > 0.6. The interpretation of the model suggests that groundwater does not affect the first peaks of the flood, but can strongly impact subsequent peaks in the case of a multi-storm event. Because this kind of model is based on a limited
Rainfall-runoff model for prediction of waterborne viral contamination in a small river catchment
Gelati, E.; Dommar, C.; Lowe, R.; Polcher, J.; Rodó, X.
2013-12-01
We present a lumped rainfall-runoff model aimed at providing useful information for the prediction of waterborne viral contamination in small rivers. Viral contamination of water bodies may occur because of the discharge of sewage effluents and of surface runoff over areas affected by animal waste loads. Surface runoff is caused by precipitation that cannot infiltrate due to its intensity and to antecedent soil water content. It may transport animal feces to adjacent water bodies and cause viral contamination. We model streamflow by separating it into two components: subsurface flow, which is produced by infiltrated precipitation; and surface runoff. The model estimates infiltrated and non-infiltrated precipitation and uses impulse-response functions to compute the corresponding fractions of streamflow. The developed methodologies are applied to the Glafkos river, whose catchment extends for 102 km2 and includes the city of Patra. Streamflow and precipitation observations are available at a daily time resolution. Waterborne virus concentration measurements were performed approximately every second week from the beginning of 2011 to mid 2012. Samples were taken at several locations: in river water upstream of Patras and in the urban area; in sea water at the river outlet and approximately 2 km south-west of Patras; in sewage effluents before and after treatment. The rainfall-runoff model was calibrated and validated using observed streamflow and precipitation data. The model contribution to waterborne viral contamination prediction was benchmarked by analyzing the virus concentration measurements together with the estimated surface runoff values. The presented methodology may be a first step towards the development of waterborne viral contamination alert systems. Predicting viral contamination of water bodies would benefit sectors such as water supply and tourism.
Guardiola-Albert, Carolina; Díez-Herrero, Andrés; Bodoque, José M.; Bermejo, Marcos; Rivero-Honegger, Carlos; Yagüe, Carlos; Monjo, Robert; Tapiador, Francisco J.
2016-04-01
The present study deals the rainfall spatial variability of a small mountainous catchment, which includes the spatial distribution and variability of convective and stratiform events. This work focuses on the precipitation events with hydrological response in Venero-Claro Basin (Avila, Spain). In this basin of 15 square kilometers, flood events of different magnitudes have been often registered. Therefore, any improvement in understanding rainfall characteristics in the area can be of special importance in rainfall estimation and hence to calibrate and validate hydrological models. These enhancements imply more objectivity of risk studies and more predictive and preventive capacity. To separate events by origin it has been used the dimensionless index defined by Monjo (2015), according to the relative temporal distribution of maximum intensities. The main advantages of this method are that it does not require thresholds, so it can be applied for each rain gauge. The geostatistical variogram tool is used to quantify the spatial characteristics of both kinds of events. Hourly rainfall accumulations over the area are computed with observations from one of the 5 existing X-band radar in Spain and 7 rain gauges located in the zone. For each hour the rainfall variogram model has been fitted with the aid of the X-band radar images. Valuable information is extracted from the stratiform and convective ensembles of variogram models. The variogram model parameters are analyzed to determine characteristics of spatial continuity that differentiates stratiform and convective events, and quartiles of sills and ranges in both ensembles are compared.
Institute of Scientific and Technical Information of China (English)
2008-01-01
Most rainfall-induced landslide forecasting models focus on the relation between landslides and rainfall,which is one of the dynamic factors,and seldom consider the stacitc factors,such as geological and geograpical factors.Landslide susceptibility,however,is determinded by both static and dynamic factors.This article proposes a static and dynamic factors-coupled forecasting model(SDFCFM) of regional rainfall-induced landslides,which quantitatively considers both the static and dynamic factors that affect landslides.The generalized additive model(GAM) is applied to coupling both factors to get the landslide susceptibility.In the case study,SDFCFM is applied to forecast the landslide occurrences in Shenzhen during a rainfall process in 2008.Compared with the rainfall logistic regression model,the resulting landslide susceptibility map illustrates that SDFCFM can reduce the forecast redundancy and improve the hit ratio.It is both applicable and practical.The application of SDFCFM in landslide warning and prevention system will improve its efficiency and also cut down the cost of human and matreial resources.
Treatment of precipitation uncertainty in rainfall-runoff modelling: a fuzzy set approach
Maskey, Shreedhar; Guinot, Vincent; Price, Roland K.
2004-09-01
The uncertainty in forecasted precipitation remains a major source of uncertainty in real time flood forecasting. Precipitation uncertainty consists of uncertainty in (i) the magnitude, (ii) temporal distribution, and (iii) spatial distribution of the precipitation. This paper presents a methodology for propagating the precipitation uncertainty through a deterministic rainfall-runoff-routing model for flood forecasting. It uses fuzzy set theory combined with genetic algorithms. The uncertainty due to the unknown temporal distribution of the precipitation is achieved by disaggregation of the precipitation into subperiods. The methodology based on fuzzy set theory is particularly useful where a probabilistic forecast of precipitation is not available. A catchment model of the Klodzko valley (Poland) built with HEC-1 and HEC-HMS was used for the application. The results showed that the output uncertainty due to the uncertain temporal distribution of precipitation can be significantly dominant over the uncertainty due to the uncertain quantity of precipitation.
DEVELOPMENT OF DETENTION VOLUME MODEL FOR DETENTION PONDS WITH LONG-DURATION RAINFALL
Institute of Scientific and Technical Information of China (English)
Jen-Yan CHEN; Hong-Yu CHEN; Jung-Yi CHANG
2004-01-01
The detention pond is one of the crucial items in detention facilities. It may effectively alleviate the occurrence of peak discharge, control the center of flood flow, and reduce the amount of soil loss. The objective of this study is analyzing the detention volume change of a detention pond with long-duration rainfall under the known isosceles trapezoidal inflow hydrograph model. The volume change of detention, which is under the influences of a given isosceles trapezoidal inflow hydrograph and the extent of peak attenuation, is investigated by using the non-dimensional detention theory and the related mathematical analyses. The minimum detention volume of a detention pond can therefore be calculated based on the estimated of volume change of detention. The proposed detention volume estimation model can be used for the design of detention of facilities during the hillside development.
