The Experts below are selected from a list of 54273 Experts worldwide ranked by ideXlab platform
Enrique Jiménez - One of the best experts on this subject based on the ideXlab platform.
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Streamflow drought time series forecasting: a case study in a small watershed in North West Spain
Stochastic Environmental Research and Risk Assessment, 2008Co-Authors: Cristina Fernández, José A. Vega, Teresa Fonturbel, Enrique JiménezAbstract:Drought is a climatic event that can cause significant damage both in natural environment and in human lives. Drought forecasting is an important issue in water resource planning. Due to the stochastic behaviour of droughts, a multiplicative seasonal autoregressive integrated moving average model was applied to forecast monthly Streamflow in a small watershed in Galicia (NW Spain). A better Streamflow forecast obtained when the Martone index was included in the model as explanatory variable. After forecasting 12 leading month Streamflow, three drought thresholds: Streamflow mean, monthly Streamflow mean and standardized Streamflow index were chosen. Both observed and forecasted Streamflow showed no drought evidence in this basin.
Ozgur Kisi - One of the best experts on this subject based on the ideXlab platform.
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Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence
Water Resources Management, 2016Co-Authors: Zaher Mundher Yaseen, Ozgur Kisi, Vahdettin DemirAbstract:Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly Streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead Streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting Streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term Streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly Streamflows in both stages of the study.
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a wavelet support vector machine conjunction model for monthly Streamflow forecasting
Journal of Hydrology, 2011Co-Authors: Ozgur Kisi, Mesut ÇimenAbstract:Summary The study investigates the accuracy of wavelet and support vector machine conjunction model in monthly Streamflow forecasting. The conjunction method is obtained by combining two methods, discrete wavelet transform and support vector machine, and compared with the single support vector machine. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in Eastern Black Sea region of Turkey are used in the study. The root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for the comparing criteria. The comparison of results reveals that the conjunction model could increase the forecast accuracy of the support vector machine model in monthly Streamflow forecasting. For the Gerdelli and Isakoy stations, it is found that the conjunction models with RMSE = 13.9 m3/s, MAE = 8.14 m3/s, R = 0.700 and RMSE = 8.43 m3/s, MAE = 5.62 m3/s, R = 0.768 in test period is superior in forecasting monthly Streamflows than the most accurate support vector regression models with RMSE = 15.7 m3/s, MAE = 10 m3/s, R = 0.590 and RMSE = 11.6 m3/s, MAE = 7.74 m3/s, R = 0.525, respectively.
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short term and long term Streamflow forecasting using a wavelet and neuro fuzzy conjunction model
Journal of Hydrology, 2010Co-Authors: Jalal Shiri, Ozgur KisiAbstract:Summary Streamflow forecasting is an important issue in hydrologic engineering, as it determines the reservoir inflow as well as the flooding events, in spite of several other applications in water resources engineering. In the present study, the application of hybrid wavelet-neuro-fuzzy model has been investigated to model daily, monthly and yearly Streamflows. Streamflow data of Derecikviran Station on the Filyos River in the Western Black Sea region of Turkey were used in the study. The data sample consisted of 31 years of Streamflow records. In the first part of the study, single neuro-fuzzy (NF) and wavelet-neuro-fuzzy (WNF) models were established based on the previously recorded Streamflow values and compared with each other. It was found that the WNF model increase the accuracy of the single NF models especially in forecasting yearly Streamflows. In the second part of the study, the single NF and WNF models were compared with each other by adding periodicity component into the their inputs. The comparison results indicated that adding periodicity component generally increased the models’ accuracy.
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neural networks and wavelet conjunction model for intermittent Streamflow forecasting
Journal of Hydrologic Engineering, 2009Co-Authors: Ozgur KisiAbstract:Intermittent Streamflow estimates are important for water quality management, planning water supplies, hydropower, and irrigation systems. This paper proposes the application of a conjunction model (neurowavelet) for forecasting daily intermittent Streamflow. The neurowavelet conjunction model is improved by combining two methods, discrete wavelet transform and artificial neural networks (ANN), for 1 day ahead Streamflow forecasting and results are compared with those of the single ANN model. Intermittent Streamflow data from two stations in the Thrace Region, the European part of Turkey, in the northwest part of the country are used in the study. The comparison results revealed that the suggested model could significantly increase the forecast accuracy of single ANN in forecasting daily intermittent Streamflows. The neurowavelet conjunction model reduced the prediction root mean square errors and mean absolute errors with respect to the single ANN model by 74–65% and 43–12%, and increased the determinati...
Balaji Rajagopalan - One of the best experts on this subject based on the ideXlab platform.
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Use of daily precipitation uncertainties in Streamflow simulation and forecast
Stochastic Environmental Research and Risk Assessment, 2011Co-Authors: Yeonsang Hwang, Martyn P. Clark, Balaji RajagopalanAbstract:Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on Streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble Streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of Streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble Streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method.
