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

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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
    Center for Climate Change Adaptation, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, 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
    Center for the Planetary Health and Innovation Science (PHIS), The IDEC Institute, Hiroshima University, Hiroshima 739-8529, Japan)

  • Lena Hobohm

    (Institute for Environmental Systems Sciences, University of Graz, Universitätsplatz 3, 8010 Graz, Austria)

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.

Suggested Citation

  • Joško Trošelj & Han Soo Lee & Lena Hobohm, 2023. "Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations," Sustainability, MDPI, vol. 15(18), pages 1-33, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13897-:d:1242848
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    References listed on IDEAS

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    1. Mariusz Starzec & Sabina Kordana-Obuch & Daniel Słyś, 2023. "Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area," Sustainability, MDPI, vol. 15(10), pages 1-43, May.
    2. Nga Thi Thanh Pham & Quang Hong Nguyen & Anh Duc Ngo & Hang Thi Thu Le & Cong Tien Nguyen, 2018. "Investigating the impacts of typhoon-induced floods on the agriculture in the central region of Vietnam by using hydrological models and satellite data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 189-204, May.
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    4. Waleed A. Hammood & Ruzaini Abdullah Arshah & Salwana Mohamad Asmara & Hussam Al Halbusi & Omar A. Hammood & Salem Al Abri, 2021. "A Systematic Review on Flood Early Warning and Response System (FEWRS): A Deep Review and Analysis," Sustainability, MDPI, vol. 13(1), pages 1-24, January.
    5. Sheng-Chi Yang & Tsun-Hua Yang & Ya-Chi Chang & Cheng-Hsin Chen & Mei-Ying Lin & Jui-Yi Ho & Kwan Tun Lee, 2020. "Development of a Hydrological Ensemble Prediction System to Assist with Decision-Making for Floods during Typhoons," Sustainability, MDPI, vol. 12(10), pages 1-20, May.
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    7. Binquan Li & Zhongmin Liang & Qingrui Chang & Wei Zhou & Huan Wang & Jun Wang & Yiming Hu, 2020. "On the Operational Flood Forecasting Practices Using Low-Quality Data Input of a Distributed Hydrological Model," Sustainability, MDPI, vol. 12(19), pages 1-16, October.
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