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. Bot...

  3. Reservoir water level forecasting using group method of data handling

    Science.gov (United States)

    Zaji, Amir Hossein; Bonakdari, Hossein; Gharabaghi, Bahram

    2018-06-01

    Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models' performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models' applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L -1) and (L, L -1, L -12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L -7) and (L, L -7, L -14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www.typingclub.com/st. Accordingly, (L, L -1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.

  4. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    OpenAIRE

    Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

    2017-01-01

    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...

  5. Radar Based Flow and Water Level Forecasting in Sewer Systems:a danisk case study

    OpenAIRE

    Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.; Neve, S. L.

    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 promis...

  6. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

    Science.gov (United States)

    Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

    2017-01-01

    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  7. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Jun-He Yang

    2017-01-01

    Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  8. The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

    Science.gov (United States)

    Dong, Sheng; Chi, Kun; Zhang, Qiyi; Zhang, Xiangdong

    2012-03-01

    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.

  9. Can we use Earth Observations to improve monthly water level forecasts?

    Science.gov (United States)

    Slater, L. J.; Villarini, G.

    2017-12-01

    Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

  10. Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

    Science.gov (United States)

    Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

    2017-12-01

    Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River

  11. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

    Science.gov (United States)

    Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

    2015-01-01

    Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

  12. Understanding Variability in Beach Slope to Improve Forecasts of Storm-induced Water Levels

    Science.gov (United States)

    Doran, K. S.; Stockdon, H. F.; Long, J.

    2014-12-01

    The National Assessment of Hurricane-Induced Coastal Erosion Hazards combines measurements of beach morphology with storm hydrodynamics to produce forecasts of coastal change during storms for the Gulf of Mexico and Atlantic coastlines of the United States. Wave-induced water levels are estimated using modeled offshore wave height and period and measured beach slope (from dune toe to shoreline) through the empirical parameterization of Stockdon et al. (2006). Spatial and temporal variability in beach slope leads to corresponding variability in predicted wave setup and swash. Seasonal and storm-induced changes in beach slope can lead to differences on the order of a meter in wave runup elevation, making accurate specification of this parameter essential to skillful forecasts of coastal change. Spatial variation in beach slope is accounted for through alongshore averaging, but temporal variability in beach slope is not included in the final computation of the likelihood of coastal change. Additionally, input morphology may be years old and potentially very different than the conditions present during forecast storm. In order to improve our forecasts of hurricane-induced coastal erosion hazards, the temporal variability of beach slope must be included in the final uncertainty of modeled wave-induced water levels. Frequently collected field measurements of lidar-based beach morphology are examined for study sites in Duck, North Carolina, Treasure Island, Florida, Assateague Island, Virginia, and Dauphin Island, Alabama, with some records extending over a period of 15 years. Understanding the variability of slopes at these sites will help provide estimates of associated water level uncertainty which can then be applied to other areas where lidar observations are infrequent, and improve the overall skill of future forecasts of storm-induced coastal change. Stockdon, H. F., Holman, R. A., Howd, P. A., and Sallenger Jr, A. H. (2006). Empirical parameterization of setup

  13. 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.

  14. 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.

  15. 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

  16. Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

    Directory of Open Access Journals (Sweden)

    Nariman Valizadeh

    2014-01-01

    Full Text Available 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.

  17. 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.

  18. Assessment of a new seasonal to inter-annual operational Great Lakes water supply, water levels, and connecting channel flow forecasting system

    Science.gov (United States)

    Gronewold, A.; Fry, L. M.; Hunter, T.; Pei, L.; Smith, J.; Lucier, H.; Mueller, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) has recently operationalized a suite of ensemble forecasts of Net Basin Supply (NBS), water levels, and connecting channel flows that was developed through a collaboration among USACE, NOAA's Great Lakes Environmental Research Laboratory, Ontario Power Generation (OPG), New York Power Authority (NYPA), and the Niagara River Control Center (NRCC). These forecasts are meant to provide reliable projections of potential extremes in daily discharge in the Niagara and St. Lawrence Rivers over a long time horizon (5 years). The suite of forecasts includes eight configurations that vary by (a) NBS model configuration, (b) meteorological forcings, and (c) incorporation of seasonal climate projections through the use of weighting. Forecasts are updated on a weekly basis, and represent the first operational forecasts of Great Lakes water levels and flows that span daily to inter-annual horizons and employ realistic regulation logic and lake-to-lake routing. We will present results from a hindcast assessment conducted during the transition from research to operation, as well as early indications of success rates determined through operational verification of forecasts. Assessment will include an exploration of the relative skill of various forecast configurations at different time horizons and the potential for application to hydropower decision making and Great Lakes water management.

  19. 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

  20. 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.

  1. Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Sepideh Karimi

    2012-06-01

    Full Text Available Forecasting lake level at various prediction intervals is an essential issue in such industrial applications as navigation, water resource planning and catchment management. In the present study, two data driven techniques, namely Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System, were applied for predicting daily lake levels for three prediction intervals. Daily water-level data from Urmieh Lake in Northwestern Iran were used to train, test and validate the used techniques. Three statistical indexes, coefficient of determination, root mean square error and variance accounted for were used to assess the performance of the used techniques. Technique inter-comparisons demonstrated that the GEP surpassed the ANFIS model at each of the prediction intervals. A traditional auto regressive moving average model was also applied to the same data sets; the obtained results were compared with those of the data driven approaches demonstrating superiority of the data driven models to ARMA.

  2. 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

    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...

  3. Forecasting Sea Water Levels at Mukho Station, South Korea Using Soft Computing Techniques

    Directory of Open Access Journals (Sweden)

    Ozgur Kisi

    2014-12-01

    Full Text Available The accuracy of three different data-driven methods, namely, Gene Expression Programming (GEP, Adaptive Neuro-Fuzzy Inference System (ANFIS and Artificial Neural Networks (ANN, is investigated for hourly sea water level prediction at the Mukho Station in the East Sea (Sea of Japan. Current and four previous level measurements are used as input variables to predict sea water levels up to 1, 24, 48, 72, 96 and 120 hours ahead. Three statistical evaluation parameters, namely, the correlation coefficient, the root mean square error and the scatter index are used to assess how the models perform. Investigation results indicate that, when compared to measurements, for +1h prediction interval, all three models perform well (with average values of R = 0.993, RMSE = 1.3 cm and SI = 0.04, with slightly better results produced by the ANNs and ANFIS, while increasing the prediction interval degrades model performance.

  4. Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation

    DEFF Research Database (Denmark)

    Liu, Dedi; Li, Xiang; Guo, Shenglian

    2015-01-01

    Dynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water...... inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental......, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from...

  5. Solid low-level waste forecasting guide

    International Nuclear Information System (INIS)

    Templeton, K.J.; Dirks, L.L.

    1995-03-01

    Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford's experience within the last six years. Hanford's forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford's annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford's forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data

  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. 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

  8. 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

  9. Weather Forecasts are for Wimps. Why Water Resource Managers Do Not Use Climate Forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Rayner, S. [James Martin Institute of Science and Civilization, Said Business School, University of Oxford, OX1 1HP (United Kingdom); Lach, D. [Oregon State University, Corvallis, OR, 97331-4501 (United States); Ingram, H. [School of Social Ecology, University of California Irvine, Irvine, CA, 92697-7075 (United States)

    2005-04-15

    Short-term climate forecasting offers the promise of improved hydrologic management strategies. However, water resource managers in the United States have proven reluctant to incorporate them in decision making. While managers usually cite poor reliability of the forecasts as the reason for this, they are seldom able to demonstrate knowledge of the actual performance of forecasts or to consistently articulate the level of reliability that they would require. Analysis of three case studies in California, the Pacific Northwest, and metro Washington DC identifies institutional reasons that appear to lie behind managers reluctance to use the forecasts. These include traditional reliance on large built infrastructure, organizational conservatism and complexity, mismatch of temporal and spatial scales of forecasts to management needs, political disincentives to innovation, and regulatory constraints. The paper concludes that wider acceptance of the forecasts will depend on their being incorporated in existing organizational routines and industrial codes and practices, as well as changes in management incentives to innovation. Finer spatial resolution of forecasts and the regional integration of multi-agency functions would also enhance their usability. The title of this article is taken from an advertising slogan for the Oldsmobile Bravura SUV.

  10. 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

  11. An ANN application for water quality forecasting.

    Science.gov (United States)

    Palani, Sundarambal; Liong, Shie-Yui; Tkalich, Pavel

    2008-09-01

    Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.

  12. Forecasting HotWater Consumption in Residential Houses

    Directory of Open Access Journals (Sweden)

    Linas Gelažanskas

    2015-11-01

    Full Text Available An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting.

  13. Reactor water level control device

    International Nuclear Information System (INIS)

    Utagawa, Kazuyuki.

    1993-01-01

    A device of the present invention can effectively control fluctuation of a reactor water level upon power change by reactor core flow rate control operation. That is, (1) a feedback control section calculates a feedwater flow rate control amount based on a deviation between a set value of a reactor water level and a reactor water level signal. (2) a feed forward control section forecasts steam flow rate change based on a reactor core flow rate signal or a signal determining the reactor core flow rate, to calculate a feedwater flow rate control amount which off sets the steam flow rate change. Then, the sum of the output signal from the process (1) and the output signal from the process (2) is determined as a final feedwater flow rate control signal. With such procedures, it is possible to forecast the steam flow rate change accompanying the reactor core flow rate control operation, thereby enabling to conduct preceding feedwater flow rate control operation which off sets the reactor water level fluctuation based on the steam flow rate change. Further, a reactor water level deviated from the forecast can be controlled by feedback control. Accordingly, reactor water level fluctuation upon power exchange due to the reactor core flow rate control operation can rapidly be suppressed. (I.S.)

  14. Forecasting daily lake levels using artificial intelligence approaches

    Science.gov (United States)

    Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

    2012-04-01

    Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

  15. 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.

  16. 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.

  17. 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

  18. Forecasting Frost Damage: Follow the Water

    Science.gov (United States)

    Rempel, A. W.

    2015-12-01

    Frost damage takes place when the pressure exerted against pore walls exceeds the cohesive strength of water-infiltrated rock and causes cracks to extend. Elegant theoretical treatments supported by meticulous field and laboratory observations have combined to unravel the basic mechanical and thermodynamic controls in idealized systems. Frost damage is most vigorous when conditions are cold enough that the net pressure exerted against the pore walls can cause crack extension, yet warm enough to enable the flow that supplies further ice growth in the newly opened space. This insight is applied here to develop practical geomorphic process laws for the effects of frost damage at the larger scales that are relevant for describing the evolution of landscapes. To this end, a direct connection is made between the intensity of frost damage and the porosity increase that results from gradients in water flux under conditions that are cold enough for ice-rock interactions to propagate cracks. This implies that the annual temperature variation at the ground surface can be combined with considerations of heat and mass transport to derive rigorous forecasts of the potential for frost damage that are tied to the increases in water mass that accompany solidification in porous rock. As an example, the image shows the depth-integrated porosity change λ promoted by crack growth at temperatures colder than -ΔTc over an annual cycle for different choices of mean annual temperature MAT and surface amplitude A (assuming a thermal diffusivity of 1 mm2/s and a power-law relationship between permeability and undercooling with exponent α=4, such that a base value of 10-14m2 is reached at a reference undercooling of 0.1 ºC). The abrupt onset in cracking once MAT decreases below a threshold is produced by the requirement that undercooling surpass ΔTc in order to generate sufficient pressures to propagate cracks. The eventual reduction and gradual tail in λ at colder MAT is produced by

  19. Optimised control and pipe burst detection by water demand forecasting

    NARCIS (Netherlands)

    Bakker, M.

    2014-01-01

    Water demand forecasting The total water demand in an area is the sum of the water demands of all individual domestic and industrial consumers in that area. These consumers behave in repetitive daily, weekly and annual patterns, and the same repetitive patterns can be observed in the drinking water

  20. 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 usually...

  1. 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 or...

  2. Forecasting Water Waves and Currents: A Space-time Approach

    NARCIS (Netherlands)

    Ambati, V.R.

    2008-01-01

    Forecasting water waves and currents in near shore and off shore regions of the seas and oceans is essential to maintain and protect our environment and man made structures. In wave hydrodynamics, waves can be classified as shallow and deep water waves based on its water depth. The mathematical

  3. Forecasting in an integrated surface water-ground water system: The Big Cypress Basin, South Florida

    Science.gov (United States)

    Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.

    2009-04-01

    The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel forecasting and real-time modelling system is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This system is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection system. Novel aspects of this system include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately forecasting river levels. Because of the interaction between surface- and groundwater a fully integrated forecast modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river system can have a direct impact on groundwater levels.

  4. Sea Level Forecasts Aggregated from Established Operational Systems

    Directory of Open Access Journals (Sweden)

    Andy Taylor

    2017-08-01

    Full Text Available A system for providing routine seven-day forecasts of sea level observable at tide gauge locations is described and evaluated. Forecast time series are aggregated from well-established operational systems of the Australian Bureau of Meteorology; although following some adjustments these systems are only quasi-complimentary. Target applications are routine coastal decision processes under non-extreme conditions. The configuration aims to be relatively robust to operational realities such as version upgrades, data gaps and metadata ambiguities. Forecast skill is evaluated against hourly tide gauge observations. Characteristics of the bias correction term are demonstrated to be primarily static in time, with time varying signals showing regional coherence. This simple approach to exploiting existing complex systems can offer valuable levels of skill at a range of Australian locations. The prospect of interpolation between observation sites and exploitation of lagged-ensemble uncertainty estimates could be meaningfully pursued. Skill characteristics define a benchmark against which new operational sea level forecasting systems can be measured. More generally, an aggregation approach may prove to be optimal for routine sea level forecast services given the physically inhomogeneous processes involved and ability to incorporate ongoing improvements and extensions of source systems.

  5. Forecasting Aggregate Productivity using Information from Firm-level Data

    NARCIS (Netherlands)

    Bartelsman, E.J.; Wolf, Z.

    2014-01-01

    In this paper, we explore whether information from firm-level data can improve forecasts of aggregate productivity growth. We generate firm-level productivity measures and aggregate them into time-series components that capture within-firm productivity and the productivity contribution of

  6. Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

    Science.gov (United States)

    Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

    2017-12-01

    The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

  7. 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 manual...

  8. 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.

  9. 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.

  10. Forecasting operational demand for an urban water supply zone

    Science.gov (United States)

    Zhou, S. L.; McMahon, T. A.; Walton, A.; Lewis, J.

    2002-03-01

    A time series forecasting model of hourly water consumption 24 h in advance for an urban zone within the Melbourne (Australia) water supply system is developed. The model comprises two modules—daily and hourly. The daily module is formulated as a set of equations representing the effects of three factors on water use namely seasonality, climatic correlation, and autocorrelation. The hourly module is developed to disaggregate the estimated daily consumption into hourly consumption. The models were calibrated using hourly and daily data for a 6 year period, and independently validated over an additional seven month period. Over this latter period, the hourly forecast model accounted for 66% of the variance in the peak hourly water consumption with a standard error of 162 l/p/d.

  11. Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

    Science.gov (United States)

    Balavalikar, Supreetha; Nayak, Prabhakar; Shenoy, Narayan; Nayak, Krishnamurthy

    2018-04-01

    The decline in groundwater is a global problem due to increase in population, industries, and environmental aspects such as increase in temperature, decrease in overall rainfall, loss of forests etc. In Udupi district, India, the water source fully depends on the River Swarna for drinking and agriculture purposes. Since the water storage in Bajae dam is declining day-by-day and the people of Udupi district are under immense pressure due to scarcity of drinking water, alternatively depend on ground water. As the groundwater is being heavily used for drinking and agricultural purposes, there is a decline in its water table. Therefore, the groundwater resources must be identified and preserved for human survival. This research proposes a data driven approach for forecasting the groundwater level. The monthly variations in groundwater level and rainfall data in three observation wells located in Brahmavar, Kundapur and Hebri were investigated and the scenarios were examined for 2000-2013. The focus of this research work is to develop an ANN based groundwater level forecasting model and compare with hybrid ANN-PSO forecasting model. The model parameters are tested using different combinations of the data. The results reveal that PSO-ANN based hybrid model gives a better prediction accuracy, than ANN alone.

  12. Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

    Science.gov (United States)

    Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

    2013-12-01

    Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so

  13. Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

    Science.gov (United States)

    Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

    2017-12-01

    The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

  14. 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.

  15. Experiences from coordinated national-level landslide and flood forecasting in Norway

    Science.gov (United States)

    Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

    2015-04-01

    While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

  16. 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,...

  17. State-level electricity demand forecasting model. [For 1980, 1985, 1990

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, H. D.

    1978-01-01

    This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

  18. 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.

  19. Adapting National Water Model Forecast Data to Local Hyper-Resolution H&H Models During Hurricane Irma

    Science.gov (United States)

    Singhofen, P.

    2017-12-01

    The National Water Model (NWM) is a remarkable undertaking. The foundation of the NWM is a 1 square kilometer grid which is used for near real-time modeling and flood forecasting of most rivers and streams in the contiguous United States. However, the NWM falls short in highly urbanized areas with complex drainage infrastructure. To overcome these shortcomings, the presenter proposes to leverage existing local hyper-resolution H&H models and adapt the NWM forcing data to them. Gridded near real-time rainfall, short range forecasts (18-hour) and medium range forecasts (10-day) during Hurricane Irma are applied to numerous detailed H&H models in highly urbanized areas of the State of Florida. Coastal and inland models are evaluated. Comparisons of near real-time rainfall data are made with observed gaged data and the ability to predict flooding in advance based on forecast data is evaluated. Preliminary findings indicate that the near real-time rainfall data is consistently and significantly lower than observed data. The forecast data is more promising. For example, the medium range forecast data provides 2 - 3 days advanced notice of peak flood conditions to a reasonable level of accuracy in most cases relative to both timing and magnitude. Short range forecast data provides about 12 - 14 hours advanced notice. Since these are hyper-resolution models, flood forecasts can be made at the street level, providing emergency response teams with valuable information for coordinating and dispatching limited resources.

  20. Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction

    CSIR Research Space (South Africa)

    Anele, AO

    2017-11-01

    Full Text Available -term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times...

  1. Forecasting of Groundwater Level using Artificial Neural Network by incorporating river recharge and river bank infiltration

    Directory of Open Access Journals (Sweden)

    Nizar Shamsuddin Mohd Khairul

    2017-01-01

    Full Text Available Groundwater tables forecasting during implemented river bank infiltration (RBI method is important to identify adequate storage of groundwater aquifer for water supply purposes. This study illustrates the development and application of artificial neural networks (ANNs to predict groundwater tables in two vertical wells located in confined aquifer adjacent to the Langat River. ANN model was used in this study is based on the long period forecasting of daily groundwater tables. ANN models were carried out to predict groundwater tables for 1 day ahead at two different geological materials. The input to the ANN models consider of daily rainfall, river stage, water level, stream flow rate, temperature and groundwater level. Two different type of ANNs structure were used to predict the fluctuation of groundwater tables and compared the best forecasting values. The performance of different models structure of the ANN is used to identify the fluctuation of the groundwater table and provide acceptable predictions. Dynamics prediction and time series of the system can be implemented in two possible ways of modelling. The coefficient correlation (R, Mean Square Error (MSE, Root Mean Square Error (RMSE and coefficient determination (R2 were chosen as the selection criteria of the best model. The statistical values for DW1 are 0.8649, 0.0356, 0.01, and 0.748 respectively. While for DW2 the statistical values are 0.7392, 0.0781, 0.0139, and 0.546 respectively. Based on these results, it clearly shows that accurate predictions can be achieved with time series 1-day ahead of forecasting groundwater table and the interaction between river and aquifer can be examine. The findings of the study can be used to assist policy marker to manage groundwater resources by using RBI method.

