WorldWideScience

Sample records for water level forecasting

  1. Forecasting Water Levels Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Shreenivas N. Londhe

    2011-06-01

    Full Text Available For all Ocean related activities it is necessary to predict the actual water levels as accurate as possible. The present work aims at predicting the water levels with a lead time of few hours to a day using the technique of artificial neural networks. Instead of using the previous and current values of observed water level time series directly as input and output the water level anomaly (difference between the observed water level and harmonically predicted tidal level is calculated for each hour and the ANN model is developed using this time series. The network predicted anomaly is then added to harmonic tidal level to predict the water levels. The exercise is carried out at six locations, two in The Gulf of Mexico, two in The Gulf of Maine and two in The Gulf of Alaska along the USA coastline. The ANN models performed reasonably well for all forecasting intervals at all the locations. The ANN models were also run in real time mode for a period of eight months. Considering the hurricane season in Gulf of Mexico the models were also tested particularly during hurricanes.

  2. Radar Based Flow and Water Level Forecasting in Sewer Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.

    2009-01-01

    This paper describes the first radar based forecast of flow and/or water level in sewer systems in Denmark. The rainfall is successfully forecasted with a lead time of 1-2 hours, and flow/levels are forecasted an additional ½-1½ hours using models describing the behaviour of the sewer system. Both...... radar data and flow/water level model are continuously updated using online rain gauges and online in-sewer measurements, in order to make the best possible predictions. The project show very promising results, and show large potentials, exploiting the existing water infrastructure in future climate...

  3. A high resolution water level forecast for the German Bight

    Science.gov (United States)

    Niehüser, Sebastian; Dangendorf, Sönke; Arns, Arne; Jensen, Jürgen

    2016-04-01

    Many coastal regions worldwide are potentially endangered by storm surges which can cause disastrous damages and loss of life. Due to climate change induced sea level rise, an accumulation of such events is expected by the end of the 21th century. Therefore, advanced storm surge warnings are needed to be prepared when another storm surge hits the coast. In the shallow southeastern North Sea these storm surge warnings are nowadays routinely provided for selected tide gauge locations along a coastline through state-of-the-art forecast systems, which are based on a coupled system of empirical tidal predictions and numerical storm surge forecasts. Along the German North Sea coastline, the Federal Maritime and Hydrographic Agency in cooperation with the German Weather Service is responsible for the storm surge warnings. They provide accurate, high frequency and real-time water level forecasts for up to six days ahead at selected tide gauge sites via internet, telephone and broadcast. Since water levels along the German North Sea coastline are dominated by shallow water effects and a very complex bathymetric structure of the seabed, the pointwise forecast is not necessarily transferable to un-gauged areas between the tide gauges. Here we aim to close this existing gap and develop water level forecasts with a high spatial (continuously with a resolution of at least 1 kilometer) as well as a high temporal (at least 15-minute values) resolution along the entire German North Sea coastline. We introduce a new methodology for water level forecasts which combines empirical or statistical and numerical models. While the tidal forecast is performed by non-parametric interpolation techniques between un-gauged and gauged sites, storm surges are estimated on the basis of statistical/empirical storm surge formulas taken from a numerical model hindcast. The procedure will be implemented in the operational mode forced with numerical weather forecasts.

  4. Flow Forecasting in Urban Drainage Systems using Deterministic Updating of Water Levels in Distributed Hydraulic Models

    DEFF Research Database (Denmark)

    Hansen, Lisbeth S.; Borup, Morten; Møller, A.;

    2011-01-01

    the performance of the updating procedure for flow forecasting. Measured water levels in combination with rain gauge input are used as basis for the evaluation. When compared to simulations without updating, the results show that it is possible to obtain an improvement in the 20 minute forecast of the water level...... to eliminate some of the unavoidable discrepancies between model and reality. The latter can partly be achieved by using the commercial tool MOUSE UPDATE, which is capable of inserting measured water levels from the system into the distributed, physically based MOUSE model. This study evaluates and documents...

  5. Flow Forecasting using Deterministic Updating of Water Levels in Distributed Hydrodynamic Urban Drainage Models

    Directory of Open Access Journals (Sweden)

    Lisbet Sneftrup Hansen

    2014-07-01

    Full Text Available There is a growing requirement to generate more precise model simulations and forecasts of flows in urban drainage systems in both offline and online situations. Data assimilation tools are hence needed to make it possible to include system measurements in distributed, physically-based urban drainage models and reduce a number of unavoidable discrepancies between the model and reality. The latter can be achieved partly by inserting measured water levels from the sewer system into the model. This article describes how deterministic updating of model states in this manner affects a simulation, and then evaluates and documents the performance of this particular updating procedure for flow forecasting. A hypothetical case study and synthetic observations are used to illustrate how the Update method works and affects downstream nodes. A real case study in a 544 ha urban catchment furthermore shows that it is possible to improve the 20-min forecast of water levels in an updated node and the three-hour forecast of flow through a downstream node, compared to simulations without updating. Deterministic water level updating produces better forecasts when implemented in large networks with slow flow dynamics and with measurements from upstream basins that contribute significantly to the flow at the forecast location.

  6. Estimation of the uncertainty in water level forecasts at ungauged locations using Quantile Regression

    Science.gov (United States)

    Roscoe, K. L.; Weerts, A. H.

    2012-04-01

    Water level predictions in rivers are used by operational managers to make water management decisions. Such decisions can concern water routing in times of drought, operation of weirs, and actions for flood protection, such as evacuation. Understanding the uncertainty in the predictions can help managers make better-informed decisions. Conditional Quantile Regression is a method that can be used to determine the uncertainty in forecasted water levels by providing an estimate of the probability density function of the error in the prediction conditional on the forecasted water level. To derive this relationship, a series of forecasts and errors in the forecasts (residuals) are required. Thus, conditional quantile regressions can be derived for locations where both observations and forecasts are available. However, 1D-hydraulic models that are used for operational forecasting produce forecasts at intermediate points where no measurements are available but for which predictive uncertainty estimates are also desired for decision making. The objective of our study is to test if interpolation methods can be used to adequately estimate conditional quantile regressions at these in-between locations. For this purpose, five years of hindcasts were used at seven stations along the IJssel River in the Netherlands. Residuals in water level hindcasts were interpolated at the five in-between lying stations. The interpolation was based solely on distance and the interpolated residuals were compared to the measured residuals at stations at the in-between locations. The resulting interpolated residuals estimated the measured residuals well, especially for longer lead times. Quantile regression was then carried out using the series of forecasts and interpolated residuals at the in-between stations. The interpolated quantile regressions were compared with regressions calibrated using the actual residuals at the in-between stations. Results show that even a simple interpolation based

  7. The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

    Institute of Scientific and Technical Information of China (English)

    DONG Sheng; CHI Kun; ZHANG Qiyi; ZHANG Xiangdong

    2012-01-01

    Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM (1,1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy.The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

  8. Hydrological forecast of maximal water level in Lepenica river basin and flood control measures

    Directory of Open Access Journals (Sweden)

    Milanović Ana

    2006-01-01

    Full Text Available Lepenica river basin territory has became axis of economic and urban development of Šumadija district. However, considering Lepenica River with its tributaries, and their disordered river regime, there is insufficient of water for water supply and irrigation, while on the other hand, this area is suffering big flood and torrent damages (especially Kragujevac basin. The paper presents flood problems in the river basin, maximum water level forecasts, and flood control measures carried out until now. Some of the potential solutions, aiming to achieve the effective flood control, are suggested as well.

  9. Correlation of air temperature above water-air sections with the forecasted low level clouds

    Science.gov (United States)

    Huseynov, N. Sh.; Malikov, B. M.

    2009-04-01

    As a case study approach the development of low clouds forecasting methods in correlation with air temperature transformational variations on the sections "water-air" is surveyed. It was evident, that transformational variations of air temperature mainly depend on peculiarities and value of advective variations of temperature. DT is the differences of initial temperature on section water-air in started area, from contrast temperature of water surface along a trajectory of movement of air masses and from the temperature above water surface in a final point of a trajectory. Main values of transformational variations of air temperature at advection of a cold masses is 0.530C•h, and at advection of warm masses is -0.370C•h. There was dimensionless quantity K determined and implemented into practice which was characterized with difference of water temperature in forecasting point and air temperature in an initial point in the ratio of dew-points deficiency at the forecasting area. It follows, that the appropriate increasing or decreasing of K under conditions of cold and warm air masses advection, contributes decreasing of low clouds level. References: Abramovich K.G.: Conditions of development and forecasting of low level clouds. vol. #78, 124 pp., Hydrometcenter USSR 1973. Abramovich K.G.: Variations of low clouds level // Meteorology and Hydrology, vol. # 5, 30-41, Moscow, 1968. Budiko M.I.: Empirical assessment of climatic changes toward the end of XX century // Meteorology and Hydrology, vol. #12, 5-13, Moscow, 1999. Buykov M.V.: Computational modeling of daily evolutions of boundary layer of atmosphere at the presence of clouds and fog // Meteorology and Hydrology, vol. # 4, 35-44, Moscow, 1981. Huseynov N.Sh. Transformational variations of air temperature above Caspian Sea / Proceedings of Conference On Climate And Protection of Environment, 118-120, Baku, 1999. Huseynov N.Sh.: Consideration of advective and transformational variations of air temperature in

  10. Application of Artificial Neural Network into the Water Level Modeling and Forecast

    Directory of Open Access Journals (Sweden)

    Marzenna Sztobryn

    2013-06-01

    Full Text Available The dangerous sea and river water level increase does not only destroy the human lives, but also generate the severe flooding in coastal areas. The rapidly changes in the direction and velocity of wind and associated with them sea level changes could be the severe threat for navigation, especially on the fairways of small fishery harbors located in the river mouth. There is the area of activity of two external forcing: storm surges and flood wave. The aim of the work was the description of an application of Artificial Neural Network (ANN methodology into the water level forecast in the case study field in Swibno harbor located is located at 938.7 km of the Wisla River and at a distance of about 3 km up the mouth (Gulf of Gdansk - Baltic Sea.

  11. Comparison of Artificial Neural Network And M5 Model Tree Technique In Water Level Forecasting of Solo River

    Science.gov (United States)

    Lasminto, Umboro; Hery Mularta, Listya

    2010-05-01

    Flood events along the Solo River flow at the end of December 2007 has caused lose of properties and lives. Floods occurred in the city of Ngawi, Madiun, Bojonegoro, Babat and surrounding areas. To reduce future losses, one of the important efforts that will occur during a flood is to get information about the magnitude and time will be floods, so that people can make an effort to reduce its impact. Flood forecasting model can provide information of water level in the river some time before the incident. This paper will compare the flood forecasting model at Bojonegoro City was built using the technique of Artificial Neural Network (ANN) and M5 Model Tree (M5MT). The model will forecast the water level of 1, 3 and 6 hours ahead at the point of water level recorders in the City of Bojonegoro using input from the water level at some point water level recorders in the upstream such as Karangnongko, Sekayu, Jurug and Wonogiri. The same data set of hourly water level records are used to build the model of ANN and M5MT technique. The selection of parameters and setup of ANN and M5MT technique is done to obtain the best result. The results of the model are evaluated by calculating the Root Mean Square Error (RMSE) between the predictions and observations. RMSE produced by the water level forecasting model 1, 3 and 6 hours ahead with M5MT technique are 0.2723, 0.6279 and 0.7176 meters. While the ANN technique are 0.1829, 0.3192 and 0517 meters. ANN technique has a better ability in predicting low flow, whereas M5 Model Tree technique has a better ability in predicting high flow. Keywords : Water level forecasting, Solo River, M5 Model Tree, Artificial Neural Network

  12. Site Level Climate Downscaling for Forecasting Water Balance Stress and Reslience of Acadian Boreal Trees

    Science.gov (United States)

    Brooks, B. G.; Serbin, S.

    2014-12-01

    A downscaling framework is presented and applied to physiological and climatic data for projecting future climate resilience of one key boreal tree species, black spruce, in Cape Breton Highlands, Nova Scotia. The technique is based on a combination of probabilistic downscaling methods and control system theory, which together are used to transform large-scale future climate input (air temperature, humidity) to local scale climate parameters important to plant biophysical processes (vapor pressure deficit). Large-scale forecast data from the Community Earth System Model were downscaled spatially then temporally based on the cumulative distributions and sub-daily patterns from corresponding observational data at North Mountain (Cape Breton). Validation over historical decades shows that this technique provides hourly temperature and vapor pressure deficit data accurate to within 0.7%. Further we applied these environmental factors to a species specific empirical model of stomatal conductance for black spruce to compare differences in predicted water regulation response when large-scale (ESM) data are used as drivers versus localized data transformed using this new site-level downscaling technique. We observe through this synthetic study that over historical to contemporary periods (1850-2006) differences between large-scale and localized forecasts of stomatal conductance were small but that future climate extremes (2006-2100) have a strong effect on derived water balance in black spruce. These results also suggest that black spruce in the Cape Breton Highlands may have biophysical responses to climate change that are not predicted by spatially coarse (1°) data, which does not include site level extremes that in this study are shown to strongly curb future growth rates in black spruce as present day climate extremes become common place.

  13. Comparison of forecasts of mean monthly water level in the Paraguay River, Brazil, from two fractionally differenced models

    Science.gov (United States)

    Prass, Taiane S.; Bravo, Juan Martin; Clarke, Robin T.; Collischonn, Walter; Lopes, SíLvia R. C.

    2012-05-01

    The paper compares forecasts of mean monthly water levels up to six months ahead at Ladário, on the Upper Paraguay River, Brazil, estimated from two long-range dependence models. In one of them, the marked seasonal cycle was removed and a fractionally differenced model was fitted to the transformed series. In the other, a seasonal fractionally differenced model was fitted to water levels without transformation. Forecasts from both models for periods up to six months ahead were compared with forecasts given by simpler "short-range dependence" Box-Jenkins models, one fitted to the transformed series, the other a seasonal autoregressive moving average (ARMA) model. Estimates of parameters in the four models (two "long-range dependence", two "short-range dependence") were updated at six-monthly intervals over a 20 year period, and forecasts were compared using root mean square errors (rmse) between water-level forecasts and observed levels. As judged by rmse, performances of the two long-range dependence models, and of the ARMA (1,1) short-range dependence model, were very similar; all three out-performed the seasonal short-range dependence ARMA model. There was evidence that all models performed better during recession periods, than on the hydrograph rising limb.

  14. Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

    Science.gov (United States)

    Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

    2017-04-01

    Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive

  15. Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.

    Science.gov (United States)

    Valizadeh, Nariman; El-Shafie, Ahmed; Mirzaei, Majid; Galavi, Hadi; Mukhlisin, Muhammad; Jaafar, Othman

    2014-01-01

    Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

  16. Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

    Science.gov (United States)

    Chang, Fi-John; Chen, Pin-An; Lu, Ying-Ray; Huang, Eric; Chang, Kai-Yao

    2014-09-01

    Urban flood control is a crucial task, which commonly faces fast rising peak flows resulting from urbanization. To mitigate future flood damages, it is imperative to construct an on-line accurate model to forecast inundation levels during flood periods. The Yu-Cheng Pumping Station located in Taipei City of Taiwan is selected as the study area. Firstly, historical hydrologic data are fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the floodwater storage pond (FSP) at the pumping station. Secondly, effective factors (rainfall stations) that significantly affect the FSP water level are extracted by the Gamma test (GT). Thirdly, one static artificial neural network (ANN) (backpropagation neural network-BPNN) and two dynamic ANNs (Elman neural network-Elman NN; nonlinear autoregressive network with exogenous inputs-NARX network) are used to construct multi-step-ahead FSP water level forecast models through two scenarios, in which scenario I adopts rainfall and FSP water level data as model inputs while scenario II adopts only rainfall data as model inputs. The results demonstrate that the GT can efficiently identify the effective rainfall stations as important inputs to the three ANNs; the recurrent connections from the output layer (NARX network) impose more effects on the output than those of the hidden layer (Elman NN) do; and the NARX network performs the best in real-time forecasting. The NARX network produces coefficients of efficiency within 0.9-0.7 (scenario I) and 0.7-0.5 (scenario II) in the testing stages for 10-60-min-ahead forecasts accordingly. This study suggests that the proposed NARX models can be valuable and beneficial to the government authority for urban flood control.

  17. Forecast level in the groundwater regime in the territory adjacent to the pond - storage devices waste mine water "SVIDOVOK"

    Directory of Open Access Journals (Sweden)

    Yevhrashkina H.P.

    2012-09-01

    Full Text Available The hydrodynamic scheme layer-bond is proposed for long – term level regime forecast. Which takes into account the rising ground waters under the influence by hydrodynamic schemes: of the pond and of the river Samara. The process is described with Fourier’s equation. The method of double superposition is used in the calculations, which the most accurately accounts for the effect of boundary condition

  18. Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Young

    2015-01-01

    Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

  19. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

    Science.gov (United States)

    Grimaldi, Stefania; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

    2016-09-01

    Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

  20. Flow Forecasting using Deterministic Updating of Water Levels in Distributed Hydrodynamic Urban Drainage Models

    DEFF Research Database (Denmark)

    Hansen, Lisbet Sneftrup; Borup, Morten; Moller, Arne

    2014-01-01

    , and then evaluates and documents the performance of this particular updating procedure for flow forecasting. A hypothetical case study and synthetic observations are used to illustrate how the Update method works and affects downstream nodes. A real case study in a 544 ha urban catchment furthermore shows...

  1. Municipal water consumption forecast accuracy

    Science.gov (United States)

    Fullerton, Thomas M.; Molina, Angel L.

    2010-06-01

    Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

  2. Factors Influencing the Iterative Accuracy of Ground Water Level in Forecasting the Water Burst of Deep Drawdown Mines

    Institute of Scientific and Technical Information of China (English)

    李铎; 杨小荟; 武强; 张志忠

    2002-01-01

    The purpose of this paper is to discuss the influential factors of iteration accuracy when we use iteration to determine the numerical model for predicting water yield of deep drawdown mines and calculating the groundwater level. The relationship among the calculation error of groundwater level, the pumping rate, the limit of iteration convergence error, the calculation time, and the aquifer parameters were discussed by using an ideal model. Finally, the water yield of Dianzi iron mine was predicted using the testified numerical model. It is indicated that the calculation error of groundwater level is related to the limit of iteration convergence error, the calculation time and the aquifer parameters, but not to the pumping rate and the variation of groundwater level.

  3. Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

    Science.gov (United States)

    Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

    2016-12-01

    Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

  4. 相应水位法在水文预报中的应用%Application of Corresponding Water Level Method in Hydrological Forecast

    Institute of Scientific and Technical Information of China (English)

    刘汉臣

    2012-01-01

    The corresponding water level method is a simple hydrological forecast method applied widely in current flood forecast.Based on the theory of flood wave motion in river course,the same phase point on the flood wave(such as flood rising point,peak and wave valley) will show a real time water level on the upper and lower river sections,calling the water level for corresponding water level each other.The corresponding water level experiential relation can be established,then forecast the future water level of lower section from current water level of upper section in river course.The flood peak water level forecast in Mudanjiang Hydrological Station is realized by constituting relevant relations between Mudanjiang Changtingzi Hydrological Station and Mudanjiang Hydrological Station in this study and it provides real-time hydrological information for Mudanjiang River flood control management.%相应水位法是目前洪水作业预报中应用较为广泛的一种简便水文预报方法。它根据河道洪水波运动原理,洪水波上同一位相点(如起涨点、洪峰、波谷)通过河段上下断面时表现出的水位,彼此称相应水位,建立相应水位经验关系,由上断面现时水位预报下断面未来时刻水位的河道洪水预报方法。主要通过对牡丹江长汀子水文站与牡丹江水文站建立洪峰水位相关关系,实现对牡丹江水文站洪峰水位的预报,为牡丹江市防汛调度提供及时的水文信息。

  5. Wavelet Analytical Forecasting Method of Water Consumption

    Institute of Scientific and Technical Information of China (English)

    刘洪波; 张宏伟

    2004-01-01

    A new method of short-term forecasting for water consumption in municipal supply water networks based on wavelet transformation is introduced. By wavelet decomposing commonly used in the signal field, water consumption per hour is decomposed into many series. Trend item, cycle item and random item are separated from the original time series in this way.Then by analyzing, building a model, forecasting every series and composing the results, the forecasting value of the original consumption is received. Simulation results show that this forecasting method is faster and more accurate, of which the error is less than 20%,indicating that the wavelet analytical method is practicable.

  6. Nambe Pueblo Water Budget and Forecasting model.

    Energy Technology Data Exchange (ETDEWEB)

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  7. Seasonal UK hydrological forecasts using rainfall forecasts - what level of skill?

    Science.gov (United States)

    Bell, Victoria; Davies, Helen; Kay, Alison; Scaife, Adam

    2017-04-01

    Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe, including the UK have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. The Hydrological Outlook UK (HOUK: www.hydoutuk.net) is the first operational hydrological forecast system for the UK that delivers monthly outlooks of the water situation for both river flow and groundwater levels. The output from the HOUK are publicly available and used each month by government agencies, practitioners and academics alongside other sources of information such as flood warnings and meteorological forecasts. The HOUK brings together information on current and forecast weather conditions, and river flows, and uses several modelling approaches to explore possible future hydrological conditions. One of the techniques combines ensembles of monthly-resolution seasonal rainfall forecasts provided by the Met Office GloSea5 forecast system with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The approach combines a high resolution, spatially distributed hydrological initial condition (HIC) provided by a hydrological model (Grid-to-Grid) driven by weather observations up to the forecast time origin. Considerable efforts have been made to accommodate the temporal and spatial resolution of the GloSea5 rainfall forecasts (monthly time-step and national-scale) in a spatially distributed forecasting system, leading to the development of a monthly resolution water balance model (WBM) to forecast regional mean river flows for the next 1 and 3 months ahead. The work presented here provides the first assessment of the skill in the HOUK national-scale flow forecasts using an ensemble of rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009). The skill in the combined modelling system has been assessed for different seasons and regions of Britain, and compared to what might be achieved using

  8. Artificial Neural Network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning

    Science.gov (United States)

    French, Jon; Mawdsley, Robert; Fujiyama, Taku; Achuthan, Kamal

    2017-04-01

    Effective prediction of tidal storm surge is of considerable importance for operators of major ports, since much of their infrastructure is necessarily located close to sea level. Storm surge inundation can damage critical elements of this infrastructure and significantly disrupt port operations and downstream supply chains. The risk of surge inundation is typically approached using extreme value analysis, while short-term forecasting generally relies on coastal shelf-scale tide and surge models. However, extreme value analysis does not provide information on the duration of a surge event and can be sensitive to the assumptions made and the historic data available. Also, whilst regional tide and surge models perform well along open coasts, their fairly coarse spatial resolution means that they do not always provide accurate predictions for estuarine ports. As part of a NERC Environmental Risks to Infrastructure Innovation Programme project, we have developed a tool that is specifically designed to forecast the North Sea storm surges on major ports along the east coast of the UK. Of particular interest is the Port of Immingham, Humber estuary, which handles the largest volume of bulk cargo in the UK including major flows of coal and biomass for power generation. A tidal surge in December 2013, with an estimated return period of 760 years, partly flooded the port, damaged infrastructure and disrupted operations for several weeks. This and other recent surge events highlight the need for additional tools to supplement the national UK Storm Tide Warning Service. Port operators are also keen to have access to less computationally expensive forecasting tools for scenario planning and to improve their resilience to actual events. In this paper, we demonstrate the potential of machine learning methods based on Artificial Neural Networks (ANNs) to generate accurate short-term forecasts of extreme water levels at estuarine North Sea ports such as Immingham. An ANN is

  9. Combining SKU-level sales forecasts from models and experts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2009-01-01

    textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we

  10. Discussion of 'Reservoir Computing approach to Great Lakes water level forecasting' by P. Coulibaly [J. Hydrol. 381(2010) 76-88

    Science.gov (United States)

    Abrahart, Robert J.; Mount, Nick J.; Shamseldin, Asaad Y.

    2012-02-01

    This paper extends published findings on the use of standard and recurrent neural network solutions for dynamical system modelling/monthly water level forecasting of the Great Lakes, North America. Earlier results are visualised and benchmarked using multiple linear regression. Feedforward solutions are observed to perform either linear or quasi-linear modelling operations. The superior performance of recurrent solutions is attributed to their highly dynamic, non-linear structure, influencing the manner in which feedback loops are incorporated. The echo state network was very powerful in such respects. Of particular interest, our research has also demonstrated that the inclusion of a recursive term in a linear regression model will have no impact on its predicted output.

  11. Water Vapor Forecasting for Chilean Sites

    Science.gov (United States)

    Marín, Julio C.; Cuevas, O.; Pozo, D.; Curé, M.

    2017-09-01

    "A number of observatories in Chile operate in the infrared region of the electromagnetic spectrum. Therefore, it is very important to them to accurately know the water vapor content of the atmosphere for a better observational planning. This talk provides an overview of the methods used to forecast water vapor over astronomical sites in Chile using observations and atmospheric numerical modeling."

  12. Joint Assimilation of InSAR and Water-level Data for Aquifer Parameter Estimation and Groundwater State Forecasting in Santa Clara Valley, California

    Science.gov (United States)

    Abdullin, Ayrat; Jonsson, Sigurjon

    2017-04-01

    Ground subsidence induced by groundwater withdrawal is a widespread problem and can cause damage to buildings and infrastructure. The challenge is to forecast, accurately and in a cost effective way, when water extraction may threaten infrastructure, so that procedures can be applied to avoid unacceptable levels of ground deformation beyond construction engineering criteria. However, many characteristics of the heterogeneity of aquifer parameters, such as hydraulic conductivity and storage coefficients, are usually uncertain. Monitoring data, such as water-level data in monitoring wells, can be used to reduce these uncertainties, but the difficulty is that they usually only provide spatially limited information about the groundwater system. To take on these problems, we use an ensemble-based assimilation framework that efficiently integrates InSAR-derived displacements and hydraulic head data for improved understanding of groundwater reservoir behavior. We apply this framework for aquifer parameter estimation of the basin-wide Santa Clara Valley groundwater system in northern California. To study the deformation patterns in the area, we use time-series analysis of InSAR data, based on more than 150 images from the ERS, Envisat and ALOS satellites from 1992-2012. Using the InSAR observations, in addition to approximate data on pumping, managed recharge and rainfall amounts, we are able to advance our understanding of the ongoing hydrogeological processes within the aquifer system. We find that including both InSAR and well water-level data as observations improves the properties estimation compared to basic statistical interpolation between the available well data. We also compare the performance of our hydraulic head predictions with previous groundwater studies in Santa Clara Valley, such as those of Chaussard et al. (2014). The results suggest that the high spatial resolution subsidence observations from InSAR are useful for accurately quantifying hydraulic

  13. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    Science.gov (United States)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  14. Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

    Science.gov (United States)

    Wang, Yao; Sridhar, Banavar

    2010-01-01

    This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in

  15. Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

    Science.gov (United States)

    Spirig, Christoph; Bhend, Jonas; Liniger, Mark

    2016-04-01

    Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

  16. Method for Water Management Considering Long-term Probabilistic Forecasts

    Science.gov (United States)

    Hwang, J.; Kang, J.; Suh, A. S.

    2015-12-01

    This research is aimed at predicting the monthly inflow of the Andong-dam basin in South Korea using long-term probabilistic forecasts to apply long-term forecasts to water management. Forecasted Cumulative Distribution Functions (CDFs) of monthly precipitation are plotted by combining the range of monthly precipitation based on proper Probability Density Function (PDF) in past data with probabilistic forecasts in each category. Ensembles of inflow are estimated by entering generated ensembles of precipitation based on the CDFs into the 'abcd' water budget model. The bias and RMSE between averages in past data and observed inflow are compared to them in forecasted ensembles. In our results, the bias and RMSE of average precipitation in the forecasted ensemble are bigger than in past data, whereas the average inflow in the forecasted ensemble is smaller than in past data. This result could be used for reference data to apply long-term forecasts to water management, because of the limit in the number of forecasted data for verification and differences between the Andong-dam basin and the forecasted regions. This research has significance by suggesting a method of applying probabilistic information in climate variables from long-term forecasts to water management in Korea. Original data of a climate model, which produces long-term probabilistic forecasts should be verified directly as input data of a water budget model in the future, so that a more scientific response in water management against uncertainty of climate change could be reached.

  17. Disaggregating residential water demand for improved forecasts and decision making

    Science.gov (United States)

    Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

    2003-04-01

    Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water

  18. The method of water-level forecasting for the Heilongjiang mainstream%黑龙江干流水位预报方法研究

    Institute of Scientific and Technical Information of China (English)

    赵思远; 郝振纯; 刘文斌

    2016-01-01

    Heilongjiang mainstream and accu-rate within acceptable range.Forecast schemes'pass rate reaches more than 85% and coefficients of de-termination are above 0.97 for six stations.The model can be used for water-level forecasting.② The paper quantifies uncertainty of the Multivariate hybrid linear regression model results using the Monte Carlo methods.The larger the standard deviation,the more uncertain the flood hydrograph forecasting,and thus model is more uncertain.The parameters'sensitivity analysis based on standardized regression coefficients shows that the parameters with minimum values in time order of mainstream are the most sen-sitive,the parameters with minimum values in time order of tributary take the second place,and the pa-rameters with higher values in time order are less sensitive.

  19. Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

    Science.gov (United States)

    Block, P. J.; Gonzalez, E.; Bonnafous, L.

    2011-12-01

    Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

  20. Forecasting ozone daily maximum levels at Santiago, Chile

    Science.gov (United States)

    Jorquera, Héctor; Pérez, Ricardo; Cipriano, Aldo; Espejo, Andrés; Victoria Letelier, M.; Acuña, Gonzalo

    In major urban areas, air pollution impact on health is serious enough to include it in the group of meteorological variables that are forecast daily. This work focusses on the comparison of different forecasting systems for daily maximum ozone levels at Santiago, Chile. The modelling tools used for these systems were linear time series, artificial neural networks and fuzzy models. The structure of the forecasting model was derived from basic principles and it includes a combination of persistence and daily maximum air temperature as input variables. Assessment of the models is based on two indices: their ability to forecast well an episode, and their tendency to forecast an episode that did not occur at the end (a false positive). All the models tried in this work showed good forecasting performance, with 70-95% of successful forecasts at two monitor sites: Downtown (moderate impacts) and Eastern (downwind, highest impacts). The number of false positives was not negligible, but this may be improved by expressing the forecast in broad classes: low, average, high, very high impacts; the fuzzy model was the most reliable forecast, with the lowest number of false positives among the different models evaluated. The quality of the results and the dynamics of ozone formation suggest the use of a forecast to warn people about excessive exposure during episodic days at Santiago.

  1. Is China's fifth-largest inland lake to dry-up? Incorporated hydrological and satellite-based methods for forecasting Hulun lake water levels

    Science.gov (United States)

    Cai, Zuansi; Jin, Taoyong; Li, Changyou; Ofterdinger, Ulrich; Zhang, Sheng; Ding, Aizhong; Li, Jiancheng

    2016-08-01

    Hulun Lake, China's fifth-largest inland lake, experienced severe declines in water level in the period of 2000-2010. This has prompted concerns whether the lake is drying up gradually. A multi-million US dollar engineering project to construct a water channel to transfer part of the river flow from a nearby river to maintain the water level was completed in August 2010. This study aimed to advance the understanding of the key processes controlling the lake water level variation over the last five decades, as well as investigate the impact of the river transfer engineering project on the water level. A water balance model was developed to investigate the lake water level variations over the last five decades, using hydrological and climatic data as well as satellite-based measurements and results from land surface modelling. The investigation reveals that the severe reduction of river discharge (-364 ± 64 mm/yr, ∼70% of the five-decade average) into the lake was the key factor behind the decline of the lake water level between 2000 and 2010. The decline of river discharge was due to the reduction of total runoff from the lake watershed. This was a result of the reduction of soil moisture due to the decrease of precipitation (-49 ± 45 mm/yr) over this period. The water budget calculation suggests that the groundwater component from the surrounding lake area as well as surface run off from the un-gauged area surrounding the lake contributed ∼ net 210 Mm3/yr (equivalent to ∼ 100 mm/yr) water inflows into the lake. The results also show that the water diversion project did prevent a further water level decline of over 0.5 m by the end of 2012. Overall, the monthly water balance model gave an excellent prediction of the lake water level fluctuation over the last five decades and can be a useful tool to manage lake water resources in the future.

  2. Value of seasonal flow forecast to reservoir operation for water supply in snow-dominated catchments

    Science.gov (United States)

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea; Pianosi, Francesca; Nijssen, Bart; Lettenmaier, Dennis

    2014-05-01

    The recursive application of forecasting and optimization can make management strategies more flexible and efficient by improving the potential for anticipating, and thus adapting, to adverse events. In the field of reservoir operation, this means enriching the information base on which release decisions are made. At a minimum, this includes the available reservoir storage, but reservoir management can greatly benefit from consideration of other pieces of information as, for instance, weather and flow forecasts. However, the utility or value of inflow forecasts is directly related to forecast quality. In this work, we focus on snow-dominated water resource systems, where the prediction of the volume and timing of snowmelt can greatly enhance the operational performance. We use the Oroville-Thermalito reservoir complex in the Feather River Basin, California, as a case study to explore the effect of forecast quality on optimal release strategies. We use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir operation performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to relate forecast skill to changes in release decisions and to determine the minimum forecast skill that is required to affect decision-making. We generate forecasted inflow sequences using the Variable Infiltration Capacity (VIC) hydrology model. Forecast initial conditions are created using observed meteorology, while inflow forecasts are based on seasonal climate forecasts. Although the forecast skill level is specific to the Feather River basin, the methodology should be transferable to other systems with strong seasonal runoff regimes. We assess the transferability of the case study results to other systems using alternative reservoir characteristics of the Oroville

  3. 易门井静水位预报指标综合研究及运用%Comprehensive study on forecasting and application of indicators of the static water level in Yinmen well

    Institute of Scientific and Technical Information of China (English)

    杨芸霞; 周萍

    2011-01-01

    地下流体的观测与研究,是捕捉地震前兆信息的主要方法之一,是做好地震监测预报的重要途径.对易门静水位十多年观测中的一些异常与地震作分析、探讨,从单项指标的分析到综合指标的研究运用,并在多年的预测预报实践中,筛选出对应率、概率较高的5个短临预报指标,其预报准确率为0.928.预报有效率为0.948,可靠率为0.938,通过双概率检验.在综合预报指标的前提下,采用"60点滑动法",给出定性、定量的综合判据.建立了以易门为圆心200 km内M≥5.0地震的综合指标预测方案.%The underground fluid observation and the research is one of the main approaches to capture earthquake precursor information. It is also an important way for earthquake monitoring and prediction. Based on the static water level in Yimen well for more than ten years, some anomalies and earthquake observation are analyzed and discussed. From the single index analysis to research comprehensive index, and in the years of forecast practice, the corresponding rate, high probability of five short forecast indexes are chosen which forecasting accuracy is 0. 928, forecasting validity is 0. 948, and reliability is 0. 938. These indexes also pass through the dual probability test. On the premise of integrated forecast indexes, using 60 point sliding method, a qualitative and quantitative criterion is further developed and the comprehensive index forecasting plan for M≥5.0 earthquake in the 200 km circle centered as Yimen has been established.

  4. Water demand forecasting: review of soft computing methods.

    Science.gov (United States)

    Ghalehkhondabi, Iman; Ardjmand, Ehsan; Young, William A; Weckman, Gary R

    2017-07-01

    Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

  5. Artificial neural network application for real time forecasting of levels in a natural water course; Un'applicazione delle reti neurali artificiali per la previsione in tempo reale delle altezze idrometriche di un corso d'acqua

    Energy Technology Data Exchange (ETDEWEB)

    Pecora, S. [Ufficio Idrografico e Mareografico per il bacino del fiume Po, Parma (Italy); Veltri, P. [Cosenza Univ. della Calabria, Cosenza (Italy). Dipt. di Difesa del Suolo

    2001-04-01

    The recent adoption of law limits in the field of environmental defence has turned attention of technicians and researchers towards characterization of receiving catchment. During a flood event, after a period far from such extreme events, water quality monitoring can be meaningful to characterize diffuse pollutant loads, carried by the first washing water of the catchment. In order to that it is necessary to adopt suitable control systems which, reliable in forecasting, can start automatic sampler form a remote place, in such a way as to draw samples during flood growth already. Artificial neural networks (ANN) have been applied to forecasting of water levels at the section of Enza at Cedogno (Reggio Emilia, Italy). [Italian] La recente adozione di limiti normativi in materia di tutela ambientale ha rivolto l'attenzione di tecnici e ricercatori verso la caratterizzazione del bacino ricettore. Il monitoraggio della qualita' idrica durante una piena, dopo un periodo lontano da tali eventi estremi, puo' essere significativo per l'individuazione dei carichi inquinanti diffusi, trasportati dalle prime acque di dilavamento del bacino. A tal fine e' necessario adottare opportuni sistemi di controllo che, affidabili nelle previsioni, possano avviare da remoto il campionamento automatico delle acque, in modo da prelevare i campioni gia' durante la fase di crescita della piena. Le reti neurali (ANN, artificial neural network) sono state applicate nella previsione delle altezze idrometriche alla sezione di Enza a Cedogno (RE).

  6. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    OpenAIRE

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have ti...

  7. High-resolution hydrological seasonal forecasting for water resources management over Europe

    Science.gov (United States)

    Pan, Ming; Wanders, Niko; Wood, Eric; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohini; Prudhomme, Christel; Houghton-Carr, Helen

    2017-04-01

    To support the decision-making process at the seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by water managers. So far high-resolution seasonal forecasts have been unavailable due to 1) lack of availability in meteorological seasonal forecasts, 2) the coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, and 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. As part of the EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project, we have created a unique dataset of hydrological seasonal forecasts derived from four atmospheric circulation models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP). The forecasts provide daily values at 5-km spatial resolution and are bias corrected against E-OBS meteorological observations. Consistency in the LSM parameterization ensures synergy in the hydrological forecasts, resulting in 208 forecasts at any given day over Europe. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been co-designed in collaboration with end-users and stakeholders inside the EDgE project. An example of an SCII is the percentage of ensemble realizations above the 10th percentile of monthly river flow or below the 90th percentile, including the persistency in the forecast with increasing lead times. Results show that skillful discharge forecasts can be made throughout Europe 3 months in advance, with predictability up to 6 months for Northern Europe due to the impact of snow. The predictability of soil moisture is limited to the first three months, due to the significant impact of precipitation and the short memory in the initial conditions (only for the first month). The groundwater recharge predictability

  8. Forecasting urban water demand: A meta-regression analysis.

    Science.gov (United States)

    Sebri, Maamar

    2016-12-01

    Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

  9. Long-range dependence and sea level forecasting

    CERN Document Server

    Ercan, Ali; Abbasov, Rovshan K

    2013-01-01

    This study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA

  10. Data-Driven Techniques for Regional Groundwater Level Forecasts

    Science.gov (United States)

    Chang, F. J.; Chang, L. C.; Tsai, F. H.; Shen, H. Y.

