Improved Flood Forecasting in Basins With No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products
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Water Resources Research - 2021 ...
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Final Published Version
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Department of Hydrology & Atmospheric Sciences, University of ArizonaIssue Date
2021
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John Wiley and Sons IncCitation
Dou, Y., Ye, L., Gupta, H. V., Zhang, H., Behrangi, A., & Zhou, H. (2021). Improved Flood Forecasting in Basins With No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products. Water Resources Research.Journal
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Copyright © 2021. 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
Satellite-based precipitation products (SPPs) with short latencies provide a new opportunity for flood forecasting in basins with no precipitation stations. However, the larger uncertainties associated with these near-real-time SPPs can influence the accuracy of flood forecasts. Here, we propose a real-time updating method, referred to as “Constrained Runoff Correction using Multiple SPPs” (CRC-M). The method is based on the hypothesis that the range of runoff volumes computed using different near-real-time SPPs provides an indication of the approximate range in which the true runoff volume lies. Accordingly, a constrained runoff correction is performed using the discharge observed at the basin outlet within this range. Evaluation using real data indicates that the new method performs well with Nash–Sutcliffe (NS) values of 0.85 and 0.87 during calibration and validation, respectively. The necessity of imposing constraints using multiple SPPs is demonstrated by comparing CRC-M with 2 controls, referred to as “Unconstrained Runoff Correction using Single SPP” (URC-S) and “Constrained Runoff Correction using Single SPP with perturbations” (CRC-S). Experiments indicate that the key factors that result in good performance are (a) relatively reliable SPPs and (b) wider constraint ranges. Overall, the CRC-M method can result in accurate and stable flood forecasts in basins with no precipitation stations, without the shortening of leading time and the need for increased model complexity (i.e., the numbers of model parameters). © 2021. American Geophysical Union. All Rights Reserved.Note
6 month embargo; first published: 12 November 2021ISSN
0043-1397Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1029/2021WR029682