  2. Integrating water data, models and forecasts - the Australian Water Resources Information System (Invited)

    Science.gov (United States)

    Argent, R.; Sheahan, P.; Plummer, N.

    2010-12-01

    Under the Commonwealth Water Act 2007 the Bureau of Meteorology was given a new national role in water information, encompassing standards, water accounts and assessments, hydrological forecasting, and collecting, enhancing and making freely available Australia’s water information. The Australian Water Resources Information System (AWRIS) is being developed to fulfil part of this role, by providing foundational data, information and model structures and services. Over 250 organisations across Australia are required to provide water data and metadata to the Bureau, including federal, state and local governments, water storage management and hydroelectricity companies, rural and urban water utilities, and catchment management bodies. The data coverage includes the categories needed to assess and account for water resources at a range of scales. These categories are surface, groundwater and meteorological observations, water in storages, water restrictions, urban and irrigation water use and flows, information on rights, allocations and trades, and a limited suite of water quality parameters. These data are currently supplied to the Bureau via a file-based delivery system at various frequencies from annual to daily or finer, and contain observations taken at periods from minutes to monthly or coarser. One of the primary keys to better data access and utilisation is better data organisation, including content and markup standards. As a significant step on the path to standards for water data description, the Bureau has developed a Water Data Transfer Format (WDTF) for transmission of a variety of water data categories, including site metadata. WDTF is adapted from the OGC’s observation and sampling-features standard. The WDTF XML schema is compatible with the OGC's Web Feature Service (WFS) interchange standard, and conforms to GML Simple Features profile (GML-SF) level 1, emphasising the importance of standards in data exchange. In the longer term we are also

  3. Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

    Science.gov (United States)

    Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

    2016-12-01

    The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

  4. 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

  5. High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

    Science.gov (United States)

    Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

    2017-12-01

    For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

  6. 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

  7. Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

    Directory of Open Access Journals (Sweden)

    B. M. Brentan

    2017-01-01

    Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

  8. 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

  9. Water level detection pipeline

    International Nuclear Information System (INIS)

    Koshikawa, Yukinobu; Imanishi, Masatoshi; Niizato, Masaru; Takagi, Masahiro

    1998-01-01

    In the present invention, water levels of a feedwater heater and a drain tank in a nuclear power plant are detected at high accuracy. Detection pipeline headers connected to the upper and lower portions of a feedwater heater or a drain tank are connected with each other. The connection line is branched at appropriate two positions and an upper detection pipeline and a lower detection pipeline are connected thereto, and a gauge entrance valve is disposed to each of the detection pipelines. A diaphragm of a pressure difference generator is connected to a flange formed to the end portion. When detecting the change of water level in the feedwater heater or the drain tank as a change of pressure difference, gauge entrance valves on the exit side of the upper and lower detection pipelines are connected by a connection pipe. The gauge entrance valve is closed, a tube is connected to the lower detection pipe to inject water to the diaphragm of the pressure difference generator passing through the connection pipe thereby enabling to calibrate the pressure difference generator. The accuracy of the calibration of instruments is improved and workability thereof upon flange maintenance is also improved. (I.S.)

  10. 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.

  11. Water level indicator

    International Nuclear Information System (INIS)

    Murase, Michio; Araki, Hidefumi.

    1996-01-01

    A difference of pressure between a standard pressure conduit in communication with a gas phase of a reactor pressure vessel and a water level pressure conduit in communication with a liquid phase of the pressure vessel is detected by a pressure difference gage. A communication pipe and a standard level vessel are disposed between the pressure vessel and the standard pressure conduit, and a standard liquid surface on the side of the standard pressure conduit is formed in the standard level vessel. A gas releaser is disposed to the gas phase portion of the standard level vessel. The gas releaser equipment is constituted by a porous material, a permeation membrane and a gas exhaustion hole. The gas phase of the standard level vessel is divided by a partition plate into a first gas phase being in contact with a connection portion with the communication pipe and a second gas phase in contact with the gas releaser. A gas flow channel hole and a condensate descending hole are disposed to the partition plate. Since incondensible gases accumulated to the standard level vessel are effectively exhausted, the incondensible gases are prevented from being dissolved into liquid. (I.N.)

  12. State of the Science for Sub-Seasonal to Seasonal Precipitation Forecasting in Support of Water Resource Managers

    Science.gov (United States)

    DeWitt, D. G.

    2017-12-01

    Water resource managers are one of the communities that would strongly benefit from highly-skilled sub-seasonal to seasonal precipitation forecasts. Unfortunately, the current state of the art prediction tools frequently fail to provide a level of skill sufficient to meet the stakeholders needs, especially on the monthly and seasonal timescale. On the other hand, the skill of precipitation forecasts on the week-2 timescale are relatively high and arguably useful in many decision-making contexts. This talk will present a comparison of forecast skill for the week-2 through the first season timescale and describe current efforts within NOAA and elsewhere to try to improve forecast skill beyond week-2, including research gaps that need to be addressed in order to make progress.

  13. iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

    Science.gov (United States)

    Sumi, S. J.; Ferreira, C.

    2017-12-01

    Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system

  14. 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.

  15. 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 onl....... 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....... 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...

  16. Reactor water level control device

    International Nuclear Information System (INIS)

    Hiramatsu, Yohei.

    1980-01-01

    Purpose: To increase the rapid response of the waterlevel control converting a reactor water level signal into a non-linear type, when the water level is near to a set value, to stabilize the water level reducting correlatively the reactor water level variation signal to stabilize greatly from the set value, and increasing the variation signal. Constitution: A main vapor flow quality transmitter detects the vapor flow generated in a reactor and introduced into a turbine. A feed water flow transmitter detects the quantity of a feed water flow from the turbine to the reactor, this detected value is sent to an addition operating apparatus. On the other hand, the power signal of the reactor water level transmitter is sent to the addition operating apparatus through a non-linear water level signal converter. The addition operation apparatus generates a signal for requesting the feed water flow quantity from both signals. Upon this occasion, the reactor water level signal converter makes small the reactor water level variation when the reactor level is close the set value, and when the water level deviates greatly from the set value, the reactor water level variation is made large thereby to improve the rapid response of the reactor coater level control. (Yoshino, Y.)

  17. Suppression device for the reactor water level lowering

    International Nuclear Information System (INIS)

    Kasuga, Hajime; Kasuga, Hiroshi.

    1984-01-01

    Purpose: To suppress the lowering in the reactor water level so as to avoid unnecessary actuation of ECCS upon generation of transient changes which forecasts the lowering of the reactor water level in a BWR type reactor. Constitution: There are provided a water level suppression signal generator for generating a water level suppression signal upon generation of a transient change signal which forecasts the water level lowering in a nuclear reactor and a recycling flow rate controller that applies a recycling flow rate control signal to a recycling pump drive motor by the water level lowering suppression signal. The velocity of the recycling pump is controlled by a reactor scram signal by way of the water level lowering suppresion signal generator and a recycling flow rate controller. Then, the recycling reactor core flow rate is decreased and the void amount in the reactor is transiently increased where the water level tends to increase. Accordingly, the water level lowering by the scram is moderated by the increasing tendency of the water level. (Ikeda, J.)

  18. Latent fluctuation periods and long-term forecasting of the level of Markakol lake

    Science.gov (United States)

    Madibekov, A. S.; Babkin, A. V.; Musakulkyzy, A.; Cherednichenko, A. V.

    2018-01-01

    The analysis of time series of the level of Markakol Lake by the method of “Periodicities” reveals in its variations the harmonics with the periods of 12 and 14 years, respectively. The verification forecasts of the lake level by the trend tendency and by its combination with these sinusoids were computed with the lead time of 5 and 10 years. The estimation of the forecast results by the new independent data permitted to conclude that forecasts by the combination of the sinusoids and trend tendency are better than by the trend tendency only. They are no worse than the mean value prediction.

  19. 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

  20. Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

    Science.gov (United States)

    Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

    2014-05-01

    Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

  1. Ensemble seasonal forecast of extreme water inflow into a large reservoir

    Directory of Open Access Journals (Sweden)

    A. N. Gelfan

    2015-06-01

    Full Text Available An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study. Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2 located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

  2. 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.

  3. Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA

    Science.gov (United States)

    Razali, S. N. A. M.; Rusiman, M. S.; Zawawi, N. I.; Arbin, N.

    2018-04-01

    This study is carried out to forecast water consumption expenditure of Malaysian university specifically at University Tun Hussein Onn Malaysia (UTHM). The proposed Holt-Winter’s and Auto-Regressive Integrated Moving Average (ARIMA) models were applied to forecast the water consumption expenditure in Ringgit Malaysia from year 2006 until year 2014. The two models were compared and performance measurement of the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) were used. It is found that ARIMA model showed better results regarding the accuracy of forecast with lower values of MAPE and MAD. Analysis showed that ARIMA (2,1,4) model provided a reasonable forecasting tool for university campus water usage.

  4. Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

    Science.gov (United States)

    Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

    2015-12-01

    The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

  5. 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.

  6. 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.

  7. Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory.

    Science.gov (United States)

    Pérez-Jordán, G.; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

    2018-04-01

    We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R =0.951 and R =0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

  8. Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings

    Directory of Open Access Journals (Sweden)

    Gde Dharma Nugraha

    2018-03-01

    Full Text Available Building energy management systems (BEMS have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.

  9. A multi-scale relevance vector regression approach for daily urban water demand forecasting

    Science.gov (United States)

    Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin

    2014-09-01

    Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.

  10. Financial Risk Reduction and Management of Water Reservoirs Using Forecasts: A Case for Pernambuco, Brazil

    Science.gov (United States)

    Kumar, I.; Josset, L.; e Silva, E. C.; Possas, J. M. C.; Asfora, M. C.; Lall, U.

    2017-12-01

    The financial health and sustainability, ensuring adequate supply, and adapting to climate are fundamental challenges faced by water managers. These challenges are worsened in semi-arid regions with socio-economic pressures, seasonal supply of water, and projected increase in intensity and frequency of droughts. Over time, probabilistic rainfall forecasts are improving and for water managers, it could be key in addressing the above challenges. Using forecasts can also help make informed decisions about future infrastructure. The study proposes a model to minimize cost of water supply (including cost of deficit) given ensemble forecasts. The model can be applied to seasonal to annual ensemble forecasts, to determine the least cost solution. The objective of the model is to evaluate the resiliency and cost associated to supplying water. A case study is conducted in one of the largest reservoirs (Jucazinho) in Pernambuco state, Brazil, and four other reservoirs, which provide water to nineteen municipalities in the Jucazinho system. The state has been in drought since 2011, and the Jucazinho reservoir, has been empty since January 2017. The importance of climate adaptation along with risk management and financial sustainability are important to the state as it is extremely vulnerable to droughts, and has seasonal streamflow. The objectives of the case study are first, to check if streamflow forecasts help reduce future supply costs by comparing k-nearest neighbor ensemble forecasts with a fixed release policy. Second, to determine the value of future infrastructure, a new source of supply from Rio São Francisco, considered to mitigate drought conditions. The study concludes that using forecasts improve the supply and financial sustainability of water, by reducing cost of failure. It also concludes that additional infrastructure can help reduce the risks of failure significantly, but does not guarantee supply during prolonged droughts like the one experienced

  11. Hybrid Stochastic Forecasting Model for Management of Large Open Water Reservoir with Storage Function

    Science.gov (United States)

    Kozel, Tomas; Stary, Milos

    2017-12-01

    The main advantage of stochastic forecasting is fan of possible value whose deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. Discharge in measurement profile could be categorized as random process. Content of article is construction and application of forecasting model for managed large open water reservoir with supply function. Model is based on neural networks (NS) and zone models, which forecasting values of average monthly flow from inputs values of average monthly flow, learned neural network and random numbers. Part of data was sorted to one moving zone. The zone is created around last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to zone. The model was compiled for forecast of 1 to 12 month with using backward month flows (NS inputs) from 2 to 11 months for model construction. Data was got ridded of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. The data were with monthly step and forecast is not recurring. 90 years long real flow series was used for compile of the model. First 75 years were used for calibration of model (matrix input-output relationship), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, was used application to management of artificially made reservoir. Course of water reservoir management using Genetic algorithm (GE) + real flow series was compared with Fuzzy model (Fuzzy) + forecast made by Moving zone model. During evaluation process was founding the best size of zone. Results show that the highest number of input did not give the best results and ideal size of zone is in interval from 25 to 35, when course of management was almost same for

  12. Using Deep Learning Techniques to Forecast Environmental Consumption Level

    Directory of Open Access Journals (Sweden)

    Donghyun Lee

    2017-10-01

    Full Text Available Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

  13. 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. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Forecast on Water Locking Damage of Low Permeable Reservoir with Quantum Neural Network

    Science.gov (United States)

    Zhao, Jingyuan; Sun, Yuxue; Feng, Fuping; Zhao, Fulei; Sui, Dianjie; Xu, Jianjun

    2018-01-01

    It is of great importance in oil-gas reservoir protection to timely and correctly forecast the water locking damage, the greatest damage for low permeable reservoir. An analysis is conducted on the production mechanism and various influence factors of water locking damage, based on which a quantum neuron is constructed based on the information processing manner of a biological neuron and the principle of quantum neural algorithm, besides, the quantum neural network model forecasting the water locking of the reservoir is established and related software is also made to forecast the water locking damage of the gas reservoir. This method has overcome the defects of grey correlation analysis that requires evaluation matrix analysis and complicated operation. According to the practice in Longxi Area of Daqing Oilfield, this method is characterized by fast operation, few system parameters and high accuracy rate (the general incidence rate may reach 90%), which can provide reliable support for the protection technique of low permeable reservoir.

  15. 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.

  16. 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.

  17. Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of Beijing.

    Science.gov (United States)

    Wei, Yigang; Wang, Zhichao; Wang, Huiwen; Yao, Tang; Li, Yan

    2018-09-01

    Water is centrally important for agricultural security, environment, people's livelihoods, and socio-economic development, particularly in the face of extreme climate changes. Due to water shortages in many cities, the conflicts between various stakeholders and sectors over water use and allocation are becoming more common and intense. Effective inclusive governance of water use is critical for relieving water use conflicts. In addition, reliable forecasting of the structure of water usage among different sectors is a basic need for effective water governance planning. Although a large number of studies have attempted to forecast water use, little is known about the forecasted structure and trends of water use in the future. This paper aims to develop a forecasting model for the structure of water usage based on compositional data. Compositional data analysis is an effective approach for investigating the internal structure of a system. A host of data transformation methods and forecasting models were adopted and compared in order to derive the best-performing model. According to mean absolute percent error for compositional data (CoMAPE), a hyperspherical-transformation-based vector autoregression model for compositional data (VAR-DRHT) is the best-performing model. The proportions of the agricultural, industrial, domestic and environmental water will be 6.11%, 5.01%, 37.48% and 51.4% by 2020. Several recommendations for water inclusive development are provided to give a better account for the optimization of the water use structure, alleviation of water shortages, and improving stake holders' wellbeing. Overall, although we focus on groundwater, this study presents a powerful framework broadly applicable to resource management. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

    Science.gov (United States)

    Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo

    2018-04-01

    Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

  19. 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

  20. The Next Level in Automated Solar Flare Forecasting: the EU FLARECAST Project

    Science.gov (United States)

    Georgoulis, M. K.; Bloomfield, D.; Piana, M.; Massone, A. M.; Gallagher, P.; Vilmer, N.; Pariat, E.; Buchlin, E.; Baudin, F.; Csillaghy, A.; Soldati, M.; Sathiapal, H.; Jackson, D.; Alingery, P.; Argoudelis, V.; Benvenuto, F.; Campi, C.; Florios, K.; Gontikakis, C.; Guennou, C.; Guerra, J. A.; Kontogiannis, I.; Latorre, V.; Murray, S.; Park, S. H.; Perasso, A.; Sciacchitano, F.; von Stachelski, S.; Torbica, A.; Vischi, D.

    2017-12-01

    We attempt an 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 achieved for the forecasting of major solar flares. We outline the consortium, top-level objectives and first results of the project, highlighting the diversity and fusion of expertise needed to deliver what was promised. The project's final product, featuring an openly accessible, fully modular and free to download flare forecasting facility will be delivered in early 2018. The project's three objectives, namely, science, research-to-operations and dissemination / communication, are also discussed: in terms of science, we encapsulate our close-to-final assessment on how close (or far) are we from a practically exploitable solar flare forecasting. In terms of R2O, we briefly describe the architecture of the FLARECAST infrastructure that includes rigorous validation for each forecasting step. From the three different communication levers of the project we finally focus on lessons learned from the two-way interaction with the community of stakeholders and governmental organizations. The FLARECAST project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 640216.

  1. Predictive Uncertainty Estimation in Water Demand Forecasting Using the Model Conditional Processor

    Directory of Open Access Journals (Sweden)

    Amos O. Anele

    2018-04-01

    Full Text Available In a previous paper, a number of potential models for short-term water demand (STWD prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017 showed that hybrid models may be considered as the accurate and appropriate forecasting models for STWD prediction. However, such best single valued forecast does not guarantee reliable and robust decisions, which can be properly obtained via model uncertainty processors (MUPs. MUPs provide an estimate of the full predictive densities and not only the single valued expected prediction. Amongst other MUPs, the purpose of this paper is to use the multi-variate version of the model conditional processor (MCP, proposed by Todini (2008, to demonstrate how the estimation of the predictive probability conditional to a number of relatively good predictive models may improve our knowledge, thus reducing the predictive uncertainty (PU when forecasting into the unknown future. Through the MCP approach, the probability distribution of the future water demand can be assessed depending on the forecast provided by one or more deterministic forecasting models. Based on an average weekly data of 168 h, the probability density of the future demand is built conditional on three models’ predictions, namely the autoregressive-moving average (ARMA, feed-forward back propagation neural network (FFBP-NN and hybrid model (i.e., combined forecast from ARMA and FFBP-NN. The results obtained show that MCP may be effectively used for real-time STWD prediction since it brings out the PU connected to its forecast, and such information could help water utilities estimate the risk connected to a decision.

  2. Water temperature forecasting and estimation using fourier series and communication theory techniques

    International Nuclear Information System (INIS)

    Long, L.L.

    1976-01-01

    Fourier series and statistical communication theory techniques are utilized in the estimation of river water temperature increases caused by external thermal inputs. An example estimate assuming a constant thermal input is demonstrated. A regression fit of the Fourier series approximation of temperature is then used to forecast daily average water temperatures. Also, a 60-day prediction of daily average water temperature is made with the aid of the Fourier regression fit by using significant Fourier components

  3. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    Science.gov (United States)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most

  4. 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.

  5. Using JPSS Retrievals to Implement a Multisensor, Synoptic, Layered Water Vapor Product for Forecasters

    Science.gov (United States)

    Forsythe, J. M.; Jones, A. S.; Kidder, S. Q.; Fuell, K.; LeRoy, A.; Bikos, D.; Szoke, E.