    2015-12-01

    Data-Driven Techniques for Regional Groundwater Level Forecasts Fi-John Changa, Li-Chiu Changb, Fong He Tsaia, Hung-Yu Shenba Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC. b Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan, ROC..Correspondence to: Fi-John Chang (email: changfj@ntu.edu.tw)The alluvial fan of the Zhuoshui River in Taiwan is a good natural recharge area of groundwater. However, the over extraction of groundwater occurs in the coastland results in serious land subsidence. Groundwater systems are heterogeneous with diverse temporal-spatial patterns, and it is very difficult to quantify their complex processes. Data-driven methods can effectively capture the spatial-temporal characteristics of input-output patterns at different scales for accurately imitating dynamic complex systems with less computational requirements. In this study, we implement various data-driven methods to suitably predict the regional groundwater level variations for making countermeasures in response to the land subsidence issue in the study area. We first establish the relationship between regional rainfall, streamflow as well as groundwater levels and then construct intelligent groundwater level prediction models for the basin based on the long-term (2000-2013) regional monthly data sets collected from the Zhuoshui River basin. We analyze the interaction between hydrological factors and groundwater level variations; apply the self-organizing map (SOM) to obtain the clustering results of the spatial-temporal groundwater level variations; and then apply the recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to predicting the monthly groundwater levels. As a consequence, a regional intelligent groundwater level prediction model can be constructed based on the adaptive results of the SOM. Results demonstrate that the development

  11. Viabilidad para pronósticos hidrológicos de niveles diarios, semanales y decadales en colombia The feasibility of daily, weekly and ten-day water-level forecasting in Colombia

    Directory of Open Access Journals (Sweden)

    Rivera Hebert

    2010-08-01

    Full Text Available El presente artículo analiza y concluye sobre la viabilidad de pronósticos hidrológicos de niveles diarios, semanales y decadales en 20 estaciones hidrológicas de la red de monitoreo hidrometeorológico que soporta al Servicio de Alertas del Instituto de Hidrología, Meteorología y Estudios Ambientales – Ideam en Colombia (www.ideam.gov.co. Esta viabilidad se determina a través de un conjunto de criterios de desempeño ortogonales y para el presente estudio recomienda la aplicación de combinaciones lineales adaptativamente óptimas (CLAO como operador viable para la configuración de un sistema de pronóstico hidrológico en tiempo real de niveles diarios, semanales y decadales. En conclusión, se muestra que los pronósticos de niveles diarios, semanales y decadales tienen una viabilidad de pronóstico satisfactoria para el 70% de los casos estudiados.This paper analyses the feasibility of forecasting daily, weekly and ten-day water-levels at 20 hydrological stations forming part of the monitoring network supporting the Institute of Hydrology, Meteorology and Environmental Studies’ (IDEAM Alert Service in Colombia (www.ideam.gov.co. Such viability was determined by a set of orthogonal performance criteria and implementing optimally adaptive linear combinations (OALC was recommended for this study as a viable operator for configuring a real-time hydrological forecast system. It is shown that the forecast for daily, weekly and ten-day levels had satisfactory viability for 70% of the cases studied.

  12. 7 CFR 612.6 - Application for water supply forecast service.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Application for water supply forecast service. 612.6... CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.6 Application for water supply forecast service. Requests for obtaining water supply forecasts...

  13. 7 CFR 612.5 - Dissemination of water supply forecasts and basic data.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Dissemination of water supply forecasts and basic data... SUPPLY FORECASTS § 612.5 Dissemination of water supply forecasts and basic data. Water supply outlook reports prepared by NRCS and its cooperators containing water supply forecasts and basic data are...

  14. Nivmar: a storm surge forecasting system for Spanish waters

    Directory of Open Access Journals (Sweden)

    Enrique Álvarez Fanjul

    2001-07-01

    Full Text Available In this paper, a storm surge prediction system for the Spanish Waters is presented. The system, named Nivmar, is based on the ocean circulation Hamsom model and on the harmonical prediction of tides computed from data measured by the tide gauge network Redmar, managed by Puertos del Estado. Nivmar is executed twice a day, running Hamsom forced by meteorological fields derived from the INM (Instituto Nacional de Meteorología operational application of Hirlam atmospheric model. Data from Redmar tide gauges is used to to forecast the tidal elevations, to validate the system and to perform data assimilation, correcting systematic errors in the mean sea level due to physicals processes that are not included in the ocean model (i. e. steric height. The forecast horizon is 48 hours. In order to validate the system with measured data from Redmar a very stormy 5 months period was selected. Results from this test (November 95 to March 96 are presented. Data from this experiment shown that Nivmar is able to correctly predict sea level in the region. A simple data assimilation scheme for sea level is described and results from its application are studied. Finally, special focus is made in future plans and potential developments and applications of the system.

  15. Forecasting drought risks for a water supply storage system using bootstrap position analysis

    Science.gov (United States)

    Tasker, Gary; Dunne, Paul

    1997-01-01

    Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

  16. Forecasting and Communicating Water-Related Disasters in Africa

    Science.gov (United States)

    Hong, Y.; Clark, R. A.; Mandl, D.; Gourley, J. J.; Flamig, Z.; Zhang, K.; Macharia, D.; Frye, S. W.; Cappelaere, P. G.; Handy, M.

    2016-12-01

    Accurate forecasting and communication of water and water-related hazards in developing regions could save untold lives and property. To this end, the CREST (Coupled Routing and Excess Storage) hydrologic model has been implemented over East Africa, and in dozens of other countries as a user-friendly, flexible, and highly extensible platform for monitoring water resources, floods, droughts, and landslides since 2009. We will present the updated CREST/EF5 hydrologic ensemble modeling framework with new model physics and better forecasts of streamflow, soil moisture, and other hydrologic states to RCMRD (the Regional Centre for Mapping of Resources for Development) and SERVIR global hub network. The central goal of this project is to develop an ensemble hydrologic prediction system, forced by weather and climate forecasts in a single continuum, to communicate forecasts on scales ranging from sub-daily to seasonal and in formats designed for better decision making about water and water-related disasters. The CREST/EF5 is a proven performer at getting researcher and officials in emerging regions excited about and confident in their ability to independently monitor, forecast, and understand water and water-related disasters, through a series of training workshops and capacity building activities in USA, Africa, Mesoamerica, and South Asia and is thus particularly well-suited for hydrologic capacity building in emerging countries.

  17. 7 CFR 612.2 - Snow survey and water supply forecast activities.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates...

  18. Medium Range Ensembles Flood Forecasts for Community Level Applications

    Science.gov (United States)

    Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

    2013-05-01

    Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

  19. FORECAST ON FUTURE LEVEL OF ECONOMY DEVELOPMENT OF CHINA

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    China is a country in the period of economy takeoff. We cannot use the common method to forecast its fu-ture econotmy level. This paper establishes an economic level forecast model of the countries whose economy is in the takeoff because of the stimulation of model country. The enlightenment of the model is from physics. If there are two sub-stances, A and B, and a medium between them, according to physics, when substance A is hotter than B, B' s tempera-ture will inevitably rise and close to that of A. Thus, this system tends to be a state of balance. Three factors affect heatconduction between substance A and B. They are the difference of temperature between two substances, the conductivityof medium and the characteristics of themselves. The model is testified through two examples. And then we forecast theeconomic development level of China in long term. This paper raises a model to solve the problem of research approaches.However, since there are some limitations on data source, problems will appear. For example, in certain years, our fore-cast results do not suit the real situation. But in the long term, the tendency is accurate. Then this model can be amendedin accordance with different situations.

  20. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  1. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  2. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  3. 基于贝叶斯定理与洪水预报误差抬高水库汛限水位的风险分析%Risk analysis of raising reservoir flood limited water level based on Bayes theorem and flood forecast error

    Institute of Scientific and Technical Information of China (English)

    周如瑞; 卢迪; 王本德; 周惠成

    2016-01-01

    The development of hydrometeorological forecast technology offers important opportunities for reservoir dynamic control of flood limited water level. Economic benefits can be improved by raising the flood limited water level, but there is certain flood control risk. The purpose of this study was to propose a risk analysis method of upper bound of dynamic control of flood limited water level in order to provide the support for the development of dynamic control of flood limited water level. The proposed risk analysis method was based on Bayes theorem and flood forecast error characteristics. Qinghe reservoir, located in the northeast of China, was taken as an example. 21 flood events of actual and forecast runoff from the year 1964 to 2013 were used. For large reservoirs that has the ability for multi-year regulation, decision makers of flood control operation concern a lot about runoff forecast accuracy because the design flood is controlled by the flood volume. First, maximum entropy method was selected to simulate the runoff prediction error probability density function of 21 flood events, also forecast error range was calculated. According to the actual need of runoff forecast error in Qinghe reservoir, the range was divided into 6 zones, and distribution probabilities of runoff forecast errors in each zone, namely the prior probability distributions of flood forecasting errors were obtained by integrating the density function. Then, the probabilities of the highest water levels being higher than corresponding designed levels within different flood forecast error bounds were studied, and the risks of different flood forecast errors were inferred by Bayes theorem when the highest water level in flood regulation met with the design flood frequency. Based on the risk analysis method, risks of each design water level considering flood forecast information were compared with risks of conventional mode. The proposed risk analysis method of upper bound of dynamic

  4. ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation

    Science.gov (United States)

    Lavers, David A.; Pappenberger, Florian; Richardson, David S.; Zsoter, Ervin

    2016-11-01

    In winter, heavy precipitation and floods along the west coasts of midlatitude continents are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within the atmospheric river of extratropical cyclones. This study builds on previous findings that showed that forecasts of IVT have higher predictability than precipitation, by applying and evaluating the European Centre for Medium-Range Weather Forecasts Extreme Forecast Index (EFI) for IVT in ensemble forecasts during three winters across Europe. We show that the IVT EFI is more able (than the precipitation EFI) to capture extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase; conversely, the precipitation EFI is better during the negative NAO phase and at shorter leads. An IVT EFI example for storm Desmond in December 2015 highlights its potential to identify upcoming hydrometeorological extremes, which may prove useful to the user and forecasting communities.

  5. Mesoscale hydrological ensemble forecasting for water resources management

    Energy Technology Data Exchange (ETDEWEB)

    Fortin, V. [Meteorological Research Div., Environment Canada, Dorval, Quebec (Canada); Turcotte, R. [Government of Quebec, Centre of Quebec' s Water Expertise, Quebec (Canada); Anctil, F. [Univ. Laval, Dept. of Civil Engineering, Laval, Quebec (Canada); Favre, A.-C. [INRS, Water Earth and Environment, Quebec (Canada); Petit, Th. [Univ. Laval, Dept. of Civil Engineering, Laval, Quebec (Canada)

    2008-07-01

    This poster discusses meso-scale hydrological ensemble forecasting for water resources management. Environment Canada will produce a 20 member meso-scale (35 km), short range (48 h) meteorological ensemble prediction system (M-EPS). Each of the 20 members of the global EPS will be dynamically down scaled from 100 to 35 km over North America using limited area model GEM-LAM. Preliminary tests have been conducted on Lie'vre watershed to assess the impact of using short range M-EPS for hydrological forecasting.

  6. FORECAST OF WATER TEMPERATURE IN RESERVOIR BASED ON ANALYTICAL SOLUTION

    Institute of Scientific and Technical Information of China (English)

    JI Shun-wen; ZHU Yue-ming; QIANG Sheng; ZENG Deng-feng

    2008-01-01

    The water temperature in reservoirs is difficult to be predicted by numerical simulations. In this article, a statistical model of forecasting the water temperature was proposed. In this model, the 3-D thermal conduction-diffusion equations were converted into a system consisting of 2-D equations with the Fourier expansion and some hypotheses. Then the statistical model of forecasting the water temperature was developed based on the analytical solution to the 2-D thermal equations. The simplified statistical model can elucidate the main physical mechanism of the temperature variation much more clearly than the numerical simulation with the Navier-Stokes equations. Finally, with the presented statistical model, the distribution of water temperature in the Shangyoujiang reservoir was determined.

  7. Enhancing water quality modelling & forecasting in the Han River basin (Korea) using data assimilation

    Science.gov (United States)

    Loos, Sibren; Sumihar, Julius; Min, Joong-Hyuk; El Serafy, Ghada; Kim, Kyunghyun; Weerts, Albrecht

    2013-04-01

    Data assimilation in operational systems is a promising method to enhance the lead-time and reduce the uncertainty of water quality forecasts and provides a good base for the setup of monitoring schemes in large catchments (locations and frequency of sampling). In the River Han (Korea) three weirs have been constructed to prevent flooding and improve the water quality in the main stream. With real-time automated data imports and two water quality models, HSPF and EFDC, embedded in the FEWS-NIER forecasting platform, information about the current water quality status and daily water quality forecasts seven days ahead is provided to -water management agencies in the basin. To improve both the quality and the lead time of the water quality forecasts the EFDC hydrodynamics and water quality model has been implemented in OpenDA, an open interface standard for data assimilation (DA) in numerical models. The setup of this real-time water quality data assimilation system to enhance the algal dynamics modelling and the forecasts in the Han River basin (20,960 km² in size) was performed by a number of steps using Ensemble Kalman Filtering (EnKF). Using a twin experiment the correct working of the algorithm was tested. Noise was applied to several water quality variables in the main tributaries with a sequential simulation algorithm, to obtain correct noise settings that result in a realistic spread between the individual ensemble members. As the next step, the inclusion of observations in the main stream for data assimilation was tested using the EnKF algorithm to define their effect on the model results. Noise was applied to global solar radiation to improve water temperature forecasts, as well as to phosphate, nitrate and chlorophyll-α concentrations in the large tributaries to improve the prediction of algal level upstream of the weirs. Different combinations of noise and observation settings (standard deviation and time correlation) to find the best model update of

  8. GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

    Science.gov (United States)

    Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

    2013-01-01

    Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

  9. Groundwater Level Fluctuation Forecasting in Birjand Aquifer Using Artificial Neural Network

    Science.gov (United States)

    Mirarabi, A.; Nakhaei, M.

    2009-04-01

    Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables such as groundwater levels. In this paper using artificial neural network three objective including determination of the influential parameters which impact fluctuation of groundwater level in birjand aquifer, investigation of the effect of temporal and spatial information by considering time series (9 years) and simulation of the fluctuation groundwater level in three selected piezometers are recognized. The reasonably good prediction of piezometric level simulated based on ANN using FNN_LM by selection of effective parameters and optimal time lag

  10. Data Assimilation to Estimate the Water Level of River

    Science.gov (United States)

    Apriliani, Erna; Hanafi, Lukman; Imron, Chairul

    2017-09-01

    Data assimilation is an estimation method for stochastic dynamic system by combining the mathematical model with measurement data. Water level and velocity of river are stochastic dynamic system, and it is important to estimate the water level and velocity of river flow to reduce flood risk disaster. Here, we estimate the water level and velocity of river flow by using data assimilation specially Kalman filter and Ensemble Kalman filter. We define mathematical model of river flow, discretize and do simulation by Kalman filter and Ensemble Kalman filter. In data assimilation, we forecast the water level and velocity by using mathematical model and based on the measurement data, the correction of forecasting is made.

  11. Gray comprehensive assessment and optimal selection of water consumption forecasting model

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A comprehensive assessing method based on the principle of the gray system theory and gray relational grade analysis was put forward to optimize water consumption forecasting models. The method provides a better accuracy for the assessment and the optimal selection of the water consumption forecasting models. The results show that the forecasting model built on this comprehensive assessing method presents better self-adaptability and accuracy in forecasting.

  12. Water Level Station History

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Images contain station history information for 175 stations in the National Water Level Observation Network (NWLON). The NWLON is a network of long-term,...

  13. Forecasting the Levels of Vector Autoregressive Log-Transformed Time Series

    NARCIS (Netherlands)

    M.A. Ariñ o; Ph.H.B.F. Franses (Philip Hans)

    1996-01-01

    textabstractIn this paper we give explicit expressions for the forecasts of levels of a vector time series when such forecasts are generated from (possibly cointegrated) vector autoregressions for the corresponding log-transformed time series. We also show that simply taking exponentials of forecast

  14. A fully adaptive forecasting model for short-term drinking water demand

    NARCIS (Netherlands)

    Bakker, M.; Vreeburg, J.H.G.; Schagen, van K.M.; Rietveld, L.C.

    2013-01-01

    For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar data as single input. Based on this input, the m

  15. Multiplexed FBG Monitoring System for Forecasting Coalmine Water Inrush Disaster

    Directory of Open Access Journals (Sweden)

    B. Liu

    2012-01-01

    Full Text Available This paper presents a novel fiber-Bragg-grating- (FBG- based system which can monitor and analyze multiple parameters such as temperature, strain, displacement, and seepage pressure simultaneously for forecasting coalmine water inrush disaster. The sensors have minimum perturbation on the strain field. And the seepage pressure sensors adopt a drawbar structure and employ a corrugated diaphragm to transmit seepage pressure to the axial strain of FBG. The pressure sensitivity is 20.20 pm/KPa, which is 6E3 times higher than that of ordinary bare FBG. The FBG sensors are all preembedded on the roof of mining area in coalmine water inrush model test. Then FBG sensing network is set up applying wavelength-division multiplexing (WDM technology. The experiment is carried out by twelve steps, while the system acquires temperature, strain, displacement, and seepage pressure signals in real time. The results show that strain, displacement, and seepage pressure monitored by the system change significantly before water inrush occurs, and the strain changes firstly. Through signal fusion analyzed it can be concluded that the system provides a novel way to forecast water inrush disaster successfully.

  16. River water temperature and fish growth forecasting models

    Science.gov (United States)

    Danner, E.; Pike, A.; Lindley, S.; Mendelssohn, R.; Dewitt, L.; Melton, F. S.; Nemani, R. R.; Hashimoto, H.

    2010-12-01

    Water is a valuable, limited, and highly regulated resource throughout the United States. When making decisions about water allocations, state and federal water project managers must consider the short-term and long-term needs of agriculture, urban users, hydroelectric production, flood control, and the ecosystems downstream. In the Central Valley of California, river water temperature is a critical indicator of habitat quality for endangered salmonid species and affects re-licensing of major water projects and dam operations worth billions of dollars. There is consequently strong interest in modeling water temperature dynamics and the subsequent impacts on fish growth in such regulated rivers. However, the accuracy of current stream temperature models is limited by the lack of spatially detailed meteorological forecasts. To address these issues, we developed a high-resolution deterministic 1-dimensional stream temperature model (sub-hourly time step, sub-kilometer spatial resolution) in a state-space framework, and applied this model to Upper Sacramento River. We then adapted salmon bioenergetics models to incorporate the temperature data at sub-hourly time steps to provide more realistic estimates of salmon growth. The temperature model uses physically-based heat budgets to calculate the rate of heat transfer to/from the river. We use variables provided by the TOPS-WRF (Terrestrial Observation and Prediction System - Weather Research and Forecasting) model—a high-resolution assimilation of satellite-derived meteorological observations and numerical weather simulations—as inputs. The TOPS-WRF framework allows us to improve the spatial and temporal resolution of stream temperature predictions. The salmon growth models are adapted from the Wisconsin bioenergetics model. We have made the output from both models available on an interactive website so that water and fisheries managers can determine the past, current and three day forecasted water temperatures at

  17. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  18. Exploratory studies into the prospects for seasonal forecasting of lake levels and outflows

    Science.gov (United States)

    Sene, Kevin; Tych, Woldek; Beven, Keith

    2017-04-01

    Some of the largest lakes in the world are in Africa and seasonal forecasts of levels and outflows can potentially help with water supply, irrigation and hydropower operations; in particular regarding the risks from floods or droughts. Some factors which increase the prospects for real-time forecasting include the significant time delays between rainfall and outflows resulting from the huge volumes of water stored, and that many studies have shown possible links between regional rainfall and climate indices for the Indian Ocean and elsewhere. On the other hand, on account of the huge areas covered, catchments can span several climate zones and rainfall and flow monitoring networks are often sparse. Exploratory studies into some of these issues are described based on case studies for two large lakes, including some preliminary findings regarding data assimilation and the complexity of models required. The studies were performed using a range of stochastic signal identification tools and are compared with the findings from an ensemble streamflow prediction approach. Preliminary conclusions are then drawn regarding the relevance of these results to the development of operational forecasting models.

  19. Forecasting Drinking and Household Water Requirement of the Thrace Region

    Directory of Open Access Journals (Sweden)

    F. Konukcu

    2007-05-01

    Full Text Available This study aims at future forecasting drinking and household water requirements of the Thrace region by the aid of a scientific perspective. To realise this, first future population of the region was predicted and then the water requirements were calculated. As results, water requirements of the city and the countryside for the years 2020, 2030, 2040 and 2050 were computed as 1.45, 1.94, 2.58 and 3.44 km3, respectively. Beside, rapidly increasing drinking and household water requirements due to fast population growth and immense amount of migration into the region, demands by agriculture and intensive industry suggest that the present total water potential of about 4.0 km3 will not be sufficient and a great water crisis may be experienced. Adverse effects of a probable global climate change on water resources make the situation more acute. To overcome this crisis, governmental agencies and civil societies are called work together to produce and implement rational strategies.

  20. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  1. Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level.

    Science.gov (United States)

    Ouyang, Huei-Tau

    2017-08-01

    Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

  2. Using additional external inputs to forecast water quality with an artificial neural network for contamination event detection in source water

    Science.gov (United States)

    Schmidt, F.; Liu, S.

    2016-12-01

    Source water quality plays an important role for the safety of drinking water and early detection of its contamination is vital to taking appropriate countermeasures. However, compared to drinking water, it is more difficult to detect contamination events because its environment is less controlled and numerous natural causes contribute to a high variability of the background values. In this project, Artificial Neural Networks (ANNs) and a Contamination Event Detection Process (CED Process) were used to identify events in river water. The ANN models the response of basic water quality sensors obtained in laboratory experiments in an off-line learning stage and continuously forecasts future values of the time line in an on-line forecasting step. During this second stage, the CED Process compares the forecast to the measured value and classifies it as regular background or event value, which modifies the ANN's continuous learning and influences its forecasts. In addition to this basic setup, external information is fed to the CED Process: A so-called Operator Input (OI) is provided to inform about unusual water quality levels that are unrelated to the presence of contamination, for example due to cooling water discharge from a nearby power plant. This study's primary goal is to evaluate how well the OI fits into the design of the combined forecasting ANN and CED Process and to understand its effects on the online forecasting stage. To test this, data from laboratory experiments conducted previously at the School of Environment, Tsinghua University, have been used to perform simulations highlighting features and drawbacks of this method. Applying the OI has been shown to have a positive influence on the ANN's ability to handle a sudden change in background values, which is unrelated to contamination. However, it might also mask the presence of an event, an issue that underlines the necessity to have several instances of the algorithm run in parallel. Other difficulties

  3. Improved Water and Energy Management Utilizing Seasonal to Interannual Hydroclimatic Forecasts

    Science.gov (United States)

    Arumugam, S.; Lall, U.

    2014-12-01

    Seasonal to interannual climate forecasts provide valuable information for improving water and energy management. Given that the climatic attributes over these time periods are typically expressed as probabilistic information, we propose an adaptive water and energy management framework that uses probabilistic inflow forecasts to allocate water for uses with pre-specified reliabilities. To ensure that the system needs are not compromised due to forecast uncertainty, we propose uncertainty reduction using model combination and based on a probabilistic constraint in meeting the target storage. The talk will present findings from recent studies from various basins that include (a) role of multimodel combination in reducing the uncertainty in allocation (b) relevant system characteristics that improve the utility of forecasts, (c) significance of streamflow forecasts in promoting interbasin transfers and (d) scope for developing power demand forecasts utilizing temperature forecasts. Potential for developing seasonal nutrient forecasts using climate forecasts for supporting water quality trading will also be presented. Findings and synthesis from the panel discussion from the recently concluded AGU chapman conference on "Seaonal to Interannual Hydroclimatic Forecasts and Water Management" will also be summarized.

  4. System Dynamics Approach to Urban Water Demand Forecasting A Case Study of Tianjin

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongwei; ZHANG Xuehua; ZHANG Baoan

    2009-01-01

    A system dynamics approach to urban water demand forecasting was developed based on the analysis of urban water resources system.which was characterized by multi.feedback and nonlinear interactions among system elements.As an example,Tianjin water resources system dynamic model was set up to forecast water resources demand of the planning years.The practical verification showed that the relative error was lower than 1O%.Furthermore,through the comparison and analysis of the simulation results under different development modes presented in this paper.the forecasting results ofthe water resources demand ofTianiin was achieved based on sustainable utilization strategy of water resources.

  5. Global operational hydrological forecasts through eWaterCycle

    Science.gov (United States)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and

  6. Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

    Science.gov (United States)

    Bélanger, Eric; Vincent, Alain

    2005-01-01

    In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.

  7. A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.

    Directory of Open Access Journals (Sweden)

    Guillaume Bal

    Full Text Available Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i an emotive simulated example, ii application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.

  8. Electricity forecasting on the individual household level enhanced based on activity patterns.

    Science.gov (United States)

    Gajowniczek, Krzysztof; Ząbkowski, Tomasz

    2017-01-01

    Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

  9. eWaterCycle: A global operational hydrological forecasting model

    Science.gov (United States)

    van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

    2015-04-01

    Development of an operational hyper-resolution hydrological global model is a central goal of the eWaterCycle project (www.ewatercycle.org). This operational model includes ensemble forecasts (14 days) to predict water related stress around the globe. Assimilation of near-real time satellite data is part of the intended product that will be launched at EGU 2015. The challenges come from several directions. First, there are challenges that are mainly computer science oriented but have direct practical hydrological implications. For example, we aim to make use as much as possible of existing standards and open-source software. For example, different parts of our system are coupled through the Basic Model Interface (BMI) developed in the framework of the Community Surface Dynamics Modeling System (CSDMS). The PCR-GLOBWB model, built by Utrecht University, is the basic hydrological model that is the engine of the eWaterCycle project. Re-engineering of parts of the software was needed for it to run efficiently in a High Performance Computing (HPC) environment, and to be able to interface using BMI, and run on multiple compute nodes in parallel. The final aim is to have a spatial resolution of 1km x 1km, which is currently 10 x 10km. This high resolution is computationally not too demanding but very memory intensive. The memory bottleneck becomes especially apparent for data assimilation, for which we use OpenDA. OpenDa allows for different data assimilation techniques without the need to build these from scratch. We have developed a BMI adaptor for OpenDA, allowing OpenDA to use any BMI compatible model. To circumvent memory shortages which would result from standard applications of the Ensemble Kalman Filter, we have developed a variant that does not need to keep all ensemble members in working memory. At EGU, we will present this variant and how it fits well in HPC environments. An important step in the eWaterCycle project was the coupling between the hydrological and

  10. Stakeholder Application of NOAA/NWS River Forecasts: Oil and Water?

    Science.gov (United States)

    Werner, K.; Averyt, K.; Bardlsey, T.; Owen, G.

    2011-12-01

    The literature strongly suggests that water management seldom uses forecasts for decision making despite the proven skill of the prediction system and the obvious application of these forecasts to mitigate risk. The literature also suggests that forecast usage is motivated most strongly by risk of failure of the water management objectives. In the semi-arid western United States where water demand has grown such that it roughly equals the long term supply, risk of failure has become pervasive. In the Colorado Basin, the US National Weather Service's Colorado Basin River Forecast Center (CBRFC) has partnered with the Western Water Assessment (WWA) and the Climate Assessment for the Southwest (CLIMAS) to develop a toolkit for stakeholder engagement and application of seasonal streamflow predictions. This toolkit has been used to facilitate several meetings both in the Colorado Basin and elsewhere to assess the factors that motivate, deter, and improve the application of forecasts in this region. The toolkit includes idealized (1) scenario exercises where participants are asked to apply forecasts to real world water management problems, (2) web based exercises where participants gain experience with forecasts and other online forecast tools, and (3) surveys that assess respondents' experience with and perceptions of forecasts and climate science. This talk will present preliminary results from this effort as well as how the CBRFC has adopted the results into its stakeholder engagement strategies.

  11. Time to death and the forecasting of macro-level health care expenditures: some further considerations.

    Science.gov (United States)

    van Baal, Pieter H; Wong, Albert

    2012-12-01

    Although the effect of time to death (TTD) on health care expenditures (HCE) has been investigated using individual level data, the most profound implications of TTD have been for the forecasting of macro-level HCE. Here we estimate the TTD model using macro-level data from the Netherlands consisting of mortality rates and age- and gender-specific per capita health expenditures for the years 1981-2007. Forecasts for the years 2008-2020 of this macro-level TTD model were compared to forecasts that excluded TTD. Results revealed that the effect of TTD on HCE in our macro model was similar to those found in micro-econometric studies. As the inclusion of TTD pushed growth rate estimates from unidentified causes upwards, however, the two models' forecasts of HCE for the 2008-2020 were similar. We argue that including TTD, if modeled correctly, does not lower forecasts of HCE.

  12. Introduction of Drought Monitoring and Forecasting System based on Real-time Water Information Using ICT

    Science.gov (United States)

    Lee, Y., II; Kim, H. S.; Chun, G.

    2016-12-01

    There were severe damages such as restriction on water supply caused by continuous drought from 2014 to 2015 in South Korea. Through this drought event, government of South Korea decided to establish National Drought Information Analysis Center in K-water(Korea Water Resources Corporation) and introduce a national drought monitoring and early warning system to mitigate those damages. Drought index such as SPI(Standard Precipitation Index), PDSI(Palmer Drought Severity Index) and SMI(Soil Moisture Index) etc. have been developed and are widely used to provide drought information in many countries. However, drought indexes are not appropriate for drought monitoring and early warning in civilized countries with high population density such as South Korea because it could not consider complicated water supply network. For the national drought monitoring and forecasting of South Korea, `Drought Information Analysis System' (D.I.A.S) which is based on the real time data(storage, flowrate, waterlevel etc.) was developed. Based on its advanced methodology, `DIAS' is changing the paradigm of drought monitoring and early warning systems. Because `D.I.A.S' contains the information of water supply network from water sources to the people across the nation and provides drought information considering the real-time hydrological conditions of each and every water source. For instance, in case the water level of a specific dam declines to predetermined level of caution, `D.I.A.S' will notify people who uses the dam as a source of residential or industrial water. It is expected to provide credible drought monitoring and forecasting information with a strong relationship between drought information and the feelings of people rely on water users by `D.I.A.S'.

  13. Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India

    Science.gov (United States)

    Shah, Reepal; Sahai, Atul Kumar; Mishra, Vimal

    2017-02-01

    Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7-45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error - MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

  14. Post-processing GCM daily rainfall and temperature forecasts for applications in water management and agriculture

    Science.gov (United States)

    Schepen, Andrew; Wang, Qj; Everingham, Yvette; Zhao, Tongtiegang

    2017-04-01

    Ensemble time series forecasts of rainfall and temperature up to six months ahead are sought for applications in water management and agricultural production. Raw GCM forecasts are generally not suitable for direct use in hydrological models or agricultural production simulators and must be post-processed first, to ensure they are reliable, as skilful as possible, and have realistic temporal patterns. In this study, we test two post-processing approaches to produce daily forecasts for cropping regions and water supply catchments in Australia. In the first approach, we apply the calibration, bridging and merging (CBaM) method to produce statistically reliable monthly forecasts based on GCM outputs of rainfall, temperature and sea surface temperatures. We then disaggregate the monthly forecasts to obtain realistic daily time series forecasts that can be used as inputs to crop and hydrological models. In the second approach, we develop a method for directly post-processing daily GCM forecasts using a Bayesian joint probability (BJP) model. We demonstrate and evaluate the two approaches through a case study for the Tully sugar region in north-eastern Australia. The daily post-processed forecasts will benefit applications in streamflow forecasting and crop yield forecasting.

  15. Water Stage Forecasting in Tidal streams during High Water Using EEMD

    Science.gov (United States)

    Chen, Yen-Chang; Kao, Su-Pai; Su, Pei-Yi

    2017-04-01

    There are so many factors may affect the water stages in tidal streams. Not only the ocean wave but also the stream flow affects the water stage in a tidal stream. During high water, two of the most important factors affecting water stages in tidal streams are flood and tide. However the hydrological processes in tidal streams during high water are nonlinear and nonstationary. Generally the conventional methods used for forecasting water stages in tidal streams are very complicated. It explains the accurately forecasting water stages, especially during high water, in tidal streams is always a difficult task. The study makes used of Ensemble Empirical Model Decomposition (EEMD) to analyze the water stages in tidal streams. One of the advantages of the EEMD is it can be used to analyze the nonlinear and nonstationary data. The EEMD divides the water stage into several intrinsic mode functions (IMFs) and a residual; meanwhile, the physical meaning still remains during the process. By comparing the IMF frequency with tidal frequency, it is possible to identify if the IMF is affected by tides. Then the IMFs is separated into two groups, affected by tide or not by tide. The IMFs in each group are assembled to become a factor. Therefore the water stages in tidal streams are only affected by two factors, tidal factor and flood factor. Finally the regression analysis is used to establish the relationship between the factors of the gaging stations in the tidal stream. The available data during 15 typhoon periods of the Tanshui River whose downstream reach is in estuary area is used to illustrate the accuracy and reliability of the proposed method. The results show that the simple but reliable method is capable of forecasting water stages in tidal streams.

  16. Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

    Indian Academy of Sciences (India)

    S K Roy Bhowmik; V R Durai

    2012-04-01

    India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25° × 0.25° utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

  17. Low-Level Turbulence Forecasts From Fine-Scale Models

    Science.gov (United States)

    2014-02-01

    Army Research Laboratory Computational and Information Sciences Directorate Battlefield Environment Division (ATTN: RDRL- CIE -M) White Sands Missile... colors show where the forecast is for LGT and MOD turbulence respectively. By 1800 UTC (figure 31) the boundary has progressed across the entire

  18. 3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The vertical structure of water vapor in atmosphere is one of the initial information of numerical weather forecast model. Because of the strong variation of water vapor in atmosphere and limited spatio-temporal solutions of traditional observation technique, the initial water vapor field of numerical weather forecast model can not accurately be described. At present, using GPS slant observations to study water vapor profile is very popular in the world. Using slant water vapor(SWV) observations from Shanghai GPS network,we diagnose the three-dimensional(3D) water vapor structure over Shanghai area firstly in China. In water vapor tomography, Gauss weighted function is used as horizontal constraint, the output of numerical forecast is used as apriori information, and boundary condition is also considered. For the problem without exact apriori weights for observations, estimation of variance components is introduced firstly in water vapor tomography to determine posteriori weights. Robust estimation is chosen for reducing the effect of blunders on solutions. For the descending characteristic of water vapor with height increasing, non-equal weights are used along vertical direction. Comparisons between tomography results and the profile provided by numerical model (MM5) show that the forecasted moisture fields of MM5 can be improved obviously by GPS slant water vapor. Using GPS slant observations to study 3D structure of atmosphere in near real-time is very important for improving initial water vapor field of short-term weather forecast and enhancing the accuracy of numerical weather forecast.

  19. Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

    Science.gov (United States)

    Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

    2016-12-01

    Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

  20. Short-Term Forecasting of Urban Storm Water Runoff in Real-Time using Extrapolated Radar Rainfall Data

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Rasmussen, Michael R.

    2013-01-01

    Model based short-term forecasting of urban storm water runoff can be applied in realtime control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel...... online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly....... The radar rainfall extrapolation (nowcast) limits the lead time of the system to two hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented....

  1. Application of artificial neural network model for groundwater level forecasting in a river island with artificial influencing factors

    Science.gov (United States)

    Lee, Sanghoon; Yoon, Heesung; Park, Byeong-Hak; Lee, Kang-Kun

    2017-04-01

    Groundwater use has been increased for various purposes like agriculture, industry or drinking water in recent years, the issue related to sustainability on the groundwater use also has been raised. Accordingly, forecasting the groundwater level is of great importance for planning sustainable use of groundwater. In a small island surrounded by the Han River, South Korea, seasonal fluctuation of the groundwater level is characterized by multiple factors such as recharge/discharge event of the Paldang dam, Water Curtain Cultivation (WCC) during the winter season, operation of Groundwater Heat Pump System (GWHP). For a period when the dam operation is only occurred in the study area, a prediction of the groundwater level can be easily achieved by a simple cross-correlation model. However, for a period when the WCC and the GWHP systems are working together, the groundwater level prediction is challenging due to its unpredictable operation of the two systems. This study performed Artificial Neural Network (ANN) model to forecast the groundwater level in the river area reflecting the various predictable/unpredictable factors. For constructing the ANN models, two monitoring wells, YSN1 and YSO8, which are located near the injection and abstraction wells for the GWHP system were selected, respectively. By training with the groundwater level data measured in January 2015 to August 2015, response of groundwater level by each of the surface water level, the WCC and the GWHP system were evaluated. Consequentially, groundwater levels in December 2015 to March 2016 were predicted by ANN models, providing optimal fits in comparison to the observed water levels. This study suggests that the ANN model is a useful tool to forecast the groundwater level in terms of the management of groundwater. Acknowledgement : Financial support was provided by the "R&D Project on Environmental Management of Geologic CO2 Storage" from the KEITI (Project Number: 2014001810003) This research was

  2. Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

    Science.gov (United States)

    Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

    2017-09-01

    In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The

  3. Modelled seasonal forecasts of snow water equivalent and runoff in alpine catchments

    Science.gov (United States)

    Förster, Kristian; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

    2016-04-01

    Seasonal forecasts of water balance components are becoming increasingly important for hydrological applications. These forecasts are typically derived from coupled atmosphere-ocean climate models, which enable physically based seasonal forecasts. In mountainous regions, however, topography is complex whilst typical spatial resolutions of the climate models are still comparably coarse, i.e in the data, ridges and valleys are not represented with sufficient accuracy. Therefore, seasonal predictions of atmospheric variables require consideration of representative gradients. We present first results of seasonal forecasts and re-forecasts processed by the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2). These are prepared for monthly time steps in order to be used for ensemble runs of water balance simulation using the Alpine Water balance And Runoff Estimation model (AWARE). This model has been designed for monthly seasonal predictions in ice- and snowmelt dominated catchments. The study area is the Inn catchment in Tyrol/Austria, including its headwaters in Switzerland. Results are evaluated for both anomalies of meteorological input data (temperature and precipitation), as well as balance components including snow water equivalent and runoff, both simulated with AWARE. Based on model skill evaluations derived from forecasts and observations, the model chain CFSv2 - AWARE proves helpful to analyse possible future hydrological system states of mountainous catchments with emphasis on spatio-temporal snow cover evolution.

