TY - JOUR
T1 - Improved Dynamic System Response Curve Method for Real-Time Flood Forecast Updating
AU - Si, Wei
AU - Gupta, Hoshin V.
AU - Bao, Weimin
AU - Jiang, Peng
AU - Wang, Wenzhuo
N1 - Funding Information:
We thank Editor Martyn Clark, the Associate Editor, and three reviewers for their constructive comments that helped to substantially improve the manuscript. We thank Zhao Yang for assistance provided throughout the process of this work. The first author was partially supported by the National Natural Science Foundation of China (51709077), National Postdoctoral Foundation of China (2017M611679), Postdoctoral Foundation of Jiangsu Province (1701019A), and the Open Research Fund of Yellow River Sediment Key Laboratory (201804). The second author acknowledges partial support by the Australian Centre of Excellence for Climate System Science (CE110001028). The data and model program used in this paper are available on HydroShare at this site (http://www.hydroshare.org/resource/7b64413c4fb1488ca614b5dd0e9d2ead). Data used in this study are also available upon request from the corresponding author.
Publisher Copyright:
© 2019. American Geophysical Union. All Rights Reserved.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - improved DSRC
KW - operational hydrology
KW - rainfall heterogeneity
KW - real-time flood forecasting
KW - ridge estimation
KW - spatially distributed rainfall error estimation
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U2 - 10.1029/2019WR025520
DO - 10.1029/2019WR025520
M3 - Article
AN - SCOPUS:85071922057
VL - 55
SP - 7493
EP - 7519
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 9
ER -