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Coupling typhoon rainfall forecasting with overland-flow modeling for early warning of inundation
Authors:Tsung-Yi Pan  Lung-Yao Chang  Jihn-Sung Lai  Hsiang-Kuan Chang  Cheng-Shang Lee  Yih-Chi Tan
Institution:1. Center for Weather Climate and Disaster Research, National Taiwan University, 158 Chow Shan Rd., Taipei, 106, Taiwan, ROC
2. Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan, ROC
3. Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei, Taiwan, ROC
4. Hydrotech Research Institute, National Taiwan University, Taipei, Taiwan, ROC
5. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan, ROC
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.
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