  4. Research on Forecasting Water Requirement of Well Irrigation Rice by Time Series Analysis Method

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The paper builds up the forecasting model of air temperature according to the data (1994~1998) of Fu Jin area.At the same time,the writer inquires into the relation of water requirement of well irrigation rice (ET) and average air temperature (T).Furthermore,the rice irrigation water requirement (ET) of Fu Jin area has been forecast in 1999.Thus,we can apply the model in irrigation management.

  5. Sources of seasonal water-supply forecast skill in the western US

    Science.gov (United States)

    Dettinger, Michael

    2007-01-01

    Many water supplies in the western US depend on water that is stored in snowpacks and reservoirs during the cool, wet seasons for release and use in the following warm seasons. Managers of these water supplies must decide each winter how much water will be available in subsequent seasons so that they can proactively capture and store water and can make reliable commitments for later deliveries. Long-lead water-supply forecasts are thus important components of water managers' decisionmaking. Present-day operational water-supply forecasts draw skill from observations of the amount of water in upland snowpacks, along with estimates of the amount of water otherwise available (often via surrogates for antecedent precipitation, soil moisture or baseflows). Occasionally, the historical hydroclimatic influences of various global climate conditions may be factored in to forecasts. The relative contributions of (potential) forecast skill for January-March and April-July seasonal water- supply availability from these sources are mapped across the western US as lag correlations among elements of the inputs and outputs from a physically based, regional land-surface hydrology model of the western US from 1950-1999. Information about snow-water contents is the most valuable predictor for forecasts made through much of the cool-season but, before the snows begin to fall, indices of El Nino-Southern Oscillation are the primary source of whatever meager skill is available. The contributions to forecast skill made available by knowledge of antecedent flows (a traditional predictor) and soil moisture at the time the long-lead forecast is issued are compared, to gain insights into the potential usefulness of new soil-moisture monitoring options in the region. When similar computations are applied to simulated flows under historical conditions, but with a uniform +2°C warming imposed, the widespread diminution of snowpacks reduces forecast skills, although skill contributed by measures

  6. Droughts in the US: Modeling and Forecasting for Agriculture-Water Management and Adaptation

    Science.gov (United States)

    Perveen, S.; Devineni, N.; Lall, U.

    2012-12-01

    been accessed for the agricultural data at the county level. Preliminary analyses show that large parts of Midwest and Southern parts of Florida and California are prone to multiyear droughts. This can primarily be attributed to high agricultural and/or urban water demands coupled with high interannual variability in supply. We propose to develop season-ahead and monthly updated forecasts of the drought index for informing the drought management plans. Given the already customized (sector specific) nature of the proposed drought index and its ability to represent the variability in both supply and demand, the early warning or forecasting of the index would not only complement the drought early warning systems in place by the national integrated drought information system (NIDIS) but also help in prescribing the ameliorative measures for adaptation.

  7. Short-Term Forecasting of Urban Water Consumption Based on the Largest Lyapunov Exponent

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    An approach for short-term forecasting of municipal water consumption was presented based on the largest Lyapunov exponent of chaos theory. The chaotic characteristics of time series of urban water consumption were examined by means of the largest Lyapunov exponent and correlation dimension. By using the largest Lyapunov exponent a short-term forecasting model for urban water consumption was developed, which was compared with the artificial neural network (ANN) approach in a case study. The result indicates that the model based on the largest Lyapunov exponent has higher prediction precision and forecasting stability than the ANN method, and its forecasting mean relative error is 9.6% within its maximum predictable time scale while it is 60.6% beyond the scale.

  8. Expert forecasts and the emergence of water scarcity on public agendas

    Science.gov (United States)

    Graffy, E.A.

    2006-01-01

    Expert forecasts of worldwide water scarcity depict conditions that call for proactive, preventive, coordinated water governance, but they have not been matched by public agendas of commensurate scope and urgency in the United States. This disconnect can not be adequately explained without some attention to attributes of forecasts themselves. I propose that the institutional fragmentation of water expertise and prevailing patterns of communication about water scarcity militate against the formulation of a common public definition of the problem and encourage reliance on unambiguous crises to stimulate social and policy agenda setting. I do not argue that expert forecasts should drive public agendas deterministically, but if their purpose is to help prevent water crises (not just predict them), then a greater effort is needed to overcome the barriers to meaningful public scrutiny of expert claims and evaluation of water strategies presently in place. Copyright ?? 2006 Taylor & Francis Group, LLC.

  9. A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

    Directory of Open Access Journals (Sweden)

    Murat Yalçıntaş

    2015-08-01

    Full Text Available The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul’s water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient technologies in households and reduction in water supply loss through investments on distribution infrastructure.

  10. Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

    Energy Technology Data Exchange (ETDEWEB)

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.; Pianosi, Francesca; Nijssen, B.; Lettenmaier, Dennis P.

    2016-04-12

    In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based on a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.

  11. Operational data assimilation for improving hydrologic, hydrodynamic, and water quality forecasting using open tools

    Science.gov (United States)

    Weerts, Albrecht; Kockx, Arno; Sumihar, Julius; Verlaan, Martin; Hummel, Stef; Kramer, Werner; de Klaermaker, Simone

    2014-05-01

    Data assimilation holds considerable potential for improving water quantity (hydrologic/ hydraulic) and water quality predictions. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. In contrast to most operational weather (related) forecast centers operational hydrologic forecast centers often are unable to support & maintain or lack the required computing support to implement such intensive DA calculations. Moreover, it remains difficult to achieve coupling of models, data, DA techniques and exploitation of high performance computing solutions in the operational forecasting process. Several potential components of a future solution have been or are being developed, one of those being the open source project OpenDA (www.openda.org). The objective of this poster is to highlight the development of OpenDA for operational forecasting and its integration with Delft-FEWS that is being used by more than 40 operational forecast centres around the world. Several applications of OpenDA using open source (and available) model codes from various fields will be highlighted.

  12. Towards a street-level pollen concentration and exposure forecast

    Science.gov (United States)

    van der Molen, Michiel; Krol, Maarten; van Vliet, Arnold; Heuvelink, Gerard

    2015-04-01

    Atmospheric pollen are an increasing source of nuisance for people in industrialised countries and are associated with significant cost of medication and sick leave. Citizen pollen warnings are often based on emission mapping based on local temperature sum approaches or on long-range atmospheric model approaches. In practise, locally observed pollen may originate from both local sources (plants in streets and gardens) and from long-range transport. We argue that making this distinction is relevant because the diurnal and spatial variation in pollen concentrations is much larger for pollen from local sources than for pollen from long-range transport due to boundary layer processes. This may have an important impact on exposure of citizens to pollen and on mitigation strategies. However, little is known about the partitioning of pollen into local and long-range origin categories. Our objective is to study how the concentrations of pollen from different sources vary temporally and spatially, and how the source region influences exposure and mitigation strategies. We built a Hay Fever Forecast system (HFF) based on WRF-chem, Allergieradar.nl, and geo-statistical downscaling techniques. HFF distinguishes between local (individual trees) and regional sources (based on tree distribution maps). We show first results on how the diurnal variation of pollen concentrations depends on source proximity. Ultimately, we will compare the model with local pollen counts, patient nuisance scores and medicine use.

  13. Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

    Science.gov (United States)

    Shukla, Shraddhanand; Arsenault, Kristi R.; Getirana, Augusto; Kumar, Sujay V.; Roningen, Jeanne; Zaitchik, Ben; McNally, Amy; Koster, Randal D.; Peters-Lidard, Christa

    2017-04-01

    LSMs, as it provides model ensemble metrics and the ability to compare against a variety of remotely sensed measurements, like different evapotranspiration (ET) and soil moisture products, and other reanalysis datasets that are available for this region. Comparison of the models' energy and hydrological budgets will be shown for this region (and sub-basin level, e.g., Blue Nile River) and time period (1981-2015), along with evaluating ET, streamflow, groundwater storage and soil moisture, using evaluation metrics (e.g., anomaly correlation, RMSE, etc.). The system uses seasonal climate forecasts from NASA's GMAO (the Goddard Earth Observing System Model, version 5) and NCEP's Climate Forecast System, version 2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region.

  14. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Antonio Candelieri

    2017-03-01

    Full Text Available This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data and individual customer water consumption (AMR data. In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

  15. Study on the non-linear forecast method for water inrush from coal seam floor based on wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    ZHOU Rong-yi; LIU Ai-qun; LI Shu-qing

    2007-01-01

    Directing at the non-linear dynamic characteristics of water inrush from coal seam floor and by the analysis of the shortages of current forecast methods for water inrush from coal seam floor,a new forecast method was raised based on wavelet neural network(WNN)that was a model combining wavelet function with artificiaI neural network.Firstly basic principle of WNN was described.then a forecast model for water inrush from coal seam floor based on WNN was established and analyzed,finally an example of forecasting the quantity of water inrush from coal floor was illustrated to verify the feasibility and superiority of this method.Conclusions show that the forecast result based on WNN is more precise and that using WNN model to forecast the quantity of water inrush from coal seam floor is feasible and practical.

  16. Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

    Directory of Open Access Journals (Sweden)

    H. Sadeghi

    2016-02-01

    costs of water subscribers between 1388 and 1390. In structural time series model, the model was generated by entering the invisibility part of the process and development of a state-space model, as well as using maximum likelihood method and the Kalman-Filter algorithm. Results and Discussion: Given the value of the test statistic ADF, with the exception of changing water use variables with a time difference of the steady rest. Superpopulation different modes of behavior were assessed based on the demand for water. Due to the likelihood ratio statistic is most suitable for the parameters, was diagnosed the steady-state level of randomness and the slope. Price and income elasticities of demand for water, respectively -0.81 and 0.85 shows that water demand is inelastic with respect to price and income and a lot of water is essential. Identify the nature of the request of one of the most important results in estimated water demand in the urban part of the state space time series structure and patterning methods, as an Alternative for variable is Technology preferences use. The model is estimated for the city's water demand time series model, respectively ARMA (3,1. Model performance metrics to compare the structural time series and time series ARMA, the result represents a structural time series model based on the fact that all the performance criteria in this study outperformed the ARMA model to forecast water city demand in the Isfahan. Conclusion: Of a time series model structure to model ARMA in this research is to estimate the model and predict the number the less time is required, and also can be used for modeling of other variables (such as income and price to this is helping to improve the models. Also, in ARMA time series the best model for data was selected according to the Schwarz Bayesian and Akaike criterion. Results indicate that the estimation of water demand using structural time series method is more efficient than when ARMA time series model is applied

  17. A Model of Debris Flow Forecast Based on the Water-Soil Coupling Mechanism

    Institute of Scientific and Technical Information of China (English)

    Shaojie Zhang; Hongjuan Yang; Fangqiang Wei; Yuhong Jiang; Dunlong Liu

    2014-01-01

    Debris flow forecast is an important means of disaster mitigation. However, the accuracy of the statistics-based debris flow forecast is unsatisfied while the mechanism-based forecast is un-available at the watershed scale because most of existing researches on the initiation mechanism of de-bris flow took a single slope as the main object. In order to solve this problem, this paper developed a model of debris flow forecast based on the water-soil coupling mechanism at the watershed scale. In this model, the runoff and the instable soil caused by the rainfall in a watershed is estimated by the distrib-uted hydrological model (GBHM) and an instable identification model of the unsaturated soil. Because the debris flow is a special fluid composed of soil and water and has a bigger density, the density esti-mated by the runoff and instable soil mass in a watershed under the action of a rainfall is employed as a key factor to identify the formation probability of debris flow in the forecast model. The Jiangjia Gulley, a typical debris flow valley with a several debris flow events each year, is selected as a case study wa-tershed to test this forecast model of debris flow. According the observation data of Dongchuan Debris Flow Observation and Research Station, CAS located in Jiangjia Gulley, there were 4 debris flow events in 2006. The test results show that the accuracy of the model is satisfied.

  18. Feasibility of large-scale water monitoring and forecasting in the Asia-Pacific region

    Science.gov (United States)

    van Dijk, A. I. J. M.; Peña-Arancibia, J. L.; Sardella, C. S. E.

    2012-04-01

    The Asian-Pacific region (including China, India and Pakistan) is home to 51% of the global population. It accounts for 53% of agricultural and 32% of domestic water use world wide. Due to the influence of Pacific Ocean and Indian Ocean circulation patterns, the region experiences strong inter-annual variations in water availability and occurrence of drought, flood and severe weather. Some of the countries in the region have national water monitoring or forecasting systems, but they are typically of fairly narrow scope. We investigated the feasibility and utility of an integrated regional water monitoring and forecasting system for water resources, floods and drought. In particular, we assessed the quality of information that can be achieved by relying on internationally available data sources, including numerical weather prediction (NWP) and satellite observations of precipitation, soil moisture and vegetation. Combining these data sources with a large scale hydrological model, we produced monitoring and forecast information for selected retrospective case studies. The information was compared to that from national systems, both in terms of information content and system characteristics (e.g. scope, data sources, and information latency). While national systems typically have better access to national observation systems, they do not always make effective use of the available data, science and technology. The relatively slow changing nature of important Pacific and Indian Ocean circulation patterns adds meaningful seasonal forecast skill for some regions. Satellite and NWP precipitation estimates can add considerable value to the national gauge networks: as forecasts, as near-real time observations and as historic reference data. Satellite observations of soil moisture and vegetation are valuable for drought monitoring and underutilised. Overall, we identify several important opportunities for better water monitoring and forecasting in the Asia-Pacific region.

  19. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  20. Real-time short-term forecast of water inflow into Bureyskaya reservoir

    Science.gov (United States)

    Motovilov, Yury

    2017-04-01

    During several recent years, a methodology for operational optimization in hydrosystems including forecasts of the hydrological situation has been developed on example of Burea reservoir. The forecasts accuracy improvement of the water inflow into the reservoir during planning of water and energy regime was one of the main goals for implemented research. Burea river is the second left largest Amur tributary after Zeya river with its 70.7 thousand square kilometers watershed and 723 km-long river course. A variety of natural conditions - from plains in the southern part to northern mountainous areas determine a significant spatio-temporal variability in runoff generation patterns and river regime. Bureyskaya hydropower plant (HPP) with watershed area 65.2 thousand square kilometers is a key station in the Russian Far Eastern energy system providing its reliable operation. With a spacious reservoir, Bureyskaya HPP makes a significant contribution to the protection of the Amur region from catastrophic floods. A physically-based distributed model of runoff generation based on the ECOMAG (ECOlogical Model for Applied Geophysics) hydrological modeling platform has been developed for the Burea River basin. The model describes processes of interception of rainfall/snowfall by the canopy, snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface, ground and river flow. The governing model's equations are derived from integration of the basic hydro- and thermodynamics equations of water and heat vertical transfer in snowpack, frozen/unfrozen soil, horizontal water flow under and over catchment slopes, etc. The model setup for Bureya river basin included watershed and river network schematization with GIS module by DEM analysis, meteorological time-series preparation, model calibration and validation against historical observations. The results showed good

  1. Estimating Water Levels with Google Earth Engine

    Science.gov (United States)

    Lucero, E.; Russo, T. A.; Zentner, M.; May, J.; Nguy-Robertson, A. L.

    2016-12-01

    Reservoirs serve multiple functions and are vital for storage, electricity generation, and flood control. For many areas, traditional ground-based reservoir measurements may not be available or data dissemination may be problematic. Consistent monitoring of reservoir levels in data-poor areas can be achieved through remote sensing, providing information to researchers and the international community. Estimates of trends and relative reservoir volume can be used to identify water supply vulnerability, anticipate low power generation, and predict flood risk. Image processing with automated cloud computing provides opportunities to study multiple geographic areas in near real-time. We demonstrate the prediction capability of a cloud environment for identifying water trends at reservoirs in the US, and then apply the method to data-poor areas in North Korea, Iran, Azerbaijan, Zambia, and India. The Google Earth Engine cloud platform hosts remote sensing data and can be used to automate reservoir level estimation with multispectral imagery. We combine automated cloud-based analysis from Landsat image classification to identify reservoir surface area trends and radar altimetry to identify reservoir level trends. The study estimates water level trends using three years of data from four domestic reservoirs to validate the remote sensing method, and five foreign reservoirs to demonstrate the method application. We report correlations between ground-based reservoir level measurements in the US and our remote sensing methods, and correlations between the cloud analysis and altimetry data for reservoirs in data-poor areas. The availability of regular satellite imagery and an automated, near real-time application method provides the necessary datasets for further temporal analysis, reservoir modeling, and flood forecasting. All statements of fact, analysis, or opinion are those of the author and do not reflect the official policy or position of the Department of Defense or any

  2. Water quality forecasting at Gongju station in Geum River using neural network model

    Energy Technology Data Exchange (ETDEWEB)

    Ahn, Sang-Jin; Yeon, In-Sung [Chungbuk National University, Cheongju(Korea); Han, Yang-Su [Kyungdong University, Sokcho(Korea); Lee, Jae-Kyung [Daewon Science College, Jecheon(Korea)

    2001-12-31

    Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested. (author). 14 refs., 4 tabs., 8 figs.

  3. Inter-basin water transfer-supply model and risk analysis with consideration of rainfall forecast information

    Institute of Scientific and Technical Information of China (English)

    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.

  4. Coastal sea level variability in the US West Coast Ocean Forecast System (WCOFS)

    Science.gov (United States)

    Kurapov, Alexander L.; Erofeeva, Svetlana Y.; Myers, Edward

    2017-01-01

    Sea level variability along the US West Coast is analyzed using multi-year time series records from tide gauges and a high-resolution regional ocean model, the base of the West Coast Ocean Forecast System (WCOFS). One of the metrics utilized is the frequency of occurrences when model prediction is within 0.15 m from the observed sea level, F. A target level of F = 90% is set by an operational agency. A combination of the tidal sea level from a shallow water inverse model, inverted barometer (IB) term computed using surface air pressure from a mesoscale atmospheric model, and low-pass filtered sea level from WCOFS representing the effect of coastal ocean dynamics (DYN) provides the most straightforward approach to reaching levels F>80%. The IB and DYN components each add between 5 and 15% to F. Given the importance of the DYN term bringing F closer to the operational requirement and its role as an indicator of the coastal ocean processes on scales from days to interannual, additional verification of the WCOFS subtidal sea level is provided in terms of the model-data correlation, standard deviation of the band-pass filtered (2-60 days) time series, the annual cycle amplitude, and alongshore sea level coherence in the range of 5-120-day periods. Model-data correlation in sea level increases from south to north along the US coast. The rms amplitude of model sea level variability in the 2-60-day band and its annual amplitude are weaker than observed north of 42 N, in the Pacific Northwest (PNW) coast region. The alongshore coherence amplitude and phase patterns are similar in the model and observations. Availability of the multi-year model solution allows computation and analysis of spatial maps of the coherence amplitude. For a reference location in the Southern California Bight, relatively short-period sea level motions (near 10 days) are incoherent with those north of the Santa Barbara Channel (in part, due to coastal trapped wave scattering and/or dissipation). At a

  5. A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water

    OpenAIRE

    2013-01-01

    Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA) and Artificial Neural Network (ANN) model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and r...

  6. Water quality forecast through application of BP neural network at Yuqiao reservoir

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, the model adopts LM (Levenberg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

  7. Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting

    Science.gov (United States)

    Rezaie-balf, Mohammad; Naganna, Sujay Raghavendra; Ghaemi, Alireza; Deka, Paresh Chandra

    2017-10-01

    In this study, two different machine learning models, Multivariate Adaptive Regression Splines (MARS) and M5 Model Trees (MT) have been applied to simulate the groundwater level (GWL) fluctuations of three shallow open wells within diverse unconfined aquifers. The Wavelet coupled MARS and MT hybrid models were developed in an attempt to further increase the GWL forecast accuracy. The Discrete Wavelet Transform (DWT) which is particularly effective in dealing with non-stationary time-series data was employed to decompose the input time series into various sub-series components. Historical data of 10 years (August-1996 to July-2006) comprising monthly groundwater level, rainfall, and temperature were used to calibrate and validate the models. The models were calibrated and tested for one, three and six months ahead forecast horizons. The wavelet coupled MARS and MT models were compared with their simple counterpart using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Normalized Nash-Sutcliffe Efficiency (NNSE) and Coefficient of Determination (R2) . The wavelet coupled MARS and MT models developed using multi-scale input data performed better compared to their simple counterpart and the forecast accuracy of W-MARS models were superior to that of W-MT models. Specifically, the DWT offered a better discrimination of non-linear and non-stationary trends that were present at various scales in the time series of the input variables thus crafting the W-MARS models to provide more accurate GWL forecasts.

  8. MOCASSIM - an operational forecast system for the Portuguese coastal waters.

    Science.gov (United States)

    Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

    2003-04-01

    An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

  9. Risk-based decision making in water management using probabilistic forecasts: results from a game experiment

    Science.gov (United States)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; van Andel, Schalk-Jan; Wood, Andy

    2014-05-01

    Probabilistic streamflow forecasts have been increasingly used or requested by practitioners in the operation of multipurpose water reservoirs. They usually integrate hydrologic inflow forecasts to their operational management rules to optimize water allocation or its economic value, to mitigate droughts, for flood and ecological control, among others. In this paper, we present an experiment conducted to investigate the use of probabilistic forecasts to make decisions on water reservoir outflows. The experiment was set up as a risk-based decision-making game. In the game, each participant acted as a water manager. A sequence of probabilistic inflow forecasts was presented to be used to make a reservoir release decision at a monthly time step, subject to a few constraints. After each decision, the actual inflow was presented and the consequences of the decisions made were discussed. Results from the application of the game to different groups of scientists and operational managers during conferences and meetings in 2013 (a total of about 150 participants) illustrate the different strategies adopted by the players. This game experiment allowed participants to experience first hand the challenges of probabilistic, quantitative decision-making.

  10. HYDROLOGICAL FORECASTS OF DANUBE FLOOD 2013 BY THE HUNGARIAN HYDROLOGICAL FORECASTING SERVICE

    Directory of Open Access Journals (Sweden)

    A. CSÍK

    2014-10-01

    Full Text Available The significant lead time resulting from the use of the OLSER system of the Hungarian Hydrological Forecasting Service is of key importance in making timely preparations for flood defence. Due to continuous improvements to the quantitative meteorological forecast models (primarily the generally used ECMWF model and the OLSER system over the past years, we have by now reached a point where the previously separately managed flood peak forecasting and continuous forecasting can no longer be interpreted independently. Continuous forecasting taking into account precipitation forecasts and monitoring spatial changes of the complex physics-based concentration process also offers a level of accuracy suitable to identify peak values. The flood wave of June 2013 along the Hungarian Danube section exceeded the ever observed highest high water levels everywhere (except for gauge Mohács. The forecasts prepared by HHFS played a crucial role both in terms of lead time and the forecasted water levels.

  11. Neural network forecasting model based on phase space re-construction in water yield of mine

    Institute of Scientific and Technical Information of China (English)

    LIU Wei-lin; DONG Zeng-chuan; CHEN Nan-xiang; CAO Lian-hai

    2007-01-01

    The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example,the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.

  12. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuatio

  13. Anticipatory water management: using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuatio

  14. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuatio

  15. Anticipatory water management: using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuatio

  16. Anticipatory water management: using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring

  17. Anticipatory Water Management: Using ensemble weather forecasts for critical events

    NARCIS (Netherlands)

    Van Andel, S.J.

    2009-01-01

    Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring

  18. From Potential to Practice: How Weather and Climate Forecasts Can Be Effectively Used in Water Resources Management Decision Making

    Science.gov (United States)

    Shafiee-Jood, M.; Cai, X.

    2015-12-01

    The last decade has witnessed tremendous scientific and technological advances in our ability to forecast climate variability and extremes, which are potentially useful to help operate and manage water resources systems with larger reliability and efficiency. However, many forecasts are rarely effectively used in practice and there is little evidence of incorporating them in real-world decision making. One of the main barriers of the uptake of forecasts, which is often cited by studies, is related to forecast uncertainty; however, even reliable forecasts alone are not sufficient to ensure the expected response. There exist other barriers that affect effective use of forecasts such as water managers' behavior and institutional impediments. In fact, water managers make decisions in a sophisticated setting, which is on one hand affected by uncertainty and on the other hand constrained by regulations and policies. Therefore, it is not only important to recognize the various key individual challenges, but also critical to understand the interdependencies among them (Figure 1) in order to properly address the effective use of forecasts. This understanding is also essential to assess the expected value of forecasts information which is of high importance for decision makers prior to incorporating forecasts. The main objectives of this talk, which builds upon an extensive literature review of using forecasts in water resources and agricultural decision making, are to 1) address the key challenges limiting the uptake of forecast, 2) highlight the interdependency among different factors, and 3) shed light on how these insights can help improve the use of forecast in real-world practices.

  19. Densified GPS Estimates of Integrated Precipitable Water Vapor Improve Weather Forecasting during the North American Monsoon

    Science.gov (United States)

    Moore, A. W.; Small, I.; Gutman, S. I.; Bock, Y.; Dumas, J.; Haase, J. S.; Laber, J. L.

    2013-12-01

    Continuous GPS (CGPS) stations for observing crustal motion in the western U.S. now number more than 1200, with over 500 of them operating in real time. Tropospheric wet delay from real-time processing of the GPS data, along with co-located or nearby surface and temperature measurements, are being operationally converted to Integrated Precipitable Water Vapor (IPW) for evaluation as a forecasting tool (Gutman, 2011). The available density of real-time GPS in southern California now allows us to explore usage of densified GPS IPW in operational weather forecasting during weather conditions involving moisture extremes. Under a NASA Advanced Information Systems Technology (AIST) project, 27 southern California stations have been added to the NOAA GPS-Met observing network providing 30-minute estimates of IPW for ingestion into operational NOAA weather models, as well as for direct use by National Weather Service forecasters in monitoring developing weather conditions. The densified network proved advantageous in the 2013 North American Monsoon season, allowing forecasters to visualize rapid moisture increases at intervals between model runs and radiosonde observations and assisting in flood watch/warning decisions. We discuss the observed relationship between IPW and onset of precipitation in monsoon events in southern California and possibilities for additional decision support tools for forecasters.

  20. Forecast-Based Operations Support Tool for the New York City Water Supply System

    Science.gov (United States)

    Pyke, G.; Porter, J.

    2012-12-01

    The NYC water supply system serves 9 million people with over 1 BGD of water drawn from 19 reservoirs. To support operation of the system to meet multiple objectives (e.g. supply reliability, water quality, environmental releases, hydropower, peak flow mitigation), the New York City Department of Environmental Protection (DEP) is developing an Operations Support Tool (OST), a forecast-based decision support system that provides a probabilistic foundation for water supply operations and planning. Key features of OST include: the ability to run both long-term simulations and short-term probabilistic simulations on the same model platform; automated processing of near-real-time (NRT) data sources; use of inflow forecasts to support look-ahead operational simulations; and water supply-water quality model linkage to account for feedback and tradeoffs between supply and quality objectives. OST supports two types of simulations. Long-term runs execute the system model over an extended historical record and are used to evaluate reservoir operating rules, infrastructure modifications, and climate change scenarios (with inflows derived from downscaled GCM data). Short-term runs for operational guidance consist of multiple (e.g. 80+) short (e.g. one year) runs, all starting from the same initial conditions (typically those of the current day). Ensemble reservoir inflow forecast traces are used to drive the model for the duration of the simulation period. The result of these runs is a distribution of potential future system states. DEP managers analyze the distributions for alternate scenarios and make operations decisions using risk-based metrics such as probability of refill or the likelihood of a water quality event. For operational simulations, the OST data system acquires NRT data from DEP internal sources (SCADA operations data, keypoint water quality, in-stream/in-reservoir water quality, meteorological and snowpack monitoring sites). OST acquires streamflow data from

  1. Forecasting surface water flooding hazard and impact in real-time

    Science.gov (United States)

    Cole, Steven J.; Moore, Robert J.; Wells, Steven C.

    2016-04-01

    Across the world, there is increasing demand for more robust and timely forecast and alert information on Surface Water Flooding (SWF). Within a UK context, the government Pitt Review into the Summer 2007 floods provided recommendations and impetus to improve the understanding of SWF risk for both off-line design and real-time forecasting and warning. Ongoing development and trial of an end-to-end real-time SWF system is being progressed through the recently formed Natural Hazards Partnership (NHP) with delivery to the Flood Forecasting Centre (FFC) providing coverage over England & Wales. The NHP is a unique forum that aims to deliver coordinated assessments, research and advice on natural hazards for governments and resilience communities across the UK. Within the NHP, a real-time Hazard Impact Model (HIM) framework has been developed that includes SWF as one of three hazards chosen for initial trialling. The trial SWF HIM system uses dynamic gridded surface-runoff estimates from the Grid-to-Grid (G2G) hydrological model to estimate the SWF hazard. National datasets on population, infrastructure, property and transport are available to assess impact severity for a given rarity of SWF hazard. Whilst the SWF hazard footprint is calculated in real-time using 1, 3 and 6 hour accumulations of G2G surface runoff on a 1 km grid, it has been possible to associate these with the effective rainfall design profiles (at 250m resolution) used as input to a detailed flood inundation model (JFlow+) run offline to produce hazard information resolved to 2m resolution. This information is contained in the updated Flood Map for Surface Water (uFMfSW) held by the Environment Agency. The national impact datasets can then be used with the uFMfSW SWF hazard dataset to assess impacts at this scale and severity levels of potential impact assigned at 1km and for aggregated county areas in real-time. The impact component is being led by the Health and Safety Laboratory (HSL) within the NHP

  2. A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2013-06-01

    Full Text Available Forecasting water quality is always an effective approach for water environmental management. This study presents a combined Wavelet transform (WA and Artificial Neural Network (ANN model for monthly ammonia nitrogen series prediction in river water. The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series. Compared to the traditional ANN, the WA-ANN models were found more accurate and reliable. The results of the study indicate that WA could remove the noise of the original datasets and the WA-ANN could help environment decision-maker manage water quality more effective.

  3. Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

    Science.gov (United States)

    Anghileri, D.; Voisin, N.; Castelletti, A.; Pianosi, F.; Nijssen, B.; Lettenmaier, D. P.

    2016-06-01

    We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20-60% of the total forecast value.

  4. Remote Sensing of Snow as a Tool to Forecast Water Shortage in the Argentinian Dry Andes

    Science.gov (United States)

    Delbart, Nicolas; Dunesme, Samuel; Lavie, Emilie; Madelin, Malika

    2016-08-01

    In the Argentinian Dry Andes the annual snow melt is the main source of superficial water and aquifer recharge, essential for the population of the oases. Interannual variability in the snow cover in the Andes mountains causes variability in the water volumes available. In this study we analyze the errors of a water discharge forecast method based on the MODIS MOD10A2 snow cover product, with regards to the mass anomalies estimated by GRACE satellite at the scale of four watersheds.Because the high-water period (September-April) discharge is directly related to the snow extent at the beginning of the snowmelt period, i.e. in September and October, we use MOD10A2 images to forecast the average high water season discharge. Despite an average uncertainty of 15%, uncertainty peaks to about 50% in several years. Comparison with mass anomalies retrieved GRACE satellite data suggests that overestimation of our forecast method comes from snowbed thickness interannual variations.

  5. A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

    Directory of Open Access Journals (Sweden)

    Francesca Gagliardi

    2017-07-01

    Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

  6. Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data.

    Science.gov (United States)

    Parker, Jason; Cuthbertson, Courtney; Loveridge, Scott; Skidmore, Mark; Dyar, Will

    2017-04-15

    Vital statistics on the number of, alcohol-induced death (AICD) drug-induced death (DICD), and suicides at the local-level are only available after a substantial lag of up to two years after the events occur. We (1) investigate how well Google Trends search data explain variation in state-level rates in the US, and (2) use this method to forecast these rates of death for 2015 as official data are not yet available. We tested the degree to which Google Trends data on 27 terms can be fit to CDC data using L1-regularization on AICD, DICD, and suicide. Using Google Trends data, we forecast 2015 AICD, DICD, and suicide rates. L1-regularization fit the pre-2015 data much better than the alternative model using state-level unemployment and income variables. Google Trends data account for substantial variation in growth of state-level rates of death: 30.9% for AICD, 23.9% for DICD, and 21.8% for suicide rates. Every state except Hawaii is forecasted to increase in all three of these rates in 2015. The model predicts state, not local or individual behavior, and is dependent on continued availability of Google Trends data. The method predicts state-level AICD, DICD, and suicide rates better than the alternative model. The study findings suggest that this methodology can be developed into a public health surveillance system for behavioral health-related causes of death. State-level predictions could be used to inform state interventions aimed at reducing AICD, DICD, and suicide. Copyright © 2017. Published by Elsevier B.V.

  7. Forecasting hot water consumption in dwellings using artificial neural networks

    OpenAIRE

    Gelazanskas, Linas; Gamage, Kelum

    2015-01-01

    The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Ar...

  8. 基于时间序列法的北京市需水量预报%Water demand forecasting of Beijing using the Time Series Forecasting Method

    Institute of Scientific and Technical Information of China (English)

    ZHAI Yuanzheng; WANG Jinsheng; TENG Yanguo; ZUO Rui

    2012-01-01

    @@%It is essential to establish the water resources exploitation and utilization planning,which is mainly based on recognizing and forecasting the water consumed structure rationally and scientifically.During the past 30 years (1980-2009),mean annual precipitation and total water resource of Beijing have decreased by 6.89% and 31.37% compared with those perennial values,respectively,while total water consumption during the same period reached pinnacle historically.Accordingly,it is of great significance for the harmony between socio-economic development and environmental development.Based on analyzing total water consumption,agricultural,industrial,domestic and environmental water consumption,and evolution of water consumed structure,further driving forces of evolution of total water consumption and water consumed structure are revealed systematically.Prediction and discussion are achieved for evolution of total water consumption,water consumed structure,and supply-demand situation of water resource in the near future of Beijing using Time Series Forecasting Method.The purpose of the endeavor of this paper is to provide scientific basis for the harmonious development between socio-economy and water resources,for the establishment of rational strategic planning of water resources,and for the social sustainable development of Beijing with scientific bases.

  9. Solar radiation and water vapor pressure to forecast chickenpox epidemics.

    Science.gov (United States)

    Hervás, D; Hervás-Masip, J; Nicolau, A; Reina, J; Hervás, J A

    2015-03-01

    The clear seasonality of varicella infections in temperate regions suggests the influence of meteorologic conditions. However, there are very few data on this association. The aim of this study was to determine the seasonal pattern of varicella infections on the Mediterranean island of Mallorca (Spain), and its association with meteorologic conditions and schooling. Data on the number of cases of varicella were obtained from the Network of Epidemiologic Surveillance, which is composed of primary care physicians who notify varicella cases on a compulsory basis. From 1995 to 2012, varicella cases were correlated to temperature, humidity, rainfall, water vapor pressure, atmospheric pressure, wind speed, and solar radiation using regression and time-series models. The influence of schooling was also analyzed. A total of 68,379 cases of varicella were notified during the study period. Cases occurred all year round, with a peak incidence in June. Varicella cases increased with the decrease in water vapor pressure and/or the increase of solar radiation, 3 and 4 weeks prior to reporting, respectively. An inverse association was also observed between varicella cases and school holidays. Using these variables, the best fitting autoregressive moving average with exogenous variables (ARMAX) model could predict 95 % of varicella cases. In conclusion, varicella in our region had a clear seasonality, which was mainly determined by solar radiation and water vapor pressure.

  10. Season-ahead streamflow forecast informed tax strategies for semi-arid water rights markets

    Science.gov (United States)

    Delorit, J. D.; Block, P. J.

    2016-12-01

    In many semi-arid regions multisectoral demands stress available water supplies. The Elqui River valley of north central Chile, which draws on limited capacity reservoirs supplied largely by annually variable snowmelt, is one of these cases. This variability forces water managers to develop demand-based allocation strategies which have typically resulted in water right volume reductions, applied equally per right. Compounding this issue is often deferred or delayed infrastructure investments, which has been linked Chile's Coasian approach to water markets, under which rights holders do not pay direct procurement costs, non-use fees, nor taxes. Here we build upon our previous research using forecasts of likely water rights reductions, informed by season-ahead prediction models of October-January (austral growing season) streamflow, to construct annual, forecast-sensitive, per right tax. We believe this tax, to be borne by right holders, will improve the beneficial use of water resources by stimulating water rights trading and improving system efficiency by generating funds for infrastructure investment, thereby reducing free-ridership and conflict between rights holders. Research outputs will include sectoral per right tax assessments, tax revenue generation, Elqui River valley economic output, and water rights trading activity.

  11. АSSESSMENT AND FORECASTING OF FLIGHT SAFETY LEVEL OF AIRLINE

    Directory of Open Access Journals (Sweden)

    E. S. Prozorov

    2015-01-01

    Full Text Available The article presents methods based on probability theory and mathematical statistics for solving a number of basic problems: formation and evaluation of the current flight safety level; forecasting the level of flight safety; ranking the objects (planes, pilots in terms of flight safety; evaluation of the presence (or absence of control actions arising in the context of the organization of corporate safety management system. At the same time as the main source of information are considered forward-looking events received from flight data.

  12. Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach

    DEFF Research Database (Denmark)

    Boldrini, Lorenzo

    In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based......) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature...

  13. Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

    Directory of Open Access Journals (Sweden)

    N. Sujay Raghavendra

    2015-12-01

    Full Text Available This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of both the SVR and WP–SVR models is assessed using different statistical indices. From the comparative result analysis of the developed models, it can be seen that WP–SVR model outperforms the classic SVR model in predicting groundwater levels at all the three well locations (e.g. NRMSE(WP–SVR = 7.14, NRMSE(SVR = 12.27; NSE(WP–SVR = 0.91, NSE(SVR = 0.8 during the test phase with respect to well location at Surathkal. Therefore, using the WP–SVR model is highly acceptable for modeling and forecasting of groundwater level fluctuations.