Zhang, Yongqiang; Vaze, Jai; Chiew, Francis H. S.; Li, Ming
2015-06-01
Predicting daily runoff time series in ungauged catchments is both important and challenging. For the last few decades, the rainfall-runoff (RR) modelling approach has been the method of choice. There have been very few studies reported in literature which attempt to use flow duration curve (FDC) to predict daily runoff time series. This study comprehensively compares the two approaches using an extensive dataset (228 catchments) for a large region of south-eastern Australia and provides guidelines for choosing the suitable method. For each approach we used the nearest neighbour method and two weightings - a 5-donor simple mathematical average (SA) and a 5-donor inverse-distance weighting (5-IDW) - to predict daily runoff time series. The results show that 5-IDW was noticeably better than a single donor to predict daily runoff time series, especially for the FDC approach. The RR modelling approach calibrated against daily runoff outperformed the FDC approach for predicting high flows. The FDC approach was better at predicting medium to low flows in traditional calibration against the Nash-Sutcliffe-Efficiency or Root Mean Square Error, but when calibrated against a low flow objective function, both the FDC and rainfall-runoff models performed equally well in simulating the low flows. These results indicate that both methods can be further improved to simulate daily hydrographs describing the range of flow metrics in ungauged catchments. Further studies should be carried out for improving the accuracy of predicted FDC in ungauged catchments, including improving the FDC model structure and parameter fitting.
Extending a rainfall-runoff model for lowland catchments from lumped to semi-distributed
Brauer, Claudia; Torfs, Paul; Teuling, Ryan; Uijlenhoet, Remko
2016-04-01
The Wageningen Lowland Runoff Simulator (WALRUS) is a parametric rainfall-runoff model for catchments with shallow groundwater (Brauer et al., 2014ab). WALRUS was developed using data and experience from two Dutch experimental catchments: the Hupsel Brook catchment (6.5 km2) and the Cabauw polder (0.5 km2). We identified key processes for runoff generation in lowland catchments, notably (1) groundwater-unsaturated zone coupling, (2) wetness-dependent flow routes, (3) groundwater-surface water feedbacks and (4) seepage and surface water supply, and accounted for these in the model structure. Up to now, WALRUS has been used in a lumped manner. However, water managers and researchers have expressed an interest in a semi-distributed version for application to larger catchments with varying forcing and catchment characteristics and to investigate the effect of groundwater flow within the catchment on modelled variables (e.g. groundwater depth). We combined WALRUS and a model for 2-dimensional groundwater flow into a simple modelling framework. WALRUS was already designed to cope with groundwater flow into or out of the model domain, because seepage and lateral groundwater flow are common in lowlands. In the semi-distributed version, we used this feature to couple different WALRUS elements (grid cells or subcatchments) to each other. Groundwater flow was computed using a digital elevation model, groundwater depths computed by WALRUS, soil transmissivity data and Darcy's law. Finally, we implemented a surface routing model including backwater effects, which are relevant in areas with little relief. With respect to the lumped version, the semi-distributed requires more data. Therefore, we investigated the added value of different data sources (forcing, elevation, soil, surface water) separately. We will present the rationale behind the semi-distributed model and show how the model structure compares to observations and and simulations without lateral transport. C.C. Brauer
Indian Academy of Sciences (India)
K Rajendran; Ravi S Nanjundiah; Sulochana Gadgil; J Srinivasan
2012-06-01
The failure of atmospheric general circulation models (AGCMs) forced by prescribed SST to simulate and predict the interannual variability of Indian/Asian monsoon has been widely attributed to their inability to reproduce the actual sea surface temperature (SST)–rainfall relationship in the warm Indo-Pacific oceans. This assessment is based on a comparison of the observed and simulated correlation between the rainfall and local SST. However, the observed SSTconvection/rainfall relationship is nonlinear and for this a linear measure such as the correlation is not an appropriate measure. We show that the SST–rainfall relationship simulated by atmospheric and coupled general circulation models in IPCC AR4 is nonlinear, as observed, and realistic over the tropical West Pacific (WPO) and the Indian Ocean (IO). The SST–rainfall pattern simulated by the coupled versions of these models is rather similar to that from the corresponding atmospheric one, except for a shift of the entire pattern to colder/warmer SSTs when there is a cold/warm bias in the coupled version.
Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli
2017-07-01
This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.
Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli
2016-04-01
This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.
Rakesh, V.; Kantharao, B.
2017-03-01
Data assimilation is considered as one of the effective tools for improving forecast skill of mesoscale models. However, for optimum utilization and effective assimilation of observations, many factors need to be taken into account while designing data assimilation methodology. One of the critical components that determines the amount and propagation observation information into the analysis, is model background error statistics (BES). The objective of this study is to quantify how BES in data assimilation impacts on simulation of heavy rainfall events over a southern state in India, Karnataka. Simulations of 40 heavy rainfall events were carried out using Weather Research and Forecasting Model with and without data assimilation. The assimilation experiments were conducted using global and regional BES while the experiment with no assimilation was used as the baseline for assessing the impact of data assimilation. The simulated rainfall is verified against high-resolution rain-gage observations over Karnataka. Statistical evaluation using several accuracy and skill measures shows that data assimilation has improved the heavy rainfall simulation. Our results showed that the experiment using regional BES outperformed the one which used global BES. Critical thermo-dynamic variables conducive for heavy rainfall like convective available potential energy simulated using regional BES is more realistic compared to global BES. It is pointed out that these results have important practical implications in design of forecast platforms while decision-making during extreme weather events
RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)
Long, Andrew J.
