Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating
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Si_et_al-2019-Water_Resources_ ...
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Final Published Version
Affiliation
Univ Arizona, Dept Hydrol & Atmospher SciIssue Date
2019-09-02Keywords
improved DSRCreal-time flood forecasting
rainfall heterogeneity
spatially distributed rainfall error estimation
ridge estimation
operational hydrology
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AMER GEOPHYSICAL UNIONCitation
Si, W., Gupta, H. V., Bao, W., Jiang, P.,& Wang, W. (2019). Improved dynamic system response curve method for real‐time flood forecast updating. Water Resources Research, 55, 7493-7519 https://doi.org/10.1029/2019WR025520Journal
WATER RESOURCES RESEARCHRights
Copyright © 2019. American Geophysical Union. All Rights Reserved.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.Note
6 month embargo; published online: 2 September 2019ISSN
0043-1397Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1029/2019wr025520