    2015-12-01

    Forecasters have been using the NOAA operational blended total precipitable water (TPW) product, developed by the Cooperative Institute for Research in the Atmosphere (CIRA), since 2009. Blended TPW has a wide variety of uses related to heavy precipitation and flooding, such as measuring the amount of moisture in an atmospheric river originating in the tropics. But blended TPW conveys no information on the vertical distribution of moisture, which is relevant to a variety of forecast concerns. Vertical profile information is particularly lacking over the oceans for landfalling storms. A blended six-satellite, four-layer, layered water vapor product demonstrated by CIRA and the NASA Short-term Prediction Research and Transition Center (SPoRT) in allows forecasters to see the vertical distribution of water vapor in near real-time. National Weather Service (NWS) forecaster feedback indicated that this new, vertically-resolved view of water vapor has a substantial impact on forecasts. This product uses NOAA investments in polar orbiting satellite sounding retrievals from passive microwave radiances, in particular, the Microwave Integrated Retrieval System (MIRS). The product currently utilizes data from the NOAA-18 and -19 spacecraft, Metop-A and -B, and the Defense Meteorological Program (DMSP) F18 spacecraft. The sounding instruments onboard the Suomi-NPP and JPSS spacecraft will be cornerstone instruments in the future evolution of this product. Applications of the product to heavy rain cases will be presented and compared to commonly used data such as radiosondes and Geostationary Operational Environmental Satellite (GOES) water vapor channel imagery. Research is currently beginning to implement advective blending, where model winds are used to move the water vapor profiles to a common time. Interactions with the NOAA Satellite Analysis Branch (SAB), National Center for Environmental Prediction (NCEP) centers including the Ocean Prediction Center (OPC) and Weather

  6. Multi-Model Prediction for Demand Forecast in Water Distribution Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo Lopez Farias

    2018-03-01

    Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

  7. Forecast Model of Urban Stagnant Water Based on Logistic Regression

    Directory of Open Access Journals (Sweden)

    Liu Pan

    2017-01-01

    Full Text Available With the development of information technology, the construction of water resource system has been gradually carried out. In the background of big data, the work of water information needs to carry out the process of quantitative to qualitative change. Analyzing the correlation of data and exploring the deep value of data which are the key of water information’s research. On the basis of the research on the water big data and the traditional data warehouse architecture, we try to find out the connection of different data source. According to the temporal and spatial correlation of stagnant water and rainfall, we use spatial interpolation to integrate data of stagnant water and rainfall which are from different data source and different sensors, then use logistic regression to find out the relationship between them.

  8. Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

    Science.gov (United States)

    Chen, Jinduan; Boccelli, Dominic L.

    2018-02-01

    Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

  9. Evaluation of the fast orthogonal search method for forecasting chloride levels in the Deltona groundwater supply (Florida, USA)

    Science.gov (United States)

    El-Jaat, Majda; Hulley, Michael; Tétreault, Michel

    2018-02-01

    Despite the broad impact and importance of saltwater intrusion in coastal aquifers, little research has been directed towards forecasting saltwater intrusion in areas where the source of saltwater is uncertain. Saline contamination in inland groundwater supplies is a concern for numerous communities in the southern US including the city of Deltona, Florida. Furthermore, conventional numerical tools for forecasting saltwater contamination are heavily dependent on reliable characterization of the physical characteristics of underlying aquifers, information that is often absent or challenging to obtain. To overcome these limitations, a reliable alternative data-driven model for forecasting salinity in a groundwater supply was developed for Deltona using the fast orthogonal search (FOS) method. FOS was applied on monthly water-demand data and corresponding chloride concentrations at water supply wells. Groundwater salinity measurements from Deltona water supply wells were applied to evaluate the forecasting capability and accuracy of the FOS model. Accurate and reliable groundwater salinity forecasting is necessary to support effective and sustainable coastal-water resource planning and management. The available (27) water supply wells for Deltona were randomly split into three test groups for the purposes of FOS model development and performance assessment. Based on four performance indices (RMSE, RSR, NSEC, and R), the FOS model proved to be a reliable and robust forecaster of groundwater salinity. FOS is relatively inexpensive to apply, is not based on rigorous physical characterization of the water supply aquifer, and yields reliable estimates of groundwater salinity in active water supply wells.

  10. Ambient noise forecasting with a large acoustic array in a complex shallow water environment.

    Science.gov (United States)

    Rogers, Jeffrey S; Wales, Stephen C; Means, Steven L

    2017-11-01

    Forecasting ambient noise levels in the ocean can be a useful way of characterizing the detection performance of sonar systems and projecting bounds on performance into the near future. The assertion is that noise forecasting can be improved with a priori knowledge of source positions coupled with the ability to resolve closely separated sources in bearing. One example of such a system is the large aperture research array located at the South Florida Test Facility. Given radar and Automatic Identification System defined source positions and environmental information, transmission loss (TL) is computed from known source positions to the array. Source levels (SLs) of individual ships are then estimated from computed TL and the pre-determined beam response of the array using a non-negative least squares algorithm. Ambient noise forecasts are formed by projecting the estimated SLs along known ship tracks. Ambient noise forecast estimates are compared to measured beam level data and mean-squared error is computed. A mean squared error as low as 3.5 dB is demonstrated in 30 min forecast estimates when compared to ground truth.

  11. 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

    Fine-grained device-level predictions of both shiftable and non-shiftable energy demand and supply is vital in order to take advantage of Demand Response (DR) for efficient utilization of Renewable Energy Sources. The selection of an effective device-level load forecast model is a challenging task......, mainly due to the diversity of the models and the lack of proper tools and datasets that can be used to validate them. In this paper, we introduce the DeMand system for fine-tuning, analyzing, and validating the device-level forecast models. The system offers several built-in device-level measurement...... 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...

  12. Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

    Science.gov (United States)

    Quilty, J.; Adamowski, J. F.

    2015-12-01

    Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

  13. Forecasting the quality of water-suppressed 1 H MR spectra based on a single-shot water scan.

    Science.gov (United States)

    Kyathanahally, Sreenath P; Kreis, Roland

    2017-08-01

    To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med 78:441-451, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  14. SHORT-TERM AND LONG-TERM WATER LEVEL PREDICTION AT ONE RIVER MEASUREMENT LOCATION

    Directory of Open Access Journals (Sweden)

    Rudolf Scitovski

    2012-12-01

    Full Text Available Global hydrological cycles mainly depend on climate changes whose occurrence is predominantly triggered by solar and terrestrial influence, and the knowledge of the high water regime is widely applied in hydrology. Regular monitoring and studying of river water level behavior is important from several perspectives. On the basis of the given data, by using modifications of general approaches known from literature, especially from investigation in hydrology, the problem of long- and short-term water level forecast at one river measurement location is considered in the paper. Long-term forecasting is considered as the problem of investigating the periodicity of water level behavior by using linear-trigonometric regression and short-term forecasting is based on the modification of the nearest neighbor method. The proposed methods are tested on data referring to the Drava River level by Donji Miholjac, Croatia, in the period between the beginning of 1900 and the end of 2012.

  15. New watershed-based climate forecast products for hydrologists and water managers

    Science.gov (United States)

    Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

    2017-12-01

    Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-­Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time

  16. Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

    Science.gov (United States)

    Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

    2018-03-01

    Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

  17. Developing integrated performance assessment and forecasting the level of financial and economic enterprise stability

    Directory of Open Access Journals (Sweden)

    Khudyakova T.A.

    2017-01-01

    Full Text Available The article deals with the problem of assessing and forecasting the level of financial and economic enterprise stability through the integrated indicators development. Currently, many enterprises operate under variable external environment, which imposes a strict requirement to consider this uncertainty. For the evaluation, analysis and prediction of the sustainability of the enterprise in the conditions of crisis we believe it possible and necessary to use the apparatus of probability theory and mathematical statistics. This problem solution will improve quantitative assessing the financial and economic stability level, forecasting possible scenarios of the enterprise development and, therefore, based on the proactive management principles and adaptation processes will greatly increase their effective functioning, as well as reduce bankruptcy probability.

  18. 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

  19. Statistical Models to Assess the Health Effects and to Forecast Ground Level Ozone

    Czech Academy of Sciences Publication Activity Database

    Schlink, U.; Herbath, O.; Richter, M.; Dorling, S.; Nunnari, G.; Cawley, G.; Pelikán, Emil

    2006-01-01

    Roč. 21, č. 4 (2006), s. 547-558 ISSN 1364-8152 R&D Projects: GA AV ČR 1ET400300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : statistical models * ground level ozone * health effects * logistic model * forecasting * prediction performance * neural network * generalised additive model * integrated assessment Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.992, year: 2006

  20. California's transition from conventional snowpack measurements to a developing remote sensing capability for water supply forecasting

    Science.gov (United States)

    Brown, A. J.; Peterson, N.

    1980-01-01

    California's Snow Survey Program and water supply forecasting procedures are described. A review is made of current activities and program direction on such matters as: the growing statewide network of automatic snow sensors; restrictions on the gathering hydrometeorological data in areas designated as wilderness; the use of satellite communications, which both provides a flexible network without mountaintop repeaters and satisfies the need for unobtrusiveness in wilderness areas; and the increasing operational use of snow covered area (SCA) obtained from satellite imagery, which, combined with water equivalent from snow sensors, provides a high correlation to the volumes and rates of snowmelt runoff. Also examined are the advantages of remote sensing; the anticipated effects of a new input of basin wide index of water equivalent, such as the obtained through microwave techniques, on future forecasting opportunities; and the future direction and goals of the California Snow Surveys Program.

  1. 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.

  2. Reactor water level measuring device

    International Nuclear Information System (INIS)

    Kuroki, Reiji; Asano, Tamotsu.

    1996-01-01

    A condensation vessel is connected to the upper portion of a reactor pressure vessel by way of a pipeline. The lower portion of the condensation vessel is connected to a low pressure side of a differential pressure transmission device by way of a reference leg pipeline. The high pressure side of the differential pressure transmission device is connected to the lower portion of the pressure vessel by way of a pipeline. The condensation vessel is equipped with a temperature sensor. When a temperature of a gas phase portion in the condensation vessel is lowered below a predetermined level, and incondensible gases in the condensation vessel starts to be dissolved in water, signals are sent from the temperature sensor to a control device and a control valve is opened. With such a constitution, CRD driving water flows into the condensation vessel, and water in which gases at the upper portion of the condensation vessel is dissolved flows into the pressure vessel by way of a pipeline. Then, gases dissolved in a reference water column in the reference leg pipeline are eliminated and the value of a reference water pressure does not change even upon abrupt lowering of pressure. (I.N.)

  3. Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming

    Science.gov (United States)

    Imani, Moslem; You, Rey-Jer; Kuo, Chung-Yen

    2014-10-01

    Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE = 0.035) and maximum coefficient of determination (R2 = 0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN.

  4. Modelling and forecasting occupational accidents of different severity levels in Spain

    International Nuclear Information System (INIS)

    Carmen Carnero, Maria; Jose Pedregal, Diego

    2010-01-01

    The control of accidents at the work place is a critical issue all over the world. The consequences of occupational accidents in terms of costs for the company in which the accidents take place is only one minor matter, being the social impact and the loss of human life the most controversial effects of this important problem. The methods used to forecast future evolution of accidents are often limited to trend estimations and projections, being the scientific literature on this topic rather scarce. This paper aims at showing and predicting the evolution of Spanish occupational accidents of different levels of severity, allowing the evaluation of the influence that preventive actions carried out by public administrations or private companies may have over the number of occupational accidents. Though some contributions may be found on this topic for Spain, this paper is the first contribution that forecast occupational accidents for different levels of severity using Multivariate Unobserved Components models developed in a State Space framework extended to deal with the irregular sampling interval of the data. Data from 1998 to 2009 have been used to test the efficacy of the forecasting system.

  5. 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

    electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems......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...

  6. Water availability forecasting for Naryn River using ground-based and satellite snow cover data

    Directory of Open Access Journals (Sweden)

    O. Y. Kalashnikova

    2017-01-01

    Full Text Available The main source of river nourishment in arid regions of Central Asia is the melting of seasonal snow accu‑ mulated in mountains during the cold period. In this study, we analyzed data on seasonal snow cover by ground‑based observations from Kyrgyzhydromet network, as well as from MODIS satellite imagery for the period of 2000–2015. This information was used to compile the forecast methods of water availability of snow‑ice and ice‑snow fed rivers for the vegetation period. The Naryn river basin was chosen as a study area which is the main tributary of Syrdarya River and belongs to the Aral Sea basin. The representative mete‑ orological stations with ground‑based observations of snow cover were identified and regression analysis between mean discharge for the vegetation period and number of snow covered days, maximum snow depth based on in‑situ data as well as snow cover area based on MODIS images was conducted. Based on this infor‑ mation, equations are derived for seasonal water availability forecasting using multiple linear regression anal‑ ysis. Proposed equations have high correlation coefficients (R = 0.89÷0.92 and  and fore‑ casting accuracy. The methodology was implemented in Kyrgyzhydromet and is used for forecasting of water availability in Naryn basin and water inflow into Toktogul Reservoir.

  7. Forecasting water flows in Pakistan's Indus River | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2011-07-15

    Jul 15, 2011 ... ... been an important but cumbersome process in this South Asian country, ... which border China and India, and the Hindu Kush, which borders Afghanistan. ... above sea level to survey one of the world's most rugged terrains.

  8. Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

    Science.gov (United States)

    Woznicki, S. A.; Wickham, J.

    2017-12-01

    Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influence future treatment processes is lacking. The drinking water treatment plant in Minneapolis, MN has recognized that land cover change threatens water quality in their source watershed, the Upper Mississippi River Basin (UMRB). Over 1,000 km2 of forests, wetlands, and grasslands in the UMRB were lost to agriculture from 2008-2013. This trend, coupled with a projected population increase of one million people in Minnesota by 2030, concerns drinking water treatment plant operators in Minneapolis with respect to meeting future demand for clean water in the UMRB. The objective of this study is to relate land cover change (forest and wetland loss, agricultural expansion, urbanization) to changes in treatment costs for the Minneapolis, MN drinking water utility. To do this, we first developed a framework to determine the relationship between land cover change and water quality in the context of recent historical changes and projected future changes in land cover. Next we coupled a watershed model, the Soil and Water Assessment Tool (SWAT) to projections of land cover change from the FOREcasting SCEnarios of Land-use Change (FORE-SCE) model for the mid-21st century. Using historical Minneapolis drinking water treatment data (chemical usage and costs), source water quality in the UMRB was linked to changes in treatment requirements as a function of projected future land cover change. These analyses will quantify the value of natural landscapes in protecting drinking water quality and future treatment processes requirements. In addition, our study provides the Minneapolis drinking water utility with information critical to their planning and capital improvement process.

  9. 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.

  10. 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...

  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. Incorporating regional growth into forecasts of greenhouse gas emissions from project-level residential and commercial development

    International Nuclear Information System (INIS)

    Rowangould, Dana; Eldridge, Melody; Niemeier, Deb

    2013-01-01

    To better understand the greenhouse gas (GHG) implications of land use planning decisions, regional planning organizations have developed tools to forecast the emissions from project-level residential and commercial development. This paper reviews the state of GHG emissions forecasting methods for project-level development. We argue that when forecasting changes in regional emissions it is important to make explicit what is assumed about a project′s effect on the population of residents and businesses in the region. We present five regional growth assumptions capturing the range of ways that project-level development might influence (i) construction and occupancy of similar developments elsewhere in a region and (ii) relocation of the initial activities that occur on-site before the project is built. We show that current forecasting tools inconsistently address the latter when they are interpreted as forecasted changes in regional emissions. Using a case study in Yolo County, California we demonstrate that forecasted changes in regional emissions are greatly affected by the regional growth assumption. In the absence of information about which regional growth assumption is accurate, we provide guidelines for selection of a conservative regional growth assumption. - Highlights: • Current tools inconsistently forecast GHG emissions from project-level development. • We outline five assumptions about how projects may affect regional growth. • Our assumptions capture a range of economic and population effects of projects. • Our case study shows that growth assumptions greatly affect regional GHG estimates. • We provide guidelines for selecting a conservative regional growth assumption

  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. Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

    Science.gov (United States)

    Dixon, Kenneth

    A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.

  16. Drinking Water Quality Forecast of Peshawar Valley on the Basis of Sample Data

    International Nuclear Information System (INIS)

    Khan, S.U.; Bangash, F.K.

    2001-01-01

    Microbiological and related parameters of 75 portable water samples collected from source, distribution line and consumer tap in 25 different locations were investigated. The findings were used to forecast statistically the quality of drinking water of hole valley at all three sites and compared with WHO's standards. The study shows that the valley has good water deposits and suitable for drinking purposes however the same quality is not maintained throughout the distribution systems. The presence of total and fecal coliform in the samples collected from distribution line and consumer tap shows the mixing of wastewater through leaky joints and corroded underground supply system. The study also shows poor disinfecting practices in the study area. On the basis of this study we can say that the area got excellent subsoil water deposits but most of the consumers are supplied with water not fit for drinking purposes which is the main cause of Heath problems in the area. (author)

  17. 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.

  18. 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

  19. Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

    Science.gov (United States)

    KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

    2013-12-01

    It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more

  20. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level.

    Directory of Open Access Journals (Sweden)

    Cecilia de Almeida Marques-Toledo

    2017-07-01

    Full Text Available Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems.In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access.Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low

  1. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level.

    Science.gov (United States)

    Marques-Toledo, Cecilia de Almeida; Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

    2017-07-01

    Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are

  2. Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

    Science.gov (United States)

    Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

    2013-03-01

    Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

  3. Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites

    Science.gov (United States)

    Marín, Julio C.; Pozo, Diana; Curé, Michel

    2015-01-01

    In this work, we describe a method to estimate the precipitable water vapor (PWV) from Geostationary Observational Environmental Satellite (GOES) data at high altitude sites. The method was applied at Atacama Pathfinder Experiment (APEX) and Cerro Toco sites, located above 5000 m altitude in the Chajnantor plateau, in the north of Chile. It was validated using GOES-12 satellite data over the range 0-1.2 mm since submillimeter/millimeter astronomical observations are only useful within this PWV range. The PWV estimated from GOES and the Final Analyses (FNL) at APEX for 2007 and 2009 show root mean square error values of 0.23 mm and 0.36 mm over the ranges 0-0.4 mm and 0.4-1.2 mm, respectively. However, absolute relative errors of 51% and 33% were shown over these PWV ranges, respectively. We recommend using high-resolution thermodynamic profiles from the Global Forecast System (GFS) model to estimate the PWV from GOES data since they are available every three hours and at an earlier time than the FNL data. The estimated PWV from GOES/GFS agrees better with the observed PWV at both sites during night time. The largest errors are shown during daytime. Short-term PWV forecasts were implemented at both sites, applying a simple persistence method to the PWV estimated from GOES/GFS. The 12 h and 24 h PWV forecasts evaluated from August to October 2009 indicates that 25% of them show a very good agreement with observations whereas 50% of them show reasonably good agreement with observations. Transmission uncertainties calculated for PWV estimations and forecasts over the studied sites are larger over the range 0-0.4 mm than over the range 0.4-1.2 mm. Thus, the method can be used over the latter interval with more confidence.

  4. Water level monitoring device in nuclear reactor

    International Nuclear Information System (INIS)

    Miura, Kiyohide; Otake, Tomohiro.