  14. Forecasting Models for Some Water Quality Parameters of Shatt Al-Hilla River, Iraq

    Directory of Open Access Journals (Sweden)

    Rafa H. Al-Suhili

    2017-07-01

    Full Text Available This paper provides Artificial Neural Networks model versions for forecasting the monthly averages of some chemical water quality parameters of Shatt Al-Hilla River, which is located at Hilla City, south of Iraq. The water quality parameters investigated were Sulphate, Magnesium, Calcium, Alkalinity, and Total Hardness. Results indicate that for Sulphate and Calcium high correlation coefficients models were observed to be (0.9 and 0.88, while for Magnesium, Alkalinity and Hardness low correlation coefficients model were observed to be (0.48,0.58, and 0.51 respectively. Serial correlation behavior of these variables indicate at that high lag time correlations sequences are observed for the first two variables and low ones for the last three water quality parameters. A serial correlation coefficient analysis was done and indicates that as the variable exhibited weak lag correlation structure, then a successful ANN forecasting model could not be obtained even if many trials were done to enhance it's performance, such as increasing the number of nodes, the lagged input variables, and/or changing the learning rate and the momentum term values, or the use of different types of activation functions. On the other hand, those variables that have a strong lag correlation structure can easily fit successful ANN forecasting models

  15. Forecasting water disaster for a coal mine under the Xiaolangdi reservoir

    Institute of Scientific and Technical Information of China (English)

    SUN Ya-jun; XU Zhi-min; DONG Qing-hong; LIU Sheng-dong; GAO Rong-bin; JIANG Yu-hai

    2008-01-01

    Xin'an coal mine, Henan Province, faces the risk of water inrush because 40% of the area of the coal mine is under the surface water of the Xiaolangdi reservoir. To forecast water disaster, an effective aquifuge and a limit of water infiltration were determined by rock-phase analysis and long term observations of surface water and groundwater. By field monitoring, as well as physical and numerical simulation experiments, we obtained data reflecting different heights of a water flow fractured zone (WFFZ)under different mining conditions, derived a formula to calculate this height and built a forecasting model with the aid of GIS. On the basis of these activities, the coal mine area was classified into three sub-areas with different potential of water inrush. In the end,our research results have been applied in and verified by industrial mining experiments at three working faces and we were able to present a successful example of coal mining under a large reservoir.

  16. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

    Directory of Open Access Journals (Sweden)

    Francisco S. de Albuquerque Filho

    2013-01-01

    Full Text Available This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

  17. Reasonable Ball Size of Ball Mill for Preparing Coal Water Fuel and Forecasting Productive Capacity

    Institute of Scientific and Technical Information of China (English)

    张荣曾; 刘炯天; 徐志强; 郑明

    2002-01-01

    By using the matrix theory, a 5-parameter grinding mathema tical model is established. Based on the properties of feed coal and requirement s for size distribution of final product, the model gives the required grinding probability for various particles and corresponding ball size distribution. By u sing this model, 3 different sizes of ball mill are designed and put into commer cial use for coal water fuel. The forecasted ball mill capacity, the particle si zes and particle size distribution as well as the coal water fuel quality parame ters are all in line with industrial operation results, which have proved the su itability of the model.

  18. Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting

    CERN Document Server

    Msiza, Ishmael S; Nelwamondo, Fulufhelo Vincent

    2007-01-01

    Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.

  19. Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging

    Directory of Open Access Journals (Sweden)

    Che-Jung Chang

    2013-01-01

    Full Text Available The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1 grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

  20. Hydrodynamics and Water Quality forecasting over a Cloud Computing environment: INDIGO-DataCloud

    Science.gov (United States)

    Aguilar Gómez, Fernando; de Lucas, Jesús Marco; García, Daniel; Monteoliva, Agustín

    2017-04-01

    Algae Bloom due to eutrophication is an extended problem for water reservoirs and lakes that impacts directly in water quality. It can create a dead zone that lacks enough oxygen to support life and it can also be human harmful, so it must be controlled in water masses for supplying, bathing or other uses. Hydrodynamic and Water Quality modelling can contribute to forecast the status of the water system in order to alert authorities before an algae bloom event occurs. It can be used to predict scenarios and find solutions to reduce the harmful impact of the blooms. High resolution models need to process a big amount of data using a robust enough computing infrastructure. INDIGO-DataCloud (https://www.indigo-datacloud.eu/) is an European Commission funded project that aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. The project addresses the development of solutions for different Case Studies using different Cloud-based alternatives. In the first INDIGO software release, a set of components are ready to manage the deployment of services to perform N number of Delft3D simulations (for calibrating or scenario definition) over a Cloud Computing environment, using the Docker technology: TOSCA requirement description, Docker repository, Orchestrator, AAI (Authorization, Authentication) and OneData (Distributed Storage System). Moreover, the Future Gateway portal based on Liferay, provides an user-friendly interface where the user can configure the simulations. Due to the data approach of INDIGO, the developed solutions can contribute to manage the full data life cycle of a project, thanks to different tools to manage datasets or even metadata. Furthermore, the cloud environment contributes to provide a dynamic, scalable and easy-to-use framework for non-IT experts users. This framework is potentially capable to automatize the processing of

  1. Assessment of reservoir system variable forecasts

    Science.gov (United States)

    Kistenmacher, Martin; Georgakakos, Aris P.

    2015-05-01

    Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

  2. Forecasting Method for Urban Rail Transit Ridership at Station Level Using Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Junfang Li

    2016-01-01

    Full Text Available Direct forecasting method for Urban Rail Transit (URT ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs, maximum relative error (MRE, smallest relative error (SRE, average relative error (ARE, and mean square root of relative error (MSRRE. Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.

  3. nowCOAST's Map Service for NOAA NOS Lake Erie Operational Forecast System (LEOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map displays the latest nowcasts and forecast guidance of water temperature, water currents, and water level guidance...

  4. nowCOAST's Map Service for NOAA NOS Lake Erie Operational Forecast System (LEOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map displays the latest nowcasts and forecast guidance of water temperature, water currents, and water level guidance...

  5. How seasonal forecast could help a decision maker: an example of climate service for water resource management

    Science.gov (United States)

    Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

    2016-04-01

    The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

  6. Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

    Science.gov (United States)

    Goodman, Brian M.; Diak, George R.; Mills, Graham A.

    1986-01-01

    A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

  7. Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

    Science.gov (United States)

    Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

    2015-12-01

    Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

  8. Forecasting recreational water quality standard violations with a linked hydrologic-hydronamic modeling system

    Science.gov (United States)

    Gronewold, A. D.; Ritzenthaler, A.; Fry, L. M.; Anderson, E. J.

    2012-12-01

    There is a clear need in the water resource and public health management communities to develop and test modeling systems which provide robust predictions of water quality and water quality standard violations, particularly in coastal communities. These predictions have the potential to supplement, or even replace, conventional human health protection strategies which (in the case of controlling public access to beaches, for example) are often based on day-old fecal indicator bacteria monitoring results. Here, we present a coupled modeling system which builds upon recent advancements in watershed-scale hydrological modeling and coastal hydrodynamic modeling, including the evolution of the Huron-Erie Connecting Waterways Forecasting System (HECWFS), developed through a partnership between NOAA's Great Lakes Environmental Research Laboratory (GLERL) and the University of Michigan Cooperative Institute for Limnology and Ecosystems Research (CILER). Our study is based on applying the modeling system to a popular beach in the metro-Detroit (Michigan, USA) area and implementing a routine shoreline monitoring program to help assess model forecasting skill. This research presents an important stepping stone towards the application of similar modeling systems in frequently-closed beaches throughout the Great Lakes region.

  9. Development of Mechanical Water Level Controller

    OpenAIRE

    Akonyi Nasiru Sule; Chinedu Cletus Obinwa; Christian Ebele Okekeze; Eyo Ifreke

    2012-01-01

    The automatic water level controller is a device designed to regulate automatically the pumping of water to an overhead tank without allowing the water in the tank to be exhausted. The design of this mechanical device was achieved using the Archimedes principle of floatation; having a float which determines the water level in the tank depending on the choice of the minimum (lower) and maximum (upper) level inscribed in the tank. The fundamental attribute of this device is the ease in design, ...

  10. Requirements and benefits of flow forecasting for improving hydropower generation

    OpenAIRE

    Dong, Xiaohua; Vrijling, J. K.; Dohmen-Janssen, Catarine M.; Ruigh, E.; Booij, Martijn J.; Stalenberg, B.; Hulscher, Suzanne J.M.H.; Van Gelder, P.H.A.J.M.; Verlaan, M.; Zijderveld, A; Waarts, P.

    2005-01-01

    This paper presents a methodology to identify the required lead time and accuracy of flow forecasting for improving hydropower generation of a reservoir, by simulating the benefits (in terms of electricity generated) obtained from the forecasting with varying lead times and accuracies. The benefit-lead time relationship was investigated only for perfect inflow forecasts, with a few selected forecasting lead times: 4, 10 days and 1 year. The water level and the release from the reservoir were ...

  11. Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

    Science.gov (United States)

    Blankenship, Clay; Zavodsky, Bradley; Jedlovec, Gary; Wick, Gary; Neiman, Paul

    2013-01-01

    Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.

  12. Diabatic forcing and initialization with assimilation of cloud and rain water in a forecast model: Methodology

    Science.gov (United States)

    Raymond, William H.; Olson, William S.; Callan, Geary

    1990-01-01

    The focus of this part of the investigation is to find one or more general modeling techniques that will help reduce the time taken by numerical forecast models to initiate or spin-up precipitation processes and enhance storm intensity. If the conventional data base could explain the atmospheric mesoscale flow in detail, then much of our problem would be eliminated. But the data base is primarily synoptic scale, requiring that a solution must be sought either in nonconventional data, in methods to initialize mesoscale circulations, or in ways of retaining between forecasts the model generated mesoscale dynamics and precipitation fields. All three methods are investigated. The initialization and assimilation of explicit cloud and rainwater quantities computed from conservation equations in a mesoscale regional model are examined. The physical processes include condensation, evaporation, autoconversion, accretion, and the removal of rainwater by fallout. The question of how to initialize the explicit liquid water calculations in numerical models and how to retain information about precipitation processes during the 4-D assimilation cycle are important issues that are addressed. The explicit cloud calculations were purposely kept simple so that different initialization techniques can be easily and economically tested. Precipitation spin-up processes associated with three different types of weather phenomena are examined. Our findings show that diabatic initialization, or diabatic initialization in combination with a new diabatic forcing procedure, work effectively to enhance the spin-up of precipitation in a mesoscale numerical weather prediction forecast. Also, the retention of cloud and rain water during the analysis phase of the 4-D data assimilation procedure is shown to be valuable. Without detailed observations, the vertical placement of the diabatic heating remains a critical problem.

  13. Assessing the skill of seasonal meteorological forecast products for predicting droughts and water scarcity in highly regulated basins

    Science.gov (United States)

    Squeri, Marika; Giuliani, Matteo; Castelletti, Andrea; Pulido-Velazquez, Manuel; Marcos-Garcia, Patricia; Macian-Sorribes, Hector

    2017-04-01

    Drought and water scarcity are important issues in Southern Europe and many predictions suggest that their frequency and severity will increase over the next years, potentially leading to negative environmental and socio-economic impacts. This work focuses on the Jucar river basin, located in the hinterland of Valencia (Eastern Spain), which is historically affected by long and severe dry periods that negatively impact several economic sectors, with irrigated agriculture representing the main consumptive demand in the basin (79%). Monitoring drought and water scarcity is crucial to activate timely drought management strategies in the basin. However, most traditional drought indexes fail in detecting critical events due to the large presence of human regulation supporting the irrigated agriculture. Over the last 20 years, a sophisticated drought monitoring system has been set up to properly capture the status of the catchment by means of the state index, a weighted linear combination of twelve indicators that depends on observations of precipitation, streamflow, reservoirs' storages and groundwater levels in representative locations at the basin. In this work, we explore the possibility of predicting the state index, which is currently used only as a monitoring tool, in order to prompt anticipatory actions before the drought/water scarcity event starts. In particular, we test the forecasting skill of retrospective seasonal meteorological predictions from the European Centre for Medium-range Weather Forecasts (ECMWF) System 4. The 7-months lead time of these products allows predicting in February the values of the state index until September, thus covering the entire agricultural season. Preliminary results suggest that the Sys4-ECMWF products are skillful in predicting the state index, potentially supporting the design of anticipatory drought management actions.

  14. Building Forecast Maps Of Water Quality For Main Rivers And Canals In Tien Giang Province, Vietnam

    Directory of Open Access Journals (Sweden)

    Anh Duc Pham

    2015-09-01

    Full Text Available This study aims to enhance the mapping of forecast for water quality assessment in Mekong Delta provinces. The data from 32 sites from main rivers and canals in an area of around 2,482 km2 in Tien Giang Province, Vietnam, were used for calculation and mapping. The ArcGIS 9.3 software, Inverse Distance Weighting (IDW interpolation method, hydrologic data, and water quality parameters in March (2010-2014 were applied to build the maps showing 2020 water quality predictions for main rivers and canals in Tien Giang Province. The estimation was based on the Water Quality Index (WQI with 6 parameters such as pH, total suspended solid (TSS, dissolved oxygen (DO, biochemical oxygen demand (BOD, total nitrogen (T_N, and coliform. The results showed that water quality in the studied area in dry season will not be improved by the year 2020. The finding could be a scientific reference for the selection of effective approaches to improve water quality in main rivers and canals in Tien Giang Province.

  15. GGOS Focus Area 3: Understanding and Forecasting Sea-Level Rise and Variability

    Science.gov (United States)

    Schöne, Tilo; Shum, Ck; Tamisiea, Mark; Woodworth, Philip

    2017-04-01

    Sea level and its change have been measured for more than a century. Especially for coastal nations, deltaic regions, and coastal-oriented industries, observations of tides, tidal extremes, storm surges, and sea level rise at the interannual or longer scales have substantial impacts on coastal vulnerability towards resilience and sustainability of world's coastal regions. To date, the observed global sea level rise is largely associated with climate related changes. To find the patterns and fingerprints of those changes, and to e.g., separate the land motion from sea level signals, different monitoring techniques have been developed. Some of them are local, e.g., tide gauges, while others are global, e.g., satellite altimetry. It is well known that sea level change and land vertical motion varies regionally, and both signals need to be measured in order to quantify relative sea level at the local scale. The Global Geodetic Observing System (GGOS) and its services contribute in many ways to the monitoring of the sea level. These includes tide gauge observations, estimation of gravity changes, satellite altimetry, InSAR/Lidar, GNSS-control of tide gauges, providing ground truth sites for satellite altimetry, and importantly the maintenance of the International Reference Frame. Focus Area 3 (Understanding and Forecasting Sea-Level Rise and Variability) of GGOS establishes a platform and a forum for researchers and authorities dealing with estimating global and local sea level changes in a 10- to 30-year time span, and its project to the next century or beyond. It presents an excellent opportunity to emphasize the global, through to regional and local, importance of GGOS to a wide range of sea-level related science and practical applications. Focus Area 3 works trough demonstration projects to highlight the value of geodetic techniques to sea level science and applications. Contributions under a call for participation (http://www.ggos.org/Applications/theme3_SL

  16. A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level.

    Science.gov (United States)

    Spaeder, M C; Fackler, J C

    2012-04-01

    Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9·3, ±7·5 and ±1·5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.

  17. Tracking and forecasting the Nation’s water quality - Priorities and strategies for 2013-2023

    Science.gov (United States)

    Rowe, Gary L.; Gilliom, Robert J.; Woodside, Michael D.

    2013-01-01

    Water-quality issues facing the Nation are growing in number and complexity, and solutions are becoming more challenging and costly. Key factors that affect the quality of our drinking water supplies and ecosystem health include contaminants of human and natural origin in streams and groundwater; excess nutrients and sediment; alteration of natural streamflow; eutrophication of lakes, reservoirs, and coastal estuaries; and changes in surface and groundwater quality associated with changes in climate, land and water use, and management practices. Tracking and forecasting the Nation's water quality in the face of these and other pressing water-quality issues are important goals for 2013-2023, the third decade of the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. In consultation with stakeholders and the National Research Council, a new strategic Science Plan has been developed that describes a strategy for building upon and enhancing assessment of the Nation's freshwater quality and aquatic ecosystems. The plan continues strategies that have been central to the NAWQA program's long-term success, but it also makes adjustments to the monitoring and modeling approaches NAWQA will use to address critical data and science information needs identified by stakeholders. This fact sheet describes surface-water and groundwater monitoring and modeling activities that will start in fiscal year 2013. It also provides examples of the types of data and information products planned for the next decade, including (1) restored monitoring for reliable and timely status and trend assessments, (2) maps and models that show the distribution of selected contaminants (such as atrazine, nitrate, and arsenic) in streams and aquifers, and (3) Web-based modeling tools that allow managers to evaluate how water quality may change in response to different scenarios of population growth, climate change, or land-use management.

  18. Pattern Recognition of High O3 Episodes in Forecasting Daily Maximum Ozone Levels

    Directory of Open Access Journals (Sweden)

    Jeong-Sook Heo

    2004-01-01

    Full Text Available In this study, a method was developed to diagnose ozone episodes exceeding environmental criteria (e.g., above 80 ppb on the basis of a multivariate statistical method and a fuzzy expert system. This method, being capable of characterizing the occurrence patterns of high-level ozone, was employed to forecast daily maximum ozone levels. The hourly data for both air pollutants and meteorological parameters, obtained both at the surface and at high elevation (500 hPa stations of Seoul City (1989-1996, were analyzed using this method. Through an application of the fuzzy expert system, the data sets were classified into 8 different types for common ozone episodes. In addition, the data sets were divided into patterns of 11 (Station A, 20 (Station B, 8 (Station C, and 10 (Station D for site-specific ozone episodes. The results of the analysis were successful in demonstrating that the method was sufficiently efficient to classify each class quantitatively with its own patterns of ozone pollution.

  19. Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

    Science.gov (United States)

    Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

    2016-04-01

    This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

  20. Development of Mechanical Water Level Controller

    Directory of Open Access Journals (Sweden)

    Akonyi Nasiru Sule

    2012-10-01

    Full Text Available The automatic water level controller is a device designed to regulate automatically the pumping of water to an overhead tank without allowing the water in the tank to be exhausted. The design of this mechanical device was achieved using the Archimedes principle of floatation; having a float which determines the water level in the tank depending on the choice of the minimum (lower and maximum (upper level inscribed in the tank. The fundamental attribute of this device is the ease in design, fabrication and mounting at a lower cost. Its testing had shown and proved that it works efficiently with Archimedes’ principle of floatation. This eliminates the frequent human intervention/monitoring of the water level in the overhead tank to control overflow manually, thereby eliminating water and energy wastages.

  1. Cost Optimization of Water Resources in Pernambuco, Brazil: Valuing Future Infrastructure and Climate Forecasts

    Science.gov (United States)

    Kumar, Ipsita; Josset, Laureline; Lall, Upmanu; Cavalcanti e Silva, Erik; Cordeiro Possas, José Marcelo; Cauás Asfora, Marcelo

    2017-04-01

    Optimal management of water resources is paramount in semi-arid regions to limit strains on the society and economy due to limited water availability. This problem is likely to become even more recurrent as droughts are projected to intensify in the coming years, causing increasing stresses to the water supply in the concerned areas. The state of Pernambuco, in the Northeast Brazil is one such case, where one of the largest reservoir, Jucazinho, has been at approximately 1% capacity throughout 2016, making infrastructural challenges in the region very real. To ease some of the infrastructural stresses and reduce vulnerabilities of the water system, a new source of water from Rio São Francisco is currently under development. Till its development, water trucks have been regularly mandated to cover water deficits, but at a much higher cost, thus endangering the financial sustainability of the region. In this paper, we propose to evaluate the sustainability of the considered water system by formulating an optimization problem and determine the optimal operations to be conducted. We start with a comparative study of the current and future infrastructures capabilities to face various climate. We show that while the Rio Sao Francisco project mitigates the problems, both implementations do not prevent failure and require the reliance on water trucks during prolonged droughts. We also study the cost associated with the provision of water to the municipalities for several streamflow forecasts. In particular, we investigate the value of climate predictions to adapt operational decisions by comparing the results with a fixed policy derived from historical data. We show that the use of climate information permits the reduction of the water deficit and reduces overall operational costs. We conclude with a discussion on the potential of the approach to evaluate future infrastructure developments. This study is funded by the Inter-American Development Bank (IADB), and in

  2. Introducing seasonal hydro-meteorological forecasts in local water management. First reflections from the Messara site, Crete, Greece.

    Science.gov (United States)

    Koutroulis, Aristeidis; Grillakis, Manolis; Tsanis, Ioannis

    2017-04-01

    Seasonal prediction is recently at the center of the forecasting research efforts, especially for regions that are projected to be severely affected by global warming. The value of skillful seasonal forecasts can be considerable for many sectors and especially for the agricultural in which water users and managers can benefit to better anticipate against drought conditions. Here we present the first reflections from the user/stakeholder interactions and the design of a tailored drought decision support system in an attempt to bring seasonal predictions into local practice for the Messara valley located in the central-south area of Crete, Greece. Findings from interactions with the users and stakeholders reveal that although long range and seasonal predictions are not used, there is a strong interest for this type of information. The increase in the skill of short range weather predictions is also of great interest. The drought monitoring and prediction tool under development that support local water and agricultural management will include (a) sources of skillful short to medium term forecast information, (b) tailored drought monitoring and forecasting indices for the local groundwater aquifer and rain-fed agriculture, and (c) seasonal inflow forecasts for the local dam through hydrologic simulation to support management of freshwater resources and drought impacts on irrigated agriculture.

  3. Groundwater-level trends and forecasts, and salinity trends, in the Azraq, Dead Sea, Hammad, Jordan Side Valleys, Yarmouk, and Zarqa groundwater basins, Jordan

    Science.gov (United States)

    Goode, Daniel J.; Senior, Lisa A.; Subah, Ali; Jaber, Ayman

    2013-01-01

    Changes in groundwater levels and salinity in six groundwater basins in Jordan were characterized by using linear trends fit to well-monitoring data collected from 1960 to early 2011. On the basis of data for 117 wells, groundwater levels in the six basins were declining, on average about -1 meter per year (m/yr), in 2010. The highest average rate of decline, -1.9 m/yr, occurred in the Jordan Side Valleys basin, and on average no decline occurred in the Hammad basin. The highest rate of decline for an individual well was -9 m/yr. Aquifer saturated thickness, a measure of water storage, was forecast for year 2030 by using linear extrapolation of the groundwater-level trend in 2010. From 30 to 40 percent of the saturated thickness, on average, was forecast to be depleted by 2030. Five percent of the wells evaluated were forecast to have zero saturated thickness by 2030. Electrical conductivity was used as a surrogate for salinity (total dissolved solids). Salinity trends in groundwater were much more variable and less linear than groundwater-level trends. The long-term linear salinity trend at most of the 205 wells evaluated was not increasing, although salinity trends are increasing in some areas. The salinity in about 58 percent of the wells in the Amman-Zarqa basin was substantially increasing, and the salinity in Hammad basin showed a long-term increasing trend. Salinity increases were not always observed in areas with groundwater-level declines. The highest rates of salinity increase were observed in regional discharge areas near groundwater pumping centers.

  4. Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

    Science.gov (United States)

    Zhang, Lei; Zou, Zhihong; Shan, Wei

    2017-06-01

    Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.

  5. Developing an industrial end-use forecast: A case study at the Los Angeles department of water and power

    Energy Technology Data Exchange (ETDEWEB)

    Mureau, T.H.; Francis, D.M. [Los Angeles Department of Water and Power, CA (United States)

    1995-05-01

    The Los Angeles Department of Water and Power (LADWP) uses INFORM 1.0 to forecast industrial sector energy. INFORM 1.0 provides an end-use framework that can be used to forecast electricity, natural gas or other fuels consumption. Included with INFORM 1.0 is a default date set including the input data and equations necessary to solve each model. LADWP has substituted service area specific data for the default data wherever possible. This paper briefly describes the steps LADWP follows in developing those inputs and application in INFORM 1.0.

  6. Informing Water Management by Direct Use of Snow Information as Surrogate of Medium-to-Long Range Streamflow Forecast

    Science.gov (United States)

    Denaro, S.; Giuliani, M.; Castelletti, A.

    2014-12-01

    Medium-to-long range streamflow forecast provide a key assistance in anticipating hydro- climatic adverse events and prompting effective adaptation measures. For instance, accurate medium-long range streamflow forecasts have a great potential to improve water reservoir operation by enabling more efficient allocation of water volumes in time (e.g. via hedging). Unfortunately, these forecasts often lacks reliability and accuracy, especially when low-frequency climate forcing (e.g. ENSO) is not intense enough to improve the forecast lead time (e.g. in Europe), and might be computationally very demanding, In this work, we explore the direct use of both rough snow data (e.g. snow depth) and snow water equivalent estimates as surrogate of medium-to-long range streamflow forecast to inform the operation of a regulated lake. The underlying idea is that snow data contains key information on current and future water availability throughout the snow melting season that might significantly improve the operation's anticipation potential. We adopt a three step methodology: First, we compute the upper bound of the system performance by assuming perfect foresight and we assess the value of additional information as the difference between this ideal solution and current operation. Using input variable selection, we then select the most relevant snow information to explain the release trajectory associated to the upper bound operating policy. Finally, we derive the optimal policy conditioned upon the selected variables by Multi-Objecting Evolutionary Direct Policy Search. The methodology is demonstrated on the snow-dominated Lake Como river basin, in the Italian Alps. Lake Como is a regulated lake primarily used to supply water to a large cultivated area and snowmelt from May-July is the most important contribution to the creation of the seasonal storage. Results show that using raw data or simple SWE estimates can largely improve anticipation capability in the daily operation of

  7. Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast system

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Zeng-Zhen [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Huang, Bohua; Schneider, Edwin K. [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); George Mason University, Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, Fairfax, VA (United States); Hou, Yu-Tai; Yang, Fanglin [NCEP/NWS/NOAA, Environmental Modeling Center, Camp Springs, MD (United States); Wang, Wanqiu [NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Stan, Cristiana [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States)

    2011-05-15

    In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics. (orig.)

  8. Improving the Predictability of Severe Water Levels along the Coasts of Marginal Seas

    Science.gov (United States)

    Ridder, N. N.; de Vries, H.; van den Brink, H.; De Vries, H.

    2016-12-01

    Extreme water levels can lead to catastrophic consequences with severe societal and economic repercussions. Particularly vulnerable are countries that are largely situated below sea level. To support and optimize forecast models, as well as future adaptation efforts, this study assesses the modeled contribution of storm surges and astronomical tides to total water levels under different air-sea momentum transfer parameterizations in a numerical surge model (WAQUA/DCSMv5) of the North Sea. It particularly focuses on the implications for the representation of extreme and rapidly recurring severe water levels over the past decades based on the example of the Netherlands. For this, WAQUA/DCSMv5, which is currently used to forecast coastal water levels in the Netherlands, is forced with ERA Interim reanalysis data. Model results are obtained from two different methodologies to parameterize air-sea momentum transfer. The first calculates the governing wind stress forcing using a drag coefficient derived from the conventional approach of wind speed dependent Charnock constants. The other uses instantaneous wind stress from the parameterization of the quasi-linear theory applied within the ECMWF wave model which is expected to deliver a more realistic forcing. The performance of both methods is tested by validating the model output with observations, paying particular attention to their ability to reproduce rapidly succeeding high water levels and extreme events. In a second step, the common features of and connections between these events are analyzed. The results of this study will allow recommendations for the improvement of water level forecasts within marginal seas and support decisions by policy makers. Furthermore, they will strengthen the general understanding of severe and extreme water levels as a whole and help to extend the currently limited knowledge about clustering events.

  9. El Niño-Southern Oscillation and water resources in the headwaters region of the Yellow River: links and potential for forecasting

    Directory of Open Access Journals (Sweden)

    A. Lü

    2011-04-01

    Full Text Available This research explores the rainfall-El Niño-Southern Oscillation (ENSO and runoff-ENSO relationships and examines the potential for water resource forecasting using these relationships. The Southern Oscillation Index (SOI, Niño1.2, Niño3, Niño4, and Niño3.4 were selected as ENSO indicators for cross-correlation analyses of precipitation and runoff. There was a significant correlation (95% confidence level between precipitation and ENSO indicators during three periods: January, March, and from September to November. In addition, monthly streamflow and monthly ENSO indictors were significantly correlated during three periods: from January to March, June, and from October to December (OND, with lag periods between one and twelve months. Because ENSO events can be accurately predicted one to two years in advance using physical modeling of the coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicators in the Headwaters Region of the Yellow River could extend from one to 36 months. Therefore, ENSO may have potential as a powerful forecasting tool for water resources in the headwater regions of Yellow River.

  10. El Niño-Southern Oscillation and water resources in the headwaters region of the Yellow River: links and potential for forecasting

    Science.gov (United States)

    Lü, A.; Jia, S.; Zhu, W.; Yan, H.; Duan, S.; Yao, Z.

    2011-04-01

    This research explores the rainfall-El Niño-Southern Oscillation (ENSO) and runoff-ENSO relationships and examines the potential for water resource forecasting using these relationships. The Southern Oscillation Index (SOI), Niño1.2, Niño3, Niño4, and Niño3.4 were selected as ENSO indicators for cross-correlation analyses of precipitation and runoff. There was a significant correlation (95% confidence level) between precipitation and ENSO indicators during three periods: January, March, and from September to November. In addition, monthly streamflow and monthly ENSO indictors were significantly correlated during three periods: from January to March, June, and from October to December (OND), with lag periods between one and twelve months. Because ENSO events can be accurately predicted one to two years in advance using physical modeling of the coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicators in the Headwaters Region of the Yellow River could extend from one to 36 months. Therefore, ENSO may have potential as a powerful forecasting tool for water resources in the headwater regions of Yellow River.

  11. El Niño-Southern Oscillation and water resources in Headwaters Region of the Yellow River: links and potential for forecasting

    Directory of Open Access Journals (Sweden)

    A. Lü

    2010-10-01

    Full Text Available Many studies have examined that El Niño-Southern Oscillation (ENSO could result in the variation of rainfall and runoff of different rivers across the world. In this paper, we will look specifically at the Headwaters Region of the Yellow River (HRYR to explore the rainfall-ENSO and runoff-ENSO relationships and discuss the potential for water resources forecasting using these relationships. Cross-correlation analyses were performed to determine the significant correlation between rainfall, runoff and ENSO indicators (e.g. SOI, Niño 1.2, Niño 3, Niño 4, and Niño 3.4 and the lag period for each relationship. Main result include: (1 there are significant correlation at 95% confidence level during three periods, i.e. January and March, from September to November; (2 there were significant correlations between monthly streamflow and monthly ENSO indictors during three periods, i.e. JFM, June, and OND, with lag periods between one and twelve months. As ENSO events can be accurately predicted one to two years in advances using physical model of coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicator in the HRYR can be extent to one to thirty-six months. Therefore, ENSO may have potential as a powerful forecast tool for water resource in headwater regions of Yellow River.

  12. Effect of citizen engagement levels in flood forecasting by assimilating crowdsourced observations in hydrological models

    Science.gov (United States)

    Mazzoleni, Maurizio; Cortes Arevalo, Juliette; Alfonso, Leonardo; Wehn, Uta; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri

    2017-04-01

    In the past years, a number of methods have been proposed to reduce uncertainty in flood prediction by means of model updating techniques. Traditional physical observations are usually integrated into hydrological and hydraulic models to improve model performances and consequent flood predictions. Nowadays, low-cost sensors can be used for crowdsourced observations. Different type of social sensors can measure, in a more distributed way, physical variables such as precipitation and water level. However, these crowdsourced observations are not integrated into a real-time fashion into water-system models due to their varying accuracy and random spatial-temporal coverage. We assess the effect in model performance due to the assimilation of crowdsourced observations of water level. Our method consists in (1) implementing a Kalman filter into a cascade of hydrological and hydraulic models. (2) defining observation errors depending on the type of sensor either physical or social. Randomly distributed errors are based on accuracy ranges that slightly improve according to the citizens' expertise level. (3) Using a simplified social model to realistically represent citizen engagement levels based on population density and citizens' motivation scenarios. To test our method, we synthetically derive crowdsourced observations for different citizen engagement levels from a distributed network of physical and social sensors. The observations are assimilated during a particular flood event occurred in the Bacchiglione catchment, Italy. The results of this study demonstrate that sharing crowdsourced water level observations (often motivated by a feeling of belonging to a community of friends) can help in improving flood prediction. On the other hand, a growing participation of individual citizens or weather enthusiasts sharing hydrological observations in cities can help to improve model performance. This study is a first step to assess the effects of crowdsourced observations in

  13. Comparing M5 Model Trees and Neural Networks for River Level Forecasting

    Science.gov (United States)

    Khan, S.; See, L.

    2005-12-01

    each partition clearly indicate the importance of different inputs; for example, one of the rain gauges is never used, despite selection through correlation analysis. The importance of different upstream stations as level increases is also clearly identified. This type of knowledge coupled with a good performing model has shown that M5 model trees have great operational potential. Solomatine, D.P. and Xue, Y.P. 2004. M5 model trees and neural networks: Application to flood forecasting in the upper reach of the Huai River in China, J. of Hydrol. Eng., 9: 491-501.

  14. Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

    Science.gov (United States)

    Kumar, Prashant; Gopalan, Kaushik; Shukla, Bipasha Paul; Shyam, Abhineet

    2016-09-01

    Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November-December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ˜10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

  15. A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

    Science.gov (United States)

    Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

    2017-04-01

    Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land

  16. Forecasting Rainfall Induced Landslide using High Resolution DEM and Simple Water Budget Model

    Science.gov (United States)

    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

  17. Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

    Science.gov (United States)

    Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

    2010-02-01

    This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

  18. Evaluation of Forecast Performance on Asian Summer Monsoon Low Level Wind Using TIGGE Dataset

    Science.gov (United States)

    Ruoyun, Niu

    2016-04-01

    The forecast performance of EASM (East Asia summer monsoon) and SASM (South Asia summer monsoon) for six TIGGE (the THORPEX Interactive Grand Global Ensemble) centers in the summers of 2008-2013 are evaluated to reflect the current predictability of the state-of-the-art numerical weather prediction. The results show that EASM is overestimated by all the TIGGE centers (except the Canadian Meteorological Center, CMC). SASM is also over-predicted by ECMWF (the European Center for Medium-Range Weather Forecasts), CMA (the China Meteorological Administration) and CMC but conversely under-predicted by JMA (the Japan Meteorological Agency), Additionally, SASM is overestimated for the early lead times and underestimated for the longer lead times by NCEP (the National Centers for Environmental Prediction) and UKMO (the United Kingdom Meteorological Office (UKMO). Further analysis suggests such biases are likely to the associated with those in the related land-sea thermal contrasts. EASM surge is basically overestimated by NCEP and CMA and mainly underestimated by the others. The bias predictabilities for SASM surge are similar to that of SASM. The peaks of SASM and EASM including their surges are mainly underestimated while the valleys are mostly overestimated. By comparison, ECMWF and UKMO have overall the highest forecast skills in predicting SASM and EASM and both have respective advantages. All the TIGGE centers generally show higher skills in predicting SASM than EASM. The forecast skills of SASM and EASM are superior to that of their respective surges. Moreover, the bias-correction forecast skills tend to be improved with higher correlation coefficients in raw forecast verification.

  19. Reconstruction of the Past and Forecast of the Future European and British Ice Sheets and Associated Sea–Level Change

    OpenAIRE

    Hagdorn, Magnus K M

    2003-01-01

    The aim of this project is to improve our understanding of the past European and British ice sheets as a basis for forecasting their future. The behaviour of these ice sheets is investigated by simulating them using a numerical model and comparing model results with geological data including relative sea–level change data. In order to achieve this aim, a coupled ice sheet/lithosphere model is developed. Ice sheets form an integral part of the Earth system. They affect the plane...

  20. A Markov Model for Forecasting Inventory Levels for U.S Navy Medical Service Corps Healthcare Administrators

    Science.gov (United States)

    2014-03-01

    Exper Psych 30 4% Clinical Dietetics 26 3% Financial Mgt 78 8% Research Psych 17 3% Optometry 109 11% MPT&E 31 3% Entomology 39 6% Pharmacy , General 136...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited A MARKOV MODEL FOR...FORECASTING INVENTORY LEVELS FOR U.S NAVY MEDICAL SERVICE CORPS HEALTHCARE ADMINISTRATORS by Sobondo Josiah March 2014 Thesis Co

  1. A Stochastic-Dynamic Model for Real Time Flood Forecasting

    Science.gov (United States)

    Chow, K. C. A.; Watt, W. E.; Watts, D. G.

    1983-06-01

    A stochastic-dynamic model for real time flood forecasting was developed using Box-Jenkins modelling techniques. The purpose of the forecasting system is to forecast flood levels of the Saint John River at Fredericton, New Brunswick. The model consists of two submodels: an upstream model used to forecast the headpond level at the Mactaquac Dam and a downstream model to forecast the water level at Fredericton. Inputs to the system are recorded values of the water level at East Florenceville, the headpond level and gate position at Mactaquac, and the water level at Fredericton. The model was calibrated for the spring floods of 1973, 1974, 1977, and 1978, and its usefulness was verified for the 1979 flood. The forecasting results indicated that the stochastic-dynamic model produces reasonably accurate forecasts for lead times up to two days. These forecasts were then compared to those from the existing forecasting system and were found to be as reliable as those from the existing system.

  2. Experiments in Objective Aviation Weather Forecasting Using Upper-Level Steering.

    Science.gov (United States)

    1983-12-13

    Estoque 2 and Reed 3 developed graphical 1000- mb forecast techniques, applying baroclinic theory, and these techniques steered thermal patterns using upper...none. 42 References 1. Fjbrtoft. R. (1952) On a numerical method of integrating the barotropic vorti- city equation. Tellus 4:179-194. 2. Estoque . M.A

  3. Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding

    Science.gov (United States)

    Lavers, David A.; Waliser, Duane E.; Ralph, F. Martin; Dettinger, Michael

    2016-01-01

    The western United States is vulnerable to socioeconomic disruption due to extreme winter precipitation and floods. Traditionally, forecasts of precipitation and river discharge provide the basis for preparations. Herein we show that earlier event awareness may be possible through use of horizontal water vapor transport (integrated vapor transport (IVT)) forecasts. Applying the potential predictability concept to the National Centers for Environmental Prediction global ensemble reforecasts, across 31 winters, IVT is found to be more predictable than precipitation. IVT ensemble forecasts with the smallest spreads (least forecast uncertainty) are associated with initiation states with anomalously high geopotential heights south of Alaska, a setup conducive for anticyclonic conditions and weak IVT into the western United States. IVT ensemble forecasts with the greatest spreads (most forecast uncertainty) have initiation states with anomalously low geopotential heights south of Alaska and correspond to atmospheric rivers. The greater IVT predictability could provide warnings of impending storminess with additional lead times for hydrometeorological applications.

  4. Critical hydraulic pressure forecasting of water inrush in coal seam floors based on a genetic algorithm-neural network

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, M.; Shi, C.; Liu, T. [China Academy of Safety Science and Technology, Beijing (China); Fu, T. [Tsinghua Univ., Beijing (China). Dept. of Thermal Engineering

    2008-08-15

    This paper presented a method of forecasting water inrush in coal seam floors. The theoretical forecasting method used a combined genetic algorithm-neural network method to analyze the relationships between the critical pressure of water inrush and the different conditions in coal seam floors. Actual measurement data from Chinese coal mines were used to train the multi-layer feedforward neural network. Genetic algorithms were used to train the neural networks and optimize the neural network topology. The topology structure of the network was selected by considering population size, mutation rate, and crossing rates. The critical hydraulic pressure of water inrush was then predicted, and predictions were compared with measurements taken to validate the method. Results of the study showed that the forecasting method improved learning efficiency and the prediction capacity of the network. It was concluded that the combined method can be used to accurately predict the critical hydraulic pressure of water inrush on coal seam floors. 28 refs., 1 tab., 7 figs.