2015-01-01
The Rainfall-Response Aquifer and Watershed Flow Model (RRAWFLOW) is a lumped-parameter model that simulates streamflow, spring flow, groundwater level, or solute transport for a measurement point in response to a system input of precipitation, recharge, or solute injection. I introduce the first version of RRAWFLOW available for download and public use and describe additional options. The open-source code is written in the R language and is available at http://sd.water.usgs.gov/projects/RRAWFLOW/RRAWFLOW.html along with an example model of streamflow. RRAWFLOW includes a time-series process to estimate recharge from precipitation and simulates the response to recharge by convolution, i.e., the unit-hydrograph approach. Gamma functions are used for estimation of parametric impulse-response functions (IRFs); a combination of two gamma functions results in a double-peaked IRF. A spline fit to a set of control points is introduced as a new method for estimation of nonparametric IRFs. Several options are included to simulate time-variant systems. For many applications, lumped models simulate the system response with equal accuracy to that of distributed models, but moreover, the ease of model construction and calibration of lumped models makes them a good choice for many applications (e.g., estimating missing periods in a hydrologic record). RRAWFLOW provides professional hydrologists and students with an accessible and versatile tool for lumped-parameter modeling.
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...
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.
Semita, Carlo; Ferrero, Elena; Calvo, Angela; Trucchi, Gabriella
2014-05-01
Sahel has been plagued by a continuous process of environment degradation since the droughts of the 70s and 80s. The desertification process has a significant impact also on the natural and artificial water bodies, on the rivers flow and the river ecosystems, damaging human activities. Environmental damage exacerbates inequality by exerting a negative impact on the population, in the meantime, the inequalities of human development amplify also environmental damage. The project "RUSSADE" (Réseau des Universités Sahéliennes pour la Sécurité Alimentaire et la Durabilité Environnement), included in the EuropeAid-ACP-EU Cooperation Programme in higher education (EDULINK II), has the specific objective to enhance higher education systems through the implementation of an interdisciplinary Master. The courses are designed with a geoethical approach in order to integrate Earth and Life Sciences to give the Sahelian students technical, scientific and methodological bases and to develop the skills of managing natural resources and to improve agriculture and food security. The systemic and multidisciplinary approach will involve several fields of knowledge. The programs of the different areas are linked to each other and Italian and Sahelian teachers cooperate together both in the courses planning and in the teaching activities. The partners, three African Higher Education Institutions (HEIs) of Niger (CRESA - Abdou Moumouni University), Burkina Faso (LERNSE - Polytechnic University of Bobo-Dioulasso) and Chad (University Institute of Sciences and Techniques of Abeché), will develop an education project with the main objective to use scientific knowledge to fight hunger and poverty and to promote environmental protection in a sustainable development perspective. The quality of higher education in the management of natural resources in agriculture will improve living conditions of Sahelian people. The Master, planned with innovative connections, includes a field training
Comparing model ensembles in an event attribution study of 2012 West African rainfall
Parker, Hannah; Lott, Fraser C.; Cornforth, Rosalind J.
2016-04-01
In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, here we investigate whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from climateprediction.net (a regional version of HadAM3P). These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation, although the magnitude and confidence intervals of the decrease depend on the model ensemble used. The influences of significant teleconnections are then removed from the CMIP5 ensemble to see how this influences the results and compares with the atmosphere-only ensembles.
Calibration of a rainfall-runoff hydrological model and flood simulation using data assimilation
Piacentini, A.; Ricci, S. M.; Thual, O.; Coustau, M.; Marchandise, A.
2010-12-01
Rainfall-runoff models are crucial tools for long-term assessment of flash floods or real-time forecasting. This work focuses on the calibration of a distributed parsimonious event-based rainfall-runoff model using data assimilation. The model combines a SCS-derived runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The SCS-derived runoff model is parametrized by the initial water deficit, the discharge coefficient for the soil reservoir and a lagged discharge coefficient. The Lag and Route routing model is parametrized by the velocity of travel and the lag parameter. These parameters are assumed to be constant for a given catchment except for the initial water deficit and the velocity travel that are event-dependent (landuse, soil type and moisture initial conditions). In the present work, a BLUE filtering technique was used to calibrate the initial water deficit and the velocity travel for each flood event assimilating the first available discharge measurements at the catchment outlet. The advantages of the BLUE algorithm are its low computational cost and its convenient implementation, especially in the context of the calibration of a reduced number of parameters. The assimilation algorithm was applied on two Mediterranean catchment areas of different size and dynamics: Gardon d'Anduze and Lez. The Lez catchment, of 114 km2 drainage area, is located upstream Montpellier. It is a karstic catchment mainly affected by floods in autumn during intense rainstorms with short Lag-times and high discharge peaks (up to 480 m3.s-1 in September 2005). The Gardon d'Anduze catchment, mostly granite and schistose, of 545 km2 drainage area, lies over the departements of Lozère and Gard. It is often affected by flash and devasting floods (up to 3000 m3.s-1 in September 2002). The discharge observations at the beginning of the flood event are assimilated so that the BLUE algorithm provides optimal values for the initial water deficit and the
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.
Nanda, Trushnamayee; Sahoo, Bhabagrahi; Beria, Harsh; Chatterjee, Chandranath
2016-08-01
Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the
Modelling the impacts of deforestation on monsoon rainfall in West Africa
Energy Technology Data Exchange (ETDEWEB)
Abiodun, B J [Department of Environmental and Geographical Science, University of Cape Town (South Africa); Pal, J S [Department of Civil Engineering and Environmental Science, Loyola Marymount University, California (United States); Afiesimama, E A [WMO Regional Research and Training Institute, Lagos (Nigeria); Gutowski, W J [Department of Geological and Atmospheric Sciences, Iowa State University, Iowa (United States); Adedoyin, A, E-mail: babiodun@csag.uct.ac.z [Department of Physics, University of Botswana, Gaborone (Botswana)
2010-08-15
The study found that deforestation causes more monsoon moisture to be retained in the mid-troposphere, thereby reducing the northward transport of moisture needed for rainfall over West Africa. Hence, deforestation has dynamical impacts on the West African monsoon and rainfall.