    1988-01-01

    Purpose: To monitor the water level in a pressure vessel of BWR type nuclear reactors at high accuracy by improving the compensation functions. Constitution: In the conventional water level monitor in a nuclear reactor, if the pressure vessel is displaced by the change of the pressure in the reactor or the temperature of the reactor water, the relative level of the reference water head in a condensation vessel is changed to cause deviation between the actual water level and the indicated water level to reduce the monitoring accuracy. According to the invention, means for detecting the position of the reference water head and means for detection the position in the condensation vessel are disposed to the pressure vessel. Then, relative positional change between the condensation vessel and the reference water head is calculated based on detection sinals from both of the means. The water level is compensated and calculated by water level calculation means based on the relative positional change, water level signals from the level gage and the pressure signals from the pressure gage. As a result, if the pressure vessel is displaced due to the change of the temperature or pressure, it is possible to measure the reactor water level accurately thereby remakably improve the reliability for the water level control in the nuclear reactor. (Horiuchi, T.)

  5. Forecasting domestic water demand in the Haihe river basin under changing environment

    Directory of Open Access Journals (Sweden)

    X.-J. Wang

    2018-02-01

    Full Text Available A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22  ×  108 m3 by GCM BNU-ESM and the minimum 107.25  ×  108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

  6. Forecasting domestic water demand in the Haihe river basin under changing environment

    Science.gov (United States)

    Wang, Xiao-Jun; Zhang, Jian-Yun; Shahid, Shamsuddin; Xie, Yu-Xuan; Zhang, Xu

    2018-02-01

    A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs) 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22 × 108 m3 by GCM BNU-ESM and the minimum 107.25 × 108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

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

    Science.gov (United States)

    Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

    2017-12-01

    A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-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. Current work suggests

  8. 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.

  9. Subseasonal to Seasonal Predictions of U.S. West Coast High Water Levels

    Science.gov (United States)

    Khouakhi, A.; Villarini, G.; Zhang, W.; Slater, L. J.

    2017-12-01

    Extreme sea levels pose a significant threat to coastal communities, ecosystems, and assets, as they are conducive to coastal flooding, coastal erosion and inland salt-water intrusion. As sea levels continue to rise, these sea level extremes - including occasional minor coastal flooding experienced during high tide (nuisance floods) - are of concern. Extreme sea levels are increasing at many locations around the globe and have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate processes such as the El Niño-Southern Oscillation (ENSO). Here, intra-seasonal to seasonal probabilistic forecasts of high water levels are computed at the Toke Point tide gage station on the US west coast. We first identify the main climate drivers that are responsible for high water levels and examine their predictability using General Circulation Models (GCMs) from the North American Multi-Model Ensemble (NMME). These drivers are then used to develop a probabilistic framework for the seasonal forecasting of high water levels. We focus on the climate controls on the frequency of high water levels using the number of exceedances above the 99.5th percentile and above the nuisance flood level established by the National Weather Service. Our findings indicate good forecast skill at the shortest lead time, with the skill that decreases as we increase the lead time. In general, these models aptly capture the year-to-year variability in the observational records.

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

    OpenAIRE

    Yalçıntaş, Murat; Bulu, Melih; Küçükvar, Murat; Samadi, Hamidreza

    2015-01-01

    Yayın, Endüstri Mühendisliği Bölümü ile ortak hazırlanmıştır; ancak tekrara düşmemek için ilk yazarın bölümü alınmıştır. 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, usi...

  11. 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.

  12. Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

    Science.gov (United States)

    Arumugam, S.; Mazrooei, A.; Ward, R.

    2017-12-01

    Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

  13. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

  14. Water level measurement uncertainty during BWR instability

    International Nuclear Information System (INIS)

    Torok, R.C.; Derbidge, T.C.; Healzer, J.M.

    1994-01-01

    This paper addresses the performance of the water-level measurement system in a boiling water reactor (BWR) during severe instability oscillations which, under some circumstances, can occur during an anticipated transient without SCRAM (ATWS). Test data from a prototypical mock-up of the water-level measurement system was used to refine and calibrate a water-level measurement system model. The model was then used to predict level measurement system response, using as boundary conditions vessel pressures calculated by ppercase RETRAN for an ATWS/instability event.The results of the study indicate that rapid pressure changes in the reactor pressure vessel which cause oscillations in downcomer water level, coupled with differences in instrument line lengths, can produce errors in the sensed water level. Using nominal parameters for the measurement system components, a severe instability transient which produced a 0.2 m peak-to-minimum water-level oscillation in the vessel downcomer was predicted to produce pressure difference equivalent to a 0.7 m level oscillation at the input to the differential pressure transmitter, 0.5 m oscillation at the output of the transmitter, and an oscillation of 0.3 m on the water-level indicator in the control room. The level measurement system error, caused by downcomer water-level oscillations and instrument line length differential, is mitigated by damping both in the differential pressure transmitter used to infer level and in the control room display instrument. ((orig.))

  15. Succeeding in deep water by combining technology qualification and production forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Hussain, A.; Oiungen, B.; Raposo, C. [Det Norske Veritas (DNV), Rio de Janeiro, RJ (Brazil)

    2008-07-01

    All the easy oil and gas is gone, and, as a result the Oil and Gas industry is continuously targeting deeper and more remote fields. The exploration and development of deep water oil and gas fields is associated with enormous costs and multiple uncertainties with regard to equipment reliability and performance. Proper risk management can be used to evaluate the impact of these uncertainties thereby helping to ensure optimal business performance of the future assets, as well as helping the decision maker target investment towards areas where the financial impact will be the greatest. This paper reviews the principles of Technology Qualification and Production Forecasting methodology, both of which are risk management solutions with a proven track record for deep water field developments. (author)

  16. Water: Local-Level Management

    International Development Research Centre (IDRC) Digital Library (Canada)

    Each publication distills IDRC's research experience with an eye to drawing out ..... in an arid area can drip as much water into the dry soil as might ever arrive ..... disintegrate without careful maintenance into smelly sources of disease and ...

  17. Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ziyi Yin

    2018-03-01

    Full Text Available Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP, the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR was introduced for comparison with the ANN. The results show the following: (a The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

  18. Method of measuring reactor water level

    International Nuclear Information System (INIS)

    Shinohara, Kaoru.

    1979-01-01

    Purpose: To provide a water level measuring system so that a reactor water level detecting signal can be corrected in correspondence to a recirculation flow, thereby to carry out a correct water level detection in a wide range of the reactor. Method: According to the operation record of a precursor reactor, the ratio Δh of the lowering of the water level due to the recirculation flow is lowered in proportion to the ratiowith respect to the rated differential pressure of the recirculation flow. Accordingly, the flow of recirculation pump is measured by an elbow differential pressure generator utilizing an elbow of a pipe, and the measured value is multiplied by a gain by a ratio setter, and therefter, an addition computation is carried out by an adder for correcting the signal from a water level detector. When the signal from the water level detector is corrected in this manner, the influence of the lowering of the water level due to the recirculation flow can be removed, and an interlocker predetermined in the defined water level can be actuated, thus the influence of the dynamic pressure due to the recirculation flow acting on the instrumental pipe line detecting the reactor water level can be removed effectively. (Yoshino, Y.)

  19. 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.

  20. 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.

  1. 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.

  2. Neuron- specific enolase level in patients with metabolic syndrome and its value forecasting acute stroke

    Directory of Open Access Journals (Sweden)

    Oral Ospanov

    2018-03-01

    Full Text Available Background Patients with metabolic syndrome are at a greater risk of experiencing a cerebrovascular event. Several studies show that patients with metabolic syndrome have asymptomatic ischemic brain injury. In this case, there is a need for rapid determination of asymptomatic brain lesions and prediction of acute stroke. Aims The aim of the study was to determine the neuron-specific enolase (NSE serum level in patients with metabolic syndrome and the value of this level for forecasting acute stroke. Methods The study used the following information to determine metabolic syndrome: waist circumference, total cholesterol, triglycerides, high-density lipoprotein cholesterol, blood pressure, and blood glucose. Doppler sonography mapping of the brachiocephalic trunk was held to determine the percentage of the carotid artery stenosis. To determine asymptomatic ischemic brain injury, the NSE serum marker was measured. Statistical processing of the measurements was performed using the H test and the Mann–Whitney test. The possible link between MS and NSE were determined by logistic regression analysis. Mathematical modeling was performed using logistic regression. Results There are statistically significant differences in NSE concentrations in groups with metabolic syndrome and ischemic stroke patients. This assertion is confirmed by logistic regression analysis, which revealed the existence of a relationship between metabolic syndrome and increased concentration of NSE. Conclusion Patients with metabolic syndrome have an increased concentration of NSE. This indicates the presence of asymptomatic ischemic neuronal damage. A prognostic model for determining the probability that patients with metabolic syndrome will have an acute stroke was developed.

  3. 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

  4. Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

    Science.gov (United States)

    Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

    2017-11-01

    Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

  5. 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.

  6. Nonlinear Stochastic Models for Water Level Dynamics in Closed Lakes

    OpenAIRE

    Mishchenko, A.S.; Zelikin, M.I.; Zelikina, L.F.

    1995-01-01

    This paper presents the results of investigation of nonlinear mathematical models of the behavior of closed lakes using the example of the Caspian Sea. Forecasting the level of the Caspian Sea is crucial both for the economy of the region and for the region's environment. The Caspian Sea is a closed reservoir; it is well known that its level changes considerably due to a variety of factors including global climate change. A series of forecasts exists based on different methods and taking...

  7. Method for steam generator water level measurement

    International Nuclear Information System (INIS)

    Srinivasan, J.S.

    1991-01-01

    This paper describes a nuclear power plant, a method of controlling the steam generator water level, wherein the steam generator has an upper level tap corresponding to an upper level, a lower level, a riser positioned between the lower and upper taps, and level sensor means for indicating water level between a first range limit and a second range limit, the sensor means being connected to at least the lower tap. It comprises: calculating a measure of velocity head at about the lower level tap; calculating a measure of full water level as the upper level less the measure of velocity head; calibrating the level sensor means to provide an output at the first limit corresponding to an input thereto representative of the measure of full level; calculating a high level setpoint equal to the level of the riser less a bias amount which is a function of the position of the riser relative to the span between the taps; and controlling the water level when the sensor means indicates that the high level setpoint has been reached

  8. Radon levels in a water distribution network

    International Nuclear Information System (INIS)

    Alabdula'aly, A.I.

    1997-01-01

    The capital city of Saudi Arabia, Riyadh, relies on both desalinated sea water as well as treated groundwater to meet all its water requirements. About 66% of the water demand is met by desalinated sea water, and the remaining is supplied by six groundwater treatment plants located in the vicinity of the city and supplied with water from 161 wells. The desalinated sea water is blended with only one plant product water and pumped to the distribution network, whereas the other five plants product water is pumped directly to the network. A study of 222 Rn levels in the city distribution network was carried out in which 89 samples were collected from different locations representing the city districts. All samples have shown low radon levels with an average concentration of 0.2 Bq l -1 and a range values of 0.1-1.0 Bq l -1 . The level of radon in different parts of the network was found to be influenced by the water sources to which they are supplied. The lowest radon levels were observed in districts supplied mostly by desalinated sea water. (Author)

  9. 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

  10. 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.)

  11. 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.

  12. 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.

  13. Assessing the spatial impact of climate on wheat productivity and the potential value of climate forecasts at a regional level

    Science.gov (United States)

    Wang, Enli; Xu, J.; Jiang, Q.; Austin, J.

    2009-03-01

    Quantification of the spatial impact of climate on crop productivity and the potential value of seasonal climate forecasts can effectively assist the strategic planning of crop layout and help to understand to what extent climate risk can be managed through responsive management strategies at a regional level. A simulation study was carried out to assess the climate impact on the performance of a dryland wheat-fallow system and the potential value of seasonal climate forecasts in nitrogen management in the Murray-Darling Basin (MDB) of Australia. Daily climate data (1889-2002) from 57 stations were used with the agricultural systems simulator (APSIM) to simulate wheat productivity and nitrogen requirement as affected by climate. On a good soil, simulated grain yield ranged from 7 t/ha in the east border regions. Optimal nitrogen rates ranged from 200 kgN/ha/yr. Simulated gross margin was in the range of -20/ha to 700/ha, increasing eastwards. Wheat yield was closely related to rainfall in the growing season and the stored soil moisture at sowing time. The impact of stored soil moisture increased from southwest to northeast. Simulated annual deep drainage ranged from zero in western inland to >200 mm in the east. Nitrogen management, optimised based on ‘perfect’ knowledge of daily weather in the coming season, could add value of 26˜79/ha compared to management optimised based on historical climate, with the maximum occurring in central to western part of MDB. It would also reduce the nitrogen application by 5˜25 kgN/ha in the main cropping areas. Comparison of simulation results with the current land use mapping in MDB revealed that the western boundary of the current cropping zone approximated the isolines of 160 mm of growing season rainfall, 2.5t/ha of wheat grain yield, and 150/ha of gross margin in QLD and NSW. In VIC and SA, the 160-mm isohyets corresponded relatively lower simulated yield due to less stored soil water. Impacts of other factors like soil

  14. 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.

  15. Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

    Science.gov (United States)

    Wu, Hua'an; Zeng, Bo; Zhou, Meng

    2017-11-15

    High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

  16. 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

  17. 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

  18. Ground Level Ozone Peak Forecast using Neural Networks and Kalman Filter

    Czech Academy of Sciences Publication Activity Database

    Pelikán, Emil; Eben, Kryštof; Vondráček, Jiří; Krejčíř, Pavel; Keder, J.

    2000-01-01

    Roč. 3, č. 2 (2000), s. 3-8 ISSN 1335-339X Grant - others:APPETISE(XE) IST-99-11764; MŽP ČR(CZ) ZZ520/2/97; MŠMT ČR(CZ) VS96008 Institutional research plan: AV0Z1030915 Keywords : ozone forecast * neural classifications * Kalman filter * genetic algorithms * Kohonen maps * Czech Republic Subject RIV: BB - Applied Statistics, Operational Research

  19. Reading Ground Water Levels with a Smartphone

    Science.gov (United States)

    van Overloop, Peter-Jules

    2015-04-01

    Most ground water levels in the world are measured manually. It requires employees of water management organizations to visit sites in the field and execute a measurement procedure that requires special tools and training. Once the measurement is done, the value is jotted down in a notebook and later, at the office, entered in a computer system. This procedure is slow and prone to human errors. A new development is the introduction of modern Information and Communication Technology to support this task and make it more efficient. Two innovations are introduced to measure and immediately store ground water levels. The first method is a measuring tape that gives a sound and light when it just touches the water in combination with an app on a smartphone with which a picture needs to be taken from the measuring tape. Using dedicated pattern recognition algorithms, the depth is read on the tape and it is verified if the light is on. The second method estimates the depth using a sound from the smartphone that is sent into the borehole and records the reflecting waves in the pipe. Both methods use gps-localization of the smartphone to store the depths in the right location in the central database, making the monitoring of ground water levels a real-time process that eliminates human errors.

  20. A improved tidal method without water level

    Science.gov (United States)

    Luo, xiaowen

    2017-04-01

    Now most tide are obtained use water Level and pressure type water gage, but it is difficult to install them and reading is in low accuracy in this method . In view of above-mentioned facts, In order to improve tide accuracy, A improved method is introduced.sea level is obtained in given time using high-precision GNSS buoy combined instantaneous position from pressure gage. two steps are as following, (1) the GNSS time service is used as the source of synchronization reference in tidal measurement; (2) centimeter-level sea surface positions are obtained in real time using difference GNSS The improved method used in seafloor topography survey,in 145 cross points, 95% meet the requirements of the Hydrographic survey specification. It is effective method to obtain higher accuracy tide.

  1. Climate-driven changes in water level

    DEFF Research Database (Denmark)

    Hansen, Rikke Bjerring; Olsen, Jesper; Jeppesen, Erik

    2013-01-01

    level rose. Moreover, Nymphaeaceae trichosclereids were abundant during the period of algal enrichment. Cladoceran taxa associated with floating leaved plants or benthic habitats responded in a complex way to changes in water level, but the cladoceran assemblages generally reflected deep lake conditions...... hydrology driven by precipitation. The isotopic, sedimentary and plant macrofossil records suggested that the lake level started to decrease around 8400 cal. yr BP, the decrease accelerating during 8350-8260 before an abrupt increase during 8260-8210. This pattern shows that the climate anomaly started...... rates of cladoceran subfossils and algal pigments, possibly due to increased turbidity and reduced nutrient input during this drier period. Pigment analysis also showed added importance of diatoms and cryptophytes during this climate anomaly, while cyanobacteria became more important when the water...

  2. The Lower Sevier River Basin Crop Monitor and Forecast Decision Support System: Exploiting Landsat Imagery to Provide Continuous Information to Farmers and Water Managers

    Science.gov (United States)

    Torres-Rua, A. F.; Walker, W. R.; McKee, M.

    2013-12-01

    The last century has seen a large number of innovations in agriculture such as better policies for water control and management, upgraded water conveyance, irrigation, distribution, and monitoring systems, and better weather forecasting products. In spite of this, irrigation management and irrigation water deliveries by farmers/water managers is still based on factors like water share amounts, tradition, and past experience on irrigation. These factors are not necessarily related to the actual crop water use; they are followed because of the absence of related information provided in a timely manner at an affordable cost. Thus, it is necessary to develop means to deliver continuous and personalized information about crop water requirements to water users/managers at the field and irrigation system levels so managers at these levels can better quantify the required versus available water for irrigation during the irrigation season. This study presents a new decision support system (DSS) platform that addresses the absence of information on actual crop water requirements and crop performance by providing continuous updated farm-based crop water use along with other farm performance indicators such as crop yield and farm management to irrigators and water managers. This DSS exploits the periodicity of the Landsat Satellite Mission (8 to 16 days, depending on the period of interest) to provide remote monitoring at the individual field and irrigation system levels. The Landsat satellite images are converted into information about crop water use, yield performance and field management through application of state-of-the-art semi-physical and statistical algorithms that provide this information at a pixel basis that are ultimately aggregated to field and irrigation system levels. A version of the DSS has been implemented for the agricultural lands in the Lower Sevier River, Utah, and has been operational since the beginning of the 2013 irrigation season. The main goal of

  3. Deep-water oilfield development cost analysis and forecasting —— Take gulf of mexico for example

    Science.gov (United States)

    Shi, Mingyu; Wang, Jianjun; Yi, Chenggao; Bai, Jianhui; Wang, Jing

    2017-11-01

    Gulf of Mexico (GoM) is the earliest offshore oilfield which has ever been developed. It tends to breed increasingly value of efficient, secure and cheap key technology of deep-water development. Thus, the analyze of development expenditure in this area is significantly important the evaluation concept of deep-water oilfield all over the world. This article emphasizes on deep-water development concept and EPC contract value in GoM in recent 10 years in case of comparison and selection to the economic efficiency. Besides, the QUETOR has been put into use in this research processes the largest upstream cost database to simulate and calculate the calculating examples’ expenditure. By analyzing and forecasting the deep-water oilfield development expenditure, this article explores the relevance between expenditure index and oil price.

