Integrated Hydrologic and Reservoir Routing Model for Real-Time Water Level Forecasts
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 9
Abstract
Reliable 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.
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Acknowledgments
The research reported in this paper was supported by the Excellent Young Scientist Foundation of the National Natural Science Foundation of China (NSFC; 51422907) and the Program for New Century Excellent Talents (NCET) in University (NCET-11-0401).
References
Ahmed, J. A., and Sarma, A. K. (2007). “Artificial neural network model for synthetic streamflow generation.” Water Resour. Manage., 21(6), 1015–1029.
Alvisi, S., and Franchini, M. (2011). “Fuzzy neural networks for water level and discharge forecasting with uncertainty.” Environ. Modell. Softw., 26(4), 523–537.
Alvisi, S., Mascellani, G., Franchini, M., and Bardossy, A. (2006). “Water level forecasting through fuzzy logic and artificial neural network approaches.” Hydrol. Earth Syst. Sci., 10(1), 1–17.
Bao, H. J., et al. (2011). “Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast.” Adv. Geosci., 29(6), 61–67.
Box, G. E. P., and Jenkins, G. M. (1970). Time series analysis forecasting and control, Holden-Day, San Francisco.
Budu, K. (2014). “Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting.” J. Hydrol. Eng., 1385–1400.
Chang, F. J., and Chang, Y. T. (2006). “Adaptive neuro-fuzzy inference system for prediction of water level in reservoir.” Adv. Water Resour., 29(1), 1–10.
Chen, H., Xiang, T. T., Zhou, X., and Xu, C. Y. (2011). “Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China.” Stochastic Environ. Res. Risk Assess., 26(6), 847–858.
Chow, V. T., Maidment, D. R., and Mays, L. W. (1988). Applied hydrology, McGraw Hill, New York.
Coulibaly, P., Haché, M., Fortin, V., and Bobée, B. (2005). “Improving daily reservoir inflow forecasts with model combination.” J. Hydrol. Eng., 91–99.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S. (1993). “Shuffled complex evolution approach for effective and efficient global minimization.” J. Optim. Theory Appl., 76(3), 501–521.
Fang, C. H., Guo, S. L., Duan, Y. H., and Yang, X. M. (2008). “A simple and new approach of reproducing inflow flood hydrograph of reservoirs.” Chin. J. Geotech. Eng., 30(11), 1743–1747 (in Chinese).
Fen, O. Y., et al. (2014). “Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology.” Stochastic Environ. Res. Risk Assess., 28(4), 991–1010.
Fiorentini, M., and Orlandini, S. (2013). “Robust numerical solution of the reservoir routing equation.” Adv. Water Resour., 59, 123–132.
Güldal, V., and Tongal, H. (2010). “Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir Lake level forecasting.” Water Resour. Manage., 24(1), 105–128.
Hipni, A., El-shafie, A., Najah, A., Karim, O. A., Hussain, A., and Mukhlisin, M. (2013). “Daily forecasting of dam water levels: Comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS).” Water Resour. Manage., 27(10), 3803–3823.
Khac-Tien Nguyen, P., and Hock-Chye Chua, L. (2012). “The data-driven approach as an operational real-time flood forecasting model.” Hydrol. Process., 26(19), 2878–2893.
Khan, M. S., and Coulibaly, P. (2006). “Application of support vector machine in lake water level prediction.” J. Hydrol. Eng., 199–205.
Kisi, O., Shiri, J., and Nikoofar, B. (2012). “Forecasting daily lake levels using artificial intelligence approaches.” Comput. Geosci., 41, 169–180.
Li, H. X., Zhang, Y. Q., Chiew, F. H. S., and Xu, S. G. (2009). “Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index.” J. Hydrol., 370(1–4), 155–162.
Lin, G. F., Chen, G. R., and Huang, P. Y. (2010). “Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting.” Adv. Water Resour., 33(8), 887–898.