Numerical model for the fall speed of raindrops in a rainfall simulator
J. van Boxel
1997-01-01
The impact velocity or kinetic energy of raindrops is an important factor when studying processes such as splash erosion and crusting. Often rainfall simulators are used to study these processes, but most rainfall simulators are not high enough for large raindrops accelerate to velocities close to t
Parth Sarthi, P.; Kumar, Praveen; Ghosh, Soumik
2016-05-01
The Gangetic Plain (GP) of India is much sensitive to rainfall due to its large spatial and temporal variability, and therefore, Coupled Model Intercomparison Project phases 3 and 5 (CMIP3 and CMIP5)-simulated rainfall is analysed over the GP. Model evaluation is carried out with observed rainfall of India Meteorological Department (IMD) and Global Precipitation and Climatology Project (GPCP). Community Climate System Model version 3 (CCSM3), Hadley Centre Global Environment Model (HadGEM) and Model for Interdisciplinary Research on Climate (MIROC) (Hires) of CMIP3 and CCSM4, CESM1 (WACCM) and CESM1 (CAM5) of CMIP5 sound well with observations. In CMIP3, projected future changes in June-July-August-September (JJAS) rainfall show either 5-15 % excess or 5 % deficit in CCSM3 (A2 scenario) and 10 % deficit in HadGEM1. In B1, MIROC (Hires) shows 5-10 % deficit. Under A1B scenario, deficit is possible in MIROC (Hires) and HadGEM1. In CMIP5, CESM1 (CAM5) shows 5-15 % deficit in Representative Concentration Pathway (RCP) 4.5. CCSM4 and CESM1 (WACCM) show 10-20 % excess while 5-15 % deficit is possible in CESM1 (CAM5) in RCP 8.5.
Kitoh, A.; Endo, H.
2015-12-01
El Nino/Southern Oscillation (ENSO) will still be the most dominant year-to-year variations of the future tropical climate system. A global high-resolution atmospheric general circulation model with grid size about 20 km is used to project future changes in rainfall extremes associated with El Nino at the end of the 21st century. Four different spatial patterns in sea surface temperature (SST) changes are used as future boundary conditions based on the CMIP5 RCP8.5 scenario. Rainfall extremes such as the maximum 5-day precipitation total (Rx5d) over the western Pacific are positively correlated to the Nino3.4 SST anomalies. It is found that Rx5d regressed to the Nino3.4 SST will increase two times in the future compared to the present value. This implies drastic increase of risk of heavy-rainfall induced disasters under by global warming over the western Pacific countries.
An Emotional ANN (EANN) approach to modeling rainfall-runoff process
Nourani, Vahid
2017-01-01
This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.
Rainfall-runoff model HEC-HMS in a small inhomogeneous basin
Ponížilová, Iva; Unucka, Jan; Říhová, Veronika
2014-05-01
The contribution focuses on the applicability of the hydrologic rainfall-runoff model HEC-HMS to verify the effect of inhomogeneities of the basin surface. The simulation of an extreme rainfall-runoff episode using the HEC-HMS model should prove the influence of basin inhomogeneity on the speed and volume of runoff and the potential of watersheds on runoff mitigation. The area of interest is situated in North Bohemia, Czech Republic. Inhomogeneity of the Robecsky stream basin is caused by different physical-geographic conditions in the basin of the main reaches of the Robecsky stream and its major left tributary which is the Bobri stream. Before their confluence, both streams have a comparable catchment area of about 130 km2. Significant differences are manifested in average altitude of the basin, basin shape, basin slope, time of concentration and the proportion of forest areas. The Bobri stream shows more extreme runoff characteristics in combination with a smaller area of forestation. Another important factor affecting runoff from the basin is the proportion of watersheds that accumulate water in the landscape and cause runoff mitigation and slowdown. To illustrate the influence of watersheds Machovo Lake on the Robecsky stream and Holansky pond on the Bobri stream were selected. Machovo Lake is the third largest watershed in the territory of the Czech Republic. Holansky pond is the largest of the system of Holansky ponds. The Robecsky stream has the lowest runoff coefficient from the entire Ploucnice basin. The lakes surface-drainage area ratio is approximately 1.7% of the total catchment area of the Robecsky stream. The rainfall-runoff model HEC-HMS was utilized for the analysis and to determine the volume of runoff the method of CN curves was used that depends on hydrological properties of soils. For schematisation of extreme runoff conditions of the basin the precipitation period from 6th to 8th August 2010 was selected. Extremeness of peak flows of the
Woodborne, Stephan; Hall, Grant; Zhang, Qiong
2016-04-01
Palaeoclimate reconstruction using isotopic analysis of tree growth increments has yielded a 1000-year record of rainfall variability in southern Africa. Isotope dendro-climatology reconstructions from baobab trees (Adansonia digitata) provide evidence for rainfall variability from the arid Namib Desert and the Limpopo River Valley. Isotopic analysis of a museum specimen of a yellowwood tree (Podocarps falcatus) yields another record from the southwestern part of the subcontinent. Combined with the limited classic denro-climatologies available in the region these records yield palaeo-rainfall variability in the summer and winter rainfall zones as well as the hyper-arid zone over the last 1000 years. Coherent shifts in all of the records indicate synoptic changes in the westerlies, the inter-tropical convergence zone, and the Congo air boundary. The most substantial rainfall shift takes place at about 1600 CE at the onset of the Little Ice Age. Another distinctive feature of the record is a widespread phenomenon that occurs shortly after 1810 CE that in southern Africa corresponds with a widespread social upheaval known as the Difequane or Mfekane. Large scale forcing of the system includes sea-surface temperatures in the Agulhas Current, the El Nino Southern Oscillation and the Southern Annular Mode. The Little Ice Age and Mfekane climate shifts result from different forcing mechanisms, and the rainfall response in the different regions at these times do not have a fixed phase relationship. This complexity provides a good scenario to test climate models. A first order (wetter versus drier) comparison between each of the tree records and a 1000-year palaeoclimate model simulation for the Little Ice Age and Mfekane transitions demonstrates a generally good correspondence.