  4. Contribution of piezometric measurement on knowledge and management of low water levels

    Science.gov (United States)

    Bessiere, Hélène; Stollsteiner, Philippe; Allier, Delphine; Nicolas, Jérôme; Gourcy, Laurence

    2014-05-01

    This article is based on a BRGM study on piezometric indicators, threshold values of discharges and groundwater levels for the assessment of potentially pumpable volumes of chalky watersheds. A method for estimating low water levels from groundwater levels is presented from three examples of chalk aquifer; the first one is located in Picardy and the two other in the Champagne Ardennes region. Piezometers with "annual" cycles, used in these examples, are supposed to be representative of the aquifer hydrodynamics. The analysis leads to relatively precise and satisfactory relationships between groundwater levels and observed discharges for this chalky context. These relationships may be useful for monitoring, validation, extension or reconstruction of the low water flow. On the one hand, they allow defining the piezometric levels corresponding to the different alert thresholds of river discharges. On the other hand, they clarify the distribution of low water flow from runoff or the draining of the aquifer. Finally, these correlations give an assessment of the minimum flow for the coming weeks using of the rate of draining of the aquifer. Nevertheless the use of these correlations does not allow to optimize the value of pumpable volumes because it seems to be difficult to integrate the amount of the effective rainfall that may occur during the draining period. In addition, these relationships cannot be exploited for multi-annual cycle systems. In these cases, the solution seems to lie on the realization of a rainfall-runoff-piezometric level model. Therefore, two possibilities are possible. The first one is to achieve each year, on a given date, a forecast for the days or months to come with various frequential distributions rainfalls. However, the forecast must be reiterated each year depending on climatic conditions. The principle of the second method is to simulate forecasts for different rainfall intensities and following different initial conditions. The results

  5. 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

  6. Seasonal prediction and predictability of Eurasian spring snow water equivalent in NCEP Climate Forecast System version 2 reforecasts

    Science.gov (United States)

    He, Qiong; Zuo, Zhiyan; Zhang, Renhe; Zhang, Ruonan

    2018-01-01

    The spring snow water equivalent (SWE) over Eurasia plays an important role in East Asian and Indian monsoon rainfall. This study evaluates the seasonal prediction capability of NCEP Climate Forecast System version 2 (CFSv2) retrospective forecasts (1983-2010) for the Eurasian spring SWE. The results demonstrate that CFSv2 is able to represent the climatological distribution of the observed Eurasian spring SWE with a lead time of 1-3 months, with the maximum SWE occurring over western Siberia and Northeastern Europe. For a longer lead time, the SWE is exaggerated in CFSv2 because the start of snow ablation in CFSv2 lags behind that of the observation, and the simulated snowmelt rate is less than that in the observation. Generally, CFSv2 can simulate the interannual variations of the Eurasian spring SWE 1-5 months ahead of time but with an exaggerated magnitude. Additionally, the downtrend in CFSv2 is also overestimated. Because the initial conditions (ICs) are related to the corresponding simulation results significantly, the robust interannual variability and the downtrend in the ICs are most likely the causes for these biases. Moreover, CFSv2 exhibits a high potential predictability for the Eurasian spring SWE, especially the spring SWE over Siberia, with a lead time of 1-5 months. For forecasts with lead times longer than 5 months, the model predictability gradually decreases mainly due to the rapid decrease in the model signal.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    S. W. D. Turner

    2017-09-01

    Full Text Available 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 behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

  9. Forecasting water demand using back propagation networks in the operation of reservoirs in the citarum cascade, west java, indonesia

    Directory of Open Access Journals (Sweden)

    Mulya R. Mashudi

    2017-11-01

    Full Text Available This study investigates the use of Neural Networks (NN as a potential means of more accurately forecasting water demand in the Citarum River basin cascade. Neural Networks have the ability to recognise nonlinear patterns when sufficiently trained with historical data. The study constructs a NN model of the cascade, based on Back Propagation Networks (BPN. Data representing physical characteristics and meteorological conditions in the Citarum River basin from 1989 through 1995 were used to train the BPN. Nonlinear activation functions (sigmoid, tangent, and gaussian functions and hidden layers in the BPN were chosen for the study.

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

    International Nuclear Information System (INIS)

    Kaiser, Mark J.

    2009-01-01

    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. Hydrostatic Water Level Systems At Homestake DUSEL

    Science.gov (United States)

    Stetler, L. D.; Volk, J. T.

    2009-12-01

    Two arrays of Fermilab-style hydrostatic water level sensors have been installed in the former Homestake gold mine in Lead, SD, the site of the new Deep Underground Science and Engineering Laboratory (DUSEL). Sensors were constructed at Fermilab from 8.5 cm diameter PVC pipe (housing) that was sealed on the ends and fit with a proximity sensor. The instrument have a height of 10 cm. Two ports in each sensor housing provide for connectivity, the upper port for air and the bottom port for water. Multiple instruments connected in series provide a precise water level and differences in readings between successive sensors provide for ground tilt to be resolved. Sensor resolution is 5 μm per count and has a range of approximately 1.25 cm. Data output from each sensor is relayed to a Fermilab-constructed readout card that also has temperature/relative humidity and barometric pressure sensors connected. All data are relayed out of the mine by fiber optic cable and can be recorded by Ethernet at remote locations. The current arrays have been installed on the 2000-ft level (610 m) and consist of six instruments in each array. Three sensors were placed in a N-S oriented drift and three in an E-W oriented drift. Using this orientation, it is anticipated that tilt direction may be resolved in addition to overall tilt magnitude. To date the data show passage of earth tides and frequency analysis has revealed five components to this signal, three associated with the semi-diurnal (~12.4 hr) and two with the diurnal (~24.9 hr) tides. Currently, installation methods are being analyzed between concrete pillar and rib-mounting using the existing setup on the 2000-ft level. Using these results, two additional arrays of Fermilab instruments will be installed on the 4550-ft and 4850-ft levels (1387 and 1478 m, respectively). In addition to Fermilab instruments, several high resolution Budker tiltmeters (1 μm resolution) will be installed in the mine workings in the near future, some

  12. Water levels shape fishing participation in flood-control reservoirs

    Science.gov (United States)

    Miranda, Leandro E.; Meals, K. O.

    2013-01-01

    We examined the relationship between fishing effort (hours fished) and average March–May water level in 3 flood control reservoirs in Mississippi. Fishing effort increased as water level rose, peaked at intermediate water levels, and decreased at high water levels. We suggest that the observed arched-shaped relationship is driven by the shifting influence of fishability (adequacy of the fishing circumstances from an angler's perspective) and catch rate along a water level continuum. Fishability reduces fishing effort during low water, despite the potential for higher catch rates. Conversely, reduced catch rates and fishability at high water also curtail effort. Thus, both high and low water levels seem to discourage fishing effort, whereas anglers seem to favor intermediate water levels. Our results have implications for water level management in reservoirs with large water level fluctuations.

  13. The relationship between energy intensity and income levels: Forecasting long term energy demand in Asian emerging countries

    International Nuclear Information System (INIS)

    Galli, R.; Univ. della Svizzera Italiana, Lugano

    1998-01-01

    This paper analyzes long-term trends in energy intensity for ten Asian emerging countries to test for a non-monotonic relationship between energy intensity and income in the author's sample. Energy demand functions are estimated during 1973--1990 using a quadratic function of log income. The long-run coefficient on squared income is found to be negative and significant, indicating a change in trend of energy intensity. The estimates are then used to evaluate a medium-term forecast of energy demand in the Asian countries, using both a log-linear and a quadratic model. It is found that in medium to high income countries the quadratic model performs better than the log-linear, with an average error of 9% against 43% in 1995. For the region as a whole, the quadratic model appears more adequate with a forecast error of 16% against 28% in 1995. These results are consistent with a process of dematerialization, which occurs as a result of a reduction of resource use per unit of GDP once an economy passes some threshold level of GDP per capita

  14. FORECASTING OF DURABILITY OF ASPHALT PAVEMENT ON THE BASIS OF LEVELS OF THEIR VIBRATION LOADING

    Directory of Open Access Journals (Sweden)

    V. A. Osinovskaya

    2015-01-01

    Full Text Available The problem of low durability of flexible pavement is one of the most important problems of road economy. For example, the actual service life of asphalt pavement in Russia about 3 … 5 years. The bad condition of highways is an obstacle for the development of the national economy and leads to a significant annual economic losses.At present, this problem has no exact solution. Even at the seeming good road conditions of Europe and America the problem of low durability is no less important in these countries. And this problem becomes more and more actual every year.Our scientific researches allowed to make a hypothesis that the projected of pavements are not have the necessary durability yet not of a stage of designing because in strength calculations did not take into account the vibration of road constructions.Very actual the vibration loading becomes today as is now significantly changed the nature of loading of pavements. As a result the deflections of a pavements are reduced, but the increased vibration of pavements accelerated processes of destruction and significantly reduced durability.The theory of vibration destruction developed by the author allows to adjust the vibration, to form the vibration resistance pavements, and also to forecast a residual life of pavements that will more effectively develop repair actions.

  15. The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

    Science.gov (United States)

    Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E

    2017-11-01

    The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. 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...

  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 Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  19. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  20. Operational Principle of Water Level Detector for Agricultural and ...

    African Journals Online (AJOL)

    This paper proposes a design to automatically detect the level of water in a reservoir (storage tank) at a preset level and initializes an information to the users in case of low water level. The functionality of this sensor depends basically on the electrical conductivity of water (probes) which varies, depending on the level of its ...

  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. Demand forecasts at national and EU level on a computer-based model taking usage costs into account

    DEFF Research Database (Denmark)

    Passamonti, Lucia; Falch, Morten; Björksten, Margareta

    1997-01-01

    The objective of this deliverable is to forecast the residential spending on selected multimedia services such as Tele-entertainment, VOD, AOD, Networked games, Teleshopping and Teleworking.......The objective of this deliverable is to forecast the residential spending on selected multimedia services such as Tele-entertainment, VOD, AOD, Networked games, Teleshopping and Teleworking....

  3. 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.

  4. 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

  5. An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method

    Science.gov (United States)

    Muñoz-Esparza, Domingo; Kosović, Branko; Jiménez, Pedro A.; Coen, Janice L.

    2018-04-01

    The level-set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high-order level-set method using fifth-order WENO scheme for the discretization of spatial derivatives and third-order explicit Runge-Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF-Fire. The algorithm includes solution of an additional partial differential equation for level-set reinitialization. The accuracy of the fire-front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level-set-based wildfire models yields to rate-of-spread errors in the range 10-35% for typical grid sizes (Δ = 12.5-100 m) and considerably underestimates fire area. Moreover, the amplitude of fire-front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF-Fire algorithm results in rate-of-spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid-order level-set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high-order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high-order accurate level-set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.

  6. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    Science.gov (United States)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  7. Portable Water Level Monitoring System via SMS

    Directory of Open Access Journals (Sweden)

    Jomar S. Vitales

    2015-11-01

    Full Text Available Damages and lives taken by the typhoon Ondoy and other super typhoons brought the researchers to think and develop a device that warns people an hour or more than an hour before the devastating phenomena. In this project the researchers have thought of using text messaging in which the country’s leading means of communication. The development of the project was guided by the Engineering Design Cycle of Dr. Allan Cheville in his book entitled “Rocket Engineering”. The researchers have identified and used the needed materials which are suited in the intended function of the project. The project was already evaluated and had gathered a favorable response from the knowledgeable respondents in the field where the design project is intended to use. The project has a high acceptability level in the respondents’ point of view. The researchers are highly recommending the implementation of the project for a better testing in the incoming rainy season and also recommending to be placed in the Pantalan Bridge in Pantalan, Nasugbu, Batangas, Philippines. The researchers are also suggesting another study for a better water proof casing of the project.

  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. The WRF model forecast-derived low-level wind shear climatology over the United States great plains

    Energy Technology Data Exchange (ETDEWEB)

    Storm, B. [Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX (United States); Basu, S. [Atmospheric Science Group, Department of Geosciences, Texas Tech University, Lubbock, TX (United States)

    2010-07-01

    For wind resource assessment projects, it is common practice to use a power-law relationship (U(z) {proportional_to} z{sup {alpha}}) and a fixed shear exponent ({alpha} = 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. (author)

  10. Marine Text Forecasts and Products Listing

    Science.gov (United States)

    FQPZ23KWNO West Coast 06z, 18z FQAC23KWNO Artic Alaska 06z, 18z Computer-generated extratropical storm surge Water Levels Tsunami Coastal/Lakeshore Hazard Message; Storm Surge Forecasts Satellite Orbit Predictions Update (Storm #1) As required TCUAT2 (alt) Tropical Cyclone Update (Storm #2) As required TCUAT3 (alt

  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

    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.......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...... 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...

  12. Gauging leaf-level contributions to landscape-level water loss within a Western US dryland fores

    Science.gov (United States)

    Murphy, P.; Potts, D. L.; Minor, R. L.; Hamerlynck, E. P.; Sutter, L., Jr.; Barron-Gafford, G.

    2017-12-01

    Western US forests represent a large constituent of the North American water and carbon cycles, yet the primary controls on water loss from these ecosystems remains unknown. In dryland forests, such as those found in the Southwestern US, water availability is key to ecosystem function, and the timing and magnitude of water loss can have lasting effects on the health of these communities. One poorly defined part of the water balance in these forests is the partitioning of evapotranspiration (ET) into evaporation (E; blue flow) to transpiration (T; green flow). A study of water fluxes at multiple scales in a semiarid montane forest in Southern Arizona speaks to the partitioning of these two water flows. Within the footprint of an eddy covariance system, which estimates ecosystem ET, we have examined the impacts of variation in climate, species makeup, and topographic position on E and T. This was done using leaf-level measures of T, pedon-scale measures of E, and whole-tree water loss by way of sap flux sensors. Where available, we have examined E, T, and ET fluxes across multiple seasons and years of highly variable precipitation records. Understanding the partitioning of ET is crucial, considering that projected changes to dryland ecosystems include longer periods of drought separated by heavier precipitation events. At a moment when potential impacts of changing climate on dryland structure and function are poorly understood, a stronger comprehension of these blue and green water flows is necessary to forecast the productivity of Western US forests into the future.

  13. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

  14. Analysis of water-level fluctuations in Wisconsin wells

    Science.gov (United States)

    Patterson, G.L.; Zaporozec, A.

    1987-01-01

    More than 60 percent of the residents of Wisconsin use ground water as their primary water source. Water supplies presently are abundant, but ground-water levels continually fluctuate in response to natural factors and human-related stresses. A better understanding of the magnitude, duration, and frequency of past fluctuations, and the factors controlling these fluctuations may help anticipate future changes in ground-water levels.

  15. Evaluation of yield and water-level relations

    International Nuclear Information System (INIS)

    Cushman, R.L.; Purtymun, W.D.

    1975-10-01

    Yield and water relations in the Los Alamos supply wells were evaluated because of the increasing demand for water. Water-level declines were extrapolated for 10 yr, to 1983, on the basis of past records. On the basis of current pumpage, the extrapolations indicate that nonpumping water levels in individual wells will decline from 10 to 30 ft. Well characteristics were compiled to provide an individual history of each well, and recommendations for improving water production are presented

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

    DEFF Research Database (Denmark)

    Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

    2018-01-01

    The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build...... spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western...... Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have...

  2. 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

  3. Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

    Directory of Open Access Journals (Sweden)

    H. Apel

    2018-04-01

    Full Text Available The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived

  4. Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

    Science.gov (United States)

    Apel, Heiko; Abdykerimova, Zharkinay; Agalhanova, Marina; Baimaganbetov, Azamat; Gavrilenko, Nadejda; Gerlitz, Lars; Kalashnikova, Olga; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

    2018-04-01

    The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from

  5. 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

  6. Minimal climate change impacts on natural organic matter forecasted for a potable water supply in Ireland.

    Science.gov (United States)

    O'Driscoll, Connie; Ledesma, José L J; Coll, John; Murnane, John G; Nolan, Paul; Mockler, Eva M; Futter, Martyn N; Xiao, Liwen W

    2018-07-15

    Natural organic matter poses an increasing challenge to water managers because of its potential adverse impacts on water treatment and distribution, and subsequently human health. Projections were made of impacts of climate change on dissolved organic carbon (DOC) in the primarily agricultural Boyne catchment which is used as a potable water supply in Ireland. The results indicated that excluding a potential rise in extreme precipitation, future projected loads are not dissimilar to those observed under current conditions. This is because projected increases in DOC concentrations are offset by corresponding decreases in precipitation and hence river flow. However, the results presented assume no changes in land use and highlight the predicted increase in DOC loads from abstracted waters at water treatment plants. Copyright © 2018. Published by Elsevier B.V.

  7. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  8. Development of reactor water level sensor for extreme conditions

    Energy Technology Data Exchange (ETDEWEB)

    Miura, K; Ogasawara, T [Sukegawa Electric Co., Ltd., Hitachi, Ibaraki (Japan); Shibata, Akira; Nakamura, Jinichi; Saito, Takashi; Tsuchiya, Kunihiko [Japan Atomic Energy Agency, Oarai Research and Development Center, Oarai, Ibaraki (Japan)

    2012-03-15

    In the Fukushima accident, measurement failure of water level was one of the most important factors which caused serious situation. The differential pressure type water level indicators are widely used in various place of nuclear power plant but after the accident of TMI-2, the need of other reliable method has been required. The BICOTH type and the TRICOTH type water level indicator for light water power reactors had been developed for in-pile water level indicator but currently those are not adopted to nuclear power plant. In this study, the development of new type water level indicator composed of thermocouple and heater is described. Demonstration test and characteristic evaluation of the water level indicator were performed and we had obtained satisfactory results. (author)

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

    International Nuclear Information System (INIS)

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

    1996-01-01

    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

  10. 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.

  11. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

  12. Energy optimization through probabilistic annual forecast water release technique for major storage hydroelectric reservoir

    International Nuclear Information System (INIS)

    Abdul Bahari Othman; Mohd Zamri Yusoff

    2006-01-01

    One of the important decisions to be made by the management of hydroelectric power plant associated with major storage reservoir is to determine the best turbine water release decision for the next financial year. The water release decision enables firm energy generated estimation for the coming financial year to be done. This task is usually a simple and straightforward task provided that the amount of turbine water release is known. The more challenging task is to determine the best water release decision that is able to resolve the two conflicting operational objectives which are minimizing the drop of turbine gross head and maximizing upper reserve margin of the reservoir. Most techniques from literature emphasize on utilizing the statistical simulations approach. Markovians models, for example, are a class of statistical model that utilizes the past and the present system states as a basis for predicting the future [1]. This paper illustrates that rigorous solution criterion can be mathematically proven to resolve those two conflicting operational objectives. Thus, best water release decision that maximizes potential energy for the prevailing natural inflow is met. It is shown that the annual water release decision shall be made in such a manner that annual return inflow that has return frequency smaller than critical return frequency (f c ) should not be considered. This criterion enables target turbine gross head to be set to the well-defined elevation. In the other words, upper storage margin of the reservoir shall be made available to capture magnitude of future inflow that has return frequency greater than or equal to f c. A case study is shown to demonstrate practical application of the derived mathematical formulas

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

    International Nuclear Information System (INIS)

    Graves, R.P.

    1998-01-01

    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

  14. 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

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

    Directory of Open Access Journals (Sweden)

    J. Cho

    2016-10-01

    Full Text Available 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.