  5. Water-level fluctuations influence sediment porewater ...

    Science.gov (United States)

    Reservoirs typically have elevated fish mercury (Hg) levels compared to natural lakes and rivers. A unique feature of reservoirs is water-level management which can result in sediment exposure to the air. The objective of this study is to identify how reservoir water-level fluctuations impact Hg cycling, particularly the formation of the more toxic and bioaccumulative methylmercury (MeHg). Total-Hg (THg), MeHg, stable isotope methylation rates and several ancillary parameters were measured in reservoir sediments (including some in porewater and overlying water) that are seasonally and permanently inundated. The results showed that sediment and porewater MeHg concentrations were over 3-times higher in areas experiencing water-level fluctuations compared to permanently inundated sediments. Analysis of the data suggest that the enhanced breakdown of organic matter in sediments experiencing water-level fluctuations has a two-fold effect on stimulating Hg methylation: 1) it increases the partitioning of inorganic Hg from the solid phase into the porewater phase (lower log Kd values) where it is more bioavailable for methylation; and 2) it increases dissolved organic carbon (DOC) in the porewater which can stimulate the microbial community that can methylate Hg. Sulfate concentrations and cycling were enhanced in the seasonally inundated sediments and may have also contributed to increased MeHg production. Overall, our results suggest that reservoir management a

  6. Skill assessment for an operational algal bloom forecast system.

    Science.gov (United States)

    Stumpf, Richard P; Tomlinson, Michelle C; Calkins, Julie A; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T

    2009-02-20

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast

  7. Skill assessment for an operational algal bloom forecast system

    Science.gov (United States)

    Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.

    2010-01-01

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast

  8. Forecasting the impact of global changes on the water resources of a mountainous catchment in the Chilean Andes

    Science.gov (United States)

    Ruelland, D.; Campéon, C.; Dezetter, A.; Jourde, H.

    2012-04-01

    This study aims to simulate the complex interrelationships between climate forcing, human pressure and dynamics of groundwater and surface water of the upper Elqui catchment (5 660 km2) in the Chilean Andes. The water resources of this mountainous, semi-arid catchment has been undergoing a growing pressure because of high climate variability and of the economic mutations of various sectors (agriculture, tourism), which have impacted water availability of the area. Due to the agriculture-based development in the region, water scarcity is thus a matter of great concern for this basin. Hydrological simulations were performed with a conceptual model that takes into account a shallow reservoir supplied by precipitation and feeding evapotranspiration, surface/sub-surface runoff and infiltration, and (ii) a deep reservoir fed by infiltration and generating the baseflow. A third reservoir, in which fluxes are controlled by temperature, has been introduced to account for the snowmelt regime of the catchment. A 30-year period (1979-2008) was chosen to capture long-term hydro-climatic variability due to alternating ENSO and LNSO events. Then water uses (dam functioning, agricultural and domestic withdrawals) were integrated into the model. The model was calibrated and validated with streamflow data on the basis of a multi-objective function that aggregates a variety of goodness-of-fit criteria. Prospective climatic and anthropogenic scenarios were finally elaborated and forced into the model in order to propose midterm (2050 horizon) simulations. The model correctly reproduces the observed discharge at the basin outlet. Depending on the modelling complexity, NSE coefficients are about 0.82-0.90 over the calibration period (1979-1990) and 0.78-0.84 over the validation period (1991-2008). The volume error between observation and simulation is lower than 15% over the whole period studied. The dynamics of both the water level in the deep conceptual reservoir and the water table

  9. A Hybrid Model for ET (Evapotranspiration) Forecasting Based on EEMD for ET-based Water Resources Management

    Science.gov (United States)

    Guo, A.; Wang, Y.

    2015-12-01

    It is widely known that the water shortage is much more serious in some regions or basins under changing environment. In this paper, ET-based (evapotranspiration-based) water resources management (ET-WRM) model is purposed for land use planning and water resources management, which mainly focus on generalized ET, i.e. agricultural, industrial, domestic, and ecological water consumption, to achieve high efficient use of water resource. To accurately predict the ET, it is decomposed to several intrinsic mode functions and one residue by the ensemble empirical mode decomposition (EEMD) method and forecasted by a hybrid model combined artificial neural network (ANN), support vector machine (SVM) and autoregressive integrated moving average (ARIMA). The model is applied in Ningxia Hui Autonomous Region, China, a typical region of water resources shortage, and the results show that: (i) the pass rate of prediction obtained by the modified hybrid forecasting model reaches up to 93% in the study area, which shows higher accuracy than applying any one method of them singly. The predicted ET in programming year, 2020, will reach to 3.48 billion m³. (ii) in the study area, to achieve water-saving goal, agricultural water-saving measures should be taken in the future, due to the existing phenomenon of low irrigation water use efficiency and wide planting area of high water-consuming crops. (iii) the water-saving volume in agriculture, industry and domestic in 2020 should be reached to 1.48×108m³, 1.11×108m³and 0.61×108m³, to balance the available water supply and future water consumption, compared with the baseline year, 2011. (iv) for water-saving in agriculture, the adjustment of planting structure, irrigation scheduling, agricultural activities and engineering measures is the main measure.

  10. Development of a System to Generate Near Real Time Tropospheric Delay and Precipitable Water Vapor in situ at Geodetic GPS Stations, to Improve Forecasting of Severe Weather Events

    Science.gov (United States)

    Moore, A. W.; Bock, Y.; Geng, J.; Gutman, S. I.; Laber, J. L.; Morris, T.; Offield, D. G.; Small, I.; Squibb, M. B.

    2012-12-01

    We describe a system under development for generating ultra-low latency tropospheric delay and precipitable water vapor (PWV) estimates in situ at a prototype network of geodetic GPS sites in southern California, and demonstrating their utility in forecasting severe storms commonly associated with flooding and debris flow events along the west coast of North America through infusion of this meteorological data at NOAA National Weather Service (NWS) Forecast Offices and the NOAA Earth System Research Laboratory (ESRL). The first continuous geodetic GPS network was established in southern California in the early 1990s and much of it was converted to real-time (latency tropospheric zenith delays, which can be converted into mm-accuracy PWV using collocated pressure and temperature measurements, the basis for GPS meteorology (Bevis et al. 1992, 1994; Duan et al. 1996) as implemented by NOAA with a nationwide distribution of about 300 GPS-Met stations providing PW estimates at subhourly resolution currently used in operational weather forecasting in the U.S. We improve upon the current paradigm of transmitting large quantities of raw data back to a central facility for processing into higher-order products. By operating semi-autonomously, each station will provide low-latency, high-fidelity and compact data products within the constraints of the narrow communications bandwidth that often occurs in the aftermath of natural disasters. The onsite ambiguity-resolved precise point positioning solutions are enabled by a power-efficient, low-cost, plug-in Geodetic Module for fusion of data from in situ sensors including GPS and a low-cost MEMS meteorological sensor package. The decreased latency (~5 minutes) PW estimates will provide the detailed knowledge of the distribution and magnitude of PW that NWS forecasters require to monitor and predict severe winter storms, landfalling atmospheric rivers, and summer thunderstorms associated with the North American monsoon. On the

  11. Enabling Solar Flare Forecasting at an Unprecedented Level: the FLARECAST Project

    Science.gov (United States)

    Georgoulis, Manolis K.; Pariat, Etienne; Massone, Anna Maria; Vilmer, Nicole; Jackson, David; Buchlin, Eric; Csillaghy, Andre; Bommier, Veronique; Kontogiannis, Ioannis; Gallagher, Peter; Gontikakis, Costis; Guennou, Chloé; Murray, Sophie; Bloomfield, D. Shaun; Alingery, Pablo; Baudin, Frederic; Benvenuto, Federico; Bruggisser, Florian; Florios, Konstantinos; Guerra, Jordan; Park, Sung-Hong; Perasso, Annalisa; Piana, Michele; Sathiapal, Hanna; Soldati, Marco; Von Stachelski, Samuel; Argoudelis, Vangelis; Caminade, Stephane

    2016-07-01

    We attempt a brief but informative description of the Flare Likelihood And Region Eruption Forecasting (FLARECAST) project, European Commission's first large-scale investment to explore the limits of reliability and accuracy for the forecasting of major solar flares. The consortium, objectives, and first results of the project - featuring an openly accessible, interactive flare forecasting facility by the end of 2017 - will be outlined. In addition, we will refer to the so-called "explorative research" element of project, aiming to connect solar flares with coronal mass ejections (CMEs) and possibly pave the way for CME, or eruptive flare, prediction. We will also emphasize the FLARECAST modus operandi, namely the diversity of expertise within the consortium that independently aims to science, infrastructure development and dissemination, both to stakeholders and to the general public. Concluding, we will underline that the FLARECAST project responds squarely to the joint COSPAR - ILWS Global Roadmap to shield society from the adversities of space weather, addressing its primary goal and, in particular, its Research Recommendations 1, 2 and 4, Teaming Recommendations II and III, and Collaboration Recommendations A, B, and D. The FLARECAST project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 640216.

  12. Theoretical basis for expert system to forecast and assess economic impact of anthropogenic pollution on population disease level

    Directory of Open Access Journals (Sweden)

    M.I. Bublyk

    2014-09-01

    Full Text Available The aim of the article. Theoretical basis of mathematical apparatus of fuzzy sets to evaluate and account the man-made (anthropogenic losses is improved in the article in order to take effective administrative decisions of their reduction and prevention. Theoretical basis for building an expert system for forecasting the economic effects of man-made (anthropogenic pollution on population levels of disease is analyzed. Practically these investigations will give the opportunity to control measures of orientation of the national economy and its individual industries on sustainable development. The results of the analysis. The theoretical foundations and applied problems of predicting man-made damage to the national economy and methods of management at the state level allowed for the following conclusions. 1. To justify the application of theoretical principles of fuzzy sets as an effective mathematical tool in conditions of incomplete information and uncertainty in future work the advantages of fuzzy expert systems, including the possibility of approximate descriptions such complex phenomena that can not be described in conventional quantitative terms, and the ability to receive, store and adjust the knowledge possessed by experts in this subject area in the process of dialogue with them in order to get real results. 2. The model of fuzzy expert system for establishing interdependencies between the amount of pollution (emissions, effluents, waste and deterioration of health in Ukraine has been proposed. 3. The model in predicting the technogenic load (discharges (drained polluted waters without treatment and emissions of sulfur dioxide and nitric oxide due to economic activity and its effects on the number of newly registered tumors in 1000 people of the population in Ukraine has been investigated. 4. During the investigation it was established as a rising idea to use the claim that the impact of emissions and discharges of pollutants to the number

  13. Hydro static water level systems at Fermilab

    Energy Technology Data Exchange (ETDEWEB)

    Volk, J.T.; Guerra, J.A.; Hansen, S.U.; Kiper, T.E.; Jostlein, H.; Shiltsev, V.; Chupyra, A.; Kondaurov, M.; Singatulin, S.

    2006-09-01

    Several Hydrostatic Water Leveling systems (HLS) are in use at Fermilab. Three systems are used to monitor quadrupoles in the Tevatron and two systems are used to monitor ground motion for potential sites for the International Linear Collider (ILC). All systems use capacitive sensors to determine the water level of water in a pool. These pools are connected with tubing so that relative vertical shifts between sensors can be determined. There are low beta quadrupoles at the B0 and D0 interaction regions of Tevatron accelerator. These quadrupoles use BINP designed and built sensors and have a resolution of 1 micron. All regular lattice superconducting quadrupoles (a total of 204) in the Tevatron use a Fermilab designed system and have a resolution of 6 microns. Data on quadrupole motion due to quenches, changes in temperature will be presented. In addition data for ground motion for ILC studies caused by natural and cultural factors will be presented.

  14. Forecasting the probability of future groundwater levels declining below specified low thresholds in the conterminous U.S.

    Science.gov (United States)

    Dudley, Robert W.; Hodgkins, Glenn A.; Dickinson, Jesse

    2017-01-01

    We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater-level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness-of-fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month-to-month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low-threshold events. We identified challenges in deriving probabilistic-forecasting models and possible approaches for addressing those challenges.

  15. Summary of the Ground-Water-Level Hydrologic Conditions in New Jersey 2006

    Science.gov (United States)

    Jones, Walter; Pope, Daryll

    2007-01-01

    Ground water is one of the Nation's most important natural resources. It provides about 40 percent of our Nation's public water supply. Currently, nearly one-half of New Jersey's drinking-water is supplied by over 300,000 wells that serve more than 4.3 million people (John P. Nawyn, U.S. Geological Survey, written commun., 2007). New Jersey's population is projected to grow by more than a million people by 2030 (U.S. Census Bureau, accessed March 2, 2006, at http://www.census.gov). As demand for water increases, managing the development and use of the ground-water resource so that the supply can be maintained for an indefinite time without causing unacceptable environmental, economic, or social consequences is of paramount importance. This report describes the U.S. Geological Survey (USGS) New Jersey Water Science Center Observation Well Networks. Record low ground-water levels during water year 2006 (October 1, 2005 to September 30, 2006) are listed, and water levels in six selected water-table observation wells and three selected confined wells are shown in hydrographs. The report describes the trends in water levels in various confined aquifers in southern New Jersey and in water-table and fracture rock aquifers throughout the State. Web site addresses to access the data also are included. The USGS has operated a network of observation wells in New Jersey since 1923 for the purpose of monitoring ground-water-level changes throughout the State. Long-term systematic measurement of water levels in observation wells provides the data needed to evaluate changes in the ground-water resource over time. Records of ground-water levels are used to evaluate the effects of climate changes and water-supply development, to develop ground-water models, and to forecast trends.

  16. Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2000-01-01

    Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment

  17. Forecasting terrestrial water storage changes in the Amazon Basin using Atlantic and Pacific sea surface temperatures

    Directory of Open Access Journals (Sweden)

    C. de Linage

    2013-10-01

    Full Text Available Floods and droughts frequently affect the Amazon River basin, impacting transportation, river navigation, agriculture, and ecosystem processes within several South American countries. Here we examined how sea surface temperatures (SSTs influence interannual variability of terrestrial water storage anomalies (TWSAs in different regions within the Amazon basin and propose a modeling framework for inter-seasonal flood and drought forecasting. Three simple statistical models forced by a linear combination of lagged spatial averages of central Pacific (Niño 4 index and tropical North Atlantic (TNAI index SSTs were calibrated against a decade-long record of 3°, monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE satellite mission. Niño 4 was the primary external forcing in the northeastern region of the Amazon basin whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit significantly (p < 0.05 for at least 64% of the grid cells within the basin, compared to models forced solely with Niño 4 or TNAI. The combined model explained 66% of the observed variance in the northeastern region, 39% in the central and western regions, and 43% for the Amazon basin as a whole with a 3 month lead time between the SST indices and TWSAs. Model performance varied seasonally: it was higher than average during the rainfall wet season in the northeastern Amazon and during the dry season in the central and western regions. The predictive capability of the combined model was degraded with increasing lead times. Degradation was smaller in the northeastern Amazon (where 49% of the variance was explained using an 8 month lead time vs. 69% for a 1 month lead time compared to the central and western Amazon (where 22% of the variance was explained at 8 months vs. 43% at 1 month. These relationships may enable the development of an early warning system for flood and drought risk. This work also

  18. Continuum: a distributed hydrological model for water management and flood forecasting

    Directory of Open Access Journals (Sweden)

    F. Silvestro

    2012-06-01

    Full Text Available Full process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, prediction of impact on the hydrologic cycle due to natural and human changes to catchment features in present and changing climates. Since they must mimic a variety of physical processes they can be very complex and with a high degree of parameterization. This complexity can be increased by the need to relate the state variables to observations in order to allow data assimilation.

    In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data scarce environments.

    All the main hydrological phenomena are modeled in a distributed way. Mass balance and energy balance are solved explicitly. Land surface temperature, which is particularly suited to being extensively observed and assimilated, is an explicit state variable.

    An objective performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been calibrated and validated using different data sets on two nested outlet sections and the capability of the model in reproducing both the stream-flow measurements and the land surface temperature retrieved by satellite measurements, has been investigated.

  19. Minimum Reservoir Water Level in Hydropower Dams

    Science.gov (United States)

    Sarkardeh, Hamed

    2017-07-01

    Vortex formation over the intakes is an undesirable phenomenon within the water withdrawal process from a dam reservoir. Calculating the minimum operating water level in power intakes by empirical equations is not a safe way and sometimes contains some errors. Therefore, current method to calculate the critical submergence of a power intake is construction of a scaled physical model in parallel with numerical model. In this research some proposed empirical relations for prediction of submergence depth in power intakes were validated with experimental data of different physical and numerical models of power intakes. Results showed that, equations which involved the geometry of intake have better correspondence with the experimental and numerical data.

  20. Scheduling satellite imagery acquisition for sequential assimilation of water level observation into flood modelling

    Science.gov (United States)

    García-Pintado, Javier; Neal, Jeff C.; Mason, David C.; Dance, Sarah L.; Bates, Paul D.

    2013-04-01

    Satellite-based imagery has proved useful for obtaining information on water levels in flood events. Microwave frequencies are generally more useful for flood detection than visible-band sensors because of its all-weather day-night capability. Specifically, the future SWOT mission, with Ka-band interferometry, will be able to provide direct Water Level Observations (WLOs), and current and future Synthetic Aperture Radar (SAR) sensors can provide information of flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides indirect WLOs. By either means, satellite-based WLOs can be assimilated into a hydrodynamic model to decrease forecast uncertainty and further to estimate river discharge into the flooded domain. Operational scenarios can even make a combined use of imagery from different uncoordinated missions to sequentially estimate river discharge. Thus, with an increasing number of operational satellites with WLO capability, information on the relationship between satellite first visit, revisit times, and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007, Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. As an example, we use different scenarios of revisit times and observational errors expected from the current COSMO-Skymed (CSK) constellation, with X-band SAR. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a

  1. nowCOAST's Map Service for NOAA NOS Extratropical Surge and Tide Operational Forecast System (ESTOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of combined (tides + wind driven) water level and...

  2. GNSS-Reflectometry based water level monitoring

    Science.gov (United States)

    Beckheinrich, Jamila; Schön, Steffen; Beyerle, Georg; Apel, Heiko; Semmling, Maximilian; Wickert, Jens

    2013-04-01

    Due to climate changing conditions severe changes in the Mekong delta in Vietnam have been recorded in the last years. The goal of the German Vietnamese WISDOM (Water-related Information system for the Sustainable Development Of the Mekong Delta) project is to build an information system to support and assist the decision makers, planners and authorities for an optimized water and land management. One of WISDOM's tasks is the flood monitoring of the Mekong delta. Earth reflected L-band signals from the Global Navigation Satellite System show a high reflectivity on water and ice surfaces or on wet soil so that GNSS-Reflectometry (GNSS-R) could contribute to monitor the water level in the main streams of the Mekong delta complementary to already existing monitoring networks. In principle, two different GNSS-R methods exist: the code- and the phase-based one. As the latter being more accurate, a new generation of GORS (GNSS Occultation, Reflectometry and Scatterometry) JAVAD DELTA GNSS receiver has been developed with the aim to extract precise phase observations. In a two week lasting measurement campaign, the receiver has been tested and several reflection events at the 150-200 m wide Can Tho river in Vietnam have been recorded. To analyze the geometrical impact on the quantity and quality of the reflection traces two different antennas height were tested. To track separately the direct and the reflected signal, two antennas were used. To derive an average height of the water level, for a 15 min observation interval, a phase model has been developed. Combined with the coherent observations, the minimum slope has been calculated based on the Least- Squares method. As cycle slips and outliers will impair the results, a preprocessing of the data has been performed. A cycle slip detection strategy that allows for automatic detection, identification and correction is proposed. To identify outliers, the data snooping method developed by Baarda 1968 is used. In this

  3. DeMand: A tool for evaluating and comparing device-level demand and supply forecast models

    DEFF Research Database (Denmark)

    Neupane, Bijay; Siksnys, Laurynas; Pedersen, Torben Bach

    2016-01-01

    datasets, forecast models, features, and errors measures, thus semi-automating most of the steps of the forecast model selection and validation process. This paper presents the architecture and data model of the DeMand system; and provides a use-case example on how one particular forecast model...

  4. Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

    Science.gov (United States)

    Shedd, R.; Reed, S. M.; Porter, J. H.

    2015-12-01

    The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional

  5. Forecasting Water Use on Fixed Army Installations within the Contiguous United States.

    Science.gov (United States)

    1984-06-22

    Virginia Ft. Gordon, Georgia Ft. Rucker, Alabama Ft. Benjamin Harrison, Indiana Ft. Sill, Oklahoma Ft. A.P. Hill, Virginia Ft. Leonard Wood, Missouri Ft...response at the installation level. Permission was also given by the Coumander, United States Army Student Detachment at Fort Benjamin Harrison, Indiana... Darr , P., S. L. Feldman, and C. Komen, 1976. The Demand for Urban Water, Leiden, the Netherlands: Martinus Nijhoff Social Sciences Division

  6. Sensitivity analysis of a data assimilation technique for hindcasting and forecasting hydrodynamics of a complex coastal water body

    Science.gov (United States)

    Ren, Lei; Hartnett, Michael

    2017-02-01

    Accurate forecasting of coastal surface currents is of great economic importance due to marine activities such as marine renewable energy and fish farms in coastal regions in recent twenty years. Advanced oceanographic observation systems such as satellites and radars can provide many parameters of interest, such as surface currents and waves, with fine spatial resolution in near real time. To enhance modelling capability, data assimilation (DA) techniques which combine the available measurements with the hydrodynamic models have been used since the 1990s in oceanography. Assimilating measurements into hydrodynamic models makes the original model background states follow the observation trajectory, then uses it to provide more accurate forecasting information. Galway Bay is an open, wind dominated water body on which two coastal radars are deployed. An efficient and easy to implement sequential DA algorithm named Optimal Interpolation (OI) was used to blend radar surface current data into a three-dimensional Environmental Fluid Dynamics Code (EFDC) model. Two empirical parameters, horizontal correlation length and DA cycle length (CL), are inherent within OI. No guidance has previously been published regarding selection of appropriate values of these parameters or how sensitive OI DA is to variations in their values. Detailed sensitivity analysis has been performed on both of these parameters and results presented. Appropriate value of DA CL was examined and determined on producing the minimum Root-Mean-Square-Error (RMSE) between radar data and model background states. Analysis was performed to evaluate assimilation index (AI) of using an OI DA algorithm in the model. AI of the half-day forecasting mean vectors' directions was over 50% in the best assimilation model. The ability of using OI to improve model forecasts was also assessed and is reported upon.

  7. Decreasing residual aluminum level in drinking water

    Institute of Scientific and Technical Information of China (English)

    王志红; 崔福义

    2004-01-01

    The relativity of coagulant dosage, residual turbidity, temperature, pH etc. with residual aluminum concentration were investigated, and several important conclusions were achieved. Firstly, dosage of alum-coagulant or PAC1 influences residual aluminum concentration greatly. There is an optimal-dosage-to-aluminum, a bit less than the optimal-dosage-to-turbidity. Secondly, it proposes that decreasing residual aluminum concentration can be theoretically divided into two methods, either decreasing (even removing) the concentration of particulate aluminum component, or decreasing dissolved aluminum. In these tests there is an optimal value of residual turbidity of postprecipitation at 7.0 NTU. Thirdly, residual aluminum level will increase while water temperature goes higher. At the last, optimal pH value corresponds a minimum dissolved aluminum at a given turbidity. Data shows the optimal pH value decreases with water temperature's increasing.

  8. Predicting Water Levels at Kainji Dam Using Artificial Neural Networks

    African Journals Online (AJOL)

    Predicting Water Levels at Kainji Dam Using Artificial Neural Networks. ... The aim of this study is to develop artificial neural network models for predicting water levels at Kainji Dam, which supplies water to Nigeria's largest ... Article Metrics.

  9. Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

    Science.gov (United States)

    Li, Yu; Giuliani, Matteo; Castelletti, Andrea

    2016-04-01

    Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in

  10. Hydrocarbon production forecast for committed assets in the shallow water Outer Continental Shelf of the Gulf of Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Kaiser, Mark J. [Center for Energy Studies, Louisiana State University, Energy Coast and Environment Building, Nicholson Extension Drive, Baton Rouge, LA 70803 (United States)

    2009-11-15

    In 2007, the federal waters of the Gulf of Mexico averaged daily production of 1.3 million barrels of oil and 7.6 billion cubic feet of natural gas. The majority of oil is produced from deepwater fields in water depth greater than 1000 ft, while most gas production is extracted from the shelf. The Outer Continental Shelf is a mature province with over 3800 fixed structures and 6500 producing wells connected into an integrated pipeline network more than 30,000 miles in length. The purpose of this paper is to develop a methodology to forecast oil and gas production in the shallow water Gulf of Mexico. Structures are categorized according to age and production characteristics, and forecast procedures for each asset class are described and illustrated. The methodology is implemented using the inventory of committed assets circa December 2006. The expected amount of hydrocarbon production arising from the inventory of committed assets under stable reservoir and investment conditions is estimated to be 1056 MMbbl oil and 13.3 Tcf gas valued between $85 and 150 billion. The results of generalized regression models are presented with a discussion of the limitations of analysis. (author)

  11. Flow Forecasting in Drainage Systems with Extrapolated Radar Rainfall Data and Auto Calibration on Flow Observations

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Grum, M.; Rasmussen, Michael R.;

    2011-01-01

    in a small urban catchment has been developed. The forecast is based on application of radar rainfall data, which by a correlation based technique, is extrapolated with a lead time up to two hours. The runoff forecast in the drainage system is based on a fully distributed MOUSE model which is auto......Forecasting of flows, overflow volumes, water levels, etc. in drainage systems can be applied in real time control of drainage systems in the future climate in order to fully utilize system capacity and thus save possible construction costs. An online system for forecasting flows and water levels......-calibrated on flow measurements in order to produce the best possible forecast for the drainage system at all times. The system shows great potential for the implementation of real time control in drainage systems and forecasting flows and water levels....

  12. Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

    Energy Technology Data Exchange (ETDEWEB)

    Turner, Sean WD; Bennett, James; Robertson, David; Galelli, Stefano

    2017-09-28

    Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling-horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this surprising behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. The main driver of system performance is the forecast accuracy at the timing during which critical decisions are made—namely during severe drought. Our results emphasise the importance of forecast skill consistency and highlight a need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

  13. Drinking Water Maximum Contaminant Levels (MCLs)

    Data.gov (United States)

    U.S. Environmental Protection Agency — National Primary Drinking Water Regulations (NPDWRs or primary standards) are legally enforceable standards that apply to public water systems. Primary standards...

  14. Online short-term heat load forecasting for single family houses

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

    2013-01-01

    This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts....... The models are optimized to fit the level of optimal adaptivity and the thermal dynamical response of the building. Identification of a model, which is suitable for application to all the houses, is carried out. The results show that the forecasting errors mainly are related to: unpredictable high frequency...

  15. Flood Forecasting via Time Lag Forward Network; Kelantan, Malaysia

    Science.gov (United States)

    Jajarmizadeh, Milad; Mohd Sidek, Lariyah; Bte Basri, Hidayah; Shakira Jaffar, Aminah

    2016-03-01

    Forecasting water level is one of the critical issues in Malaysia for Kelantan region. Based on the flood events in 2014, this study investigates the hourly-forecasting of water level in one station namely Kg Jenob in Kelantan. For this issue, Time Lag Forward Network (TLFN) is evaluated for forecasting the water level as dynamic model. Heuristic method in stepwise forward methodology is performed. Rainfall and water level are the input and output of the modelling respectively. For selected flood period 15/12/2014 to 30/12/2014, 8 scenarios are developed to obtain a minimum error in water level forecasting. By monitoring the error, it will show that the optimum configuration of network has 2 processors in hidden layer and 7 lags have enough contribution on the result of hourly forecasting. Transfer functions in hidden and output layers are is Tangent hyperbolic and bias. Observed and simulated data are compared with usual error criteria called Mean Square Error (MSE) and Root Mean Square Error (RMSE) which obtained 0.005 and 0.07 respectively. In conclusion, this study will be as a baseline for Kelantan to show that TLFN has promising result to forecast the flood events.

  16. 基于PDL模型的城市化水平预测%Forecasting Urbanization Level Based on PDL Model

    Institute of Scientific and Technical Information of China (English)

    丁刚; 赵萍萍

    2005-01-01

    In the previous research, when the economic factor forecasting solution of Urbanization was used into practice, the users almost always ignored the effects of the lagged economic development factors. The accuracy of the forecasting should be decreased seriously by this way. In this paper, taking Gansu province as an example, the authors try to improve the forecasting accuracy on the basis of the unrestricted PDL (Polynomial Distributed Lag')Models. In the context, the ARIMA Models and CPPS software are also applied to finish the forecasting more accurately.

  17. Forecasting and prevention of water inrush during the excavation process of a diversion tunnel at the Jinping II Hydropower Station, China.

    Science.gov (United States)

    Hou, Tian-Xing; Yang, Xing-Guo; Xing, Hui-Ge; Huang, Kang-Xin; Zhou, Jia-Wen

    2016-01-01

    Estimating groundwater inflow into a tunnel before and during the excavation process is an important task to ensure the safety and schedule during the underground construction process. Here we report a case of the forecasting and prevention of water inrush at the Jinping II Hydropower Station diversion tunnel groups during the excavation process. The diversion tunnel groups are located in mountains and valleys, and with high water pressure head. Three forecasting methods are used to predict the total water inflow of the #2 diversion tunnel. Furthermore, based on the accurate estimation of the water inrush around the tunnel working area, a theoretical method is presented to forecast the water inflow at the working area during the excavation process. The simulated results show that the total water flow is 1586.9, 1309.4 and 2070.2 m(3)/h using the Qshima method, Kostyakov method and Ochiai method, respectively. The Qshima method is the best one because it most closely matches the monitoring result. According to the huge water inflow into the #2 diversion tunnel, reasonable drainage measures are arranged to prevent the potential disaster of water inrush. The groundwater pressure head can be determined using the water flow velocity from the advancing holes; then, the groundwater pressure head can be used to predict the possible water inflow. The simulated results show that the groundwater pressure head and water inflow re stable and relatively small around the region of the intact rock mass, but there is a sudden change around the fault region with a large water inflow and groundwater pressure head. Different countermeasures are adopted to prevent water inrush disasters during the tunnel excavation process. Reasonable forecasting the characteristic parameters of water inrush is very useful for the formation of prevention and mitigation schemes during the tunnel excavation process.

  18. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  19. Forecast Forecasts the Trend

    Institute of Scientific and Technical Information of China (English)

    Wang Ting

    2009-01-01

    @@ The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.

  20. A new hybrid model for filling gaps and forecast in sea level: application to the eastern English Channel and the North Atlantic Sea (western France)

    Science.gov (United States)

    Turki, Imen; Laignel, Benoit; Kakeh, Nabil; Chevalier, Laetitia; Costa, Stephane

    2015-04-01

    This research is carried out in the framework of the program Surface Water and Ocean Topography (SWOT) which is a partnership between NASA and CNES. Here, a new hybrid model is implemented for filling gaps and forecasting the hourly sea level variability by combining classical harmonic analyses to high statistical methods to reproduce the deterministic and stochastic processes, respectively. After simulating the mean trend sea level and astronomical tides, the nontidal residual surges are investigated using an autoregressive moving average (ARMA) methods by two ways: (1) applying a purely statistical approach and (2) introducing the SLP in ARMA as a main physical process driving the residual sea level. The new hybrid model is applied to the western Atlantic sea and the eastern English Channel. Using ARMA model and considering the SLP, results show that the hourly sea level observations of gauges with are well reproduced with a root mean square error (RMSE) ranging between 4.5 and 7 cm for 1 to 30 days of gaps and an explained variance more than 80 %. For larger gaps of months, the RMSE reaches 9 cm. The negative and the positive extreme values of sea levels are also well reproduced with a mean explained variance between 70 and 85 %. The statistical behavior of 1-year modeled residual components shows good agreements with observations. The frequency analysis using the discrete wavelet transform illustrate strong correlations between observed and modeled energy spectrum and the bands of variability. Accordingly, the proposed model presents a coherent, simple, and easy tool to estimate the total sea level at timescales from days to months. The ARMA model seems to be more promising for filling gaps and estimating the sea level at larger scales of years by introducing more physical processes driving its stochastic variability.

  1. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  2. Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method

    Science.gov (United States)

    Adamowski, J. F.; Quilty, J.; Khalil, B.; Rathinasamy, M.

    2014-12-01

    This paper explores forecasting short-term urban water demand (UWD) (using only historical records) through a variety of machine learning techniques coupled with a novel input variable selection (IVS) procedure. The proposed IVS technique termed, bootstrap rank-ordered conditional mutual information for real-valued signals (brCMIr), is multivariate, nonlinear, nonparametric, and probabilistic. The brCMIr method was tested in a case study using water demand time series for two urban water supply system pressure zones in Ottawa, Canada to select the most important historical records for use with each machine learning technique in order to generate forecasts of average and peak UWD for the respective pressure zones at lead times of 1, 3, and 7 days ahead. All lead time forecasts are computed using Artificial Neural Networks (ANN) as the base model, and are compared with Least Squares Support Vector Regression (LSSVR), as well as a novel machine learning method for UWD forecasting: the Extreme Learning Machine (ELM). Results from one-way analysis of variance (ANOVA) and Tukey Honesty Significance Difference (HSD) tests indicate that the LSSVR and ELM models are the best machine learning techniques to pair with brCMIr. However, ELM has significant computational advantages over LSSVR (and ANN) and provides a new and promising technique to explore in UWD forecasting.

  3. Accurate Linking of Lake Erie Water Level with Shoreline Datum Using GPS Buoy and Satellite Altimetry

    Directory of Open Access Journals (Sweden)

    Kai-Chien Cheng

    2008-01-01

    Full Text Available There is a need to accurately link the water level to the shoreline vertical datum for various applications including coastal management, lake/river/estuary/wetland hydrological or storm surge modeling/forecasting. Coastal topography is historically surveyed and referenced to the predetermined vertical datum in terms of orthometric heights, or the heights above the geoid, which is poorly known in terms of accuracy and lack of adequate spatial resolution for coastal applications such as estuary or storm surge modeling. We demonstrate an accurate linking of the lake surface to a shoreline datum using satellite techniques, including GPS buoy and satellite altimetry, water level gauges, and local geoid and lake circulation models. The possible error sources are analyzed and an error budget is reported in this study. An innovated method to estimate geoid height near the water level gauge using a GPS buoy is proposed. It is found that at a 95% confidence interval, the method is consistent with the National Geodetic Survey GEOID03 geoid model. The lake surface represented using a lake circulation model provided by the Great Lakes Forecasting Systems is also verified with kriging based on the data (1999 - 2001 from the water level gauge, and TOPEX/POSEIDON altimeter. Mean discrepancies of 2.7 and 7.2 cm are found with the data from the gauges around Lake Erie, and from the combination of the gauges and the altimeter, respectively. It reveals that the current dominant limitation of more accurate linking of water surface to shoreline is the insufficient knowledge of geoid in the current models. Further improvement is feasible through more accurate and higher resolution modeling of the lake geoid.

  4. Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities

    Science.gov (United States)

    Lv, Baolei; Cobourn, W. Geoffrey; Bai, Yuqi

    2016-12-01

    Empirical regression models for next-day forecasting of PM2.5 and O3 air pollution concentrations have been developed and evaluated for three large Chinese cities, Beijing, Nanjing and Guangzhou. The forecast models are empirical nonlinear regression models designed for use in an automated data retrieval and forecasting platform. The PM2.5 model includes an upwind air quality variable, PM24, to account for regional transport of PM2.5, and a persistence variable (previous day PM2.5 concentration). The models were evaluated in the hindcast mode with a two-year air quality and meteorological data set using a leave-one-month-out cross validation method, and in the forecast mode with a one-year air quality and forecasted weather dataset that included forecasted air trajectories. The PM2.5 models performed well in the hindcast mode, with coefficient of determination (R2) values of 0.54, 0.65 and 0.64, and normalized mean error (NME) values of 0.40, 0.26 and 0.23 respectively, for the three cities. The O3 models also performed well in the hindcast mode, with R2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities. The O3 models performed better in summertime than in winter in Beijing and Guangzhou, and captured the O3 variations well all the year round in Nanjing. The overall forecast performance of the PM2.5 and O3 models during the test year varied from fair to good, depending on location. The forecasts were somewhat degraded compared with hindcasts from the same year, depending on the accuracy of the forecasted meteorological input data. For the O3 models, the model forecast accuracy was strongly dependent on the maximum temperature forecasts. For the critical forecasts, involving air quality standard exceedences, the PM2.5 model forecasts were fair to good, and the O3 model forecasts were poor to fair.

  5. eWaterCycle: Building an operational global Hydrological forecasting system based on standards and open source software

    Science.gov (United States)

    Drost, Niels; Bierkens, Marc; Donchyts, Gennadii; van de Giesen, Nick; Hummel, Stef; Hut, Rolf; Kockx, Arno; van Meersbergen, Maarten; Sutanudjaja, Edwin; Verlaan, Martin; Weerts, Albrecht; Winsemius, Hessel

    2015-04-01

    At EGU 2015, the eWaterCycle project (www.ewatercycle.org) will launch an operational high-resolution Hydrological global model, including 14 day ensemble forecasts. Within the eWaterCycle project we aim to use standards and open source software as much as possible. This ensures the sustainability of the software created, and the ability to swap out components as newer technologies and solutions become available. It also allows us to build the system much faster than would otherwise be the case. At the heart of the eWaterCycle system is the PCRGLOB-WB Global Hydrological model (www.globalhydrology.nl) developed at Utrecht University. Version 2.0 of this model is implemented in Python, and models a wide range of Hydrological processes at 10 x 10km (and potentially higher) resolution. To assimilate near-real time satellite data into the model, and run an ensemble forecast we use the OpenDA system (www.openda.org). This allows us to make use of different data assimilation techniques without the need to implement these from scratch. As a data assimilation technique we currently use (variant of) an Ensemble Kalman Filter, specifically optimized for High Performance Computing environments. Coupling of the model with the DA is done with the Basic Model Interface (BMI), developed in the framework of the Community Surface Dynamics Modeling System (CSDMS) (csdms.colorado.edu). We have added support for BMI to PCRGLOB-WB, and developed a BMI adapter for OpenDA, allowing OpenDA to use any BMI compatible model. We currently use multiple different BMI models with OpenDA, already showing the benefits of using this standard. Throughout the system, all file based input and output is done via NetCDF files. We use several standard tools to be used for pre- and post-processing data. Finally we use ncWMS, an NetCDF based implementation of the Web Map Service (WMS) protocol to serve the forecasting result. We have build a 3D web application based on Cesium.js to visualize the output. In

  6. Forecasting the effects of EU policy measures on the nitrate pollution of groundwater and surface waters

    Science.gov (United States)

    Kunkel, R.; Kreins, P.; Tetzlaff, B.; Wendland, F.