Pipunic, Robert C.; Ryu, Dongryeol; Costelloe, Justin F.; Su, Chun-Hsu
2015-10-01
In providing uniform spatial coverage, satellite-based rainfall estimates can potentially benefit hydrological modeling, particularly for flood prediction. Maximizing the value of information from such data requires knowledge of its error. The most recent Tropical Rainfall Measuring Mission (TRMM) 3B42RT (TRMM-RT) satellite product version 7 (v7) was used for examining evaluation procedures against in situ gauge data across mainland Australia at a daily time step, over a 9 year period. This provides insights into estimating uncertainty and informing quantitative error model development, with methodologies relevant to the recently operational Global Precipitation Measurement mission that builds upon the TRMM legacy. Important error characteristics highlighted for daily aggregated TRMM-RT v7 include increasing (negative) bias and error variance with increasing daily gauge totals and more reliability at detecting larger gauge totals with a probability of detection of data have increasing (positive) bias and error variance with increasing TRMM-RT estimates. Difference errors binned within 10 mm/d increments of TRMM-RT v7 estimates highlighted negatively skewed error distributions for all bins, suitably approximated by the generalized extreme value distribution. An error model based on this distribution enables bias correction and definition of quantitative uncertainty bounds, which are expected to be valuable for hydrological modeling and/or merging with other rainfall products. These error characteristics are also an important benchmark for assessing if/how future satellite rainfall products have improved.
Soil moisture plays a key role in runoff generation processes. As a result, the assimilation of soil moisture observations into rainfall-runoff models is increasingly being investigated. Given the scarcity of ground-based in situ measurements, satellite soil moisture observations offer a valuable da...
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.
Modeled Influence of East Asian Black Carbon on Inter-Decadal Shifts in East China Summer Rainfall
Institute of Scientific and Technical Information of China (English)
Rashed MAHMOOD; LI Shuang-Lin
2011-01-01
Two inter-decadal shifts in East China sum- mer rainfall during the last three decades of the 20th century have been identified. One shift occurred in the late 1970s and featured more rainfall in the Yangtze River valley and prolonged drought in North China. The other shift occurred in the early 1990s and featured increased rainfall in South China. The role of black carbon （BC） aerosol in the first shift event is controversial, and it has not been documented for the second event. In this study, the authors used Geophysical Fluid Dynamics Laboratory＇s （GFDL＇s） atmospheric general circulation model known as Atmosphere and Land Model （AM2.1）, which has been shown to capture East Asian climate variability well, to investigate these issues by conducting sensitive experiments with or without historical BC in East Asia. The results suggest that the model reproduces the first shift well, including intensified rainfall in the Yangtze River and weakened monsoonal circulation. However, the model captures only a fraction of the observed variations for the second shift event. Thus, the role of BC in modu- lating the two shift events is different, and its impact is relatively less important for the early 1990s event.
Directory of Open Access Journals (Sweden)
Weijian Guo
2015-05-01
Full Text Available Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin.
Directory of Open Access Journals (Sweden)
Abdouramane Gado Djibo
2015-09-01
New hydrological insights for the region: The model that allows a change in the predictor–predictand relationship according to rainfall amplitude (M1 and uses air temperature as predictor is the best model for seasonal rainfall forecasting in the study area.
Modelling the statistical dependence of rainfall event variables by a trivariate copula function
Directory of Open Access Journals (Sweden)
M. Balistrocchi
2011-01-01
Full Text Available In many hydrological models, such as those derived by analytical probabilistic methods, the precipitation stochastic process is represented by means of individual storm random variables which are supposed to be independent of each other. However, several proposals were advanced to develop joint probability distributions able to account for the observed statistical dependence. The traditional technique of the multivariate statistics is nevertheless affected by several drawbacks, whose most evident issue is the unavoidable subordination of the dependence structure assessment to the marginal distribution fitting. Conversely, the copula approach can overcome this limitation, by splitting the problem in two distinct items. Furthermore, goodness-of-fit tests were recently made available and a significant improvement in the function selection reliability has been achieved. Herein a trivariate probability distribution of the rainfall event volume, the wet weather duration and the interevent time is proposed and verified by test statistics with regard to three long time series recorded in different Italian climates. The function was developed by applying a mixing technique to bivariate copulas, which were formerly obtained by analyzing the random variables in pairs. A unique probabilistic model seems to be suitable for representing the dependence structure, despite the sensitivity shown by the dependence parameters towards the threshold utilized in the procedure for extracting the independent events. The joint probability function was finally developed by adopting a Weibull model for the marginal distributions.
Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection
Naveau, Philippe
2016-04-09
In statistics, extreme events are often defined as excesses above a given large threshold. This definition allows hydrologists and flood planners to apply Extreme-Value Theory (EVT) to their time series of interest. Even in the stationary univariate context, this approach has at least two main drawbacks. First, working with excesses implies that a lot of observations (those below the chosen threshold) are completely disregarded. The range of precipitation is artificially shopped down into two pieces, namely large intensities and the rest, which necessarily imposes different statistical models for each piece. Second, this strategy raises a nontrivial and very practical difficultly: how to choose the optimal threshold which correctly discriminates between low and heavy rainfall intensities. To address these issues, we propose a statistical model in which EVT results apply not only to heavy, but also to low precipitation amounts (zeros excluded). Our model is in compliance with EVT on both ends of the spectrum and allows a smooth transition between the two tails, while keeping a low number of parameters. In terms of inference, we have implemented and tested two classical methods of estimation: likelihood maximization and probability weighed moments. Last but not least, there is no need to choose a threshold to define low and high excesses. The performance and flexibility of this approach are illustrated on simulated and hourly precipitation recorded in Lyon, France.