  16. Controlling taste and odour levels in water

    Energy Technology Data Exchange (ETDEWEB)

    Bowers, A J

    1980-12-01

    Taste and odor of drinking water supplies act as indicator mechanisms, indicating increased degrees of biological activity, possible contamination of the supply, treatment inadequacies, or contamination of the distribution systems. Disinfection and coagulation are effective preventive measures. Taste and odor problems may arise even with the application of preventive measures, so protective and treatment techniques must be implemented. These include chlorination and activated carbon absorption. (1 photo, 3 references, 1 table)

  17. Lake Erie Water Level Study. Main Report.

    Science.gov (United States)

    1981-07-01

    indirectly through excessive turbidity, current or depth would impact the higher life forms. Phytoplankton , periphyton and aquatic macrophyte comprise...System. The hydro-electric interest relates to the facilities at the St. Marys River, Welland Canal, Niagara River, St. Lawrence River at Cornwall... relates to three components: water quality, fish, and wildlife. The economic evaluations of regulation plans were also made to determine effects on

  18. Climatology of the Iberia coastal low-level wind jet: weather research forecasting model high-resolution results

    Directory of Open Access Journals (Sweden)

    Pedro M. M. Soares

    2013-01-01

    Full Text Available Coastal low-level jets (CLLJ are a low-tropospheric wind feature driven by the pressure gradient produced by a sharp contrast between high temperatures over land and lower temperatures over the sea. This contrast between the cold ocean and the warm land in the summer is intensified by the impact of the coastal parallel winds on the ocean generating upwelling currents, sharpening the temperature gradient close to the coast and giving rise to strong baroclinic structures at the coast. During summertime, the Iberian Peninsula is often under the effect of the Azores High and of a thermal low pressure system inland, leading to a seasonal wind, in the west coast, called the Nortada (northerly wind. This study presents a regional climatology of the CLLJ off the west coast of the Iberian Peninsula, based on a 9 km resolution downscaling dataset, produced using the Weather Research and Forecasting (WRF mesoscale model, forced by 19 years of ERA-Interim reanalysis (1989–2007. The simulation results show that the jet hourly frequency of occurrence in the summer is above 30% and decreases to about 10% during spring and autumn. The monthly frequencies of occurrence can reach higher values, around 40% in summer months, and reveal large inter-annual variability in all three seasons. In the summer, at a daily base, the CLLJ is present in almost 70% of the days. The CLLJ wind direction is mostly from north-northeasterly and occurs more persistently in three areas where the interaction of the jet flow with local capes and headlands is more pronounced. The coastal jets in this area occur at heights between 300 and 400 m, and its speed has a mean around 15 m/s, reaching maximum speeds of 25 m/s.

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

    International Nuclear Information System (INIS)

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

    1998-01-01

    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

  20. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  1. Method and device for forecasting remaining lifetime for material constituting light water reactor plant

    International Nuclear Information System (INIS)

    Anzai, Hideya; Nakada, Kiyotomo; Shimanuki, Sei; Kida, Toshitaka; Fuse, Motomasa; Shigenaka, Naoto; Kuniya, Jiro; Izumiya, Masakiyo; Hattori, Shigeo; Saito, Takashi.

    1994-01-01

    A pressure vessel of a light water type reactor comprises a crack development sensor at the inside and a crack development monitor at the outside to monitor the development of cracks detected by the crack progress sensor. In addition, the reactor also comprises, at the outside thereof, a dissolved oxygen meter, a dissolved hydrogen peroxide meter and a conductivity meter for reactor water. A computer is connected, on line, to the crack development monitor, the dissolved oxygen meter, the dissolved hydrogen peroxide meter and the conductivity meter. A crack development rate measured by the crack development monitor, as well as the dissolved oxygen concentration, the dissolved peroxide hydrogen concentration and the conductivity of reactor water measured at the outside of the reactor by the dissolved oxygen meter, the dissolved hydrogen peroxide meter and the conductivity meter are inputted to the computer. The computer calculates the effective dissolved oxygen concentration for each portion of the plant based on these measured values. Further, the period of time till the crack reaches a predetermined limit value is calculated based on the measured values. Then, the period of time is displayed as a remaining life time of the materials due to stress corrosion crackings. (I.N.)

  2. Design and skill assessment of an Operational Forecasting System for currents and sea level variability to the Santos Estuarine System - Brazil

    Science.gov (United States)

    Godoi Rezende Costa, C.; Castro, B. M.; Blumberg, A. F.; Leite, J. R. B., Sr.

    2017-12-01

    Santos City is subject to an average of 12 storm tide events per year. Such events bring coastal flooding able to threat human life and damage coastal infrastructure. Severe events have forced the interruption of ferry boat services and ship traffic through Santos Harbor, causing great impacts to Santos Port, the largest in South America, activities. Several studies have focused on the hydrodynamics of storm tide events but only a few of those studies have pursued an operational initiative to predict short term (operational forecasting system built to predict sea surface elevation and currents in the Santos Estuarine System and (ii) to evaluate model performance in simulating observed sea surface elevation. The Santos Operational Forecasting System (SOFS) hydrodynamic module is based on the Stevens Institute Estuarine and Coastal Ocean Model (sECOM). The fully automated SOFS is designed to provide up to 71 h forecast of sea surface elevations and currents every day. The system automatically collects results from global models to run the SOFS nested into another sECOM based model for the South Brazil Bight (SBB). Global forecasting results used to force both models come from Mercator Ocean, released by Copernicus Marine Service, and from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) stablished by the Center for Weather Forecasts and Climate Studies (with Portuguese acronym CPTEC). The complete routines task take about 8 hours of run time to finish. SOFS was able to hindcast a severe storm tide event that took place in Santos on August 21-22, 2016. Comparisons with observed sea level provided skills of 0.92 and maximum root mean square errors of 25 cm. The good agreement with observed data shows the potential of the designed system to predict storm tides and to support both human and assets protection.

  3. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China.

    Science.gov (United States)

    Wang, Yi; Zheng, Tong; Zhao, Ying; Jiang, Jiping; Wang, Yuanyuan; Guo, Liang; Wang, Peng

    2013-12-01

    In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH(4+)-N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH(4+)-N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing-refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH(4+)-N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering "real" data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.

  5. Critical water stress levels in Pinus patula seedlings and their ...

    African Journals Online (AJOL)

    Critical water stress levels in Pinus patula seedlings and their relation to measures of seedling morphology. ... Southern Forests: a Journal of Forest Science ... A pot trial was implemented to determine the effect of soil water stress following transplanting on shoot water potential and stomatal conductance of Pinus patula ...

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

    African Journals Online (AJOL)

    Similarly, the control unit of the prototype performs automatic switching control of on and off on a single phase centrifugal water pump, 220volts, 0.5hp motor via a motor driver circuit (relay). It also incorporates a buzzer that beeps briefly when water level hits 100%, thus causing the pump to be switched off but when water ...

  7. 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.

  8. Inflow forecasting at BPA

    Energy Technology Data Exchange (ETDEWEB)

    McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

    2007-07-01

    The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.

  9. Short-time variations of the ground water level

    International Nuclear Information System (INIS)

    Nilsson, Lars Y.

    1977-09-01

    Investigations have demonstrated that the ground water level of aquifers in the Swedish bedrock shows shorttime variations without changing their water content. The ground water level is among other things affected by regular tidal movements occuring in the ''solid'' crust of the earth variations in the atmospheric pressure strong earthquakes occuring in different parts of the world These effects proves that the system of fissures in the bedrock are not stable and that the ground water flow is influenced by both water- and airfilled fissures

  10. Contamination levels of domestic water sources in Maiduguri ...

    African Journals Online (AJOL)

    The study examines the levels of contamination of domestic water sources in Maiduguri Metropolis area of Borno State based on their physicochemical and bacteriological properties. It was informed by the global concern on good drinking water quality which is an indicator of development level; hence the focus on domestic ...

  11. Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

    Science.gov (United States)

    Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

    2016-12-01

    The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In

  12. 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.

  13. Climate change and prairie pothole wetlands: mitigating water-level and hydroperiod effects through upland management

    Science.gov (United States)

    Renton, David A.; Mushet, David M.; DeKeyser, Edward S.

    2015-01-01

    Prairie pothole wetlands offer crucial habitat for North America’s waterfowl populations. The wetlands also support an abundance of other species and provide ecological services valued by society. The hydrology of prairie pothole wetlands is dependent on atmospheric interactions. Therefore, changes to the region’s climate can have profound effects on wetland hydrology. The relevant literature related to climate change and upland management effects on prairie pothole wetland water levels and hydroperiods was reviewed. Climate change is widely expected to affect water levels and hydroperiods of prairie pothole wetlands, as well as the biota and ecological services that the wetlands support. In general, hydrologic model projections that incorporate future climate change scenarios forecast lower water levels in prairie pothole wetlands and longer periods spent in a dry condition, despite potential increases in precipitation. However, the extreme natural variability in climate and hydrology of prairie pothole wetlands necessitates caution when interpreting model results. Recent changes in weather patterns throughout much of the Prairie Pothole Region have been in increased precipitation that results in increased water inputs to wetlands above losses associated with warmer temperatures. However, observed precipitation increases are within the range of natural climate variability and therefore, may not persist. Identifying management techniques with the potential to affect water inputs to prairie pothole wetlands would provide increased options for managers when dealing with the uncertainties associated with a changing climate. Several grassland management techniques (for example, grazing and burning) have the potential to affect water levels and hydroperiods of prairie pothole by affecting infiltration, evapotranspiration, and snow deposition.

  14. Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level

    OpenAIRE

    KAUFFELD Anna; WETTERHALL F.; Pappenberger F.; SALAMON Peter; THIELEN DEL POZO Jutta

    2014-01-01

    The uncertainty in operational hydrological forecast systems driven with numerical weather predictions inputs are often assessed by quantifying the uncertainty from the inputs and not from the hydrological model itself. However, part of the uncertainty in modelled discharge stems from the hydrological model and some models may be more suitable than others for particular processes. A hydrological multi-model hydrological system can account for some of this uncertainty, but there exists a p...

  15. Denitrifying Bioreactors Resist Disturbance from Fluctuating Water Levels

    Directory of Open Access Journals (Sweden)

    Sarah K. Hathaway

    2017-06-01

    Full Text Available Nitrate can be removed from wastewater streams, including subsurface agricultural drainage systems, using woodchip bioreactors to promote microbial denitrification. However, the variations in water flow in these systems could make reliable performance from this microbially-mediated process a challenge. In the current work, the effects of fluctuating water levels on nitrate removal, denitrifying activity, and microbial community composition in laboratory-scale bioreactors were investigated. The performance was sensitive to changing water level. An average of 31% nitrate was removed at high water level and 59% at low water level, despite flow adjustments to maintain a constant theoretical hydraulic retention time. The potential activity, as assessed through denitrifying enzyme assays, averaged 0.0008 mg N2O-N/h/dry g woodchip and did not show statistically significant differences between reactors, sampling depths, or operational conditions. In the denitrifying enzyme assays, nitrate removal consistently exceeded nitrous oxide production. The denitrifying bacterial communities were not significantly different from each other, regardless of water level, meaning that the denitrifying bacterial community did not change in response to disturbance. The overall bacterial communities, however, became more distinct between the two reactors when one reactor was operated with periodic disturbances of changing water height, and showed a stronger effect at the most severely disturbed location. The communities were not distinguishable, though, when comparing the same location under high and low water levels, indicating that the communities in the disturbed reactor were adapted to fluctuating conditions rather than to high or low water level. Overall, these results describe a biological treatment process and microbial community that is resistant to disturbance via water level fluctuations.

  16. Modelling soil water dynamics and crop water uptake at the field level

    NARCIS (Netherlands)

    Kabat, P.; Feddes, R.A.

    1995-01-01

    Parametrization approaches to model soil water dynamics and crop water uptake at field level were analysed. Averaging and numerical difficulties in applying numerical soil water flow models to heterogeneous soils are highlighted. Simplified parametrization approaches to the soil water flow, such as

  17. Velocity flow field and water level measurements in shoaling and breaking water waves

    CSIR Research Space (South Africa)

    Mukaro, R

    2010-01-01

    Full Text Available In this paper we report on the laboratory investigations of breaking water waves. Measurements of the water levels and instantaneous fluid velocities were conducted in water waves breaking on a sloping beach within a glass flume. Instantaneous water...

  18. Forecasting Ocean Acidification in the coastal waters of the Pacific Northwest

    Science.gov (United States)

    Siedlecki, S. A.; Alin, S. R.; Feely, R. A.; Hermann, A. J.; Bednarsek, N.; Nguyen, T.; Officer, S.; Kaplan, I.; Bond, N.; Newton, J.; Fisher, J. L.; Morgan, C.; Saenger, C.

    2016-12-01

    hypersaline water along the basin walls and potentially out of the confining basin. Local chemosynthetic marine communities could have been affected as they were bathed in the brine, which has been previously measured to be a factor of eight higher than normal seawater salinity.

  19. Measurement of low levels of cesium-137 in water

    International Nuclear Information System (INIS)

    Milham, R.C.; Kantelo, M.V.

    1984-10-01

    Large volume water sampling systems were developed to measure very low levels of cesium-137 in river water and in finished water from water treatment plants. Three hundred to six hundred liters of filtered water are passed through the inorganic ion exchanger potassium cobalti-ferrocyanide to remove greater than 90% of the cesium. Measurement of cesium-137 by gamma ray spectrometry results in a sensitivity of 0.001 pCi/L. Portable as well as stationary samplers were developed to encompass a variety of applications. Results of a one year study of water from the Savannah River and from water treatment plants processing Savannah River water are presented. 3 references, 7 figures

  20. Effects of Water Level Increase on Phytoplankton Assemblages in a Drinking Water Reservoir

    Directory of Open Access Journals (Sweden)

    Yangdong Pan

    2018-03-01

    Full Text Available Excessive water level fluctuation may affect physico-chemical characteristics, and consequently ecosystem function, in lakes and reservoirs. In this study, we assessed the changes of phytoplankton assemblages in response to water level increase in Danjiangkou Reservoir, one of the largest drinking water reservoirs in Asia. The water level increased from a low of 137 m to 161 m in 2014 as a part of the South–North Water Diversion Project. Phytoplankton assemblages were sampled four times per year before, during and after the water level increase, at 10 sites. Environmental variables such as total nitrogen as well as phytoplankton biomass decreased after the water level increase. Non-metric multi-dimensional scaling analysis indicated that before the water level increase, phytoplankton assemblages showed distinct seasonal variation with diatom dominance in both early and late seasons while such seasonal variation was much less evident after the water level increase. Month and year (before and after explained 13% and 6% of variance in phytoplankton assemblages (PERMANOVA, p < 0.001 respectively, and phytoplankton assemblages were significantly different before and after the water level increase. Both chlorophytes and cyanobacteria became more abundant in 2015. Phytoplankton compositional change may largely reflect the environmental changes, such as hydrodynamics mediated by the water level increase.

  1. Levels of toxaphene congeners in fish from Danish waters

    DEFF Research Database (Denmark)

    Fromberg, Arvid; Cederberg, Tommy Licht; Hilbert, G.

    2000-01-01

    The levels of toxaphene congeners, in addition to PCB congeners and organochlorine pesticides, were determined in various fish samples from different Danish waters. While PCB-153 and p,p'-DDE show different levels depending on the fishing area, with highest levels in fish from the Western Baltic...... Sea, toxaphene was detected in all the samples investigated at a more constant level. The distribution of the three toxaphene congeners Parlar #26, #50 and #62 depends on the fishing area, with the Western Baltic Sea being different from the other waters by having almost equal levels of toxaphene...

  2. Evaluate prevailing climate change on Great Lakes water levels

    International Nuclear Information System (INIS)

    Islam, M.

    2009-01-01

    'Full text:'In this paper, results of a comprehensive water mass balance modeling for the Great Lakes against prevailing and different anticipated climate change scenarios would be presented. Modeling is done in evaluating the changes in the lake storages and then changes in the lake's water level considering present condition, uncertainty and variability of climate and hydrologic conditions in the future. Inflow-outflow and consequent changes in the five Great Lake's storages are simulated for the last 30 years and then projected to evaluate the changes in the lake storages for the next 50 years. From the predicted changes in the lake storage data, water level is calculated using mass to linear conversion equation. Modeling and analysis results are expected to be helpful in understanding the possible impacts of the climate change on the Great Lakes water environment and preparing strategic plan for the sustainable management of lake's water resources. From the recent past, it is observed that there is a depleting trend in the lakes water level and hence there is a potential threat to lake's water environment and uncertainty of the availability of quality and quantity of water for the future generations, especially against prevailing and anticipated climate changes. For this reason, it is an urgent issue of understanding and quantifying the potential impacts of climate change on the Great Lake's water levels and storages. (author)

  3. Auto Detection For High Level Water Content For Oil Well

    Science.gov (United States)

    Janier, Josefina Barnachea; Jumaludin, Zainul Arifin B.

    2010-06-01

    Auto detection of high level water content for oil well is a system that measures the percentage of water in crude oil. This paper aims to discuss an auto detection system for measuring the content of water level in crude oil which is applicable for offshore and onshore oil operations. Data regarding water level content from wells can be determined by using automation thus, well with high water level can be determined immediately whether to be closed or not from operations. Theoretically the system measures the percentage of two- fluid mixture where the fluids have different electrical conductivities which are water and crude oil. The system made use of grid sensor which is a grid pattern like of horizontal and vertical wires. When water occupies the space at the intersection of vertical and horizontal wires, an electrical signal is detected which proved that water completed the circuit path in the system. The electrical signals are counted whereas the percentage of water is determined from the total electrical signals detected over electrical signals provided. Simulation of the system using the MultiSIM showed that the system provided the desired result.

  4. 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)...

  5. Ground-water levels and quality data for Georgia

    Science.gov (United States)

    ,

    1979-01-01

    This report begins a publication format that will present annually both water-level and water-quality data in Georgia. In this format the information is presented in two-page units: the left page includes text which summarizes the information for an area or subject and the right page consists of one or more illustrations. Daily mean water-level fluctuations and trends are shown in hydrographs for the previous year and fluctuations for the monthly mean water level the previous 10 years for selected observation wells. The well data best illustrate the effects of changes in recharge and discharge in the various ground-water reservoirs in the State. A short narrative explains fluctuations and trends in each hydrograph. (Woodard-USGS)

  6. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Experience of water chemistry and radiation levels in Swedish BWRs

    International Nuclear Information System (INIS)

    Ivars, R.; Elkert, J.

    1981-01-01

    From the BWR operational experience in Sweden it has been found that the occupational radiation exposures have been comparatively low in an international comparison. One main reason for the favourable conditions is the good water chemistry performance. This paper deals at first with the design considerations of water chemistry and materials selection. Next, the experience of water chemistry and radiation levels are provided. Finally, some methods to further reduce the radiation sources are discussed. (author)

  8. 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.

  9. Carboxyhaemoglobin levels in water-pipe and cigarette smokers ...

    African Journals Online (AJOL)

    South African Medical Journal ... Water-pipe smoking is growing in popularity, especially among young people, because of the social nature of the smoking session and the assumption that the ... We aimed to measure carboxyhaemoglobin (COHb) blood levels before and after water-pipe and cigarette smoking sessions.

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

    African Journals Online (AJOL)

    Communities are dependent on wetlands resources for income generation. However, anthropogenic activities that result into pollution of water are one of the major public health problems. Assessment of socio–economic activities and pollution levels of domestic water sources in Gulu Municipality, Pece wetland was done.

