Coupling typhoon rainfall forecasting with overland-flow modeling for early warning of inundation

Tsung-Yi Pan, Lung-Yao Chang, Jihn-Sung Lai, Hsiang-Kuan Chang, Cheng-Shang Lee and Yih-Chi Tan ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2014, vol. 70, issue 3, 1763-1793

Abstract: Taiwan suffers from an average of three or four typhoons annually, and the inundation caused by the heavy precipitation that is associated with typhoons frequently occurs in lowlands and floodplains. Potential inundation maps have been widely used as references to set up non-structural strategies for mitigating flood hazards. However, spatiotemporal rainfall distributions must be addressed to improve the accuracy of inundation forecasting for emergency response operations. This study presents a system for 24-h-ahead early warning of inundation, by coupling the forecasting of typhoon rainfall with the modeling of overland flow. A typhoon rainfall climatology model (TRCM) is introduced to forecast dynamically the spatiotemporal rainfall distribution based on typhoon tracks. The systematic scheme for early warning of inundation based on the spatiotemporal downscaling of rainfall and 2D overland-flow modeling yields not only the extent of inundation, but also the time to maximum inundation depth. The scheme is superior to traditional early warning method referring to the maximum extent and depth of inundation determined from conditional uniform rainfall. Analytical results show that coupling TRCM with an overland-flow model yields satisfactory inundation hydrographs for warning of the extent and peak time of inundation. This study also shows that the accuracy of forecasting spatiotemporal rainfall patterns determines the performance of inundation forecasting, which is critical to emergency response operations. Copyright Springer Science+Business Media B.V. 2014

Keywords: Typhoon rainfall forecasting; Overland-flow model; Inundation; Early warning; Spatiotemporal downscaling; Emergency response operation (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11069-011-0061-9

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