Real-Time Equivalent Conversion Correction on River Stage Forecasting with Manning’s Formula
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 1
Abstract
Channel geometry can affect the performance of the autoregressive (AR) model on river stage correction. To evaluate the effect, three ideal models were established by using the one-dimensional (1D) hydrodynamic model with imagined sets of data in assumed channels (i.e., rectangle, V-type, and complex type). To evaluate the performance of the AR model on river stage and discharge correction in a real system with unavailable observed discharges, an equivalent conversion technique by using the Manning’s formula was proposed to convert stages into equivalent conversion discharges. The results from the ideal models and real systems in the Fuchun River show that the AR model performs better on river discharge correction than on river stage correction, which is also verified at Shaowu station in the Minjiang River with available observed discharges. It indicates that the correlation of discharges is better than that of river stages. To transform corrected discharges to corrected stages, a transformation technique based on an assumed rating curve calculated by the Manning’s formula was employed. Furthermore, by combining the equivalent conversion technique, the AR model and the transformation technique, an equivalent conversion correction (ECC) method is proposed to improve the AR model performance on river stage correction. The results show that the ECC method can obtain better accuracy on river stage correction than the AR model.
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Acknowledgments
The study was supported by the Eleven-Five Program for Science and Technology of China (Grant Nos. UNSPECIFIED2006BAC05B02 and UNSPECIFIED2008BAB29B08-02), Development Program for Changjiang Scholars and Innovation Team (Grant No. UNSPECIFIEDIRT0717), and Program for the Ministry of Education and State Administration of Foreign Experts Affairs, People’s Republic of China (Grant No. UNSPECIFIEDB08408). Finally, the writers would like to thank the anonymous reviewers for their valuable comments.
References
Bao, W. M., Zhang, X. Q., and Qu, S. M. (2009). “Dynamic correction of roughness in the hydrodynamic model.” J. Hydrodynam., 21(2), 255–263.
Bjerklie, D. M., Dingman, S. L., and Bolster, C. H. (2005). “Comparison of constitutive flow resistance equations based on the Manning and Chezy equations applied to natural rivers.” Water Resour. Res., 41, W11502.
Blackburn, J., and Hicks, F. E. (2002). “Combined flood routing and flood level forecasting.” Can. J. Civ. Eng., 29, 64–75.
Campolo, M., Andreussi, P., and Soldati, A. (1999). “River flood forecasting with a neural network model.” Water Resour. Res., 35(4), 1191–1197.
Franchini, M., and Lamberti, P. (1994). “A flood routing Muskingum type simulation and forecasting model based on level data alone.” Water Resour. Res., 30(7), 2183–2196.
Guganesharajah, K., Lyons, D. J., Parsons, S. B., and Lloyd, B. J. (2006). “Influence of uncertainties in the estimation procedure of floodwater level.” J. Hydraul. Eng., 132(10), 1052–1060.
He, H. M., Yu, Q., Zhou, J., Tian, Y. Q., and Chen, R. F. (2008). “Modeling complex flood flow evolution in the middle Yellow River basin, China.” J. Hydrol., 353, 76–92.
Hsu, M. H., Fu, J. C., and Liu, W. C. (2006). “Dynamic routing model with real-time roughness updating for flood forecasting.” J. Hydraul. Eng., 132(6), 605–619.
Lamberti, P., and Pilati, S. (1996). “Flood propagation models for real-time forecasting.” J. Hydrol., 175, 239–265.
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, 302–312.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models. Part 1: Discussion of principles.” J. Hydrol., 10(3), 282–290.
Neal, J. C., Atkinson, P. M., and Hutton, C. W. (2007). “Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements.” J. Hydrol., 336, 401–415.
Qu, S. M., and Bao, W. M. (2003). “Comprehensive correction of real-time flood forecasting.” Adv. Water Sci., 14(2), 167–172 (in Chinese).
Qu, S. M., Bao, W. M., Shi, P., Yu, Z. B., and Jiang, P. (2009). “Water-stage forecasting in a multitributary tidal river using a bidirectional Muskingum method.” J. Hydrol. Eng., 14(12), 1299–1308.
Romanowicz, R. J., Young, P. C., Beven, K. J., and Pappenberger, F. (2008). “A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting.” Adv. Water Resour., 31, 1048–1056.
Wong, T. S. W. (2008). “Effect of channel shape on time of travel and equilibrium detention storage in channel.” J. Hydrol. Eng., 13(3), 189–196.
Wu, C. L., Chau, K. W., and Li, Y. S. (2009a). “River stage prediction based on a distributed support vector regression.” J. Hydrol., 358, 96–111.
Wu, X. L., Wang, C. H., Chen, X., and Xiang, X. H. (2009b). “Kalman filtering correction in real-time forecasting with hydrodynamic model.” J. Hydrodynam., 20(3), 391–397.
Yen, B. C. (2002). “Open channel flow resistance.” J. Hydraul. Eng., 128(1), 20–39.
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Information
Published In
Journal of Hydrologic Engineering
Volume 16 • Issue 1 • January 2011
Pages: 1 - 9
Copyright
© 2011 ASCE.
History
Received: Jun 16, 2009
Accepted: May 6, 2010
Published online: Dec 15, 2010
Published in print: Jan 2011
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