Integrated Hydrologic and Reservoir Routing Model for Real-Time Water Level Forecasts

AbstractReliable forecasts of reservoir water levels are important for reservoir operations and water resources management. A reservoir water level forecasting model was developed by integrating the Xinanjiang model and a reservoir routing model. The integrated model, of which the hydrologic parameters are calibrated based on the observed water level directly, is used to forecast the reservoir water levels, while that of the conventional method calibrates the hydrologic model using the estimated reservoir inflows from water balance. Through an application to the China’s Shuibuya Reservoir, the integrated model shows a much higher accuracy than the conventional method, with an average RMS error of 0.10 m, whereas that of the conventional method is 5.13 m. The presented model provides a reliable tool for real-time forecasts of reservoir water levels.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[2]  Özgür Kisi,et al.  Forecasting daily lake levels using artificial intelligence approaches , 2012, Comput. Geosci..

[3]  Stefano Alvisi,et al.  Fuzzy neural networks for water level and discharge forecasting with uncertainty , 2010, Environ. Model. Softw..

[4]  Paulin Coulibaly,et al.  Improving Daily Reservoir Inflow Forecasts with Model Combination , 2005 .

[5]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence , 2009 .

[6]  Stefano Alvisi,et al.  Water level forecasting through fuzzy logic and artificial neural network approaches , 2005 .

[7]  Hakan Tongal,et al.  Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting , 2010 .

[8]  Xujie Zhang,et al.  Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models , 2013, Water Resources Management.

[9]  Robert J. Abrahart,et al.  Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan , 2014 .

[10]  Chong-yu Xu,et al.  Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China , 2013 .

[11]  Zhao Ren-jun,et al.  The Xinanjiang model applied in China , 1992 .

[12]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[13]  Ximing Cai,et al.  Effect of streamflow forecast uncertainty on real-time reservoir operation , 2010 .

[14]  Gwo-Fong Lin,et al.  An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model , 2011 .

[15]  A. K. Lohani,et al.  Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques , 2012 .

[16]  Pan Liu,et al.  Multi-site evaluation to reduce parameter uncertainty in a conceptual hydrological modeling within the GLUE framework , 2014 .

[17]  Lloyd H.C. Chua,et al.  The data-driven approach as an operational real-time flood forecasting model , 2012 .

[18]  Gwo-Fong Lin,et al.  An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons , 2009 .

[19]  Marcello Fiorentini,et al.  Robust numerical solution of the reservoir routing equation , 2013 .

[20]  Chong-Yu Xu,et al.  Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China , 2012, Stochastic Environmental Research and Risk Assessment.

[21]  K. Budu,et al.  Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting , 2014 .

[22]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[23]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[24]  Florian Pappenberger,et al.  Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast , 2011 .

[25]  Xi Chen,et al.  The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method , 2013 .

[26]  Alberto Montanari,et al.  Estimating the uncertainty of hydrological forecasts: A statistical approach , 2008 .

[27]  D. E. Rheinheimer,et al.  Parameter uncertainty analysis of reservoir operating rules based on implicit stochastic optimization. , 2014 .

[28]  Gwo-Fong Lin,et al.  Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting , 2010 .

[29]  Gwo-Fong Lin,et al.  Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods , 2009 .

[30]  Ahmed El-Shafie,et al.  Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS , 2013, Water Resources Management.

[31]  Ahmed El-Shafie,et al.  Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network , 2011 .

[32]  Arup Kumar Sarma,et al.  Artificial neural network model for synthetic streamflow generation , 2007 .

[33]  Zhongbo Yu,et al.  Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology , 2014, Stochastic Environmental Research and Risk Assessment.

[34]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[35]  Yongqiang Zhang,et al.  Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index , 2009 .