Lin, G. F., Chen, G. R., Huang, P. Y., and Chou, Y. C. (2009a). “Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods.” J. Hydrol., 372(1–4), 17–29.
Lin, G. F., and Wu, M. C. (2011). “An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model.” J. Hydrol., 405(3–4), 439–450.
Lin, G. F., Wu, M. C., Chen, G. R., and Tsai, F. Y. (2009b). “An RBF-based model with an information processor for forecasting hourly reservoir inflow during typhoons.” Hydrol. Process., 23(25), 3598–3609.
Lin, K. R., Liu, P., He, Y. H., and Guo, S. L. (2014). “Multi-site evaluation to reduce parameter uncertainty in a conceptual hydrological modeling within the GLUE framework.” J. Hydroinf., 16(1), 60–73.
Liu, P., Li, L. P., Chen, G. J., and Rheinheimer, D. E. (2014). “Parameter uncertainty analysis of reservoir operating rules based on implicit stochastic optimization.” J. Hydrol., 514, 102–113.
Lohani, A. K., Kumar, R., and Singh, R. D. (2012). “Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques.” J. Hydrol., 442–443, 23–35.
Loucks, D. P., Van Beek, E., Stedinger, J. R., Dijkman, J. P., and Villars, M. T. (2005). Water resources systems planning and management: An introduction to methods, models and applications, UNESCO, Paris.
Lü, H. S., et al. (2013). “The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method.” J. Hydrol., 480, 102–114.
Montanari, A., and Grossi, G. (2008). “Estimating the uncertainty of hydrological forecasts: A statistical approach.” Water Resour. Res., 44(12), W00B08.
Sulaiman, M., El-Shafie, A., Karim, O., and Basri, H. (2011). “Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network.” Water Resour. Manage., 25(10), 2525–2541.
Tian, Y., Xu, Y. P., and Zhang, X. J. (2013). “Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models.” Water Resour. Manage., 27(8), 2871–2888.
Tsai, M. J., Abrahart, R. J., Mount, N. J., and Chang, F. J. (2014). “Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan.” Hydrol. Process., 28(3), 1055–1070.
Valipour, M., Banihabib, M. E., and Behbahani, S. M. R. (2013). “Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir.” J. Hydrol., 476, 433–441.
Valizadeh, N., and El-Shafie, A. (2013). “Forecasting the level of reservoirs using multiple input fuzzification in ANFIS.” Water Resour. Manage., 27(9), 3319–3331.
Wang, D. (2011). “On the base flow recession at the Panola Mountain Research Watershed, Georgia, United States.” Water Resour. Res., 47(3), W03527.
Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L. (2009). “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series.” J. Hydrol., 374(3–4), 294–306.
Xu, H. L., Xu, C. Y., Chen, H., Zhang, Z. X., and Li, L. (2013). “Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China.” J. Hydrol., 505, 1–12.
Zhao, R. J. (1992). “The Xinanjiang model applied in China.” J. Hydrol., 135(1), 371–381.
Zhao, R. J., and Liu, X. R. (1995). “The Xinanjiang model.” Computer models of watershed hydrology, V. P. Singh, ed., Water Resources Publications, Littleton, CO.
Zhao, R. J., Zhuang, Y. L., Fang, L. R., Liu, X. R., and Zhang, Q. S. (1980). “The Xinanjiang model.” Hydrological Forecasting Proc. Oxford Symp., Vol. 129, IAHS, Wallingford, U.K., 351–356.
Zhao, T. T. G., Cai, X. M., and Yang, D. W. (2011). “Effect of streamflow forecast uncertainty on real-time reservoir operation.” Adv. Water Resour., 34(4), 495–504.
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© 2014 American Society of Civil Engineers.
History
Received: Jun 13, 2014
Accepted: Oct 30, 2014
Published online: Dec 4, 2014
Discussion open until: May 4, 2015
Published in print: Sep 1, 2015
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