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    Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

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    Name:
    Si_et_al-2019-Water_Resources_ ...
    Size:
    4.083Mb
    Format:
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    Description:
    Final Published Version
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    Author
    Si, Wei
    Gupta, Hoshin V.
    Bao, Weimin
    Jiang, Peng cc
    Wang, Wenzhuo
    Affiliation
    Univ Arizona, Dept Hydrol & Atmospher Sci
    Issue Date
    2019-09-02
    Keywords
    improved DSRC
    real-time flood forecasting
    rainfall heterogeneity
    spatially distributed rainfall error estimation
    ridge estimation
    operational hydrology
    
    Metadata
    Show full item record
    Publisher
    AMER GEOPHYSICAL UNION
    Citation
    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/10.1029/2019WR025520
    Journal
    WATER RESOURCES RESEARCH
    Rights
    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 2019
    ISSN
    0043-1397
    DOI
    10.1029/2019wr025520
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1029/2019wr025520
    Scopus Count
    Collections
    UA Faculty Publications

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