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modeling hydrologic and water quality extremes in a changing climate a statistical approach based on extreme value theory
Water Resources Research, 2010Co-Authors: Balaji Rajagopalan, Erin Towler, Eric Gilleland, Scott R Summers, David Yates, Richard W KatzAbstract:[1] Although information about climate change and its implications is becoming increasingly available to water utility managers, additional tools are needed to translate this information into secondary products useful for local assessments. The anticipated intensification of the hydrologic cycle makes quantifying changes to hydrologic extremes, as well as associated water quality effects, of particular concern. To this end, this paper focuses on using extreme value statistics to describe maximum monthly flow distributions at a given site, where the nonstationarity is derived from concurrent climate information. From these statistics, flow quantiles are reconstructed over the historic record and then projected to 2100. This paper extends this analysis to an associated source water quality impact, whereby the corresponding risk of exceeding a water quality threshold is examined. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values correlated with high Streamflow. Results demonstrate that based on climate change information from the most recent Intergovernmental Panel on Climate Change assessment report, the variability and magnitude of extreme Streamflows substantially increase over the 21st century. Consequently, the likelihood of a turbidity exceedance increases, as do the associated relative costs. The framework is general and could be applied to estimate extreme Streamflow under climate change at other locations, with straightforward extensions to other water quality variables that depend on extreme hydroclimate.
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A new method to produce categorical Streamflow forecasts
Water Resources Research, 2006Co-Authors: Satish Kumar Regonda, Balaji Rajagopalan, Martyn P. ClarkAbstract:[1] Categorical forecasts of Streamflow are important for effective water resources management. Typically, these are obtained by generating ensemble forecasts of Streamflow and counting the proportion of ensembles in the desired category. Here we develop a simple and direct method to produce categorical Streamflow forecasts at multiple sites. The method involves predicting the probability of the leading mode (or principal component) of the basin Streamflows above a given threshold and subsequently translating the predicted probabilities to all the sites in the basin. The categorical probabilistic forecasts are obtained via logistic regression using a set of large-scale climate predictors. Application to categorical forecasts of the spring (April–June) Streamflows at six locations in the Gunnison River Basin exhibited significant long-lead forecast skill.
Otto Corrêa Rotunno - One of the best experts on this subject based on the ideXlab platform.
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Climatological and hydrological patterns and verified trends in precipitation and Streamflow in the basins of Brazilian hydroelectric plants
Theoretical and Applied Climatology, 2018Co-Authors: Wanderson Luiz Silva, Luciano Nóbrega Rodrigues Xavier, Maria Elvira Piñeiro Maceira, Otto Corrêa RotunnoAbstract:This study focuses on investigating possible changes in hydrometeorological behavior on important Brazilian river basins for power generation purposes. Thereby, this research analyzes the historical averages and observed trends regarding rainfall and Streamflow and their impact on the hydrological regime. Ten river basins were selected for the assessment of alterations in the precipitation and Streamflow series throughout descriptive measures and statistical significance tests (Mann-Kendall and Sen’s slope). These data are available over different time spans, but most of the records include information from 1961 to 2006. As long as these river basins are subjected to different climate types, their corresponding rainfall and Streamflow patterns vary accordingly the basin location in the country, in addition to the season. Most of the country has a predominantly tropical climate, with a wet period concentrated between October and March and a dry period between April and September. Some exceptions are the Northeastern region, where the climate is semi-arid, besides the Southern region, where there is abundant precipitation throughout the year. According to the obtained results, it could be noted that the Streamflow response to precipitation is faster in the smaller river basins, what can be due especially to the water travel time. It was found that Belo Monte basin, in Northern Brazil, presents a statistically significant reduction in the annual rainfall. Furthermore, a significant decrease in the annual Streamflow was also identified in Xingó and Sobradinho basins, in the Northeastern region. In contrast, Itaipu basin, located in Southern Brazil, showed increasing statistically significant trends in annual rainfall and Streamflow during the second half of the twentieth century. Relevant decreasing trends were also identified in the minimum Streamflows of the Brazilian Northern and Northeastern basins and increasing ones in the maximum Streamflows in the Southern region basins. The results obtained in this work will support the assessment of the impact of rainfall and Streamflow future scenarios in regulating capacity of the hydroelectric power plant reservoirs.
Cristina Fernández - One of the best experts on this subject based on the ideXlab platform.
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Streamflow drought time series forecasting: a case study in a small watershed in North West Spain
Stochastic Environmental Research and Risk Assessment, 2008Co-Authors: Cristina Fernández, José A. Vega, Teresa Fonturbel, Enrique JiménezAbstract:Drought is a climatic event that can cause significant damage both in natural environment and in human lives. Drought forecasting is an important issue in water resource planning. Due to the stochastic behaviour of droughts, a multiplicative seasonal autoregressive integrated moving average model was applied to forecast monthly Streamflow in a small watershed in Galicia (NW Spain). A better Streamflow forecast obtained when the Martone index was included in the model as explanatory variable. After forecasting 12 leading month Streamflow, three drought thresholds: Streamflow mean, monthly Streamflow mean and standardized Streamflow index were chosen. Both observed and forecasted Streamflow showed no drought evidence in this basin.