    2009-04-01

    be expected to be reduced only by about 10 kg N ha-1 a-1 on average for the whole Weser basin. However, for the agriculturally intensive used regions the expected N surpluses reduction may be much higher and can amount 40 kg N ha-1 a-1 or more. The REGLUD model system is used to quantify the potential effects of these projected N surpluses on the intakes into the groundwater the nitrogen pollution of surface waters. A comparison to the present situation shows that the potential nitrate concentration in the leachate will decrease in almost all regions of the Weser basin, mostly by about 10 mg NO3/L. In the agriculturally intensive used regions much higher reductions in the order of 40 mg NO3/L may be expected. Consequently, reduced nitrogen outflows to surface waters via the different pathways are obtained. Using environmental target values for groundwater and surface waters, e.g. a concentration of 50 mg NO3/L in the leachate as a target for groundwater protection, the model results can be used directly to identify those regions where additional agri-environmental reduction measures are required. Additionally, a backward calculation by the REGFLUD allows the quantification of maximal permissible nitrogen surplus levels, which can be used as a reference for the derivation of additional regionally adapted and hence effective nitrogen reduction measures. The research work presented here is carried out in the framework of the still ongoing AGRUM Weser project which is funded on behalf of the German Federal Ministry of Food, Agriculture and Consumer protection (BMELV) and the River Basin Commission Weser (FGG).

  7. Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

    Science.gov (United States)

    Anghileri, D.; Castelletti, A.; Burlando, P.

    2016-12-01

    European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow

  8. Improving Garch Volatility Forecasts

    NARCIS (Netherlands)

    Klaassen, F.J.G.M.

    1998-01-01

    Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH effects are allowed within each regime, so that our model

  9. Confidence in Coastal Forecasts

    NARCIS (Netherlands)

    Baart, F.

    2013-01-01

    This thesis answers the question "How can we show and improve our confidence in coastal forecasts?", by providing four examples of common coastal forecasts. The first example shows how to improve the estimate of the one in ten thousand year storm-surge level. The three dimensional reconstruction,

  10. Aviation Forecasting in ICAO

    Science.gov (United States)

    Mcmahon, J.

    1972-01-01

    Opinions or plans of qualified experts in the field are used for forecasting future requirements for air navigational facilities and services of international civil aviation. ICAO periodically collects information from Stators and operates on anticipated future operations, consolidates this information, and forecasts the future level of activity at different airports.

  11. Recent and late quaternary changes in water level

    Science.gov (United States)

    Walcott, R. I.

    1975-01-01

    Water level changes of both the Great Lakes and the sea are described along with methods of analyzing water level data. The influence of elastic deformation of the earth and viscosity is discussed. Causes of water level changes reviewed include: earth movements, geoid changes, storm surges or meteorological phenomena, and melting ice in Antarctica, Greenland, and the mountain glaciers.

  12. Navigating the "Research-to-Operations" Bridge of Death: Collaborative Transition of Remotely-Sensed Snow Data from Research into Operational Water Resources Forecasting

    Science.gov (United States)

    Miller, W. P.; Bender, S.; Painter, T. H.; Bernard, B.

    2016-12-01

    Water and resource management agencies can benefit from hydrologic forecasts during both flood and drought conditions. Improved predictions of seasonal snowmelt-driven runoff volume and timing can assist operational water managers with decision support and efficient resource management within the spring runoff season. Using operational models and forecasting systems, NOAA's Colorado Basin River Forecast Center (CBRFC) produces hydrologic forecasts for stakeholders and water management groups in the western United States. Collaborative incorporation of research-oriented remote sensing data into CBRFC operational models and systems is one route by which CBRFC forecasts can be improved, ultimately for the benefit of water managers. Successful navigation of research-oriented remote sensing products across the "research-to-operations"/R2O gap (also known as the "valley of death") to operational destinations requires dedicated personnel on both the research and operations sides, working in a highly collaborative environment. Since 2012, the operational CBRFC has collaborated with the research-oriented Jet Propulsion Laboratory (JPL) under funding from NASA to transition remotely-sensed snow data into CBRFC's operational models and forecasting systems. Two specific datasets from JPL, the MODIS Dust Radiative Forcing in Snow (MODDRFS) and the MODIS Snow Covered-Area and Grain size (MODSCAG) products, are used in CBRFC operations as of 2016. Over the past several years, JPL and CBRFC have worked together to analyze patterns in JPL's remote sensing snow datasets from the operational perspective of the CBRFC and to develop techniques to bridge the R2O gap. Retrospective and real-time analyses have yielded valuable insight into the remotely-sensed snow datasets themselves, CBRFC's operational systems, and the collaborative R2O process. Examples of research-oriented JPL snow data, as used in CBRFC operations, are described. A timeline of the collaboration, challenges

  13. Considering the Influence of Multi-weather-factors to Forecasting the Water Requirement of Well Irrigation Rice Based on ANN Model

    Institute of Scientific and Technical Information of China (English)

    FU Qiang; FU Hong; LIANG Chuan

    2004-01-01

    The author considered the influences of several weather factors, such as air temperature, sunlight, saturation deficiency, wind speed and so on to forecasting the water requirement of well irrigation rice based on Artificial Neutron Network. Through dealing with the time series of water requirement and its influence factors, the author applied the multi-dimension data correlation analysis to ensure the net structure. Thus, the ANN model to forecast the water requirement of well irrigation rice has been built. By means of the ANN model, uncertainty relation between water requirement and many influence factors among the interior and exterior can be discovered. The results of ANN model is good, and can provide some references for establishing the water saving irrigation system.

  14. The WRF Model Forecast-Derived Low-Level Wind Shear Climatology over the United States Great Plains

    Directory of Open Access Journals (Sweden)

    Sukanta Basu

    2010-02-01

    Full Text Available For wind resource assessment projects, it is common practice to use a power-law relationship (U(z ~ zα and a fixed shear exponent (α = 1=7 to extrapolate the observed wind speed from a low measurement level to high turbine hub-heights. However, recent studies using tall-tower observations have found that the annual average shear exponents at several locations over the United States Great Plains (USGP are significantly higher than 1=7. These findings highlight the critical need for detailed spatio-temporal characterizations of wind shear climatology over the USGP, where numerous large wind farms will be constructed in the foreseeable future. In this paper, a new generation numerical weather prediction model—the Weather Research and Forecasting (WRF model, a fast and relatively inexpensive alternative to time-consuming and costly tall-tower projects, is utilized to determine whether it can reliably estimate the shear exponent and the magnitude of the directional shear at any arbitrary location over the USGP. Our results indicate that the WRF model qualitatively captures several low-level wind shear characteristics. However, there is definitely room for physics parameterization improvements for the WRF model to reliably represent the lower part of the atmospheric boundary layer.

  15. Case studies of extended model-based flood forecasting: prediction of dike strength and flood impacts

    Science.gov (United States)

    Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet

    2017-04-01

    Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global

  16. Towards Sustaining Water Resources and Aquatic Ecosystems: Forecasting Watershed Risks to Current and Future Land Use Change

    Science.gov (United States)

    Lohse, K. A.; Newburn, D.; Opperman, J. J.; Brooks, C.; Merenlender, A.

    2005-05-01

    Sustaining aquatic resources requires managing existing threats and anticipating future impacts. Resource managers and planners often have limited understanding of the relative effects of human activities on stream conditions and how these effects will change over time. Here we assess and forecast the relative impacts of land use on sediment concentrations in Mediterranean-climate watersheds in California. We focus on the Russian River basin, which supports threatened salmonid populations vulnerable to high levels of fine sediment. We ask the following questions: (1) What are the relative impacts of three different land uses (urban, exurban and agriculture) on the patterns of fine sediment in streams? (2) What is the relative contribution of past and current changes in land use activities on these patterns? and (3) What are the effects of future development on these sediment levels? First, we characterized land use at the parcel scale to calibrate the relative impacts of exurban and urban land use on stream substrate quality, characterized by the concentration of fine sediment surrounding spawning gravels (`embeddedness') in 105 stream reaches. Second, we built multiple ordinal logistic regression models on a subset of watersheds (n=64) and then evaluated substrate quality predictions against observed data from another set of watersheds (n=41). Finally, we coupled these models with spatially explicit land use change models to project future stream conditions and associated uncertainties under different development scenarios for the year 2010. We found that the percent of urban housing and agriculture were significant predictors of in-stream embeddedness. Model results from parcel-level land use data indicated that changes in development were better predictors of fine sediment than total development in a single time period. In addition, our results indicate that exurban development is an important threat to stream systems; increases in the percent of total exurban

  17. Assessing flood forecast uncertainty with fuzzy arithmetic

    Directory of Open Access Journals (Sweden)

    de Bruyn Bertrand

    2016-01-01

    Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

  18. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  19. County-Level Climate Uncertainty for Risk Assessments: Volume 23 Appendix V - Forecast Sea Ice Thickness

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A.; Lowry, Thomas Stephen; Jones, Shannon M; Walker, La Tonya Nicole; Roberts, Barry L; Malczynski, Leonard A.

    2017-04-01

    This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

  20. County-Level Climate Uncertainty for Risk Assessments: Volume 25 Appendix X - Forecast Sea Ice Age.

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lowry, Thomas Stephen [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Shannon M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Walker, La Tonya Nicole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Roberts, Barry L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Malczynski, Leonard A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

  1. County-Level Climate Uncertainty for Risk Assessments: Volume 17 Appendix P - Forecast Soil Moisture

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A.; Lowry, Thomas Stephen; Jones, Shannon M; Walker, La Tonya Nicole; Roberts, Barry L; Malczynski, Leonard A.

    2017-04-01

    This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

  2. County-Level Climate Uncertainty for Risk Assessments: Volume 15 Appendix N - Forecast Surface Runoff.

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lowry, Thomas Stephen [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Shannon M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Walker, La Tonya Nicole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Roberts, Barry L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Malczynski, Leonard A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

  3. County-Level Climate Uncertainty for Risk Assessments: Volume 27 Appendix Z - Forecast Ridging Rate.

    Energy Technology Data Exchange (ETDEWEB)

    Backus, George A.; Lowry, Thomas Stephen; Jones, Shannon M; Walker, La Tonya Nicole; Roberts, Barry L; Malczynski, Leonard A.

    2017-06-01

    This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

  4. NOAA NOS SOS, EXPERIMENTAL - Water Level

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA NOS SOS server is part of the IOOS DIF SOS Project. The stations in this dataset have water surface height above a reference datum. *These services are for...

  5. Assessing maize foliar water stress levels under field conditions ...

    African Journals Online (AJOL)

    Assessing maize foliar water stress levels under field conditions using in-situ ... is non-destructive to the crops as opposed to other traditional ground-based methods. ... water indices that could monitor the water status at leaf level on maize (Zea ... about AJOL · AJOL's Partners · Contact AJOL · Terms and Conditions of Use.

  6. Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

    Science.gov (United States)

    Bergeron, Jean M.; Trudel, Mélanie; Leconte, Robert

    2016-10-01

    The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered

  7. Water levels in the Yucca Mountain area, Nevada, 1993

    Energy Technology Data Exchange (ETDEWEB)

    Tucci, P.; Goemaat, R.L.; Burkhardt, D.J.

    1996-07-01

    Water levels were monitored in 28 wells in the Yucca Mountain area, Nevada, during 1993. Seventeen wells were monitored periodically, generally on a monthly basis, and 11 wells representing 18 intervals were monitored hourly. All wells monitor water levels in Tertiary volcanic rocks, except one that monitors water levels in Paleozoic carbonate rocks. Water levels were measured using calibrated steel tapes and pressure transducers; steel-tape measurements were corrected for mechanical stretch, thermal expansion, and borehole deviation to obtain precise water-level altitudes. Water-level altitudes in the Tertiary volcanic rocks ranged from about 728 meters above sea level east of Yucca Mountain to about 1,034 meters above sea level north of Yucca Mountain. Water-level altitudes in the well monitoring the Paleozoic carbonate rocks varied between 752 and 753 meters above sea level during 1993. Water levels were an average of about 0.04 meter lower than 1992 water levels. All data were acquired in accordance with a quality-assurance program to support the reliability of the data.

  8. Groundwater level forecasting using an artificial neural network trained with particle swarm optimization.

    Science.gov (United States)

    Tapoglou, E.; Trichakis, I. C.; Dokou, Z.; Karatzas, G. P.

    2012-04-01

    period is minimal. Finally, climate change scenarios were examined, based on the prediction that on the island of Crete during the period of 2010-2040, it will be a 12(±25)% average reduction in precipitation and a 1.9(±0.9)oC increase in mean temperature (Tsanis et al., 2011). In order to study these scenarios, data time series were created for the period 2010-2020, using a stochastic weather generator for three cases (best, worst and average case scenarios). The prediction results indicate a significant negative effect on the groundwater level only for the worst case scenario (37% reduction in precipitation), while in the other cases the results vary from neutral to positive.

  9. Forecasting of Water Resource of China based on Grey Prediction Model

    Directory of Open Access Journals (Sweden)

    Shuqing Hou

    2015-08-01

    Full Text Available Water resource planning is very important for water resources management. A desirable water resource planning is typically made in order to satisfy multiple objectives as much as possible. Thus the water resource planning problem is actually a Multiple Attribute Decision Making (MADM problem. The aim of this study is to put forward a new decision method to solve the problem of water resource planning in which attribute values expressed with triangular fuzzy numbers. The new method is an extension of projection method. To avoid the subjective randomness, the coefficient of variation method is used to determine the attribute weights. A practical example is given to illustrate the effectiveness and feasibility of the proposed method.

  10. Development of mathematical models for forecasting hydraulic loads of water and wastewater networks

    Energy Technology Data Exchange (ETDEWEB)

    Studzinki, Jan [Polish Academy of Sciences, Warsaw (Poland). Systems Research Institute; Bartkiewicz, Lidia [Technical Univ. Kielce (Poland); Stachura, Marcin [Warsaw University of Technology (Poland)

    2013-07-01

    In municipal waterworks the operation of water and wastewater networks decides about the functioning of the sewage treatment plant that is the last element of the whole water and sewage system. The both networks are connected each other and the work of the water net affects the operation of the wastewater one. The parameters which are important for right leading of all waterworks objects are their hydraulic loads that have to be not exceeded. Too large loads can cause accidents in the wastewater net or the treatment plant and an early knowledge of them is of importance for undertaking some counteractions. In the paper different algorithms to model hydraulic loads of municipal water and wastewater nets are described and compared regarding their computation velocity and accuracy. Some exemplary computations have been done with some real data received from a Polish water company. (orig.)

  11. Socio–economic benefits and pollution levels of water resources ...

    African Journals Online (AJOL)

    user

    Assessment of socio–economic activities and pollution levels of domestic water sources in Gulu Municipality, Pece ... The communities should be sensitized to treat water before drinking. ..... primarily related to the poor maintenance of sanitary.

  12. Short-term forecasting of the chloride content in the mineral waters of the Ustroń Health Resort using SARIMA and Holt-Winters models

    Directory of Open Access Journals (Sweden)

    Dąbrowska Dominika

    2015-12-01

    Full Text Available The Ustroń S.A. Health Resort (southern Poland uses iodide-bromide mineral waters taken from Middle and Upper Devonian limestones and dolomites with a mineralisation range of 110-130 g/dm3 for curative purposes. Two boreholes - U-3 and U3-A drilled in the early 1970s were exploited. The aim of this paper is to estimate changes in mineral water quality of the Ustroń Health Resort by taking into consideration chloride content in the water from the U-3 borehole. The data has included the results of monthly analyses of chlorides from 2005 to 2015 during the tests carried out by the Mining Department of the Health Resort. The triple exponential smoothing (ETS function and the Seasonal Autoregressive Integrated Moving Average (SARIMA method of modelling time series were used for the calculations. The ability to properly forecast mineral water quality can result in a good status of the exploitation borehole and a limited number of failures in the exploitation system. Because of the good management of health resorts, it is possible to acquire more satisfied customers. The main goal of the article involves the real-time forecast accuracy, obtained results show that the proposed methods are effective for such situations. Presented methods made it possible to obtain a 24-month point and interval forecast. The results of these analyses indicate that the chloride content is forecast to be in the range of 72 to 83 g/l from 2015 to 2017. While comparing the two methods of analysis, a narrower range of forecast values and, therefore, greater accuracy were obtained for the ETS function. The good performance of the ETS model highlights its utility compared with complicated physically based numerical models.

  13. PREFACE: XXIVth Conference of the Danubian Countries on the Hydrological Forecasting and Hydrological Bases of Water Management

    Science.gov (United States)

    Brilly, Mitja; Bonacci, Ognjen; Nachtnebel, Peter Hans; Szolgay, Ján; Balint, Gabor

    2008-10-01

    This volume of IOP Conference Series: Earth and Environmental Science presents a selection of papers that were given at the 24th Conference of the Danube Countries. Within the framework of the International Hydrological Program IHP of UNESCO. Since 1961 the Danube countries have successfully co-operated in organizing conferences on Hydrological Forecasting and Hydrological Water Management Issues. The 24th Conference of the Danube Countries took place between 2-4 June 2008 in Bled, Slovenia and was organized by the National Committee of Slovenia for the International Hydrological Program of UNESCO, under the auspices of the President of Republic of Slovenia. It was organized jointly by the Slovenian National Commission for UNESCO and the Environmental Agency of the Republic of Slovenia, under the support of UNESCO, WMO, and IAHS. Support for the attendance of some participants was provided by UNESCO. Additional support for the symposium was provided by the Slovene Commission for UNESCO, Environmental Agency of Slovenia, Karst Research Institute, Hydropower plants on the lower Sava River and Chair of Hydraulics Engineering FGG University of Ljubljana. All participants expressed great interest and enthusiasm in presenting the latest research results and sharing practical experiences in the Hydrology of the Danube River basin. The Editorial Board, who were nominated at the Conference, initially selected 80 full papers for publication from 210 submitted extended abstracts and papers provided by authors from twenty countries. Altogether 51 revised papers were accepted for publishing in this volume. Papers are divided by conference topics: Hydrological forecasting Hydro-meteorological extremes, floods and droughts Global climate change and antropogenic impacts on hydrological processes Water management Floods, morphological processes, erosion, sediment transport and sedimentation Developments in hydrology Mitja Brilly, Ognjen Bonacci, Peter Hans Nachtnebel, Ján Szolgay

  14. Exploring Water Level Sensitivity for Metropolitan New York during Sandy (2012 Using Ensemble Storm Surge Simulations

    Directory of Open Access Journals (Sweden)

    Brian A. Colle

    2015-06-01

    Full Text Available This paper describes storm surge simulations made for Sandy (2012 for the Metropolitan New York (NYC area using the Advanced Circulation (ADCIRC model forced by the Weather Research and Forecasting (WRF model. The atmospheric forecast uncertainty was quantified using 11-members from an atmospheric Ensemble Kalman Filter (EnKF system. A control WRF member re-initialized every 24 h demonstrated the capability of the WRF-ADCIRC models to realistically simulate the 2.83 m surge and 4.40 m storm tide (surge + astronomical tide above mean lower low water (MLLW for NYC. Starting about four days before landfall, an ensemble of model runs based on the 11 “best” meteorological predictions illustrate how modest changes in the track (20–100 km and winds (3–5 m s−1 of Sandy approaching the New Jersey coast and NYC can lead to relatively large (0.50–1.50 m storm surge variations. The ensemble also illustrates the extreme importance of the timing of landfall relative to local high tide. The observed coastal flooding was not the worst case for this particular event. Had Sandy made landfall at differing times, locations and stages of the tide, peak water levels could have been up to 0.5 m higher than experienced.

  15. Ruby Lake National Wildlife Refuge : Ruby Valley Nevada : 1992 Annual water management report 1993 Annual water management plan

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Ruby Lake NWR 1992 Annual Water Management Report 1993 Annual Water Management Plan. Includes summary of 1992 weather, 1992 water levels, water availability forecast...

  16. Water levels in the Yucca Mountain Area, Nevada, 1996

    Energy Technology Data Exchange (ETDEWEB)

    Graves, R.P.

    1998-11-01

    Water levels were monitored in 24 wells in the Yucca Mountain area, Nevada, during 1996. Twenty-two wells representing 28 depth intervals were monitored periodically, generally on a monthly basis, and 2 wells representing 3 depth intervals were monitored both hourly and periodically. All wells monitor water levels in Tertiary volcanic rocks except one that monitors water levels in paleozoic carbonate rocks. Water levels were measured using either calibrated steel tapes or a pressure sensor. Mean water-level altitudes in the Tertiary volcanic rocks ranged from about 727.86 to about 1,034.58 meters above sea level during 1996. The mean water-level altitude in the well monitoring the Paleozoic carbonate rocks was about 752.57 meters above sea level during 1996. Mean water-level altitudes for 1996 were an average of about 0.06 meter lower than 1995 mean water-level altitudes and 0.03 meter lower than 1985--95 mean water-level altitudes. During 1996, water levels in the Yucca Mountain area could have been affected by long-term pumping at the C-hole complex that began on May 8, 1996. Through December 31, 1996, approximately 196 million liters were pumped from well UE-25 c{number_sign}3 at the C-hole complex. Other ground-water pumpage in the Yucca Mountain area includes annual pumpage from water-supply wells UE-25 J-12 and UE-25 J-13 of approximately 163 and 105 million liters, respectively, and pumpage from well USW G-2 for hydraulic testing during February and April 1996 of approximately 6 million liters.

  17. A decision support system for use of probability forecasts

    NARCIS (Netherlands)

    De Kleermaeker, S.; Verkade, J.S.

    2013-01-01

    Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers’ increased awareness of forecasting uncert

  18. A decision support system for use of probability forecasts

    NARCIS (Netherlands)

    De Kleermaeker, S.; Verkade, J.S.

    2013-01-01

    Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers’ increased awareness of forecasting

  19. A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

    Science.gov (United States)

    Holtschlag, David J.; Sweat, M.J.

    1999-01-01

    Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26

  20. Animating ground water levels with Excel.

    Science.gov (United States)

    Shikaze, Steven G; Crowe, Allan S

    2003-01-01

    This note describes the use of Microsoft Excel macros (programs written in Excel's internal language, Visual Basic for Applications) to create simple onscreen animations of transient ground water data within Excel. Compared to many specialized visualization software packages, the use of Excel macros is much cheaper, much simpler, and can rapidly be learned. The Excel macro can also be used to create individual GIF files for each animation frame. This series of frames can then be used to create an AVI video file using any of a number of graphics packages, such as Corel PhotoPaint. The technique is demonstrated through a macro that animates changes in the elevation of a water table along a transect over several years.

  1. Assimilating SAR-derived water level data into a hydraulic model: a case study

    Directory of Open Access Journals (Sweden)

    L. Giustarini

    2011-02-01

    Full Text Available Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction to the model forecast uncertainty. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

  2. Water levels in the Yucca Mountain area, Nevada, 1994

    Energy Technology Data Exchange (ETDEWEB)

    Graves, R.P.; Tucci, P.; Goemaat, R.L.

    1996-12-31

    Water levels were monitored in 28 wells in the Yucca Mountain area, Nevada, during 1994. Twelve wells representing 13 intervals were monitored periodically, generally on a monthly basis, 6 wells representing 10 intervals were monitored hourly, and 10 wells representing 13 intervals were monitored both periodically and hourly. All wells monitor water levels in Tertiary volcanic rocks, except one, that monitors water levels in Paleozoic carbonate rocks. Water levels were measured using calibrated steel tapes, a multiconductor cable unit, and pressure transducers. Water-level altitudes in the Tertiary volcanic rocks ranged from about 728 to about 1,034 meters above sea level during 1994. The mean-annual water-level altitude in the well monitoring the Paleozoic carbonate rocks was about 753 meters above sea level during 1994. Water levels were only an average of about 0.01 meters lower than 1993 water levels. All data were acquired in accordance with a quality-assurance program to support the reliability of the data.

  3. Water levels in the Yucca Mountain area, Nevada, 1995

    Energy Technology Data Exchange (ETDEWEB)

    Graves, R.P.; Goemaat, R.L.

    1998-09-01

    Water levels were monitored in 28 wells in the Yucca Mountain area, Nevada, during 1995. Seventeen wells representing 18 depth intervals were monitored periodically, generally on a monthly basis, 2 wells representing 3 depth intervals were monitored hourly, and 9 wells representing 15 depth intervals were monitored both periodically and hourly. All wells monitor water levels in Tertiary volcanic rocks except one that monitors water levels in Paleozoic carbonate rocks. Water levels were measured using calibrated steel tapes, a multiconductor cable unit, and/or pressure transducers. Mean water-level altitudes in the Tertiary volcanic rocks ranged from about 728 to about 1,034 meters above sea level during 1995. The mean water-level altitude in the well monitoring the Paleozoic carbonate rocks was about 753 meters above sea level during 1995. Mean water level altitudes were only an average of about 0.01 meters higher than 1994 mean water level altitudes. A single-well aquifer test was conducted on well UE-25 WT{number_sign}12 during August and September 1995. Well USW 0-2 was also pumped during October and November 1995, in preparation for single-well aquifer test at that well. All data were acquired in accordance with a quality-assurance program to support the reliability of the data.

  4. Exploring What Determines the Use of Forecasts of Varying Time Periods in Guanacaste, Costa Rica

    Science.gov (United States)

    Babcock, M.; Wong-Parodi, G.; Grossmann, I.; Small, M. J.

    2016-12-01

    Weather and climate forecasts are promoted as ways to improve water management, especially in the face of changing environmental conditions. However, studies indicate many stakeholders who may benefit from such information do not use it. This study sought to better understand which personal factors (e.g., trust in forecast sources, perceptions of accuracy) were important determinants of the use of 4-day, 3-month, and 12-month rainfall forecasts by stakeholders in water management-related sectors in the seasonally dry province of Guanacaste, Costa Rica. From August to October 2015, we surveyed 87 stakeholders from a mix of government agencies, local water committees, large farms, tourist businesses, environmental NGO's, and the public. The result of an exploratory factor analysis suggests that trust in "informal" forecast sources (traditional methods, family advice) and in "formal" sources (government, university and private company science) are independent of each other. The result of logistic regression analyses suggest that 1) greater understanding of forecasts is associated with a greater probability of 4-day and 3-month forecast use, but not 12-month forecast use, 2) a greater probability of 3-month forecast use is associated with a lower level of trust in "informal" sources, and 3), feeling less secure about water resources, and regularly using many sources of information (and specifically formal meetings and reports) are each associated with a greater probability of using 12-month forecasts. While limited by the sample size, and affected by the factoring method and regression model assumptions, these results do appear to suggest that while forecasts of all times scales are used to some extent, local decision makers' decisions to use 4-day and 3-month forecasts appear to be more intrinsically motivated (based on their level of understanding and trust) and the use of 12-month forecasts seems to be more motivated by a sense of requirement or mandate.

  5. Forecasting cyanobacteria dominance in Canadian temperate lakes.

    Science.gov (United States)

    Persaud, Anurani D; Paterson, Andrew M; Dillon, Peter J; Winter, Jennifer G; Palmer, Michelle; Somers, Keith M

    2015-03-15

    Predictive models based on broad scale, spatial surveys typically identify nutrients and climate as the most important predictors of cyanobacteria abundance; however these models generally have low predictive power because at smaller geographic scales numerous other factors may be equally or more important. At the lake level, for example, the ability to forecast cyanobacteria dominance is of tremendous value to lake managers as they can use such models to communicate exposure risks associated with recreational and drinking water use, and possible exposure to algal toxins, in advance of bloom occurrence. We used detailed algal, limnological and meteorological data from two temperate lakes in south-central Ontario, Canada to determine the factors that are closely linked to cyanobacteria dominance, and to develop easy to use models to forecast cyanobacteria biovolume. For Brandy Lake (BL), the strongest and most parsimonious model for forecasting % cyanobacteria biovolume (% CB) included water column stability, hypolimnetic TP, and % cyanobacteria biovolume two weeks prior. For Three Mile Lake (TML), the best model for forecasting % CB included water column stability, hypolimnetic TP concentration, and 7-d mean wind speed. The models for forecasting % CB in BL and TML are fundamentally different in their lag periods (BL = lag 1 model and TML = lag 2 model) and in some predictor variables despite the close proximity of the study lakes. We speculate that three main factors (nutrient concentrations, water transparency and lake morphometry) may have contributed to differences in the models developed, and may account for variation observed in models derived from large spatial surveys. Our results illustrate that while forecast models can be developed to determine when cyanobacteria will dominate within two temperate lakes, the models require detailed, lake-specific calibration to be effective as risk-management tools.

  6. Forecasting the underground waters regime in the pitching aquifers in Huainan mining area

    Institute of Scientific and Technical Information of China (English)

    GUI He-rong; SONG Xiao-mei

    2001-01-01

    Huainan area is an important coal base of the east of China. In the early part of the 1980s, the study of the underground waters dynamic state in the area was gradually paid dose attention to. This paper introduces the observation system of the groundwater dynamic state in the multilayered pitching aquifer, and expounds the hydrogaologic feature and the waterpower reletions among aquifers. Furthermore, based on the analysis of the relations of the groundwater dynamic state to surface water, meteoric water and mining shaft outflow rate, this paper establishes main water filled aquifers of mining shaft (C3-1, C3-2, C3-3 and O2). In the light of the actual situation of the greatly changing aquifer occurrence and steep dip angle, the “two-layer space curved surface seepage model” and the calculating step are all suggested. Since 1991, the groundwater dynamic state of the next year has been predicted (numerical simulation) every year. Contracting with the measured data, wa gain a relatively ideal effect.

  7. Optimal Control of Water Systems Under Forecast Uncertainty: Robust, Proactive, and Integrated

    NARCIS (Netherlands)

    Raso, L.

    2013-01-01

    Water systems consist of natural and man-made objects serving multiple essential purposes. They are affected by many types of meteorological disturbances. In order to deal with these disturbances and to serve the desired objectives, infrastructures have been built and managed by societies for specif

  8. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    Science.gov (United States)

    Cho, Jaepil; Shin, Chang-Min; Choi, Hwan-Kyu; Kim, Kyong-Hyeon; Choi, Ji-Yong

    2016-10-01

    The APEC Climate Center (APCC) produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1) the Simple Bias Correction (SBC) method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; (3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and (4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  9. Underwater noise levels in UK waters

    Science.gov (United States)

    Merchant, Nathan D.; Brookes, Kate L.; Faulkner, Rebecca C.; Bicknell, Anthony W. J.; Godley, Brendan J.; Witt, Matthew J.

    2016-11-01

    Underwater noise from human activities appears to be rising, with ramifications for acoustically sensitive marine organisms and the functioning of marine ecosystems. Policymakers are beginning to address the risk of ecological impact, but are constrained by a lack of data on current and historic noise levels. Here, we present the first nationally coordinated effort to quantify underwater noise levels, in support of UK policy objectives under the EU Marine Strategy Framework Directive (MSFD). Field measurements were made during 2013-2014 at twelve sites around the UK. Median noise levels ranged from 81.5-95.5 dB re 1 μPa for one-third octave bands from 63-500 Hz. Noise exposure varied considerably, with little anthropogenic influence at the Celtic Sea site, to several North Sea sites with persistent vessel noise. Comparison of acoustic metrics found that the RMS level (conventionally used to represent the mean) was highly skewed by outliers, exceeding the 97th percentile at some frequencies. We conclude that environmental indicators of anthropogenic noise should instead use percentiles, to ensure statistical robustness. Power analysis indicated that at least three decades of continuous monitoring would be required to detect trends of similar magnitude to historic rises in noise levels observed in the Northeast Pacific.

  10. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

    Companies operate in an increasingly challenging environment that requires them to continuously improve all areas of the business process. Demand forecasting is one area in manufacturing companies where we can hope to gain great advantages. Improvements in forecasting can result in cost savings throughout the supply chain, improve the reliability of information and the quality of the service for our customers. In the company Danfoss Trata, d. o. o. we did not have a system for demand forecast...

  11. A Survey of Water Demand Forecasting Procedures on Fixed Army Installations.

    Science.gov (United States)

    1985-02-01

    Fort Dix, New Jersey Fort Hamilton, New York Fort Eustis, Virginia Fort Pickett, Virginia Fort Gordon, Georgia Fort Rucker, Alabama Fort Benjamin ...Vol 15, No. 4 (August 1979), pp 763-767. Darr , P., S. L. Feldman, and C. Komen, The Demand for Urban Water (Leiden, the Netherlands: Martinus...Fort Knox 5090 6635 -1545 628,6 -1196 Fort Leavenworth 2055 2070 -15 2144 -90 Fort Benjamin Harrison 552 2656 -2104 1554 -1001 Fort Lee 1655 2878 -1223

  12. Experience of the forecast of water and power resources changes at warming of the 21st century

    Institute of Scientific and Technical Information of China (English)

    Alexander; Kislov; Eugenie; Evstigneev; Galina; Surkova

    2009-01-01

    Global warming causes changes of those natural resources like power resources, water resources, agroclimatic and ecological resources etc. And these changes depend on climate. Dynamics of resources depends both on planned economic activities and on changes of a climate. In this work the climatic component of changes is discussed. Projecting results used in this paper are based on the data of the CMIP3 (coupled model intercomparison project) in the framework of Working Group on Coupled Modelling. This project includes the world’s best climate models. Results of modelling and the data of meteorological observations expressed by probability distribution functions (or empirical orthogonal functions) were compared. It was shown that the modelling data were much more reliable over flat territories. In result 11 most successful models were selected. They can be used for the forecast within the framework of known IPCC scenario А2 at the 21st century. Estimations of changes of volumes of a river runoff and conditions of humidity of territory have been allowed to determine that the serious reduction of resources is expected in the southern part of the East European plain. Its central and northern parts practically will not demonstrate changes.

  13. Experience of the forecast of water and power resources changes at warming of the 21st century

    Institute of Scientific and Technical Information of China (English)

    Alexander Kislov; Eugenie Evstigneev; Galina Surkova

    2009-01-01

    Global warming causes changes of those natural resources like power resources, water resources, agroclimatic and ecological resources etc.And these changes depend on climate.Dynamics of re-sources depends both on planned economic activities and on changes of a climate.In this work the climatic component of changes is discussed.Projecting results used in this paper are based on the data of the CMIP3 (coupled model intercomparison project) in the framework of Working Group on Coupled Modelling, This project includes the world's best climate models, Results of modelling and the data of meteorological observations expressed by probability distribution functions (or empirical or-thogonal functions) were compared, It was shown that the modelling data were much more reliable over flat territories.In result 11 most successful models were selected, They can be used for the forecast within the framework of known IPCC scenario A2 at the 21st century, Estimations of changes of vol-umes of a river runoff and conditions of humidity of territory have been allowed to determine that the serious reduction of resources is expected in the southern part of the East European plain.Its central and northern parts practically will not demonstrate changes.

  14. 用人工神经网络预测时用水量的方法%STUDY ON ARTIFICIAL NEURAL NETWORK FORECASTING METHOD OF WATER CONSUMPTION PER HOUR

    Institute of Scientific and Technical Information of China (English)

    刘洪波; 张宏伟; 田林; 王新芳

    2001-01-01

    根据城市时段用水量序列季节性、趋势性及随机扰动性的特点,利用人工神经网络方法,建立了时间水量短期预报模型.选取不同的隐层结点数,采用相同的输入样本及预测数据进行训练和预测,并通过比较其相对误差的大小,确定了神经网络的结构,并应用Matlab语言进行了具体的建模和预报.实例考核证明,该方法与常用的时间序列三角函数分析法相比,具有预测误差小、计算速度快等特点,可满足供水系统调度运行的实际需要.%An artificial neural network (ANN) short-term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden-layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.

  15. Comparing complementary NWP model performance for hydrologic forecasting for the river Rhine in an operational setting

    Science.gov (United States)

    Davids, Femke; den Toom, Matthijs

    2016-04-01

    This paper investigates the performance of complementary NWP models for hydrologic forecasting for the river Rhine, a large river catchment in Central Europe. An operational forecasting system, RWsOS-Rivieren, produces daily forecasts of discharges and water levels at the Water Management Centre Netherlands. A combination of HBV (rainfall-runoff) and SOBEK (hydrodynamic routing) models is used to produce simulations and forecasts for the catchment. Data assimilation is applied both to the model state of SOBEK and to model outputs. The primary function of the operational forecasting system is to provide reliable and accurate forecasts during periods of high water. The secondary main function is producing daily predictions for water management and water transport in The Netherlands. In addition, predicting water levels during drought periods is becoming increasingly important as well. At this moment several complementary deterministic and ensemble NWP models are used to provide the forecasters with predictions with varied initial conditions, such as ICON, ICON-EU Nest, ECMWF-DET, ECMWF-EPS, HiRLAM, COSMO-LEPS and GLAMEPS. ICON and ICON-EU have recently replaced DWD-GME and DWD COSMO-EU. These models provide weather forecasts with different lengths of lead times and also different periods of operational usage. A direct and quantitative comparison is therefore challenging. Nevertheless, it is important to investigate the suitability of the different NWP models for certain lead times and certain weather situations to help support the hydrological forecasters make an informed forecast during an operational crisis. A hindcast study will investigate the performance of these models in the operational system for different lead times and focusing on periods of both high and low water for Lobith, the location of entry of the river Rhine into The Netherlands.

  16. Precipitable water and surface humidity over global oceans from special sensor microwave imager and European Center for Medium Range Weather Forecasts

    Science.gov (United States)

    Liu, W. T.; Tang, Wenqing; Wentz, Frank J.

    1992-01-01

    Global fields of precipitable water W from the special sensor microwave imager were compared with those from the European Center for Medium Range Weather Forecasts (ECMWF) model. They agree over most ocean areas; both data sets capture the two annual cycles examined and the interannual anomalies during an ENSO episode. They show significant differences in the dry air masses over the eastern tropical-subtropical oceans, particularly in the Southern Hemisphere. In these regions, comparisons with radiosonde data indicate that overestimation by the ECMWF model accounts for a large part of the differences. As a check on the W differences, surface-level specific humidity Q derived from W, using a statistical relation, was compared with Q from the ECMWF model. The differences in Q were found to be consistent with the differences in W, indirectly validating the Q-W relation. In both W and Q, SSMI was able to discern clearly the equatorial extension of the tongues of dry air in the eastern tropical ocean, while both ECMWF and climatological fields have reduced spatial gradients and weaker intensity.