  11. Typhoon and elevated radon level in a municipal water supply

    International Nuclear Information System (INIS)

    Mao, Cheng-Hsin; Weng, Pao-Shan

    2000-01-01

    The Municipal Water Supply at Hsinchu City is a water treatment plant using poly- aluminum chloride (PAC) for coagulation and then followed by precipitation and filtration. Its capacity is 70,000 m 3 /day. The raw water is drawn from the nearby river. Since the subject of interest is the radon level during typhoon season, the sampling period was from March to December 1999. Commercially available electret was used for water samples taken from the five ponds in the plant. This technique relies on the measurement of radon in air above a water sample enclosed in a sealed vessel. The concentration of airbone radon released from water was determined by means of the electret ion chamber. During the first sampling period there came two typhoons. One is called Magie during June 10-17, and the other called Sam during August 20-26. The first typhoon led to the radon level measured from the water samples as high as 705 Bq/m 3 , while the second caused even higher radon level as high as 772 Bq/m 3 . Similar results were obtained for the second sampling period after August till December 1999. For those measured without typhoon influence, the average radon was lower from the coagulation pond yet without coagulation process during March through August 1999. However, water samples taken from the pond after precipitation did not show similar results in radon level. (author)

  12. Trace-level mercury removal from surface water

    International Nuclear Information System (INIS)

    Klasson, K.T.; Bostick, D.T.

    1998-01-01

    Many sorbents have been developed for the removal of mercury and heavy metals from waters; however, most of the data published thus far do not address the removal of mercury to the target levels represented in this project. The application to which these sorbents are targeted for use is the removal of mercury from microgram-per-liter levels to low nanogram-per-liter levels. Sorbents with thiouronium, thiol, amine, sulfur, and proprietary functional groups were selected for these studies. Mercury was successfully removed from surface water via adsorption onto Ionac SR-4 and Mersorb resins to levels below the target goal of 12 ng/L in batch studies. A thiol-based resin performed the best, indicating that over 200,000 volumes of water could be treated with one volume of resin. The cost of the resin is approximately $0.24 per 1,000 gal of water

  13. 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...

  14. 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)....

  15. 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...

  16. radio frequency based radio frequency based water level monitor

    African Journals Online (AJOL)

    eobe

    ABSTRACT. This paper elucidates a radio frequency (RF) based transmission and reception system used to remotely monitor and .... range the wireless can cover but in this prototype, it ... power supply to the system, the sensed water level is.

  17. 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)....

  18. 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...

  19. 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...

  20. 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...

  1. Negotiating water across levels: A peace and conflict "Toolbox" for water diplomacy

    Science.gov (United States)

    Grech-Madin, Charlotte; Döring, Stefan; Kim, Kyungmee; Swain, Ashok

    2018-04-01

    As a key policy tool, water diplomacy offers greater political engagement in the cooperative management of shared water. A range of initiatives has been dedicated to this end, almost invariably oriented around the interactions of nation states. Crucially, however, practitioners of water diplomacy also need to address water governance at sub-state levels. As a political, multi-level, and normative field, peace and conflict research offers a pluralism of approaches designed to bring actors together at all levels. Drawing upon this research, this paper offers new focal points for water diplomacy that can enhance its policy effectiveness and enrich its underlying academic current. More specifically, it presents three hitherto undervalued tools for water diplomacy: at the interstate level, to uncover the rich body of political norms that bind states to shared understandings of acceptable practice around water. At the intrastate level, to incorporate ethnography of water users and civil society groups' responses to state-led waterworks projects, and at the communal level to employ disaggregated georeferenced data on water resources in conflict-prone areas. Taken together, these analytical tools provide a multi-faceted political gauge of the dynamics of water diplomacy, and add vital impetus to develop water diplomacy across multiple levels of policy engagement.

  2. Tsunami Forecasting in the Atlantic Basin

    Science.gov (United States)

    Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

    2012-12-01

    The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre

  3. 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

  4. Radium-226 levels in Italian drinking waters and foods

    International Nuclear Information System (INIS)

    Mastinu, G.G.; Santaroni, G.P.

    1980-01-01

    Levels of 226 Ra in Italian waters and foods were measured. Results were similar to those found in other countries, except for some mineral waters with 226 Ra concentrations above 1 pCi/liter andup to 19 pCi/liter. No difinite correlation was found between the 226 Ra concentrations measured and the high natural background radiation levels determined in central Italy in previous work

  5. Separating decadal global water cycle variability from sea level rise.

    Science.gov (United States)

    Hamlington, B D; Reager, J T; Lo, M-H; Karnauskas, K B; Leben, R R

    2017-04-20

    Under a warming climate, amplification of the water cycle and changes in precipitation patterns over land are expected to occur, subsequently impacting the terrestrial water balance. On global scales, such changes in terrestrial water storage (TWS) will be reflected in the water contained in the ocean and can manifest as global sea level variations. Naturally occurring climate-driven TWS variability can temporarily obscure the long-term trend in sea level rise, in addition to modulating the impacts of sea level rise through natural periodic undulation in regional and global sea level. The internal variability of the global water cycle, therefore, confounds both the detection and attribution of sea level rise. Here, we use a suite of observations to quantify and map the contribution of TWS variability to sea level variability on decadal timescales. In particular, we find that decadal sea level variability centered in the Pacific Ocean is closely tied to low frequency variability of TWS in key areas across the globe. The unambiguous identification and clean separation of this component of variability is the missing step in uncovering the anthropogenic trend in sea level and understanding the potential for low-frequency modulation of future TWS impacts including flooding and drought.

  6. Determination of Heavy Metal Levels in Various Industrial Waste Waters

    Directory of Open Access Journals (Sweden)

    Mustafa Şahin Dündar

    2012-06-01

    Full Text Available Important part of the environmetal pollution consists of waste water and water pollution. The water polluted by anthropogenical, industrial, and agricultural originated sources are defined as waste waters which are the main pollution sources for reservoirs, rivers, lakes, and seas. In this work, waste waters of leather, textile, automotive side, and metal plating industries were used to determine the levels of Cu, Zn, Cr, Pb and Ni by using Flame Atomic Absorption Spectrometer. As a result, highest mean levels of copper in supernatants of plating and textile industries were observed as 377,18 ng ml-1, respectively 103 ng ml-1 lead and 963,6 ng ml-1 nickel in plating industry, 1068,2 ng ml-1 zinc and 14557,1 ng ml-1 chromium in plating and leather industries were determined.

  7. Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

    Directory of Open Access Journals (Sweden)

    Hugo Carrão

    2018-06-01

    Full Text Available Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated. Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin

  8. Water resources data for Virginia, water year 1991. Volume 2. Ground-water-level and ground-water-quality records. Water-data report (Annual), 1 October 1991-30 September 1992

    International Nuclear Information System (INIS)

    Prugh, B.J.; Powell, E.D.

    1993-01-01

    Water-resources data for the 1992 water year for Virginia consist of records of water levels and water quality of ground-water wells. The report (Volume 2. Ground-Water-Level and Ground-Water-Quality Records) contains water levels at 356 observation wells and water quality at 2 wells. Locations of these wells are given in the report

  9. Water levels of the Ozark aquifer in northern Arkansas, 2013

    Science.gov (United States)

    Schrader, Tony P.

    2015-07-13

    The Ozark aquifer is the largest aquifer, both in area of outcrop and thickness, and the most important source of freshwater in the Ozark Plateaus physiographic province, supplying water to northern Arkansas, southeastern Kansas, southern Missouri, and northeastern Oklahoma. The study area includes 16 Arkansas counties lying completely or partially within the Ozark Plateaus of the Interior Highlands major physiographic division. The U.S. Geological Survey, in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey, conducted a study of water levels in the Ozark aquifer within Arkansas. This report presents a potentiometric-surface map of the Ozark aquifer within the Ozark Plateaus of northern Arkansas, representing water-level conditions for the early spring of 2013 and selected water-level hydrographs.

  10. 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

  11. Uncertainties in Forecasting Streamflow using Entropy Theory

    Science.gov (United States)

    Cui, H.; Singh, V. P.

    2017-12-01

    Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

  12. Short-term forecasting model for aggregated regional hydropower generation

    International Nuclear Information System (INIS)

    Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo

    2014-01-01

    Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations

  13. Water quality characteristics and pollution levels of heavy metals in ...

    African Journals Online (AJOL)

    The main aim of this study was to assess the level of water quality of Lake Haiq, Ethiopia with respect to selected physical ... gated using standard analytical procedures. the level of the studied heavy metals (Pb, Cd, Cu and Zn) was determined using the .... no known discharge and hence used as reference site. Sampling ...

  14. Estimation Of Height Of Oil -Water Contact Above Free Water Level ...

    African Journals Online (AJOL)

    An estimate of oil-water contact (OWC) and the understanding of the capillary behaviour of hydrocarbon reservoirs are vital for optimum reservoir characterization, hydrocarbon exploration and production. Hence, the height of oil-water contact above free water level for different rock types from some Niger Delta reservoirs ...

  15. Cosine components in water levels at Yucca Mountain

    International Nuclear Information System (INIS)

    Rice, J.; Lehman, L.; Keen, K.

    1990-01-01

    Water-level records from wells at Yucca Mountain, Nevada are analyzed periodically to determine if they contain periodic (cosine) components. Water-level data from selected wells are input to an iterative numerical procedure that determines a best fitting cosine function. The available water-level data, with coverage of up to 5 years, appear to be representative of the natural water-level changes. From our analysis of 9 water-level records, it appears that there may be periodic components (periods of 2-3 years) in the groundwater-level fluctuations at Yucca Mountain, Nevada, although some records are fit better than others by cosine functions. It also appears that the periodic behavior has a spatial distribution. Wells west of Yucca Mountain have different periods and phase shifts from wells on and east of Yucca Mountain. Interestingly, a similar spatial distribution of groundwater chemistry at Yucca Mountain is reported by Matuska (1988). This suggests a physical cause may underlie the different physical and chemical groundwater conditions. Although a variety of natural processes could cause water-level fluctuations, hydrologic processes are the most likely, because the periodicities are only a few years. A possible cause could be periodic recharge related to a periodicity in precipitation. It is interesting that Cochran et al., (1988), show a crude two-year cycle of precipitation for 1961 to 1970 in southern Nevada. Why periods and phase shifts may differ across Yucca Mountain is unknown. Different phase shifts could indicate different lag times of response to hydrologic stimuli. Difference in periods could mean that the geologic media is heterogeneous and displays heterogeneous response to a single stimulus, or that stimuli differ in certain regions, or that a hydraulic barrier separates the groundwater system into two regions having different water chemistry and recharge areas. 13 refs., 5 figs., 1 tab

  16. Infrastructure Improvements for Snowmelt Runoff Forecasting and Assessments of Climate Change Impacts on Water Supplies in the Rio Grande Basin

    Science.gov (United States)

    Rango, A.; Steele, C. M.; Demouche, L.

    2009-12-01

    In the Southwest US, the southern Rocky Mountains provide a significant orographic barrier to prevailing moisture-laden Westerly winds, which results in snow accumulation and melt, both vitally important to the region’s water resources. The inherent variability of meteorological conditions in the Southwest, during both snowpack buildup and depletion, requires improved spatially-distributed data. The population of ground-based networks (SNOTEL, SCAN, and weather stations) is sparse and does not satisfactorily represent the variability of snow accumulation and melt. Remote sensing can be used to supplement data from ground networks, but the most frequently available remotely sensed product with the highest temporal and spatial resolution, namely snow cover, only provides areal data and not snow volume. Fortunately, the Snowmelt Runoff Model(SRM), which was developed in mountainous regions of the world, including the Rio Grande basin, accepts snow covered area as one of its major input variables along with temperature and precipitation. With the growing awareness of atmospheric warming and the southerly location of Southwest watersheds, it has become apparent that the effects of climate change will be especially important for Southwestern water users. The NSF-funded EPSCoR project “Climate Change Impacts on New Mexico’s Mountain Sources of Water” (started in 2009) has focused on improving hydrometeorological measurements, developing basin-wide and sub-basin snow cover mapping methods, generating snowmelt runoff simulations, forecasts, and long-term climate change assessments, and informing the public of the results through outreach and educational activities. Five new SNOTEL and four new SCAN sites are being installed in 2009-2010 and 12 existing basic SNOTEL sites are being upgraded. In addition, 30 automated precipitation gages are being added to New Mexico measurement networks. The first phase of snow mapping and modeling has focused on four sub basins

  17. Fog prediction using the modified asymptotic liquid water content vertical distribution formulation with the Weather Research and Forecasting model

    Science.gov (United States)

    Kim, E.; Lee, S.; Kim, J.; Chae, D.

    2017-12-01

    Fog forecasts have difficulty in forecasting due to temporal and spatial resolution problems, high numerical computations, complicated mechanisms related to turbulence in order to analyze the fog in the model, and a lack of appropriate fog physical processes. Conventional fog prediction is based on the surface visibility threshold "fog diagnosis method is based on the fog related variables near the surface, such as visibility, low stratus, relative humidity and wind speed but this method only predicts fog occurrence not fog intensity. To improve this, a new fog diagnostic scheme, based on an asymptotic analytical study of radiation fog (Zhou and Ferrier 2008, ZF08) is to increase the accuracy of fog prediction by calculating the vertical LWC considering cooling, turbulence and droplet settling, visibility, surface relative humidity and low stratus. In this study, we intend to improve fog prediction through the Weather Research and Forecasting (WRF) model using high-resolution data. Although the prediction accuracy can be improved by combining the WRF Planetary Boundary Layer (PBL) scheme and 1 dimension (1D) model, it is necessary to increase the vertical resolution in the boundary layer to implement the fog formation and persistence mechanism in the internal boundary layer in the PBL more accurately, we'll modify the algorithm to enhance the effects of turbulence and then compare the newly predicted fog and observations to determine the accuracy of the forecast of the fog occurring on the Korean peninsula.

  18. 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...

  19. Forecast of auroral activity

    International Nuclear Information System (INIS)

    Lui, A.T.Y.

    2004-01-01

    A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting, namely, the area, the power, and the rates of change in area and power. This new technique is quite accurate, as indicated by the high true skill scores for forecasting three different levels of auroral dissipation during the activity lifetime. The corresponding advanced warning time ranges from 22 to 79 min from low to high dissipation levels

  20. 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...

  1. Assessment of storm forecast

    DEFF Research Database (Denmark)

    Cutululis, Nicolaos Antonio; Hahmann, Andrea N.; Huus Bjerge, Martin

    When wind speed exceeds a certain value, wind turbines shut-down in order to protect their structure. This leads to sudden wind plants shut down and to new challenges concerning the secure operation of the pan-European electric system with future large scale offshore wind power. This task aims...... stopped, completely or partially, producing due to extreme wind speeds. Wind speed and power measurements from those events are presented and compared to the forecast available at Energinet.dk. The analysis looked at wind speed and wind power forecast. The main conclusion of the analysis is that the wind...... to consider it an EWP) and that the available wind speed forecasts are given as a mean wind speed over a rather large area. At wind power level, the analysis shows that prediction of accurate production levels from a wind farm experiencing EWP is rather poor. This is partially because the power curve...

  2. Wind power forecast

    Energy Technology Data Exchange (ETDEWEB)

    Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

    2012-07-01

    Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

  3. 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.

  4. Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment

    Science.gov (United States)

    David N. Wear

    2011-01-01

    Accurately forecasting future forest conditions and the implications for ecosystem services depends on understanding land use dynamics. In support of the 2010 Renewable Resources Planning Act (RPA) Assessment, we forecast changes in land uses for the coterminous United States in response to three scenarios. Our land use models forecast urbanization in response to the...

  5. Measurement of water potential in low-level waste management

    International Nuclear Information System (INIS)

    Jones, T.L.; Gee, G.W.; Kirkham, R.R.; Gibson, D.D.

    1982-08-01

    The measurement of soil water is important to the shallow land burial of low-level waste. Soil water flow is the principle mechanism of radionuclide transport, allows the establishment of stabilizing vegetation and also governs the dissolution and release rates of the waste. This report focuses on the measurement of soil water potential and provides an evaluation of several field instruments that are available for use to monitor waste burial sites located in arid region soils. The theoretical concept of water potential is introduced and its relationship to water content and soil water flow is discussed. Next, four major areas of soils research are presented in terms of their dependence on the water potential concept. There are four basic types of sensors used to measure soil water potential. These are: (1) tensiometers; (2) soil psychrometers; (3) electrical resistance blocks; and (4) heat dissipation probes. Tensiometers are designed to measure the soil water potential directly by measuring the soil water pressure. Monitoring efforts at burial sites require measurements of soil water over long time periods. They also require measurements at key locations such as waste-soil interfaces and within any barrier system installed. Electrical resistance blocks are well suited for these types of measurements. The measurement of soil water potential can be a difficult task. There are several sensors commercially available; however, each has its own limitations. It is important to carefully select the appropriate sensor for the job. The accuracy, range, calibration, and stability of the sensor must be carefully considered. This study suggests that for waste management activities, the choice of sensor will be the tensiometer for precise soil characterization studies and the electrical resistance block for long term monitoring programs

  6. 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...

  7. 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

  8. 7 CFR 612.7 - Forecast user responsibility.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Forecast user responsibility. 612.7 Section 612.7 Agriculture Regulations of the Department of Agriculture (Continued) NATURAL RESOURCES CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.7 Forecast user responsibility. The forecast use...

  9. Coastal and Riverine Flood Forecast Model powered by ADCIRC

    Science.gov (United States)

    Khalid, A.; Ferreira, C.

    2017-12-01

    Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which

  10. 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.

  11. Levels of trace elements in MWSS drinking water

    International Nuclear Information System (INIS)

    Andal, T.T.

    1998-01-01

    As a water supplier for the metropolis, vigilance over the water quality has not been taken for granted at the Metropolitan Waterworks and Sewerage System (MWSS). By the early 1980's, a control laboratory equipped with modern facilities had been set up to supplement the already existing control laboratory at Filter Plant II handling physical, chemical, bacteriological, biological and mineral analyses and examinations, efficiently. The new central laboratory is intended to monitor trace elements, organic constituents and other elements with health related impact so as to assure the consumers of a safe drinking water supply at all times. This presentation reviews the levels of trace element pollution in MWSS tap water, then and now, in justification of the rehabilitation projects along the distribution network, in the treatment plants and other pertinent innovations corresponding to budgeted capital outlays as invested by the system. (author)

  12. TRIHALOMETHANE LEVELS IN HOME TAP WATER AND SEMEN QUALITY

    Science.gov (United States)

    Trihalomethane Levels in Home Tap Water and Semen QualityLaura Fenster, 1 Kirsten Waller, 2 Gayle Windham, 1 Tanya Henneman, 2 Meredith Anderson, 2 Pauline Mendola, 3 James W. Overstreet, 4 Shanna H. Swan51California Department of Health Services, Division of Environm...

  13. Water-Level Analysis for Cumberland Sound, Georgia

    National Research Council Canada - National Science Library

    Kraus, Nicholas

    1997-01-01

    .... The channel through St Marys Entrance is maintained at a 50-ft depth through significant dredging that occurred from 1986-1988 Questions arose as to whether this dredging had raised the water level in Cumberland Sound. The U.S...

  14. Lake St. Clair: Storm Wave and Water Level Modeling

    Science.gov (United States)

    2013-06-01

    R. A. Luettich, C. Dawson, V. J. Cardone , A. T. Cox, M. D. Powell, H. J. Westerink, and H. J. Roberts. 2010. A high resolution coupled riverine flow...Storm Wave and Water Level Modeling 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Tyler J. Hesser

  15. design and implementation of a water level controller

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... Nigerian Journal of Technology (NIJOTECH) ... in real time application by using it to control the level of water in a tank fed by a ... chine when a cow is finished in a milking par- .... Robotics Arm. IEEE Control Systems 10(1).

