Flow Updating in Real-Time Flood Forecasting Based on Runoff Correction by a Dynamic System Response Curve
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 4
Abstract
In order to improve the accuracy of real-time flood forecasting, a new accurate and efficient real-time flood forecasting error correction method based on a dynamic system response curve (DSRC) is developed. The dynamic system response curve was introduced into the flood forecasting error correction to establish the dynamic error feedback updating model tracing the source of the error. In this study, the flow concentration of the Xinanjiang (XAJ) model is generalized into a system. The physical basis of the system response curve is the flow concentration of the hydrological model. The theoretical basis of the concept is the differential of the system response function of the runoff time series. Based on the observed and calculated discharge, the calculated runoff series was corrected using least-squares estimation, and then the flow was recalculated with the corrected runoff. The Xinanjiang model was selected to calculate runoff. The method was tested in both an ideal scenario and in a real case study. The proposed method was applied to 26 floods in the Wangjiaba basin. The ratio of qualified flood increased from 65.4 to 92.3% after correction by the DSRC. Comparison with the second-order autoregressive error forecast model [AR(2)] shows that the method can improve the forecasting results effectively. The method has a simple structure, the performance indices will not deteriorate as the forecasting period (i.e., lead time) increases, and the method does not increase the number of model parameters.
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Acknowledgments
This study is supported by the Colleges and Universities in Jiangsu Province Plans to Graduate Research and Innovation (CXZZ13_0250), the National Natural Science Foundation of China (No. 51279057/41371048/40901015), as a Major Program of National Natural Science Foundation of China (51190091), the Fundamental Research Funds for the Central Universities (B1020062/B1020072), and the Special Fund of the State Key Laboratory of China (2009585412).
References
Babovic, V., Jensen, H. R., and Klinting, A. (2001). “Neural networks as routine for error updating of numerical models.” J. Hydraul. Eng., 181–193.
Bao, W. M. (2006). Hydrological forecasting, China Water Power, Beijing, 222–245.
Bao, W. M., and Li, Q. (2012). “Estimating selected parameters for the XAJ model under multicollinearity among watershed characteristics.” J. Hydrol. Eng., 118–128.
Bao, W. M., Si, W., Shen, G. H., Zhang, X. Q., and Li, Q. (2012). “Runoff error updating based on unit hydrograph inversion.” J. Adv. Water Sci., 23(3), 315–322.
Cai, T., Li, Q. F., Yu, M. X., Lu, G. B., Li, P. C., and Xie, W. (2012). “Investigation into the impacts of land-use change on sediment yield characteristics in the upper Huaihe River basin, China.” Phys. Chem. Earth, 53–54, 1–9.
DeChant, C. M., and H. Moradkhani (2012). “Examining the effectiveness and robustness of data assimilation methods for calibration and quantification of uncertainty in hydrologic forecasting.” Water Resour. Res., 48(4), W04518.
Hasebe, M., Hino, M., and Hoshi, K. (1989). “Flood forecasting by the filer separation AR method and comparison with modeling efficiencies by some rainfall-runoff models.” J. Hydrol., 110(1–2), 1–2, 107–136.
Jasper, A. V., Hoshin, V. G., Breanndan, Ó. N., and Wiliem, B. (2006). “Real-time data assimilation for operational ensemble streamflow forecasting.” J. Hydrometeorol., 7(3), 548–565.
Ju, Q., Yu, Z. B., Hao, Z. C., Ou, G. X., Zhao, J., and Liu, D. D. (2009). “Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model.” Neurocomput., 72(13–15), 2873–2883.
Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H., and Muttil, N. (2001). “Genetic programming and its application in real-time runoff forecasting.” J. Am. Water Resour. Assoc., 37(2), 439–451.
Kirkby, M. J. (1978). Hillslope hydrology, Wiley, New York.
Komma, J., Blöschl, G., and Reszler, C. (2008). “Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting.” J. Hydrol., 357(3–4), 228–242.
Li, F. G. (2007). Study on flood forecasting and regulating of the Linhuaigang Project on Huaihe River, Hohai Univ., Nanjing, China, 16–20.
Liu, Y., et al. (2012). “Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunites.” Hydrol. Earth Syst. Sci., 16(10), 3863–3887.
Madsen, H., Butts, M. B., Khu, S. T., and Liong, S. Y. (2000). “Data assimilation in rainfall-runoff forecasting.” Proc., 4th Int. Conf. on Hydroinformatics, Cedar Rapids, IA.
Madsen, H., and Skotner, C. (2005). “Adaptive state updating in real-time river flow forecasting-a combined filtering and error forecasting procedure.” J. Hydrol., 308(1–4), 302–312.
Moore, R. J., Bell, V. A., and Jones, D. A. (2005). “Forecasting for flood warning.” External Geophys. Clim. Environ. C. R. Geosci., 337(1–2), 203–217.
Moradkhani, H., DeChant, C. M., and Sorooshian, S. (2012). “Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method.” Water Resour. Res., 48(12), W12520,.
Moradkhani, H., Hsu, K. L., Gupta, H., and Sorooshian, S. (2005).“Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter.” Water Resour. Res., 41(5), W05012.
Qu, S. M., and Bao, W. M. (2003). “Comprehensive correction of real-time flood forecasting.” J. Adv. Water Sci., 14(2), 167–171 (in Chinese).
Refsgaard, J. C. (1997). “Validation and intercomparison of different updating procedures for real-time forecasting.” Nordic Hydrol., 28(2), 65–84.
Wang, W. C., Cheng, C. T., Chau, K. W., and Xu, D. M. (2012). “Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model.” J. Hydroinf., 14(3), 784–799.
Weerts, A. H., and El, S. G. (2006). “Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models.” Water Resour. Res., 42(9), W09403.
World Meteorological Organisation (WMO). (1992). “Simulated real-time intercomparison of hydrological models.”, Geneva.
Yanga, C. S., Li, Q. F., Wen, H., and Cai, T. (2012). “Simulation of soil and water loss in the Upper Huaihe River Basin using the Xinanjiang model.” Int. Conf. on Modern Hydraulic Engineering, Procedia Engineering, 28, 501–505.
Yao, C., Li, Z. J., Yu, Z. B., and Zhang, K. (2012). “A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information.” J. Hydrol., 468–469, 47–62.
Yapo, P. O., Gupat, H. V., and Sorooshian, S. (1996). “Automatic calibration of conceptual rainfall-runoff model: Sensitivity to calibration data.” J. Hydrol., 181(1–4), 23–48.
Yu, P. S., and Chen, S. T. (2005). “Updating real-time flood forecasting using a fuzzy rule-based model.” Hydrol. Sci. J., 50(2), 265–278.
Zhao, C., Hong, H. S., Bao, W. M., and Zhang, L. P. (2008). “Robust recursive estimation of auto-regressive updating model parameters for real-time flood forecasting.” J. Hydrol., 349(3–4), 376–382.
Zhao, R. J. (1992). “The Xinanjiang model applied in China.” J. Hydrol., 135(1–4), 371–381.
Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 4 • April 2014
Pages: 747 - 756
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Oct 28, 2012
Accepted: May 16, 2013
Published online: May 18, 2013
Discussion open until: Oct 18, 2013
Published in print: Apr 1, 2014
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