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.
Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection
Naveau, Philippe; Huser, Raphael; Ribereau, Pierre; Hannart, Alexis
2016-04-01
In statistics, extreme events are often defined as excesses above a given large threshold. This definition allows hydrologists and flood planners to apply Extreme-Value Theory (EVT) to their time series of interest. Even in the stationary univariate context, this approach has at least two main drawbacks. First, working with excesses implies that a lot of observations (those below the chosen threshold) are completely disregarded. The range of precipitation is artificially shopped down into two pieces, namely large intensities and the rest, which necessarily imposes different statistical models for each piece. Second, this strategy raises a nontrivial and very practical difficultly: how to choose the optimal threshold which correctly discriminates between low and heavy rainfall intensities. To address these issues, we propose a statistical model in which EVT results apply not only to heavy, but also to low precipitation amounts (zeros excluded). Our model is in compliance with EVT on both ends of the spectrum and allows a smooth transition between the two tails, while keeping a low number of parameters. In terms of inference, we have implemented and tested two classical methods of estimation: likelihood maximization and probability weighed moments. Last but not least, there is no need to choose a threshold to define low and high excesses. The performance and flexibility of this approach are illustrated on simulated and hourly precipitation recorded in Lyon, France.
Ibrahim, B.; Wisser, D.; Barry, B.
2014-12-01
Many studies have been undertaken on climate variability analysis in West Africa since the drastic drought recorded at the beginning of the 1970s. The variability highlighted by these studies relies in many cases on different baseline periods chosen with regard to the data available or to the reference periods defined by the World Meteorological Organization (WMO). However, the significance of the change in a time series for a given period is determined from some statistical tests. We develop in this study a statistical method to identify a pertinent reference period for rainfall and temperature variability analysis in the West African Soudano-Sahelian zone. The method is based on an application of three tests of homogeneity in time series and three tests of shift detection in time series. The pertinent reference period is defined as a period of more than 20 years and homogeneous with regard to the main climate parameters (rainfall and temperature). The application of the method on four different gridded climate data from 1901 to 2012 shows that the 1945-1970 period is the longest homogeneous period with regard to the annual rainfall amount. An assessment of the significance of the difference between the confidence interval at the level of 95% around the average during this period and the annual rainfall amounts time series shows that the normal amount is between -10% and +10% of that average. Thus, with regard to that referential period, a wet (dry) year is defined with a surplus (gap) of 10% in the annual rainfall amount above (below) this average. The decadal proportions of wet and dry years reveal that the 1971-1980 period presents the most important number of significant dry years with 1984 as the driest year over the whole 1901-2012 period. The drought periods recorded in the region are mainly characterized by segments of consecutive dry years that had severe impacts on crop production and livestock. Key words: climate variability; climate change; drought
Olivier de Sardan, Jean-Pierre; Ridde, Valéry
2015-01-01
This research on user fee removal in three African countries is located at the interface of public policy analysis and health systems research. Public policy analysis has gradually become a vast and multifaceted area of research consisting of a number of perspectives. But the context of public policies in Sahelian Africa has some specific characteristics. They are largely shaped by international institutions and development agencies, on the basis of very common 'one-size-fits-all' models; the practical norms that govern the actual behaviour of employees are far removed from official norms; public goods and services are co-delivered by a string of different actors and institutions, with little coordination between them; the State is widely regarded by the majority of citizens as untrustworthy. In such a context, setting up and implementing health user fee exemptions in Burkina Faso, Mali and Niger was beset by major problems, lack of coherence and bottlenecks that affect public policy-making and implementation in these countries.
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
Monitoring suspended sediments and turbidity in Sahelian basins
Robert, Elodie; Grippa, Manuela; Kergoat, Laurent; Martinez, Jean-Michel; Pinet, Sylvain; Nogmana, Soumaguel
2017-04-01
Suspended matter can carry viruses and bacteria that are pathogenic to humans and can foster their development. Therefore, turbidity can be considered a vector of microbiological contaminants, which cause diarrheal diseases, and it can be used as a proxy for fecal bacteria. Few studies have focused on water turbidity in rural Africa, where many cases of intestinal parasitic infections are due to the consumption of unsafe water from ponds, reservoirs, lakes and rivers. Diarrheal diseases are indeed the second cause of infant mortality in sub-Saharan Africa. Furthermore, in this region, environment survey is minimal or inexistent. Monitoring water turbidity therefore represents a challenge for health improvement. Turbidity refers to the optical properties of water and it is well suited to monitoring by remote sensing. Because it varies in space and time and because the small water bodies (< 250m2) are critical for Sahelian societies, monitoring turbidity requires the use of high temporal and spatial resolution sensors like Landsat 7 and 8, Sentinel-2 as well SPOT5-TAKE5 data. Compared to many other regions of the world, the particularly high turbidity values found in tropical Africa challenges the use of remote sensing and questions the methods developed for less turbid waters. In addition, high aerosol loadings (mineral dust and biomass burning) may be detrimental to turbidity retrieval in this region because of inaccurate atmospheric corrections. We propose a method to monitor water quality of Sahelian ponds, lakes and rivers using in-situ and remote sensing data, which is tested at different sites for which in-situ water turbidity and suspended sediments concentration (SSSC) measurements are acquired. Water sample are routinely collected at two sites within the AMMA-CATCH observatory part of the Réseau de Bassin Versants (RBV) French network: the Agoufou pond in northern Mali (starting September 2014), and the Niger River at Niamey in Niger (starting June 2015
Kuczera, George; Kavetski, Dmitri; Franks, Stewart; Thyer, Mark
2006-11-01
SummaryCalibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash-Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash-Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors
Chern, J. D.; Tao, W. K.
