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Improved Flood Forecasting in Basins with no Precipitation Station: Constrained Runoff Correction Using Multiple Satellite Precipitation Products


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Created: Aug 02, 2021 at 6:10 a.m.
Last updated: Oct 26, 2021 at 9:31 a.m.
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Abstract

Satellite-based precipitation products (SPPs) with short latencies provide a new opportunity for flood forecasting in basins with no precipitation station. However, the larger uncertainties associated with such 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 evaluation, respectively. The necessity of imposing constraints using multiple SPPs is demonstrated by comparing CRC-M against 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 1) relatively reliable SPPs, and 2) wider constraint ranges. Overall, the CRC-M method can result in accurate and stable flood forecasts in basins with no precipitation station, without the shortening of leading time and the need for increased model complexity (i.e., the numbers of model parameters).

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How to Cite

Dou, Y., L. Ye, H. V. Gupta, H. Zhang, A. Behrangi, H. Zhou (2021). Improved Flood Forecasting in Basins with no Precipitation Station: Constrained Runoff Correction Using Multiple Satellite Precipitation Products, HydroShare, http://www.hydroshare.org/resource/20f34ab024d94ce19333414b77626bf2

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