Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

Joško Trošelj (), Han Soo Lee and Lena Hobohm
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Joško Trošelj: Transdisciplinary Science and Engineering Program, Graduate School of Advance Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 738-8529, Japan
Han Soo Lee: Transdisciplinary Science and Engineering Program, Graduate School of Advance Science and Engineering, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 738-8529, Japan
Lena Hobohm: Institute for Environmental Systems Sciences, University of Graz, Universitätsplatz 3, 8010 Graz, Austria

Sustainability, 2023, vol. 15, issue 18, 1-33

Abstract: This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications.

Keywords: hydrograph reproducibility; extreme river discharge; 2018 western Japan floods; physically distributed hydrological model; SCE-UA optimization; real-time forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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