  17. Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

    Science.gov (United States)

    Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

    2015-12-01

    Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases

  18. The Status of Budget Forecasting

    Directory of Open Access Journals (Sweden)

    Daniel W. Williams

    2016-11-01

    Full Text Available This article examines the breadth of the current forecast literature as it relates to public budget making. It serves to provide summary information to decision-makers who otherwise do not have the resources to learn more than a small amount focused on much more narrowly defined areas of forecasting (such as the politics of forecast bias. Next, it serves those who perform forecasting related to budgeting by reviewing the current methods and practices commonly used in this domain. It also provides a ground level for future public budget forecasting research. Finally, this article identifies several areas in which the public forecasting literature needs additional development. Several of these areas, such as the effectiveness of nonregression-based forecasting techniques, are quite important to the majority of governments in the United States and other subnational jurisdictions, where budget offices are limited and resource investments in technology are scarce.

  19. Regional and State Level Water Scarcity Report: Northeast United States

    Science.gov (United States)

    Nicoletti, C. K.; Lopez-Morales, C. A.; Hoover, J. H.; Voigt, B. G.; Vorosmarty, C. J.; Mohammed, I. N.

    2010-12-01

    There are an abundance of large-scale, coarse resolution global water scarcity studies, but the existing literature fails to address regional and state specific scarcity measures. Moreover, while environmental water requirements are an integral factor in the development and implementation of sustainable water management practices, only recently has this notion been introduced to water scarcity research. In this paper, we argue that developing a preliminary measure of water scarcity, at the regional and state levels, will allow for more informed policy development. The goal of this study is to generate a more comprehensive understanding of water scarcity in the Northeast, by gathering fine scale data, applying a consistent methodology to the calculation of a scarcity index, and analyzing the results to see relative trends in spatio-temporal water scarcity. Public supply, irrigation, rural, industrial and thermo-power withdrawals have been compiled from USGS state water use publications from 1950 to 1985. Using the WBMplus water model runoff data, state specific in-stream environmental water requirements were calculated using the accepted hydro-ecological methodology. Water scarcity was then calculated as a ratio of water withdrawals to total available water minus environmental flow requirements for the system. In so doing, this study generates a spatially explicit and temporally varying water scarcity indicator (WSI) for the Northeastern United States between 1950 and 2000 at the regional and state levels at a five-year time interval. Calculation of a spatial and temporal water scarcity indicator enabled us to identify regions and specific states that were: slightly exploited (WSI 1.0). The minimum environmental water requirements to maintain in-stream aquatic and riparian ecosystems for the Northeastern states ranged between 27.5 to 36.3 percent of the mean annual runoff within Vermont and Maryland, respectively. The regional WSI values ranged between 0.199 in 1950

  20. A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

    Science.gov (United States)

    Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2015-04-01

    Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple

  1. National level water quality simulation and climate change scenarios in Finland with WSFS-Vemala model

    Science.gov (United States)

    Huttunen, M.; Huttunen, I.; Seppänen, V.; Vehviläinen, B.

    2012-04-01

    included in the model. For natural background leaching and loading from forestry are used estimated values, process based description is under development. Sedimentation, erosion and denitrification are modelled for rivers. In lakes sedimentation, resuspension, release from sediments and denitrification are modelled. The WSFS-Vemala model is applied for load reduction and country wide climate change scenarios. In load reduction scenarios farming practices and fertilization of each field can be adjusted separately by the characteristics of the field. In climate change scenarios water quality until year 2060 is simulated. For the effects of climate change on agriculture we are using DREMFIA sector model scenarios from MTT Agrifood Research Finland. DREMFIA model gives scenarios as hectars of different crops, fertilization levels and number of cattle in four regions in Finland. Scenarios for point loading, scattered settlements, forestry and background leaching are based on expert estimates. WSFS-Vemala model is then simulated with modified weather, loading and farming input and results include concentrations in rivers and lakes and finally loading into the Baltic Sea. Preliminary scenario results show a slight increase in annual loading and remarkable shift in seasonal loading, with increased loading in winter. WSFS-Vemala model is also applied for real time simulation and forecasting of water quality, including forecasts for chlorophyll-a concentration. Forecasts are provided for the public by www pages at www.environment.fi/waterforecast.

  2. Sea Levels Online: Sea Level Variations of the United States Derived from National Water Level Observation Network Stations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Water level records are a combination of the fluctuations of the ocean and the vertical land motion at the location of the station. Monthly mean sea level (MSL)...

  3. Financial Analysts’ Forecasts

    DEFF Research Database (Denmark)

    Stæhr, Simone

    in the financial forecasts on which they base investment decisions they may end up losing money as a consequence of biased forecasts. Thus, relying primarily on decision theories such as social comparison theory and theories on confirmation bias this thesis investigates how and why pronounced biases in financial......This thesis is broadly concentrated on decision making under uncertainty. It seeks to investigate how agents in financial markets make decisions at the individual level and how these decisions can sometimes be affected by personal traits and cognitive biases rather than being perfectly rational....... The primary focus is on financial analysts in the task of conducting earnings forecasts while a secondary focus is on investors’ abilities to interpret and make use of these forecasts. Simply put, financial analysts can be seen as information intermediators receiving inputs to their analyses from firm...

  4. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

    relating to “ injectors ”) to develop a map of the related technologies [33.] Another approach is to develop a “tree” showing a system branching into its...additional terms such as “trend,” “forecast,” “ delphi ,” “assessment,” and so forth may call up other forecasts and assessments relating to the topic...present and future engine technologies. A preliminary search (Step 1, Table 5) located prior forecasts, in particular, a Delphi study [36]. The Delphi

  5. Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Carlos Peña-Guzmán

    2016-01-01

    Full Text Available The Colombian capital, Bogotá, has undergone massive growth in a short period of time. Naturally, this growth has increased the city’s water demand. The prediction of this demand will help understand and analyze consumption behavior, thereby allowing for effective management of the urban water cycle. This paper uses the Least-Squares Support Vector Machines (LS-SVM model for forecasting residential, industrial, and commercial water demand in the city of Bogotá. The parameters involved in this study include the following: monthly water demand, number of users, and total water consumption bills (price for the three studied uses. Results provide evidence of the model’s accuracy, producing R2 between 0.8 and 0.98, with an error percentage under 12%.

  6. Estimation of Peak Water Level in Pearl River Estuary under the Background of Sea Level Rise

    Institute of Scientific and Technical Information of China (English)

    KONG; Lan; CHEN; Xiao-hong; ZHUANG; Cheng-bin; CHEN; Dong-wei

    2012-01-01

    [Objective] The study aimed to predict the peak water level in Pearl River Estuary under the background of sea level rise. [Method] The changing trends of peak water level at Denglongshan station and Hengmen station were analyzed firstly on the basis of regression models, and then sea level rise in Pearl River Estuary in 2050 was predicted to estimate the 1-in-50-year peak water level in the same year. [Result] Regression analyses showed that the increasing rate of peak water level over past years was 6.3 mm/a at Denglongshan station and 5.8 mm/a at Hengmen station. In addition, if sea level will rise by 20, 30 and 60 cm respectively in 2050, it was predicted that the 1-in-50-year peak water level will reach 3.04, 3.14 and 3.44 m at Denglongshan station, and 3.19, 3.29 and 3.59 m at Hengmen station separately. [Conclusion] The estimation of peak water level in Pearl River Estuary could provide theoretical references for water resources planning.

  7. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2016-04-01

    Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative

  8. Terrestrial Waters and Sea Level Variations on Interannual Time Scale

    Science.gov (United States)

    Llovel, W.; Becker, M.; Cazenave, A.; Jevrejeva, S.; Alkama, R.; Decharme, B.; Douville, H.; Ablain, M.; Beckley, B.

    2011-01-01

    On decadal to multi-decadal time scales, thermal expansion of sea waters and land ice loss are the main contributors to sea level variations. However, modification of the terrestrial water cycle due to climate variability and direct anthropogenic forcing may also affect sea level. For the past decades, variations in land water storage and corresponding effects on sea level cannot be directly estimated from observations because these are almost non-existent at global continental scale. However, global hydrological models developed for atmospheric and climatic studies can be used for estimating total water storage. For the recent years (since mid-2002), terrestrial water storage change can be directly estimated from observations of the GRACE space gravimetry mission. In this study, we analyse the interannual variability of total land water storage, and investigate its contribution to mean sea level variability at interannual time scale. We consider three different periods that, each, depend on data availability: (1) GRACE era (2003-2009), (2) 1993-2003 and (3) 1955-1995. For the GRACE era (period 1), change in land water storage is estimated using different GRACE products over the 33 largest river basins worldwide. For periods 2 and 3, we use outputs from the ISBA-TRIP (Interactions between Soil, Biosphere, and Atmosphere-Total Runoff Integrating Pathways) global hydrological model. For each time span, we compare change in land water storage (expressed in sea level equivalent) to observed mean sea level, either from satellite altimetry (periods 1 and 2) or tide gauge records (period 3). For each data set and each time span, a trend has been removed as we focus on the interannual variability. We show that whatever the period considered, interannual variability of the mean sea level is essentially explained by interannual fluctuations in land water storage, with the largest contributions arising from tropical river basins.

  9. Levels of Cadmium and Lead in Water, Sediments and Selected ...

    African Journals Online (AJOL)

    Daisy Ouya

    Key words: heavy metals, cadmium, lead, water, sediment, fish, Kenya coast. Abstract—Flame ... accumulate some metals within food chains ... levels of toxic heavy metals (particularly cadmium ... In order to have impact on aquatic organisms,.

  10. Radio Frequency Based Water Level Monitor and Controller for ...

    African Journals Online (AJOL)

    Radio Frequency Based Water Level Monitor and Controller for Residential Applications. ... Nigerian Journal of Technology ... This paper elucidates a radio frequency (RF) based transmission and reception system used to remotely monitor ...

  11. NOS CO-OPS Water Level Data, Verified, 6-Minute

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has verified (quality-controlled), 6-minute, water level (tide) data from NOAA NOS Center for Operational Oceanographic Products and Services (CO-OPS)....

  12. NOS CO-OPS Water Level Data, Verified, High Low

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has verified (quality-controlled), daily, high low water level (tide) data from NOAA NOS Center for Operational Oceanographic Products and Services...

  13. 2012 Water Levels - Mojave River and the Morongo Groundwater Basins

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — During 2012, the U.S. Geological Survey and other agencies made approximately 2,500 water-level measurements in the Mojave River and Morongo groundwater basins....

  14. Water-level change, High Plains aquifer, 1980 to 1995

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This raster data set represents water-level change in the High Plains aquifer of the United States from 1980 to 1995, in feet. The High Plains aquifer underlies...

  15. Water-level change, High Plains aquifer, 1995 to 2000

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This raster data set represents water-level change in the High Plains aquifer of the United States from 1995 to 2000, in feet. The High Plains aquifer underlies...

  16. Water-level change, High Plains aquifer, 2005 to 2009

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This raster data set represents water-level change in the High Plains aquifer of the United States from 2005 to 2009, in feet. The High Plains aquifer underlies...

  17. Water-level change, High Plains aquifer, 2000 to 2005

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This raster data set represents water-level change in the High Plains aquifer of the United States from 2000 to 2005, in feet. The High Plains aquifer underlies...

  18. NOS CO-OPS Water Level Data, Verified, Hourly

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has verified (quality-controlled), hourly, water level (tide) data from NOAA NOS Center for Operational Oceanographic Products and Services (CO-OPS)....

  19. NOS CO-OPS Water Level Data, Preliminary, 1-Minute

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has recent, preliminary (not quality-controlled), 1-minute, water level (tide) data from NOAA NOS Center for Operational Oceanographic Products and...

  20. Water level and vegetation change analysis at Stillwater Wildlife Refuge

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — The aim of the project summarized in this report was to determine the feasibility of detecting change in surface water levels and associated wetland biomass at the...

  1. NOS CO-OPS Water Level Data, Preliminary, 6-Minute

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset has recent, preliminary (not quality-controlled), 6-minute, water level (tide) data from NOAA NOS Center for Operational Oceanographic Products and...

  2. Initial Survey Instructions for management unit water monitoring : level

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Initial survey instructions for 1.08 management unit water monitoring (level) survey on Fish Springs National Wildlife Refuge. This survey is conducted weekly and is...

  3. Life cycle assessment of forecasting scenarios for urban water management: A first implementation of the WaLA model on Paris suburban area.

    Science.gov (United States)

    Loubet, Philippe; Roux, Philippe; Guérin-Schneider, Laetitia; Bellon-Maurel, Véronique

    2016-03-01

    A framework and an associated modeling tool to perform life cycle assessment (LCA) of urban water system, namely the WaLA model, has been recently developed. In this paper, the WaLA model is applied to a real case study: the urban water system of the Paris suburban area, in France. It aims to verify the capacity of the model to provide environmental insights to stakeholder's issues related to future trends influencing the system (e.g., evolution of water demand, increasing water scarcity) or policy responses (e.g., choices of water resources and technologies). This is achieved by evaluating a baseline scenario for 2012 and several forecasting scenarios for 2022 and 2050. The scenarios are designed through the modeling tool WaLA, which is implemented in Simulink/Matlab: it combines components representing the different technologies, users and resources of the UWS. The life cycle inventories of the technologies and users components include water quantity and quality changes, specific operation (electricity, chemicals) and infrastructures data (construction materials). The methods selected for the LCIA are midpoint ILCD, midpoint water deprivation impacts at the sub-river basin scale, and endpoint Impact 2002+. The results of the baseline scenario show that wastewater treatment plants have the highest impacts compared to drinking water production and distribution, as traditionally encountered in LCA of UWS. The results of the forecasting scenarios show important changes in water deprivation impacts due to water management choices or effects of climate change. They also enable to identify tradeoffs with other impact categories and to compare several scenarios. It suggests the capacity of the model to deliver information for decision making about future policies.

  4. Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

    Science.gov (United States)

    Shutler, J. D.; Warren, M. A.; Miller, P. I.; Barciela, R.; Mahdon, R.; Land, P. E.; Edwards, K.; Wither, A.; Jonas, P.; Murdoch, N.; Roast, S. D.; Clements, O.; Kurekin, A.

    2015-04-01

    Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008-2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

  5. Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

    Science.gov (United States)

    Buyukyildiz, Meral; Tezel, Gulay

    2017-04-01

    In this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-ɛ support vector regression (PSO-ɛSVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ( R 2) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-ɛSVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; min R 2 = 0.9169, max R 2 = 0.9995, median R 2 = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations.

  6. Predictive models applied to groundwater level forecasting: a preliminary experience on the alluvial aquifer of the Magra River (Italy).

    Science.gov (United States)

    Brozzo, Gianpiero; Doveri, Marco; Lelli, Matteo; Scozzari, Andrea

    2010-05-01

    Computer-based decision support systems are getting a growing interest for water managing authorities and water distribution companies. This work discusses a preliminary experience in the application of computational intelligence in a hydrological modeling framework, regarding the study area of the alluvial aquifer of the Magra River (Italy). Two sites in the studied area, corresponding to two distinct groups of wells (Battifollo and Fornola) are managed by the local drinkable water distribution company (ACAM Acque), which serves the area of La Spezia, on the Ligurian coast. Battifollo has 9 wells with a total extraction rate of about 240 liters per second, while Fornola has 44 wells with an extraction rate of about 900 liters per second. Objective of this work is to make use of time series coming from long-term monitoring activities in order to assess the trend of the groundwater level with respect to a set of environmental and exploitation parameters; this is accomplished by the experimentation of a suitable model, eligible to be used as a predictor. This activity moves on from the modeling of the system behavior, based on a set of Input/Output data, in order to characterize it without necessarily a prior knowledge of any deterministic mechanism (system identification). In this context, data series collected by continuous hydrological monitoring instrumentation installed in the studied sites, together with meteorological and water extraction data, have been analyzed in order to assess the applicability and performance of a predictive model of the groundwater level. A mixed approach (both data driven and process-based) has been experimented on the whole dataset relating to the last ten years of continuous monitoring activity. The system identification approach presented here is based on the integration of an adaptive technique based on Artificial Neural Networks (ANNs) and a blind deterministic identification approach. According to this concept, the behavior of

  7. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

  8. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  9. Back-propagation neural network in tidal-level forecasting by Ching-PiaoTsai and Tsong-Lin Lee - Discussion

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    an interesting study on the ap- plication of an artificial neural network (ANN) for forecasting tidal levels. This technique is comparable to the already pop- ular time series modeling with an added advantage in that the functional form between the input variable... of hydrologic models.’’ J. Hydrology, 81, 57–77. McCuen, R. H. (1993). Statistical hydrology, Prentice-Hall, Englewood Cliffs, N.J. Tokar, A. S., and Johnson, P. A. (1999). ‘‘Rainfall-runoff modeling using artificial neural networks.’’ J. Hydrologic Engrg., ASCE...

  10. Numerical simulation of the impacts of water level variation on water age in Dahuofang Reservoir

    Science.gov (United States)

    Li, Xinwen; Shen, Yongming

    2015-06-01

    The transport timescales were investigated in response to water level variation under different constant flow rates in Dahuofang Reservoir. The concept of water age was applied to quantify the transport timescales. A three-dimensional hydrodynamic model was developed based on the Environmental Fluid Dynamics Code (EFDC). The model was calibrated for water surface elevation and temperature profiles from April 1, 2008 to October 31, 2008. Comparisons of observed and modeled data showed that the model reproduced the water level fluctuation and thermal stratification during warm season and vertical mixing during cold season fairly well. The calibrated model was then applied to investigate the response of water age to water level changes in Dahuofang Reservoir. Model results showed that water age increases from confluence toward dam zone. In the vertical direction, the water age is relatively uniform at upstream and stratifies further downstream, with a larger value at bottom layer than at surface layer. Comparisons demonstrated that water level variation has a significant impact on transport timescales in the reservoir. The impact of water level drawdown on water age is stronger at bottom layer than at surface layer. Under high flow conditions, the water age decreases 0-20 days at surface layer and 15-25 days at bottom layer. Under mean flow conditions, the water age decreases 20-30 days at surface layer and 30-50 days at bottom layer. Furthermore, the impact is minor in the upstream and increases further downstream. The vertical stratification of water age weakens as the water level decreases. This study provides a numerical tool to quantify the transport timescale in Dahuofang Reservoir and supports adaptive management of regional water resources by local authorities.

  11. Image-based Water Level Measurement Method under Stained Ruler

    Institute of Scientific and Technical Information of China (English)

    Jae-do KIM; Young-joon HAN; Hern-soo HAHN

    2010-01-01

    This paper proposes the water level measuring method based on the image,while the ruler used to indicate the water level is stained.The contamination of the ruler weakens or eliminates many features which are required for the image processing.However,the feature of the color difference between the ruler and the water surface are firmer on the environmental change compare to the other features.As the color differences are embossed,only the region of the ruler is limited to eliminate the noise,and the average image is produced by using several continuous frames.A histogram is then produced based on the height axis of the produced intensity average image.Local peaks and local valleys are detected,and the section between the peak and valley which have the greatest change is looked for.The valley point at this very moment is used to detect the water level.The detected water level is then converted to the actual water level by using the mapping table.The proposed method is compared to the ultrasonic based method to evaluate its accuracy and efficiency on the various contaminated environments.

  12. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  13. Short-range forecast of Shershnevskoie (South Ural) water-storage algal blooms: preliminary results of predictors' choosing and membership functions' construction

    Science.gov (United States)

    Gayazova, Anna; Abdullaev, Sanjar

    2014-05-01

    Short-range forecasting of algal blooms in drinking water reservoirs and other waterbodies is an actual element of water treatment system. Particularly, Shershnevskoie reservoir - the source of drinking water for Chelyabinsk city (South Ural region of Russia) - is exposed to interannual, seasonal and short-range fluctuations of blue-green alga Aphanizomenon flos-aquae and other dominant species abundance, which lead to technological problems and economic costs and adversely affect the water treatment quality. Whereas the composition, intensity and the period of blooms affected not only by meteorological seasonal conditions but also by ecological specificity of waterbody, that's important to develop object-oriented forecasting, particularly, search for an optimal number of predictors for such forecasting. Thereby, firstly fuzzy logic and fuzzy artificial neural network patterns for blue-green alga Microcystis aeruginosa (M. aeruginosa) blooms prediction in nearby undrained Smolino lake were developed. These results subsequently served as the base to derive membership functions for Shernevskoie reservoir forecasting patterns. Time series with the total lenght about 138-159 days of dominant species seasonal abundance, water temperature, cloud cover, wind speed, mineralization, phosphate and nitrate concentrations were obtained through field observations held at Lake Smolino (Chelyabinsk) in the warm season of 2009 and 2011 with time resolution of 2-7 days. The cross-correlation analysis of the data revealed the potential predictors of M. aeruginosa abundance quasi-periodic oscillations: green alga Pediastrum duplex (P. duplex) abundance and mineralization for 2009, P. duplex abundance, water temperature and concentration of nitrates for 2011. According to the results of cross-correlation analysis one membership function "P. duplex abundance" and one rule linking M. aeruginosa and P. duplex abundances were set up for database of 2009. Analogically, for database of 2011

  14. WATER-LEVEL MONITOR FOR BOREWELL AND WATER TANK BASED ON GSM

    Directory of Open Access Journals (Sweden)

    R.Ramani

    2012-10-01

    Full Text Available Now a days, home automation & remote control and monitoring systems have seen a rapid growth in terms of technology. Apparently there is no early warning system to monitor the tank water level and bore well water level when it has reached the critical level. In this paper we have provided water level monitoring in the tank as well as in the bore well. If the water level in a bore well drops below the threshold level for pumping its pump motor may get air locked or more burn out due to dry running. It is awkward for farmers to walk all the way to their fields at night just to switch the pump motor off. Besides, he may never get to identify the problem. This problem can be solved by using this GSM based system that will automatically make a call to the user mobile phone, when the water Level in the bore well drops threshold below or rises to the threshold level for pumping. The user can also remotely switch on or off the pump motor by sending a SMS from his mobile phone. The system is simple, reliable, portable and affordable. We proposed the work in which, Whenever water level in the tankdrops below the required level the system try to fill the tank by switching on the bore well motor to pump the water into the tank It is must to have enough water in the bore well to avoid the formation of air gap or empty running of bore well motor. High precision water level sensor is used to identify the reference water level to activate and deactivate the motor and system properly by interfacing the sensor devices into the well definedembedded system.

  15. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  16. Hindcast and Forecast of 137Cs Activities in the North Pacific Ocean Waters from 1945 to 2020 by Eddy-resolving ROMS

    Science.gov (United States)

    Tsubono, T.; Misumi, K.; Tsumune, D.; Aoyama, M.; Hirose, K.

    2015-12-01

    We conducted a hindcast and forecast of 137Cs activities in the North Pacific waters from 1945 to 2020, before and after the Fukushima Dai-ichi Nuclear Power Plant (F1NPP) accident. We used the Regional Ocean Model System (ROMS) with high resolution (1/12º-1/4º in horizontal, 45 levels in vertical), of which domain was the North Pacific Ocean. The model was driven by the exactly repeating "Normal Year" forcing Coordinated Ocean Reference Experiment (CORE) forcing dataset (Large and Yeager, 2008) using bulk formulae and the model-predicted sea surface temperature and the 50 years averaged SODA data as boundary conditions. The reconstructed global fallout due to atmospheric nuclear weapons' tests and Chernobyl accident was employed for atmospheric flux of 137Cs from 1945 to 2011. After the accident, the atmospheric deposition and direct release of 137Cs from F1NPP were also employed for input condition. Five ensemble calculations of 137Cs activities in seawater were conducted under different initial conditions, but had identical forcing. The net input of 16 PBq of 137Cs from F1NPP, which was employed in this study, corresponded to 26% of the total amount (61 PBq) of 137Cs that was estimated in the North Pacific before the F1NPP accident in 2011. Before the accident in 2011, the 137Cs on surface ranged from 0.75 to 1.7 Bq m-3. The direct comparison between simulated and observed 134Cs activities in the surface layer represented that the root-mean-square error and correlation coefficient were 5.6 Bq m-3 and 0.86, respectively, suggesting the model result were consistent with the observations. The main body of high 137Cs activity water from F1NPP was transported to south of the Subarctic Front around 42°N via the Oyashio Coastal Current, the Oyashio intrusion, and the Kuroshio bifurcation and then to the western North Pacific. This model simulation suggested that the 137Cs activities in surface waters at P26 (P04) would increase to 4.1 Bq m-3 (4.3 Bq m-3 ) in 2015

  17. Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting

    CSIR Research Space (South Africa)

    Monteiro, PMS

    2006-09-01

    Full Text Available or mortality of rock lobster in the southern Benguela; Medium term (2 month) forecasting of the intensification of the remote forcing of ETSA derived LOW which has a bearing on the Namibian hake fishery These two scales are discussed in detail in the companion...

  18. The effect of domain length and parameter estimation on observation impact in data assimilation for flood inundation forecasting.

    Science.gov (United States)

    Cooper, Elizabeth; Dance, Sarah; Garcia-Pintado, Javier; Nichols, Nancy; Smith, Polly

    2017-04-01

    Timely and accurate inundation forecasting provides vital information about the behaviour of fluvial flood water, enabling mitigating actions to be taken by residents and emergency services. Data assimilation is a powerful mathematical technique for combining forecasts from hydrodynamic models with observations to produce a more accurate forecast. We discuss the effect of both domain size and channel friction parameter estimation on observation impact in data assimilation for inundation forecasting. Numerical shallow water simulations are carried out in a simple, idealized river channel topography. Data assimilation is performed using an Ensemble Transform Kalman Filter (ETKF) and synthetic observations of water depth in identical twin experiments. We show that reinitialising the numerical inundation model with corrected water levels after an assimilation can cause an initialisation shock if a hydrostatic assumption is made, leading to significant degradation of the forecast for several hours immediately following an assimilation. We demonstrate an effective and novel method for dealing with this. We find that using data assimilation to combine observations of water depth with forecasts from a hydrodynamic model corrects the forecast very effectively at time of the observations. In agreement with other authors we find that the corrected forecast then moves quickly back to the open loop forecast which does not take the observations into account. Our investigations show that the time taken for the forecast to decay back to the open loop case depends on the length of the domain of interest when only water levels are corrected. This is because the assimilation corrects water depths in all parts of the domain, even when observations are only available in one area. Error growth in the forecast step then starts at the upstream part of the domain and propagates downstream. The impact of the observations is therefore longer-lived in a longer domain. We have found that the

  19. predicting water levels at kainji dam using artificial neural networks

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... number of power generation plants, existing ones are facing declining output due to ... become extremely popular for prediction and forecast- ing in a number of areas, ..... Monthly Weather Review, 126,. 1998, pp470–482. 12.

  20. Verification of Ensemble Forecasts for the New York City Operations Support Tool

    Science.gov (United States)

    Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

    2012-12-01

    The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble

  1. Forecasting irrigation demand by assimilating satellite images and numerical weather predictions

    Science.gov (United States)

    Pelosi, Anna; Medina, Hanoi; Villani, Paolo; Falanga Bolognesi, Salvatore; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    Forecasting irrigation water demand, with small predictive uncertainty in the short-medium term, is fundamental for an efficient planning of water resource allocation among multiple users and for decreasing water and energy consumptions. In this study we present an innovative system for forecasting irrigation water demand, applicable at different spatial scales: from the farm level to the irrigation district level. The forecast system is centred on a crop growth model assimilating data from satellite images and numerical weather forecasts, according to a stochastic ensemble-based approach. Different sources of uncertainty affecting model predictions are represented by an ensemble of model trajectories, each generated by a possible realization of the model components (model parameters, input weather data and model state variables). The crop growth model is based on a set of simplified analytical relations, with the aim to assess biomass, leaf area index (LAI) growth and evapotranspiration rate with a daily time step. Within the crop growth model, LAI dynamics is let be governed by temperature and leaf dry matter supply, according to the development stage of the crop. The model assimilates LAI data retrieved from VIS-NIR high-resolution multispectral satellite images. Numerical weather model outputs are those from the European limited area ensemble prediction system (COSMO-LEPS), which provides forecasts up to five days with a spatial resolution of seven kilometres. Weather forecasts are sequentially bias corrected based on data from ground weather stations. The forecasting system is evaluated in experimental areas of southern Italy during three irrigation seasons. The performance analysis shows very accurate irrigation water demand forecasts, which make the proposed system a valuable support for water planning and saving at farm level as well as for water management at larger spatial scales.

  2. Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis.

    Science.gov (United States)

    Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng

    2014-08-01

    The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.

  3. Water Security at Local Government Level: What do People Think?

    CSIR Research Space (South Africa)

    Meissner, Richard

    2016-06-01

    Full Text Available stream_source_info Meissner_2016.pdf.txt stream_content_type text/plain stream_size 2853 Content-Encoding UTF-8 stream_name Meissner_2016.pdf.txt Content-Type text/plain; charset=UTF-8 Water Security at Local... Government Level: What do People Think? By Dr. Richard Meissner Integrated Water Assessment Group Natural Resources and the Environment Council for Scientific and Industrial Research Presented at the Sustainable Water Seminar 2016, CSIR ICC, 2...

  4. A neural network model for predicting aquifer water level elevations.

    Science.gov (United States)

    Coppola, Emery A; Rana, Anthony J; Poulton, Mary M; Szidarovszky, Ferenc; Uhl, Vincent W

    2005-01-01

    Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.

  5. Water-quality and ground-water-level data, Bernalillo County, central New Mexico, 1995

    Science.gov (United States)

    Rankin, D.R.

    1996-01-01

    Water-quality and ground-water-level data were collected in two areas of eastern Bernalillo County in central New Mexico between March and July of 1995. Fifty-one wells, two springs, and the Ojo Grande Acequia in the east mountain area of Bernalillo County and nine wells in the northeast area of the city of Albuquerque were sampled. The water samples were analyzed for selected nutrient species; total organic carbon; major dissolved constituents; dissolved arsenic, boron, iron, and manganese; and methylene blue active substances. Analytical results were used to compute hardness, sodium adsorption ratio, and dissolved solids. Specific conductance, pH, temperature, and alkalinity were measured in the field at the time of sample collection. Ground- water-level and well-depth measurements were made at the time of sample collection when possible. Water-quality data, ground- water-level data, and well-depth data are presented in tabular form.

  6. AUTOMATED WATER LEVEL MEASUREMENTS IN SMALL-DIAMETER AQUIFER TUBES

    Energy Technology Data Exchange (ETDEWEB)

    PETERSEN SW; EDRINGTON RS; MAHOOD RO; VANMIDDLESWORTH PE

    2011-01-14

    Groundwater contaminated with hexavalent chromium, strontium-90, and uranium discharges into the Columbia River along approximately 16 km (10 mi) of the shoreline. Various treatment systems have and will continue to be implemented to eliminate the impact of Hanford Site contamination to the river. To optimize the various remediation strategies, it is important to understand interactions between groundwater and the surface water of the Columbia River. An automated system to record water levels in aquifer sampling tubes installed in the hyporheic zone was designed and tested to (1) gain a more complete understanding of groundwater/river water interactions based on gaining and losing conditions ofthe Columbia River, (2) record and interpret data for consistent and defensible groundwater/surface water conceptual models that may be used to better predict subsurface contaminant fate and transport, and (3) evaluate the hydrodynamic influence of extraction wells in an expanded pump-and-treat system to optimize the treatment system. A system to measure water levels in small-diameter aquifer tubes was designed and tested in the laboratory and field. The system was configured to allow manual measurements to periodically calibrate the instrument and to permit aquifer tube sampling without removing the transducer tube. Manual measurements were collected with an e-tape designed and fabricated especially for this test. Results indicate that the transducer system accurately records groundwater levels in aquifer tubes. These data are being used to refine the conceptual and numeric models to better understand interactions in the hyporheic zone of the Columbia River and the adjacent river water and groundwater, and changes in hydrochemistry relative to groundwater flux as river water recharges the aquifer and then drains back out in response to changes in the river level.

  7. Water-Table Levels and Gradients, Nevada, 1947-2004

    Science.gov (United States)

    Lopes, Thomas J.; Buto, Susan G.; Smith, J. LaRue; Welborn, Toby L.

    2006-01-01

    In 1999, the U.S. Environmental Protection Agency began a program to protect the quality of ground water in areas other than ground-water protection areas. These other sensitive ground water areas (OSGWA) are areas that are not currently, but could eventually be, used as a source of drinking water. The OSGWA program specifically addresses existing wells that are used for underground injection of motor-vehicle waste. To help determine whether a well is in an OSGWA, the Nevada Division of Environmental Protection needs statewide information on depth to water and the water table, which partly control the susceptibility of ground water to contamination and contaminant transport. This report describes a study that used available maps and data to create statewide maps of water-table and depth-to-water contours and surfaces, assessed temporal changes in water-table levels, and characterized water-table gradients in selected areas of Nevada. A literature search of published water-table and depth-to-water contours produced maps of varying detail and scope in 104 reports published from 1948 to 2004. Where multiple maps covered the same area, criteria were used to select the most recent, detailed maps that covered the largest area and had plotted control points. These selection criteria resulted in water-table and depth-to-water contours that are based on data collected from 1947 to 2004 being selected from 39 reports. If not already available digitally, contours and control points were digitized from selected maps, entered into a geographic information system, and combined to make a statewide map of water-table contours. Water-table surfaces were made by using inverse distance weighting to estimate the water table between contours and then gridding the estimates. Depth-to-water surfaces were made by subtracting the water-table altitude from the land-surface altitude. Water-table and depth-to-water surfaces were made for only 21 percent of Nevada because of a lack of

  8. Politics of innovation in multi-level water governance systems

    Science.gov (United States)

    Daniell, Katherine A.; Coombes, Peter J.; White, Ian

    2014-11-01

    Innovations are being proposed in many countries in order to support change towards more sustainable and water secure futures. However, the extent to which they can be implemented is subject to complex politics and powerful coalitions across multi-level governance systems and scales of interest. Exactly how innovation uptake can be best facilitated or blocked in these complex systems is thus a matter of important practical and research interest in water cycle management. From intervention research studies in Australia, China and Bulgaria, this paper seeks to describe and analyse the behind-the-scenes struggles and coalition-building that occurs between water utility providers, private companies, experts, communities and all levels of government in an effort to support or block specific innovations. The research findings suggest that in order to ensure successful passage of the proposed innovations, champions for it are required from at least two administrative levels, including one with innovation implementation capacity, as part of a larger supportive coalition. Higher governance levels can play an important enabling role in facilitating the passage of certain types of innovations that may be in competition with currently entrenched systems of water management. Due to a range of natural biases, experts on certain innovations and disciplines may form part of supporting or blocking coalitions but their evaluations of worth for water system sustainability and security are likely to be subject to competing claims based on different values and expertise, so may not necessarily be of use in resolving questions of "best courses of action". This remains a political values-based decision to be negotiated through the receiving multi-level water governance system.

  9. The response of mire vegetation to water level drawdown

    Science.gov (United States)

    Kurki, Kirsi; Laine, Jukka; Vasander, Harri; Tuittila, Eeva-Stiina

    2010-05-01

    Mires have a significant role in climate change mitigation due to their enormous carbon storage and due to the fluxes of greenhouse gases between ecosystem and the atmosphere. Mire vegetation is controlled by ecohydrology, climate and by the competition of plants on light and nutrients. The water logged conditions create a challenging environment for both vascular plants and bryophytes; therefore majority of plants growing in these habitats are highly specialized. Global warming is predicted to affect mire vegetation indirectly through increased evapotranspiration leading to decreased water table levels down to 14-22 centimeters. Water level drawdown is likely to affect the vegetation composition and consequently the ecosystem functioning of mires. Previous studies covering the first years following water table level drawdown have shown that vascular plants benefit from a lower water table and hollow-specific Sphagnum species suffer. In addition to changes in plant abundances the diversity of plant communities decreases. The lawn and hollow communities of Sphagna and sedges are found to be the most sensitive plant groups. It has been shown that surveys on vegetation changes can have different results depending on the time scale. The short and long term responses are likely vary in heterogenous mire vegetation; therefore predictions can be done more reliably with longer surveys. We applied BACI (before-after-control-impact) experimental approach to study the responses of different functional mire plant groups to water level drawdown. There are 3 control plots, 3 treatment plots with moderate water level drawdown and 3 plots drained for forestry 40 years ago as a reference. The plots are located in meso-, oligo- and ombrotrophic sites in Lakkasuo (Orivesi, Finland). The vegetation was surveyed from permanent sampling points before ditching in 2000 and during the years 2001-2003 and 2009. The data was analyzed with NMDS (PC-Ord) and DCA (CANOCO). Overall results show

  10. NOAA’s Nested Northern Gulf of Mexico Operational Forecast Systems Development

    Directory of Open Access Journals (Sweden)

    Eugene Wei

    2014-01-01

    Full Text Available The NOAA National Ocean Service’s (NOS Northern Gulf of Mexico Operational Forecast System (NGOFS became operational in March 2012. Implemented with the Finite Volume Coastal Ocean Model (FVCOM as its core three-dimensional oceanographic circulation model, NGOFS produces a real-time nowcast (−6 h to zero and six-hourly, two-day forecast guidance for water levels and three-dimensional currents, water temperature and salinity over the northern Gulf of Mexico continental shelf. Designed as a regional scale prediction system, NGOFS lacks sufficient spatial coverage and/or resolution to fully resolve hydrodynamic features in critical seaports and estuaries. To overcome this shortcoming and better support the needs of marine navigation, emergency response, and environmental management, two FVCOM-based, high-resolution, estuary-scale nested forecast modeling systems, namely the Northwest and Northeast Gulf of Mexico Operational Forecast Systems (NWGOFS and NEGOFS, have been developed through one-way nesting in NGOFS. Using the atmospheric forecast guidance from the NOAA (National Oceanic and Atmospheric Administration/NWS (National Weather Services’ North American Mesoscale (NAM Forecast System, US Geological Survey (USGS river discharge observations, and the NGOFS water level, current, water temperature and salinity as the surface, river, and open ocean boundary forcing, respectively, a six-month model hindcast for the period October 2010–March 2011 has been conducted. Modeled water levels, currents, salinity and water temperature are compared with observations using the NOS standard skill assessment software. Skill assessment scores indicated that NWGOFS and NEGOFS demonstrate improvement over NGOFS. The NWGOFS and NEGOFS are under real-time nowcast/forecast test and evaluation by NOS’s Center for Operational Oceanographic Products and Services (CO-OPS. The forecast systems are scheduled to be implemented operational on NOAA Weather

  11. Influence of nutrient level on methylmercury content in water spinach.

    Science.gov (United States)

    Greger, Maria; Dabrowska, Beata

    2010-08-01

    Widely consumed vegetables are often cultivated in sewage waters with high nutrient levels. They can contain high levels of methylmercury (MeHg), because they can form MeHg from inorganic Hg in their young shoots. We determined whether the MeHg uptake and the MeHg formation in the shoots of water spinach (Ipomoea aquatica) were affected by the presence of a high nutrient level in the growth medium. Water spinach shoots were rooted and pretreated in growth medium containing 7% (low) or 70% (high) Hoagland nutrient solution; thereafter, the plants were treated with either 0.02 microM MeHg or 0.2 microM HgCl2 for 3 d. Half the plants were then analyzed for total Hg and MeHg. The remaining plants were transferred to mercury-free medium with low or high nutrient levels and posttreated for 3 days before analysis of total Hg and MeHg in order to measure MeHg formation in the absence of external Hg. The results indicate that nutrient level did not influence MeHg uptake, but that a high nutrient level reduced the distribution of MeHg to the shoots 2.7-fold versus low nutrient level. After treatment with HgCl2, MeHg was found in roots and new shoots but not in old shoots. The MeHg:total-Hg ratio was higher in new shoots than in roots, being 13 times higher at high versus low nutrient levels. Thus, MeHg formation was the same in new shoots independent of inorganic Hg concentration, since the total Hg level decreased at a high nutrient level.