2016-12-01
The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has become a promising approach for climate modeling. Despite the overall success of MMFs in simulating the MJO, diurnal variability, and mesoscale convective systems, phenomena difficult for traditional GCMs to simulate well, systematic model deficits in MMFs do exist. One of the major biases for all MMFs is the overestimation of surface rainfall over the West Pacific, South Pacific Convergence Zone (SPCZ), and Indian Ocean. Many hypotheses have been proposed such as the use of cyclic lateral boundary condition in embedded 2D CRMs, the orientation of the 2D CRM and momentum transport, and the nonlinear feedback of wind and surface evaporation, but the tropical positive precipitation biases still persist in all MMFs. In this study, the Goddard MMFs and the stand-alone Goddard Cumulus Ensemble (GCE, a CRM) are used to study the impacts of model resolution and model domain size on the surface precipitation and rainfall characteristics. A series of GCE and MMF simulations have been carried out with different model configuration. The changes of cloud structure, occurrence, and properties such as cloud types, updraft and downdraft, latent heating profile, and cold pool strength in both stand-alone CRMs and the ones embedded inside the MMF are examined in details. We also focus on the multi-scale interaction and feedback in the MMF to understand why the tropica positive precipitation bias and root-mean-square error decrease with decreasing (increasing) GCE grid size (domain).
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.
Yang, S. K.; Kar, K. K.; Lee, J. H.
2015-12-01
Rainfall-runoff modeling is a basic tool for assessing hydrological processes where natural features (geology and geography) play a pivotal role. Due to global warming, the trends of torrential rainfall and typhoon events have been found to increase spontaneously in Jeju Island of Korea. As such, the island has been shown distinctive hydrologic characteristics. The study therefore, attempts to analyze the diversified rainfall-runoff characteristics of Jeju Island during extreme hydrologic events. The study domain covers mostly the urban areas of island and the most prominent Hancheon Stream which restrains most of its overland runoff during rainfall. For watershed delineation, 30-m resolution's digital elevation model (DEM) generated from contours and 50 years' (1964-2013) historical rainfall data from the Korea meteorological administration (KMA) were used. Furthermore, geo-spatial data collected from the Korean society of agriculture engineers (KSAE) has been used for soil texture and land use classification. Some identical studies implied to predict semi-distributed (e.g. SWAT and WMS) watershed model runoff in the island. However, the significance of this study is that it considers a GIS semi-distributed model to imply NRCS curve number technique and predict accurate results for unique runoff characteristics, by considering high catchment slope. Rainfall data from 2009 to 2013 has been used as baseline information to estimate annual runoff variations, which has been used in the spatial and statistical analyses. The study infers that the simulated runoff percentages varied from 18% to 44%, accounting for the temporal fluctuations of rainfall. Afterwards, to assess the ten year interval relationship between rainfall-runoff, the study uses historical rainfall data of Jeju-si meteorological station and four rainfall station. Lastly, the ongoing rainfall-runoff analysis will be concluded by comparing the runoff result with SWAT model result.Keywords: NRCS curve
Nytch, C. J.; Meléndez-Ackerman, E. J.
2014-12-01
There is a pressing need to generate spatially-explicit models of rainfall-runoff dynamics in the urban humid tropics that can characterize flow pathways and flood magnitudes in response to erratic precipitation events. To effectively simulate stormwater runoff processes at multiple scales, complex spatio-temporal parameters such as rainfall, evapotranspiration, and antecedent soil moisture conditions must be accurately represented, in addition to uniquely urban factors including stormwater conveyance structures and connectivity between green and gray infrastructure elements. In heavily urbanized San Juan, Puerto Rico, stream flashiness and frequent flooding are major issues, yet still lacking is a hydrological analysis that models the generation and movement of fluvial and pluvial stormwater through the watershed. Our research employs a novel and multifaceted approach to dealing with this problem that integrates 1) field-based rainfall interception and infiltration methodologies to quantify the hydrologic functions of natural and built infrastructure in San Juan; 2) remote sensing analysis to produce a fine-scale typology of green and gray cover types in the city and determine patterns of spatial distribution and connectivity; 3) assessment of precipitation and streamflow variability at local and basin-wide scales using satellite and radar precipitation estimates in concert with rainfall and stream gauge point data and participatory flood mapping; 4) simulation of historical, present-day, and future stormwater runoff scenarios with a fully distributed hydrologic model that couples diverse components of urban socio-hydrological systems from formal and informal knowledge sources; and 5) bias and uncertainty analysis of parameters and model structure within a Bayesian hierarchical framework. Preliminary results from the rainfall interception study suggest that canopy structure and leaf area index of different tree species contribute to variable throughfall and
Green, Daniel; Pattison, Ian; Yu, Dapeng
2017-04-01
Surface water (pluvial) flooding occurs when excess rainfall from intense precipitation events is unable to infiltrate into the subsurface or drain via natural or artificial drainage channels. Surface water flood events pose a major hazard to urban regions across the world, with nearly two thirds of flood damages in the UK being caused by surface water flood events. The perceived risk of surface water flooding appears to have increased in recent years due to several factors, including (i) precipitation increases associated with climatic change and variability; (ii) population growth meaning more people are occupying flood risk areas, and; (iii) land-use changes. Because urban areas are often associated with a high proportion of impermeable land-uses (e.g. tarmacked or paved surfaces and buildings) and a reduced coverage of vegetated, permeable surfaces, urban surface water flood risk during high intensity precipitation events is often exacerbated. To investigate the influence of urbanisation and terrestrial factors on surface water flood outputs, rainfall intensity, catchment slope, permeability, building density/layout scenarios were designed within a novel, 9m2 physical modelling environment. The two-tiered physical model used consists of (i) a low-cost, nozzle-type rainfall simulator component which is able to simulate consistent, uniformly distributed rainfall events of varying duration and intensity, and; (ii) a reconfigurable, modular plot surface. All experiments within the physical modelling environment were subjected to a spatiotemporally uniform 45-minute simulated rainfall event, while terrestrial factors on the physical model plot surface were altered systematically to investigate their hydrological response on modelled outflow and depth profiles. Results from the closed, controlled physical modelling experiments suggest that meteorological factors, such as the duration and intensity of simulated rainfall, and terrestrial factors, such as model slope
Modeling of soil water infiltration with rainfall simulator in different agricultural systems
Directory of Open Access Journals (Sweden)
Thais E. M. dos Santos
2016-06-01
Full Text Available ABSTRACT This study aimed to compare models for predicting soil water infiltration rate and erosive rates using a rainfall simulator in different systems of common bean (Phaseolus vulgaris L. cultivation. The evaluated mathematical models were: Kostiakov, Kostiakov-Lewis, Green-Ampt and Horton. Infiltration tests were carried out considering six treatments: bean cultivated on contour with rock barriers spaced at 0.5 m between crop rows (BC1; bean cultivated on contour with rock barriers spaced at 1.0 m between crop rows (BC2; bean cultivated downslope (BDS; bean cultivated on contour with mulch (BCM; bare soil (BS and soil under natural cover (NC. Four replicates were considered, totaling 24 field tests. Kostiakov-Lewis's equation showed the lowest values of standard error. Soil water infiltration rate was equal to 53.3 mm h-1 in the natural vegetation treatment and to 9.49 mm h-1 in the downslope treatment. Surface roughness and the time of beginning of surface runoff were significantly higher for the conditions with mulch cover.
