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

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