  12. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river

    Science.gov (United States)

    Bowden, Gavin J.; Maier, Holger R.; Dandy, Graeme C.

    2005-01-01

    This paper is the second of a two-part series in this issue that presents a methodology for determining an appropriate set of model inputs for artificial neural network (ANN) models in hydrologic applications. The first paper presented two input determination methods. The first method utilises a measure of dependence known as the partial mutual information (PMI) criterion to select significant model inputs. The second method utilises a self-organising map (SOM) to remove redundant input variables, and a hybrid genetic algorithm (GA) and general regression neural network (GRNN) to select the inputs that have a significant influence on the model's forecast. In the first paper, both methods were applied to synthetic data sets and were shown to lead to a set of appropriate ANN model inputs. To verify the proposed techniques, it is important that they are applied to a real-world case study. In this paper, the PMI algorithm and the SOM-GAGRNN are used to find suitable inputs to an ANN model for forecasting salinity in the River Murray at Murray Bridge, South Australia. The proposed methods are also compared with two methods used in previous studies, for the same case study. The two proposed methods were found to lead to more parsimonious models with a lower forecasting error than the models developed using the methods from previous studies. To verify the robustness of each of the ANNs developed using the proposed methodology, a real-time forecasting simulation was conducted. This validation data set consisted of independent data from a six-year period from 1992 to 1998. The ANN developed using the inputs identified by the stepwise PMI algorithm was found to be the most robust for this validation set. The PMI scores obtained using the stepwise PMI algorithm revealed useful information about the order of importance of each significant input.

  13. Water level oscillations in Monterey Bay and Harbor

    Directory of Open Access Journals (Sweden)

    J. Park

    2015-06-01

    Full Text Available Seiches are normal modes of water bodies responding to geophysical forcings with potential to significantly impact ecology and maritime operations. Analysis of high-frequency (1 Hz water level data in Monterey, California, identifies harbor modes between 10 and 120 s that are attributed to specific geographic features. It is found that modal amplitude modulation arises from cross-modal interaction and that offshore wave energy is a primary driver of these modes. Synchronous coupling between modes is observed to significantly impact dynamic water levels. At lower frequencies with periods between 15 and 60 min, modes are independent of offshore wave energy, yet are continuously present. This is unexpected since seiches normally dissipate after cessation of the driving force, indicating an unknown forcing. Spectral and kinematic estimates of these low-frequency oscillations support the idea that a persistent anticyclonic mesoscale gyre adjacent to the bay is a potential mode driver, while discounting other sources.

  14. Water level oscillations in Monterey Bay and Harbor

    Directory of Open Access Journals (Sweden)

    J. Park

    2014-11-01

    Full Text Available Seiches are normal modes of water bodies responding to geophysical forcings with potential to significantly impact ecology and maritime operations. Analysis of high-frequency (1 Hz water level data in Monterey California identifies Harbor modes between 10 and 120 s that are attributed with specific geographic features. It found that modal amplitude modulation arises from cross-modal interaction and that offshore wave energy is a primary driver of these modes. Synchronous coupling between modes is observed to significantly impact dynamic water levels. At lower frequencies between 15 and 60 min modes are independent of offshore wave energy, yet are continuously present. This is unexpected since seiches normally dissipate after cessation of the driving force, indicating an unknown forcing. Spectral and kinematic estimates of these low frequency oscillations supports the idea that a persistent anticyclonic mesoscale gyre adjacent to the Bay is a potential mode driver, while discounting other sources.

  15. General forecasting correcting formula

    OpenAIRE

    Harin, Alexander

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  16. General forecasting correcting formula

    OpenAIRE

    2009-01-01

    A general forecasting correcting formula, as a framework for long-use and standardized forecasts, is created. The formula provides new forecasting resources and new possibilities for expansion of forecasting including economic forecasting into the areas of municipal needs, middle-size and small-size business and, even, to individual forecasting.

  17. design and implementation of a water level controller

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... ... E.C. Anoliefod,a a. National Ctr. For Energy Research and Development, Univ. of Nigeria Nsukka ... Experimental performance results indicated that the device is quite suitable for .... the water level indicator. In the design ...

  18. Information Forecasting.

    Science.gov (United States)

    Hanneman, Gerhard J.

    Information forecasting provides a means of anticipating future message needs of a society or predicting the necessary types of information that will allow smooth social functioning. Periods of unrest and uncertainty in societies contribute to "societal information overload," whereby an abundance of information channels can create communication…

  19. Towards an operational system for oil-spill forecast over Spanish waters: initial developments and implementation test.

    Science.gov (United States)

    Sotillo, M G; Fanjul, E Alvarez; Castanedo, S; Abascal, A J; Menendez, J; Emelianov, M; Olivella, R; García-Ladona, E; Ruiz-Villarreal, M; Conde, J; Gómez, M; Conde, P; Gutierrez, A D; Medina, R

    2008-04-01

    The ESEOO Project, launched after the Prestige crisis, has boosted operational oceanography capacities in Spain, creating new operational oceanographic services and increasing synergies between these new operational tools and already existing systems. In consequence, the present preparedness to face an oil-spill crisis is enhanced, significantly improving the operational response regarding ocean, meteorological and oil-spill monitoring and forecasting. A key aspect of this progress has been the agreement between the scientific community and the Spanish Search and Rescue Institution (SASEMAR), significantly favoured within the ESEOO framework. Important achievements of this collaboration are: (1) the design of protocols that at the crisis time provide operational state-of-the-art information, derived from both forecasting and observing systems; (2) the establishment, in case of oil-spill crisis, of a new specialized unit, named USyP, to monitor and forecast the marine oceanographic situation, providing the required met-ocean and oil-spill information for the crisis managers. The oil-spill crisis scenario simulated during the international search and rescue Exercise "Gijón-2006", organized by SASEMAR, represented an excellent opportunity to test the capabilities and the effectiveness of this USyP unit, as well as the protocols established to analyze and transfer information. The results presented in this work illustrate the effectiveness of the operational approach, and constitute an encouraging and improved base to face oil-spill crisis.

  20. Stochastic Analysis and Forecasts of the Patterns of Speed, Acceleration, and Levels of Material Stock Accumulation in Society.

    Science.gov (United States)

    Fishman, Tomer; Schandl, Heinz; Tanikawa, Hiroki

    2016-04-01

    The recent acceleration of urbanization and industrialization of many parts of the developing world, most notably in Asia, has resulted in a fast-increasing demand for and accumulation of construction materials in society. Despite the importance of physical stocks in society, the empirical assessment of total material stock of buildings and infrastructure and reasons for its growth have been underexplored in the sustainability literature. We propose an innovative approach for explaining material stock dynamics in society and create a country typology for stock accumulation trajectories using the ARIMA (Autoregressive Integrated Moving Average) methodology, a stochastic approach commonly used in business studies and economics to inspect and forecast time series. This enables us to create scenarios for future demand and accumulation of building materials in society, including uncertainty estimates. We find that the so-far overlooked aspect of acceleration trends of material stock accumulation holds the key to explaining material stock growth, and that despite tremendous variability in country characteristics, stock accumulation is limited to only four archetypal growth patterns. The ability of nations to change their pattern will be a determining factor for global sustainability.

  1. Forecasting the Allocative Efficiency of Carbon Emission Allowance Financial Assets in China at the Provincial Level in 2020

    Directory of Open Access Journals (Sweden)

    Shihong Zeng

    2016-05-01

    Full Text Available As the result of climate change and deteriorating global environmental quality, nations are under pressure to reduce their emissions of greenhouse gases per unit of GDP. China has announced that it is aiming not only to reduce carbon emission per unit of GDP, but also to consume increased amounts of non-fossil energy. The carbon emission allowance is a new type of financial asset in each Chinese province and city that also affects individual firms. This paper attempts to examine the allocative efficiency of carbon emission reduction and non-fossil energy consumption by employing a zero sum gains data envelopment analysis (ZSG-DEA model, given the premise of fixed CO2 emissions as well as non-fossil energy consumption. In making its forecasts, the paper optimizes allocative efficiency in 2020 using 2010 economic and carbon emission data from 30 provinces and cities across China as its baseline. An efficient allocation scheme is achieved for all the provinces and cities using the ZSG-DEA model through five iterative calculations.

  2. Comparison of nitrate levels in raw water and finished water from historical monitoring data on Iowa municipal drinking water supplies.

    Science.gov (United States)

    Weyer, Peter J; Smith, Brian J; Feng, Zhen-Fang; Kantamneni, Jiji R; Riley, David G

    2006-05-01

    Nitrate contamination of water sources is a concern where large amounts of nitrogen fertilizers are regularly applied to soils. Ingested nitrate from dietary sources and drinking water can be converted to nitrite and ultimately to N-nitroso compounds, many of which are known carcinogens. Epidemiologic studies of drinking water nitrate and cancer report mixed findings; a criticism is the use of nitrate concentrations from retrospective drinking water data to assign exposure levels. Residential point-of-use nitrate data are scarce; gaps in historical data for municipal supply finished water hamper exposure classification efforts. We used generalized linear regression models to estimate and compare historical raw water and finished water nitrate levels (1960s-1990s) in single source Iowa municipal supplies to determine whether raw water monitoring data could supplement finished water data to improve exposure assessment. Comparison of raw water and finished water samples (same sampling date) showed a significant difference in nitrate levels in municipalities using rivers; municipalities using other surface water or alluvial groundwater had no difference in nitrate levels. A regional aggregation of alluvial groundwater municipalities was constructed based on results from a previous study showing regional differences in nitrate contamination of private wells; results from this analysis were mixed, dependent upon region and decade. These analyses demonstrate using historical raw water nitrate monitoring data to supplement finished water data for exposure assessment is appropriate for individual Iowa municipal supplies using alluvial groundwater, lakes or reservoirs. Using alluvial raw water data on a regional basis is dependent on region and decade.

  3. Evaluation of seasonal ensemble forecasts in Norway

    Science.gov (United States)

    Tore Sinnes, Svein; Engeland, Kolbjørn; Langsholt, Elin; Roar Sælthun, Nils

    2017-04-01

    Throughout the winter and spring season, seasonal forecasts are used by the Norwegian Water Resources and Energy Directorate (NVE) in order to assess the probability for sever floods or for low seasonal runoff volumes. The latter is especially important for hydropower production. The seasonal forecasts are generated by a set of 145 lumped, elevation distributed HBV models distributed all over Norway. The observed weather is used to establish the initial snow cover, soil moisture and groundwater levels in the HBV model. Subsequently, scenarios are created by using time series of observed weather the previous 50 years, creating a total of 50 ensembles. The predictability of this seasonal forecasting system depends therefore on the importance of the initial conditions, and in Norway the seasonal snow cover is especially important. The aim of this study is to evaluate the performance of the seasonal forecasts of flood peaks and seasonal runoff volumes and especially to evaluate of the predictability depends on (i) catchment climatology and (ii) issue dates and lead times. For achieving these aims, evaluation criterions assessing reliability and sharpness were used. The results shows that the predictability is the highest for catchments where the spring runoff is dominated by snow melt. The predictability is the highest for the shortest lead times (up to 1 months ahead).The predictive performance is higher for runoff volumes than for the flood peaks.

  4. Analytical approach for predicting fresh water discharge in an estuary based on tidal water level observations

    NARCIS (Netherlands)

    Cai, H.; Savenije, H.H.G.; Jiang, C.

    2014-01-01

    As the tidal wave propagates into an estuary, the tidally averaged water level tends to rise in landward direction due to the density difference between saline and fresh water and the asymmetry of the friction. The effect of friction on the residual slope is even more remarkable when accounting for

  5. Water level observations from Unmanned Aerial Vehicles for improving estimates of surface water-groundwater interaction

    DEFF Research Database (Denmark)

    Bandini, Filippo; Butts, Michael; Vammen Jacobsen, Torsten

    2017-01-01

    . However, traditional river gauging stations are normally spaced too far apart to capture spatial patterns in the water surface, while spaceborne observations have limited spatial and temporal resolution. UAVs (Unmanned Aerial Vehicles) can retrieve river water level measurements, providing: i) high...

  6. System for water level measurement based on pressure transducer

    Science.gov (United States)

    Paczesny, Daniel; Marzecki, Michał; Woyke, Michał; Tarapata, Grzegorz

    2016-09-01

    The paper reports system for water level measurement, which is designed to be used for measuring liquid levels in the tanks of an autonomous industrial cleaning robot. The selected method of measurement utilized by the designed system is based on pressure measurement. Such system is insensitive on vibrations, foams presence and liquid impurities. The influences of variable pressure on the measurements were eliminated by utilizing the differential method and as well as the system design. The system is capable of measuring water level in tanks up to 400 mm of height with accuracy of about 2,5%. The system was tested in a container during filling and emptying with various liquids. Performed tests exhibited the linearity of the sensor characteristic and the lack of hysteresis. Obtained sensitivity of the sensor prototype was approximately 6,2 mV/mm H2O.

  7. Influence of periodic water level increase on flow in Poznań Water Ways System

    Directory of Open Access Journals (Sweden)

    Tomasz Kałuża

    2013-06-01

    Full Text Available In the period 1968-1972, a project named “Rebuilding of the Poznań Water Ways System” was carried out. Within the scope of the project the Chwaliszewo Meander of the Warta river was cut off and covered. A discussion about reconstruction of Chwaliszewo Meander has been run for many years. The results of hydraulic computations of the influence of a weir on water table distribution in Poznań Water Ways System have been presented in the paper. Two different localizations of the weir were considered. Initial maximum water level of upper side of the weir was calculated. The influence of damming up on water level distribution in the Poznań Water Ways System was analysed. One-dimensional unsteady open channel flow computer systems HEC-RAS and SPRuNeR were used to carry out calculations. Building the weir, regardless of its localization, allows to raise water level in the main channel of the Warta river, increase minimum water depth and point to the architecture and recreation values of the Warta river. It is assumed that damming up is necessary only for flow rate below 100 m3/s in both localizations of the weir. The weir in focus should not create obstacles to the inland navigation and fish migration. To meet these requirements two additional hydraulic constructions must be projected: sluice and fish migration water gate.

  8. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    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.

  9. Accuracy of forecasts in strategic intelligence.

    Science.gov (United States)

    Mandel, David R; Barnes, Alan

    2014-07-29

    The accuracy of 1,514 strategic intelligence forecasts abstracted from intelligence reports was assessed. The results show that both discrimination and calibration of forecasts was very good. Discrimination was better for senior (versus junior) analysts and for easier (versus harder) forecasts. Miscalibration was mainly due to underconfidence such that analysts assigned more uncertainty than needed given their high level of discrimination. Underconfidence was more pronounced for harder (versus easier) forecasts and for forecasts deemed more (versus less) important for policy decision making. Despite the observed underconfidence, there was a paucity of forecasts in the least informative 0.4-0.6 probability range. Recalibrating the forecasts substantially reduced underconfidence. The findings offer cause for tempered optimism about the accuracy of strategic intelligence forecasts and indicate that intelligence producers aim to promote informativeness while avoiding overstatement.

  10. Quadratic controller syntheses for the steam generator water level

    Energy Technology Data Exchange (ETDEWEB)

    Arzelier, D.; Daafouz, J.; Bernussou, J.; Garcia, G

    1998-06-01

    The steam generator water level, (SGWL), control problem in the pressurized water reactor of a nuclear power plant is considered from robust control techniques point of view. The plant is a time-varying system with a non minimum phase behavior and an unstable open-loop response. The time-varying nature of the plant due to change in operating power is taken into account by including slowly time-varying uncertainty in the model. A linear Time-Invariant, (LTI) guaranteed cost quadratic stabilizing controller is designed in order to address some of the particular issues arising for such a control problem. (author) 17 refs.

  11. Predicting the residual aluminum level in water treatment process

    Directory of Open Access Journals (Sweden)

    J. Tomperi

    2012-06-01

    Full Text Available In water treatment processes, aluminum salts are widely used as coagulation chemical. High dose of aluminum has been proved to be at least a minor health risk and some evidence points out that aluminum could increase the risk of Alzheimer's disease thus it is important to minimize the amount of residual aluminum in drinking water and water used at food industry. In this study, the data of a water treatment plant (WTP was analyzed and the residual aluminum in drinking water was predicted using Multiple Linear Regression (MLR and Artificial Neural Network (ANN models. The purpose was to find out which variables affect the amount of residual aluminum and create simple and reliable prediction models which can be used in an early warning system (EWS. Accuracy of ANN and MLR models were compared. The new nonlinear scaling method based on generalized norms and skewness was used to scale all measurement variables to range [−2...+2] before data-analysis and modeling. The effect of data pre-processing was studied by comparing prediction results to ones achieved in an earlier study. Results showed that it is possible to predict the baseline level of residual aluminum in drinking water with a simple model. Variables that affected the most the amount of residual aluminum were among others: raw water temperature, raw water KMnO4 and PAC / KMnO4-ratio. The accuracies of MLR and ANN models were found to be almost equal. Study also showed that data pre-processing affects to the final prediction result.

  12. Predicting the residual aluminum level in water treatment process

    Directory of Open Access Journals (Sweden)

    J. Tomperi

    2013-06-01

    Full Text Available In water treatment processes, aluminum salts are widely used as coagulation chemical. High dose of aluminum has been proved to be at least a minor health risk and some evidence points out that aluminum could increase the risk of Alzheimer's disease. Thus it is important to minimize the amount of residual aluminum in drinking water and water used at food industry. In this study, the data of a water treatment plant (WTP was analyzed and the residual aluminum in drinking water was predicted using Multiple Linear Regression (MLR and Artificial Neural Network (ANN models. The purpose was to find out which variables affect the amount of residual aluminum and create simple and reliable prediction models which can be used in an early warning system (EWS. Accuracy of ANN and MLR models were compared. The new nonlinear scaling method based on generalized norms and skewness was used to scale all measurement variables to range [−2...+2] before data-analysis and modeling. The effect of data pre-processing was studied by comparing prediction results to ones achieved in an earlier study. Results showed that it is possible to predict the baseline level of residual aluminum in drinking water with a simple model. Variables that affected the most the amount of residual aluminum were among others: raw water temperature, raw water KMnO4 and PAC/KMnO4 (Poly-Aluminum Chloride/Potassium permanganate-ratio. The accuracies of MLR and ANN models were found to be almost the same. Study also showed that data pre-processing affects to the final prediction result.

  13. Understanding the Impact of Ground Water Treatment and Evapotranspiration Parameterizations in the NCEP Climate Forecast System (CFS) on Warm Season Predictions

    Science.gov (United States)

    Ek, M. B.; Yang, R.

    2016-12-01

    Skillful short-term weather forecasts, which rely heavily on quality atmospheric initial conditions, have a fundamental limit of about two weeks owing to the chaotic nature of the atmosphere. Useful forecasts at sub-seasonal to seasonal time scales, on the other hand, require well-simulated large-scale atmospheric response to slowly varying lower boundary forcings from both the ocean and land surface. The critical importance of ocean has been recognized, where the ocean indices have been used in a variety of climate applications. In contrast, the impact of land surface anomalies, especially soil moisture and associated evaporation, has been proven notably difficult to demonstrate. The Noah Land Surface Model (LSM) is the land component of NCEP CFS version 2 (CFSv2) used for seasonal predictions. The Noah LSM originates from the Oregon State University (OSU) LSM. The evaporation control in the Noah LSM is based on the Penman-Monteith equation, which takes into account the solar radiation, relative humidity, air temperature, and soil moisture effects. The Noah LSM is configured with four soil layers with a fixed depth of 2 meters and free drainage at the bottom soil layer. This treatment assumes that the soil water table depth is well within the specified range, and also potentially misrepresents the soil moisture memory effects at seasonal time scales. To overcome the limitation, an unconfined aquifer is attached to the bottom of the soil to allow the water table to move freely up and down. In addition, in conjunction with the water table, an alternative Ball-Berry photosynthesis-based evaporation parameterization is examined to evaluate the impact from using a different evaporation control methodology. Focusing on the 2011 and 2012 intense summer droughts in the central US, seasonal ensemble forecast experiments with early May initial conditions are carried out for the two years using an enhanced version of CFSv2, where the atmospheric component of the CFSv2 is

  14. Ditch water levels manages for environmental aims: effects on field soil water regimes

    Directory of Open Access Journals (Sweden)

    A. Armstrong

    1999-01-01

    Full Text Available The effects of ditch water management regimes on water tables are examined for two test sites in England, Halvergate in the Broads and Southlake Moor in the Somerset Levels and Moors Environmentally Sensitive Areas. It is observed that in some fields the effects of water management are only poorly transferred from the ditch to the field centre, especially where the hydraulic conductivity of the subsoil is small. Where there are large variations in the ditch water levels, reflecting the influence of major ditches subject to pump drainage, field soil water regimes differ significantly. Nevertheless, the effects of even quite small changes in the ditch regime cam be noticeable. Simple modelling studies show that much greater effects can be achieved by increasing the frequency of ditches within wetlands.

  15. Economic sustainability, water security and multi-level governance of local water schemes in Nepal

    Directory of Open Access Journals (Sweden)

    Emma Hakala

    2017-07-01

    Full Text Available This article explores the role of multi-level governance and power structures in local water security through a case study of the Nawalparasi district in Nepal. It focuses on economic sustainability as a measure to address water security, placing this thematic in the context of a complicated power structure consisting of local, district and national administration as well as external development cooperation actors. The study aims to find out whether efforts to improve the economic sustainability of water schemes have contributed to water security at the local level. In addition, it will consider the interactions between water security, power structures and local equality and justice. The research builds upon survey data from the Nepalese districts of Nawalparasi and Palpa, and a case study based on interviews and observation in Nawalparasi. The survey was performed in water schemes built within a Finnish development cooperation programme spanning from 1990 to 2004, allowing a consideration of the long-term sustainability of water management projects. This adds a crucial external influence into the intra-state power structures shaping water management in Nepal. The article thus provides an alternative perspective to cross-regional water security through a discussion combining transnational involvement with national and local points of view.

  16. Fluctuations of Lake Orta water levels: preliminary analyses

    Directory of Open Access Journals (Sweden)

    Helmi Saidi

    2016-04-01

    Full Text Available While the effects of past industrial pollution on the chemistry and biology of Lake Orta have been well documented, annual and seasonal fluctuations of lake levels have not yet been studied. Considering their potential impacts on both the ecosystem and on human safety, fluctuations in lake levels are an important aspect of limnological research. In the enormous catchment of Lake Maggiore, there are many rivers and lakes, and the amount of annual precipitation is both high and concentrated in spring and autumn. This has produced major flood events, most recently in November 2014. Flood events are also frequent on Lake Orta, occurring roughly triennially since 1917. The 1926, 1951, 1976 and 2014 floods were severe, with lake levels raised from 2.30 m to 3.46 m above the hydrometric zero. The most important event occurred in 1976, with a maximum level equal to 292.31 m asl and a return period of 147 years. In 2014 the lake level reached 291.89 m asl and its return period was 54 years. In this study, we defined trends and temporal fluctuations in Lake Orta water levels from 1917 to 2014, focusing on extremes. We report both annual maximum and seasonal variations of the lake water levels over this period. Both Mann-Kendall trend tests and simple linear regression were utilized to detect monotonic trends in annual and seasonal extremes, and logistic regression was used to detect trends in the number of flood events. Lake level decreased during winter and summer seasons, and a small but statistically non-significant positive trend was found in the number of flood events over the period. We provide estimations of return period for lake levels, a metric which could be used in planning lake flood protection measures.

  17. Modeling and forecasting electricity price jumps in the Nord Pool power market

    DEFF Research Database (Denmark)

    Knapik, Oskar

    extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account...... i) price drivers, ii) persistence, iii) seasonality of electricity prices. The models are shown to outperform commonly-used benchmark. The paper shows how crucial for price jumps forecasting is to incorporate additional knowledge on price drivers like loads, temperature and water reservoir level...

  18. How accurate are the weather forecasts for Bierun (southern Poland)?

    Science.gov (United States)

    Gawor, J.

    2012-04-01

    Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why

  19. Radon concentration levels in ground water from Toluca, Mexico.

    Science.gov (United States)

    Olguin, M T; Segovia, N; Tamez, E; Alcántara, M; Bulbulian, S

    1993-03-25

    Concentration levels of 222Rn have been analysed in water samples from deep wells of the aquifers around the City of Toluca, Mexico. The 222Rn source is the decay of 226Ra within the solid matrix of the aquifer. With a half life of 1600 years the 226Ra continuously releases 222Rn to the pores, from which it diffuses into the main body of water. This paper describes the methods used for sampling and measuring solubilized and 226Ra-supported 222Rn in the water samples, in order to evaluate possible health hazards due to the presence of radon in the drinking water supplies. The relationship of 222Rn with the hydrogeologic characteristics of the zone is also described. The analytical method involves laboratory extraction of 222Rn into toluene. Alpha disintegrations of 222Rn and contributions from short-lived daughters are counted by the liquid scintillation technique. The system was calibrated using a 226Ra standard solution. Results up to 11.3 Bq/l of 222Rn were obtained in the water samples.

  20. Multi variate regression model of the water level and production rate time series of the geothermal reservoir Waiwera (New Zealand)

    Science.gov (United States)

    Kühn, Michael; Schöne, Tim

    2017-04-01

    Water management tools are essential to ensure the conservation of natural resources. The geothermal hot water reservoir below the village of Waiwera, on the Northern Island of New Zealand is used commercially since 1863. The continuous production of 50 °C hot geothermal water, to supply hotels and spas, has a negative impact on the reservoir. Until the year 1969 from all wells drilled the warm water flow was artesian. Due to overproduction the water needs to be pumped up nowadays. Further, within the years 1975 to 1976 the warm water seeps on the beach of Waiwera ran dry. In order to protect the reservoir and the historical and tourist site in the early 1980s a water management plan was deployed. The "Auckland Council" established guidelines to enable a sustainable management of the resource [1]. The management plan demands that the water level in the official and appropriate observation well of the council is 0.5 m above sea level throughout the year in average. Almost four decades of data (since 1978 until today) are now available [2]. For a sustainable water management, it is necessary to be able to forecast the water level as a function of the production rates in the production wells. The best predictions are provided by a multivariate regression model of the water level and production rate time series, which takes into account the production rates of individual wells. It is based on the inversely proportional relationship between the independent variable (production rate) and the dependent variable (measured water level). In production scenarios, a maximum total production rate of approx. 1,100 m3 / day is determined in order to comply with the guidelines of the "Auckland Council". [1] Kühn M., Stöfen H. (2005) A reactive flow model of the geothermal reservoir Waiwera, New Zealand. Hydrogeology Journal 13, 606-626, doi: 10.1007/s10040-004-0377-6 [2] Kühn M., Altmannsberger C. (2016) Assessment of data driven and process based water management tools for

  1. Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Mirocha, Jeff D.; Simpson, Matthew D.; Fast, Jerome D.; Berg, Larry K.; Baskett, R.

    2016-04-01

    Simulations of two periods featuring three consecutive low level jet (LLJ) events in the US Upper Great Plains during the autumn of 2011 were conducted to explore the impacts of various setup configurations and physical process models on simulated flow parameters within the lowest 200 m above the surface, using the Weather Research and Forecasting (WRF) model. Sensitivities of simulated flow parameters to the horizontal and vertical grid spacing, planetary boundary layer (PBL) and land surface model (LSM) physics options, were assessed. Data from a Light Detection and Ranging (lidar) system, deployed to the Weather Forecast Improvement Project (WFIP; Finley et al. 2013) were used to evaluate the accuracy of simulated wind speed and direction at 80 m above the surface, as well as their vertical distributions between 120 and 40 m, covering the typical span of contemporary tall wind turbines. All of the simulations qualitatively captured the overall diurnal cycle of wind speed and stratification, producing LLJs during each overnight period, however large discrepancies occurred at certain times for each simulation in relation to the observations. 54-member ensembles encompassing changes of the above discussed configuration parameters displayed a wide range of simulated vertical distributions of wind speed and direction, and potential temperature, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error (RMSE) statistics show that different ensemble members performed better and worse in various simulated parameters at different times, with no clearly superior configuration . Simulations using a PBL parameterization designed specifically for the stable conditions investigated herein provided superior overall simulations of wind speed at 80 m, demonstrating the efficacy of targeting improvements of physical process models in areas of known deficiencies. However, the considerable magnitudes of the

  2. Forecasting military expenditure

    Directory of Open Access Journals (Sweden)

    Tobias Böhmelt

    2014-05-01

    Full Text Available To what extent do frequently cited determinants of military spending allow us to predict and forecast future levels of expenditure? The authors draw on the data and specifications of a recent model on military expenditure and assess the predictive power of its variables using in-sample predictions, out-of-sample forecasts and Bayesian model averaging. To this end, this paper provides guidelines for prediction exercises in general using these three techniques. More substantially, however, the findings emphasize that previous levels of military spending as well as a country’s institutional and economic characteristics particularly improve our ability to predict future levels of investment in the military. Variables pertaining to the international security environment also matter, but seem less important. In addition, the results highlight that the updated model, which drops weak predictors, is not only more parsimonious, but also slightly more accurate than the original specification.

  3. Forecast Collaboration in Grocery Supply Chains

    DEFF Research Database (Denmark)

    Aastrup, Jesper; Gammelgaard, Britta

    -requisites, degree of forecast collaboration, demand related contingency factors and outcomes/KPIs based. The hypotheses are tested in a survey among Danish grocery suppliers. The survey findings provide evidence of a positive effect of collaborative orientation and retailer competencies and trustworthiness...... on the degress of forecast collaboration. Also, campaign frequency as a demand related contingency variable is found to positively affect degree of forecast collaboration. Finally, the survey findings provide evidence of a positive effect of degree of forecast collaboration on inventory levels and forecast...

  4. Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

    KAUST Repository

    Butler, T.

    2012-07-01

    Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

  5. Tide forecasting method based on dynamic weight distribution for operational evaluation

    Institute of Scientific and Technical Information of China (English)

    Shao-wei QIU; Zeng-chuan DONG; Fen XU; Li SUN; Sheng CHEN

    2009-01-01

    Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one's forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.

  6. [Residual levels of acetochlor in source water and drinking water of China's major cities].

    Science.gov (United States)

    Yu, Zhi-Yong; Jin, Fen; Li, Hong-Yan; An, Wei; Yang, Min

    2014-05-01

    The concentration levels of acetochlor were investigated in source water and drinking water from 36 major cities in China by solid phase extraction (SPE) combined with gas chromatography - mass spectrometry (GC-MS). Acetochlor detection rate was 66.9% in all the 145 source water samples collected with an average concentration of 33.9 ng L-1. The average removal rate of acetochlor was limited through the drinking water treatment process. The detection concentration of the northeast region was the highest. The concentrations of acetochlor detected in lake were higher than those in river and groundwater as source water. The detection rate and concentration of Liaohe river watershed and the coastal watershed were the highest.

  7. Earthquake forecasting: Statistics and Information

    CERN Document Server

    Gertsik, V; Krichevets, A

    2013-01-01

    We present an axiomatic approach to earthquake forecasting in terms of multi-component random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different earthquake forecasts in terms of the increase of Shannon information. 'Forecasting' and 'prediction' of earthquakes are equivalent in this approach.

  8. Setting up an atmospheric-hydrologic model for seasonal forecasts of water flow into dams in a mountainous semi-arid environment (Cyprus)

    Science.gov (United States)

    Camera, Corrado; Bruggeman, Adriana; Zittis, Georgios; Hadjinicolaou, Panos

    2017-04-01

    Due to limited rainfall concentrated in the winter months and long dry summers, storage and management of water resources is of paramount importance in Cyprus. For water storage purposes, the Cyprus Water Development Department is responsible for the operation of 56 large dams total volume of 310 Mm3) and 51 smaller reservoirs (total volume of 17 Mm3) over the island. Climate change is also expected to heavily affect Cyprus water resources with a 1.5%-12% decrease in mean annual rainfall (Camera et al., 2016) projected for the period 2020-2050, relative to 1980-2010. This will make reliable seasonal water inflow forecasts even more important for water managers. The overall aim of this study is to set-up the widely used Weather Research and Forecasting (WRF) model with its hydrologic extension (WRF-hydro), for seasonal forecasts of water inflow in dams located in the Troodos Mountains of Cyprus. The specific objectives of this study are: i) the calibration and evaluation of WRF-Hydro for the simulation of stream flows, in the Troodos Mountains, for past rainfall seasons; ii) a sensitivity analysis of the model parameters; iii) a comparison of the application of the atmospheric-hydrologic modelling chain versus the use of climate observations as forcing. The hydrologic model is run in its off-line version with daily forcing over a 1-km grid, while the overland and channel routing is performed on a 100-m grid with a time-step of 6 seconds. Model outputs are exported on a daily base. First, WRF-Hydro is calibrated and validated over two 1-year periods (October-September), using a 1-km gridded observational precipitation dataset (Camera et al., 2014) as input. For the calibration and validation periods, years with annual rainfall close to the long-term average and with the presence of extreme rainfall and flow events were selected. A sensitivity analysis is performed, for the following parameters: partitioning of rainfall into runoff and infiltration (REFKDT), the

  9. Influence of Closing Storm Surge Barrier on Extreme Water Levels and Water Exchange; The Limfjord, Denmark

    DEFF Research Database (Denmark)

    Nørgaard, Jørgen Quvang Harck; Bentzen, Thomas Ruby; Larsen, Torben;

    2014-01-01

    of the fjord. The reduction is obtained by blocking the ingoing flow with a sluice in due time before the storm surge peaks in the North Sea. In order to avoid problems with reduced water quality and salinity, the water exchange should be controlled by only keeping the sluice open for ingoing currents...... the increased risk of flooding in the estuary has revitalized the discussion whether this connection should be closed. In this paper, it is shown by numerical simulation that the establishment of a storm surge barrier across Thyborøn Channel can significantly reduce the peak water levels in the central...

  10. Adaptation level as the basic health status characteristics: possibilitics of its assessment and forecasting of desadaptation violations

    Directory of Open Access Journals (Sweden)

    Vysochyna I.L.

    2015-09-01

    Full Text Available On the basis of comprehensive survey with integrative assessment of health state (medical history data, physical examination, anthropometry, battery of psychological tests (Eysenck, Shmishek’s Personality Inventory (teen version, tapping - test by E.P. Ilyin, children's questionnaire of neuroses; test for rapid assessment of health, activity and mood, anxiety diagnosis by Spielberg - Khanin; Luscher test, color relations test level of adaptation was defined in 236 children from orphanages aged from 4 to 18 years. The manifestations of maladjustment were registered both on psychological level (neuroticism, high anxiety, decreased performance, activity and psychological endurance, sleep disturbance, presence of accentuation and neurotic disorders and somatic level (recurrent acute respiratory infections, poor physical development, exacerbation of chronic foci of infection and burdened biological history; this summarizes conclusions on a low level of health status of children in orphanages. The author has developed mathematical models of adaptation assessment and prediction of desadaptation, which allowed to identify children at risk for the development of adaptation disorders and children with maladjustment; according to the level and severity of maladaptive disorders correction programs are designed.

  11. Considering rating curve uncertainty in water level predictions

    Science.gov (United States)

    Sikorska, A. E.; Scheidegger, A.; Banasik, K.; Rieckermann, J.

    2013-11-01

    Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

  12. Considering rating curve uncertainty in water level predictions

    Directory of Open Access Journals (Sweden)

    A. E. Sikorska

    2013-03-01

    Full Text Available Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully tested on a small urbanized basin in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i enables the modeler to incorporate informal knowledge from easily accessible information and (ii better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

  13. Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count.

    Science.gov (United States)

    Nowosad, Jakub; Stach, Alfred; Kasprzyk, Idalia; Weryszko-Chmielewska, Elżbieta; Piotrowska-Weryszko, Krystyna; Puc, Małgorzata; Grewling, Łukasz; Pędziszewska, Anna; Uruska, Agnieszka; Myszkowska, Dorota; Chłopek, Kazimiera; Majkowska-Wojciechowska, Barbara

    The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

  14. Water Inflow Forecasting System Development and Application of Coastal Reclamation Area%江苏省沿海围垦区来水预测系统开发与应用

    Institute of Scientific and Technical Information of China (English)

    陈左杰; 董增川; 管西柯; 谈娟娟

    2016-01-01

    来水预测系统是水资源一体化管理决策系统的重要组成部分。通过对江苏省沿海围垦区的水源进行划分,选定AR( p)自回归模型和BP神经网络模型进行来水预测,并在Java平台上运用Oracle数据库构建了江苏省沿海围垦区多水源来水预测系统。系统主要有来水预测和实时更新两个功能,可为沿海围垦区水资源一体化管理和开发利用提供参考。%Water⁃inflow forecasting system is one of the most important parts of decision⁃making system of integrated water resources management. According to the analysis of water sources classification and inflow forecasting methods of the coastal reclamation area, this paper built AR( p) autoregressive model, BP intelligent algorithm and oracle database on the Java platform and built the inflow forecasting system in coastal reclamation area of Jiangsu Province. It mainly includes two sub⁃modules, which is water inflow forecasting sub⁃module and real⁃time updating sub⁃module, which aims to provide references for integrated water resources management and development in the coastal reclamation area.

  15. kwmc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kont Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kcrg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kjac Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. krdu Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kiwd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. krbl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kssf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. ksaw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. kmot Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kiso Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kgck Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kcvg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. pafa Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kcrq Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. ksun Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kpia Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. krow Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kbtv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kbke Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kbpt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kact Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kavl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kbur Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. krsw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. klnd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kpuw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kbis Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kcmx Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. kipt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kteb Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kely Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kfat Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. phny Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kdsm Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kbos Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kpdx Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. tjsj Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kpae Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kpbi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kalb Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. paga Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kjax Terminal Aerodrome Forecast

    Data.gov (United States)

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