  16. Effect of electrolyzed reduced water on malondialdehyde levels and ...

    African Journals Online (AJOL)

    Purpose: To evaluate the effects of electrolyzed reduced water (ERW) on malondialdehyde (MDA) levels and neutrophil cells in Wistar rats suffering from aggressive periodontitis. Methods: Wistar rats (Rattus norvegicus) were infected with A. actinomycetemcomitans before being divided into a control group and a treatment ...

  17. High-level water purifying technology. Kodo josui shori gijutsu

    Energy Technology Data Exchange (ETDEWEB)

    Tsugura, H; Tsukiashi, K [Meidensha Corp., Tokyo (Japan)

    1993-07-01

    Research and development have been carried out on a high-level water purifying system using ozone and activated charcoals to supply drinking water free of carcinogenic matters and odors. This system comprises a system to utilize ozone by using silent discharge and oxygen enriching device, and a living organism/activated charcoal treatment system. The latter system utilizes living organisms deposited on activated charcoal surfaces to remove polluting substances including ammonia. The treatment experimenting equipment comprises an ozone generating system, an ozone treating column, an activated charcoal treating column, an ozone/activated charcoal control device, and a water amount and quality measuring system. An experiment was carried out using an experimental plant with a capacity of 20 m[sup 3]/day on water taken from the sedimentation process at an actual water purifying plant. As a result, trihalomethane formation potential was removed at about 40% in the ozone treatment, and at 70% in the whole treatment combining the ozone and living organism/activated charcoal treatments. For parameterization of palatability of water, a method is being studied that utilizes nuclear magnetic resonance to investigate degrees of water cluster. The method is regarded promising. 1 ref., 4 figs.

  18. 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

  19. An analysis of the water-level monitoring system for a boiling-water reactor

    International Nuclear Information System (INIS)

    Carlson, R.W.; Belblidia, L.A.; Russell, J.L. Jr.

    1985-01-01

    The water-level instrumentation system is very important to the overall safety of a BWR. This system is being monitored by the Safety Parameter Display System (SPDS) that is being installed in Georgia Power Company's Plant Hatch. One of the most significant functions of the SPDS is the comparison of redundant instrument readings and formation of the best estimate of each parameter from those readings which are consistent. When comparing water-level instrument readings, it is necessary to correct the individual readings for differences between current and calibration conditions as well as for differences between calibration conditions for the multiple instruments. This paper documents the examination of the water-level instrumentation system at Plant Hatch and presents the development of the equations that were used to determine the differences between indicated and actual water levels. (author)

  20. Hydraulics and drones: observations of water level, bathymetry and water surface velocity from Unmanned Aerial Vehicles

    DEFF Research Database (Denmark)

    Bandini, Filippo

    -navigable rivers and overpass obstacles (e.g. river structures). Computer vision, autopilot system and beyond visual line-of-sight (BVLOS) flights will ensure the possibility to retrieve hyper-spatial observations of water depth, without requiring the operator to access the area. Surface water speed can......The planet faces several water-related threats, including water scarcity, floods, and pollution. Satellite and airborne sensing technology is rapidly evolving to improve the observation and prediction of surface water and thus prevent natural disasters. While technological developments require....... Although UAV-borne measurements of surface water speed have already been documented in the literature, a novel approach was developed to avoid GCPs. This research is the first demonstration that orthometric water level can be measured from UAVs with a radar system and a GNSS (Global Navigation Satellite...

  1. Intercomparison of low-level tritium in water

    International Nuclear Information System (INIS)

    Sipka, V.; Zupancic, M.; Hadzisehovic, M.; Bacic, S.; Vukovic, Z.

    1989-01-01

    In 1985 the Section of Isotope Hydrology of the IAEA organized the fourth intercomparison for low-level tritium counting in waters. Four water samples with different 3 H concentration were sent to 85 laboratories willing to participate. The results from the different laboratories were presented in the unified questionnaires and coded. The participation in the intercomparisons for every laboratory doing low-level 3 H measurements in the waters is very important and useful. This is a best way to check the entire procedure and methods of the measurements and the reliability of the standards used. Since our laboratories are doing the natural 3 H concentration measurement in the waters for the environmental control and hydrology reasons it was necessary to take part in this intercomparison. Our standard procedure was applied. The 3 H activity in the samples was measured by liquid scintillation counting after an electrolytic enrichment. The results of our measurements of the four water samples, received from the organizers, are presented on the figures and tables presenting summary of the intercomparison. It is clear that our measurement (procedure and standards) have given satisfactory results (author)

  2. Coupling of sea level and tidal range changes, with implications for future water levels.

    Science.gov (United States)

    Devlin, Adam T; Jay, David A; Talke, Stefan A; Zaron, Edward D; Pan, Jiayi; Lin, Hui

    2017-12-05

    Are perturbations to ocean tides correlated with changing sea-level and climate, and how will this affect high water levels? Here, we survey 152 tide gauges in the Pacific Ocean and South China Sea and statistically evaluate how the sum of the four largest tidal constituents, a proxy for the highest astronomical tide (HAT), changes over seasonal and interannual time scales. We find that the variability in HAT is significantly correlated with sea-level variability; approximately 35% of stations exhibit a greater than ±50 mm tidal change per meter sea-level fluctuation. Focusing on a subset of three stations with long records, probability density function (PDF) analyses of the 95% percentile exceedance of total sea level (TSL) show long-term changes of this high-water metric. At Hong Kong, the increase in tides significantly amplifies the risk caused by sea-level rise. Regions of tidal decrease and/or amplification highlight the non-linear response to sea-level variations, with the potential to amplify or mitigate against the increased flood risk caused by sea-level rise. Overall, our analysis suggests that in many regions, local flood level determinations should consider the joint effects of non-stationary tides and mean sea level (MSL) at multiple time scales.

  3. 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.

  4. 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.

  5. Total Water Level Fun Facts: The Relative Contribution of Extreme Total Water Levels Along the US West Coast

    Science.gov (United States)

    Serafin, K.; Ruggiero, P.; Stockdon, H. F.

    2016-02-01

    In the fall of 2014, parts of the US West Coast endured some of the highest monthly mean sea level anomalies on record, likely due to the presence of "the blob" (Bond et al., 2015), an anomalously warm water mass in the NE Pacific. However, despite the significantly above average water levels, the coastline experienced only marginal coastal flooding and erosion hazards because the ensuing winter lacked significant storms, underscoring the fact that extreme total water levels (TWLs) are compound events. To better understand how several individual processes combine to cause devastating coastal hazards, we investigate the relative contribution that each component (waves, tides, and non-tidal residuals) has on extreme TWLs on sandy beaches. Water level records along the US West Coast are decomposed into mean sea level, astronomical tide, and non-tidal residuals (NTRs). The NTR is further split into an intra-annual seasonal signal, monthly mean sea level anomalies (inter-annual variability), and meteorological surge. TWL time series are then generated by combining water levels with wave runup, computed using wave data and beach morphology. We use this data-driven, structural function approach to investigate the spatial variability of the relative contribution of each component to the maximum TWL event on record. We also use a probabilistic, full simulation TWL model (Serafin and Ruggiero, 2014) to generate multiple, synthetic TWL records, to explore the relative contribution of each component to extreme TWL return levels. We assess the sensitivity to local beach morphology by computing TWLs for a range of observed beach slopes. Extreme TWLs are higher in Oregon and Washington than in California. Wave runup typically comprises > 50% of the TWL signal, while NTRs often compose < 5%, illustrating the importance wave climate has on the potential for extreme TWLs. While waves are typically larger in the North, California experiences greater contributions to extreme TWLs from

  6. Aquaponic Growbed Water Level Control Using Fog Architecture

    Science.gov (United States)

    Asmi Romli, Muhamad; Daud, Shuhaizar; Raof, Rafikha Aliana A.; Awang Ahmad, Zahari; Mahrom, Norfadilla

    2018-05-01

    Integrated Multi-Trophic Aquaculture (IMTA) is an advance method of aquaculture which combines species with different nutritional needs to live together. The combination between aquatic live and crops is called aquaponics. Aquatic waste that normally removed by biofilters in normal aquaculture practice will be absorbed by crops in this practice. Aquaponics have few common components and growbed provide the best filtration function. In growbed a siphon act as mechanical structure to control water fill and flush process. Water to the growbed comes from fish tank with multiple flow speeds based on the pump specification and height. Too low speed and too fast flow rate can result in siphon malfunctionality. Pumps with variable speed do exist but it is costly. Majority of the aquaponic practitioner use single speed pump and try to match the pump speed with siphon operational requirement. In order to remove the matching requirement some control need to be introduced. Preliminarily this research will show the concept of fill-and-flush for multiple pumping speeds. The final aim of this paper is to show how water level management can be done to remove the speed dependency. The siphon tried to be controlled remotely since wireless data transmission quite practical in vast operational area. Fog architecture will be used in order to transmit sensor data and control command. This paper able to show the water able to be retented in the growbed within suggested duration by stopping the flow in once predefined level.

  7. The study and improvement of water level control of pressurizer

    International Nuclear Information System (INIS)

    Gao Peng; Zhang Qinshun

    2006-01-01

    The PI controller which is used widely in water level control of pressurizer in reactor control system usually leads dynamic overshoot and long setting time. The improvement project for intelligent fuzzy controller to take the place of PI controller is advanced. This paper researches the water level control of pressurizer in reactor control system of Daya Bay Phase I, and describes the method of intelligent fuzzy control in practice. Simulation indicates that the fuzzy control has advantages of small overshoot and short settling time. It can also improve control system's real time property and anti-interference ability. Especially for non-linear and time-varying complicated control systems, it can obtain good control results. (authors)

  8. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Justin A. Schulte

    2017-12-01

    Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.

  9. Voyageurs National Park: Water-level regulation and effects on water quality and aquatic biology

    Science.gov (United States)

    Christensen, Victoria G.; Maki, Ryan P.; LeDuc, Jaime F.

    2018-01-01

    Following dam installations in the remote Rainy Lake Basin during the early 1900s, water-level fluctuations were considered extreme (1914–1949) compared to more natural conditions. In 1949, the International Joint Commission (IJC), which sets rules governing dam operation on waters shared by the United States and Canada, established the first rule curves to regulate water levels on these waterbodies. However, rule curves established prior to 2000 were determined to be detrimental to the ecosystem. Therefore, the IJC implemented an order in 2000 to change rule curves and to restore a more natural water regime. After 2000, measured chlorophyll-a concentrations in the two most eutrophic water bodies decreased whereas concentrations in oligotrophic lakes did not show significant water-quality differences. Fish mercury data were inconclusive, due to the variation in water levels and fish mercury concentrations, but can be used by the IJC as part of a long term data set.

  10. 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

  11. 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.

  12. 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

  13. Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

    Science.gov (United States)

    Shafiee-Jood, M.; Cai, X.

    2017-12-01

    Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

  14. 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

  15. Water level control for a nuclear steam generator

    International Nuclear Information System (INIS)

    Wen Tan

    2011-01-01

    Research highlights: → A water level control system for a nuclear steam generator (SG) is proposed. → The parameters of the control system are directly related to those of the plant model thus scheduling is easy to implement in practice. → The proposed gain-scheduled controller can achieve good performance at both low and high power levels. - Abstract: A water level control system for a nuclear steam generator (SG) is proposed. The control system consists of a feedback controller and a feedforward controller. The feedback controller is of first order, the feedforward controller is of second order, and parameters of the two controllers are directly related to the parameters of plant model thus scheduling is easy to implement in practice. Robustness and performance of the feedback and the feedforward controllers are analyzed in details and tuning of the two parameters of the controllers are discussed. Comparisons among a single robust controller, a multi-model controller and a gain-scheduled controller are studied. It is shown that the proposed gain-scheduled controller can achieve good performance at both low and high power levels.

  16. Forecasting Interest Rates Using Geostatistical Techniques

    Directory of Open Access Journals (Sweden)

    Giuseppe Arbia

    2015-11-01

    Full Text Available Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014 using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered.

  17. Comparative Analysis of Nitrate Levels in Pensacola Area Rain Water

    Science.gov (United States)

    Jacobs, J.; Caffrey, J. M.; Maestre, A.; Landing, W. M.

    2017-12-01

    Nitrate is an important constituent of acid rain and often correlated with atmospheric NOx levels. This link between air and water quality was tested over a course of summer 2017 and compared to data from 2005-2012. Rain water samples collected from late May through early July of 2017 were tested for pH and nitrate concentrations. These months were among the stormiest on record for the Northwest Florida region with a total rainfall of 648 mm. The data analyzed from these rain events was compared to previous data to show the trends of nitrate and pH levels in the rainwater. Median pH for this study was 5.2, higher than the medians between 2015-2012 which ranged from 4.2 to 5.0, while nitrate concentrations for this study were 15.2 µM. This contrasts with a significant drop in nitrate concentrations from 41 µM in 2005 and 2006 to around 12 µM between 2007 and 2012. The drop between 2006-7 was suspected to be a result of implementation of NOx controls at Plant Crist coal fired power plant and other Clean Air Act requirements. These inputs of nitrate and H+ ions from rainwater can have a significant influence water quality throughout the region.

  18. Forecasting potential crises

    International Nuclear Information System (INIS)

    Neufeld, W.P.

    1984-01-01

    Recently, the Trend Analysis Program (TAP) of the American Council of Life Insurance commissioned the Futures Group of Glastonbury, Connecticut, to examine the potential for large-scale catastrophic events in the near future. TAP was specifically concerned with five potential crises: the warming of the earth's atmosphere, the water shortage, the collapse of the physical infrastructure, the global financial crisis, and the threat of nuclear war. We are often unprepared to take action; in these cases, we lose an advantage we might have otherwise had. This is the whole idea behind forecasting: to foresee possibilities and to project how we can respond. If we are able to create forecasts against which we can test policy options and choices, we may have the luxury of adopting policies ahead of events. Rather than simply fighting fires, we have the option of creating a future more to our choosing. Short descriptions of these five potential crises and, in some cases, possible solutions are presented

  19. 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.

  20. Amount of leachant and water absorption levels of wood treated with borates and water repellents.

    Science.gov (United States)

    Baysal, Ergun; Sonmez, Abdullah; Colak, Mehmet; Toker, Hilmi

    2006-12-01

    Wood protection efficacy of borates against biological agents, flame retardancy, and suitability to the environment is well known. Since borates can be applied to timber as water based solutions, they are preferred economically as well. Even though they are highly mobile in wood, boron compounds are widely used in timber preservation. Borates migrate in liquid and increase the hygroscopicity of wood in damp conditions. This study deals with the physical restriction of water access in wood by impregnating water repellent agents into wood to limit amount of leachant and water absorption levels of wood after boron treatment. Borates were incorporated with polyethylene glycol-400 (PEG-400) their bulking effect in wood was considered. Results indicated that the amount of leachates from wood treated with borates in PEG-400 was remarkably higher compared to those of wood treated with the aqueous solutions of borates. Water absorption (WA) levels of wood treated with aqueous solutions of borates were higher than those of their treated samples with the solutions in PEG-400. Secondary treatments of wood with the water repellent (WR) chemicals following borate impregnation reduced the leaching of chemicals from wood in water and also WA of the specimens were less than those of the wood treated with only borates from aqueous and PEG solutions. Styrene (St) was the most effective monomer among the other agents used in terms of immobility effect on borates and WA.

  1. 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.

  2. Using simulations to forecast homeowner response to sea level rise in South Florida: Will they stay or will they go?

    Science.gov (United States)

    Treuer, G.

    2017-12-01

    Sea level rise threatens coastal communities around the world, including South Florida which may be the most financially vulnerable region in the world. Proactive investments in sea level rise adaptive flood protections could reduce South Florida's financial vulnerability. However, it is unclear if local governments and homeowners will be willing to make those investments before it is too late. Our research explores this issue by reporting the results of a novel online simulation that accelerates 348 South Florida homeowners thirty-five years into the future so that they can `live' the effects of sea level rise. The results contain a mix of optimism and caution for the prospects of future adaptation. On the positive side over 75% of participants indicated a willingness to support bond issues to pay for adaptation, even as the costs of the measures and effects of sea level rise increased over the years. Likewise, we find little evidence that politically conservative residents who normally have more skeptical views about climate change would be any less inclined to support adaptation, or only look to information sources that downplay the threat. On the negative side, homeowner interest in moving out of the region increases steadily over time as the sea level rises. This is driven by an increase in worry associated with viewing more information within the simulation.

  3. Bayesian analyses of seasonal runoff forecasts

    Science.gov (United States)

    Krzysztofowicz, R.; Reese, S.

    1991-12-01

    Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

  4. Relative Sea Level, Tidal Range, and Extreme Water Levels in Boston Harbor from 1825 to 2016

    Science.gov (United States)

    Talke, S. A.; Kemp, A.; Woodruff, J. D.

    2017-12-01

    Long time series of water-level measurements made by tide gauges provide a rich and valuable observational history of relative sea-level change, the frequency and height of extreme water levels and evolving tidal regimes. However, relatively few locations have available tide-gauge records longer than 100 years and most of these places are in northern Europe. This spatio-temporal distribution hinders efforts to understand global-, regional- and local-scale trends. Using newly-discovered archival measurements, we constructed a 200 year, instrumental record of water levels, tides, and storm surges in Boston Harbor. We detail the recovery, datum reconstruction, digitization, quality assurance, and analysis of this extended observational record. Local, decadally-averaged relative sea-level rose by 0.28 ± 0.05 m since the 1820s, with an acceleration of 0.023 ±0.009 mm/yr2. Approximately 0.13 ± 0.02 m of the observed RSL rise occurred due to ongoing glacial isostatic adjustment, and the remainder occurred due to changes in ocean mass and volume associated with the onset of modern mean sea-level rise. Change-point analysis of the new relative sea level record confirms that anthropogenic rise began in 1924-1932, which is in agreement with global mean sea level estimates from the global tide gauge network. Tide range decreased by 5.5% between 1830 and 1910, likely due in large part to anthropogenic development. Storm tides in Boston Harbor are produced primarily by extratropical storms during the November-April time frame. The three largest storm tides occurred in 1851, 1909, and 1978. Because 90% of the top 20 storm tides since 1825 occurred during a spring tide, the secular change in tide range contributes to a slight reduction in storm tide magnitudes. However, non-stationarity in storm hazard was historically driven primarily by local relative sea-level rise; a modest 0.2 m increase in relative sea level reduces the 100 year high water mark to a once-in-10 year event.

  5. kosh 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. kpdt 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. kewr 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. 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...

  9. kpga 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. kbkw 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. ktcl 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. pgwt 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. kpsp 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. kbih 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. kdnl 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. kart 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. kilm 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. kpne 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. kabi 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. ptpn 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. kblf 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. panc 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. 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...

  4. kgdv 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. 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...

  6. kdls 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. koaj 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. krhi 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. kbpk 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. khuf 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. kbpi 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...

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    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. khbg 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. kpbf 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. konp 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. pkwa 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. ktvf 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. 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...

  9. khks 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. 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...

  11. kpsm 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. kgrb 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. kgmu 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. papg 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. kbgm 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. pamc 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. klrd 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. ksan 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. patk 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. kowb 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...