Seepage and slope stability modelling of rainfall-induced slope failures in topographic hollows
Directory of Open Access Journals (Sweden)
Kiran Prasad Acharya
2016-03-01
Full Text Available This study focuses on topographic hollows, their flow direction and flow accumulation characteristics, and highlights discharge of hillslope seepage so as to understand porewater pressure development phenomena in relation with slope failure in topographic hollows. For this purpose, a small catchment in Niihama city of Shikoku Island in western Japan, with a record of seven slope failures triggered by typhoon-caused heavy rainfall on 19–20 October 2004, was selected. After extensive fieldwork and computation of hydro-mechanical parameters in unsaturated and saturated conditions through a series of laboratory experiments, seepage and slope stability modellings of these slope failures were done in GeoStudio environment using the precipitation data of 19–20 October 2004. The results of seepage modelling showed that the porewater pressure was rapid transient in silty sand, and the maximum porewater pressure measured in an area close to the base of topographic hollows was found to be higher with bigger topographic hollows. Furthermore, a threshold relationship between the topographic hollow area and maximum porewater pressure in this study indicates that a topographic hollow of 1000 sq. m area can develop maximum porewater pressure of 1.253 kPa. However, the porewater pressures required to initiate slope instability in the upper part of the topographic hollows is relatively smaller than those in the lower part of the topographic hollows.
Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer
2016-12-01
Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.
Coupling Radar Rainfall Estimation and Hydrological Modelling For Flash-flood Hazard Mitigation
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.
Stochastic Modelling of Floods Generated By Rainfall In Russia and The Uk
Lobanova, H.; Lobanov, V.
A comparison is carried out between different approaches, which are used in Russia and UK for the statistical modelling and computation of maximum discharges gen- erated by rainfall. The following main results have been obtained on the basis of the analysis of the longest records of observation in UK: - Restoration of long-term time series is necessary, although the results of restoration by using of multi-regression with analogues are not very reliable. - The use of classical criteria for the assessment of the homogeneity of empirical distributions of maximum floods gives more extreme outliers that would be found were the actual non-symmetry of the distributions to be taken into account. Extended tests of homogeneity for extremes events in rainfall and floods records have thus been developed and the result of their application is given as a comparison to the classical statistical tests. - Taking into account the bias in param- eters of empirical distribution, which connect with the impact of autocorrelation and the application of the method of moments, larger values are obtained for the quantiles. This feature is used in Russian practice of maximum runoff computations and realized in the software. - An assessment of efficiency of computations has been carried out on independent data. The WINFAP-FEH gave smaller random errors of computed values (on 4- A general formula for the assessment of empirical probabilities for non-regular values has been developed. This formula allows to build an empirical distribution of peaks over threshold (POT) data. Empirical relationships between the parameters of the POT and annual maxima (AM) empirical distributions have been established for UK area. The POT distribution has a very big ratio of coefficient of skewness (Cs) to variation coefficient (Cv) and an appropriate analytic distribution for fitting has to be found or developed. - Empirical approaches for an assessment of random errors in quantiles of distributions have been
On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution
Bruni, G.; Reinoso Rondinel, R.R.; Van de Giesen, N.C.; Clemens, F.H.L.R.; Ten Veldhuis, J.A.E.
2014-01-01
Cities are increasingly vulnerable to floods generated by intense rainfall, because of their high degree of imperviousness, implementation of infrastructures, and changes in precipitation patterns due to climate change. Accurate information of convective storm characteristics at high spatial and tem
No-Rainfall Origin of Melas Chasma Valley Networks by Salt Dehydration: Numerical Thermal Model
Kargel, J. S.; Furfaro, R.; Rodriguez, J. A. P.; Candelaria, P.; Prieto-Ballesteros, O.; Marion, G. M.; Crowley, J.; Hook, S.
2009-03-01
Salts in Melas Chasma should produce large positive thermal anomalies and warm hypersaline conditions at shallow depths. Dewatering may yield brine eruptions, and we argue that in Melas Chasma valley networks were produced this way, not by rainfall.
Rainfall interstation correlation functions derived for a class of generalized storm models
Stol, P.T.
1983-01-01
The complete derivation and solution of the rainfall interstation correlation function is described. The report emphasizes the mathematical treatment and the way in which the analytical solution can be obtained by calculus