Application of Radar-based Quantitative Precipitation Estimation on River Flood Forecast
Date Issued
2009
Date
2009
Author(s)
Tsai, Meng-Yuan
Abstract
Taiwan, located on subtropical zone of west Pacific, encounters typhoons around 3.6 times annually in average. The typhoons disaster caused about 17 billions NT dollars loss per year. The most part of loss in typhoons is the flooding in low-lying areas by the torrential rain. Due to the torrential rain, the sudden rise of flood stage will not only destroy water conservancy facilities, but also threaten the life and property of the residents near the riverbank. If the variation trend of flood stage precisely forecast in advance, the flood management agency will take proper actions for response and mitigation before the disaster happens. n recent years, Taiwan made an effort on improving the technique of real-time rainfall observations. After the completion and applications of the Doppler Radar Networks developed from Center Weather Bureau and Water Resources Agency with the cooperation of U.S. National Severe Storm Laboratory, QPESUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensors) makes the technique of real-time spatial rainfall estimation better. At present, QPE (Quantitative Precipitation Estimation) is capable of providing the information of high-resolution rainfall. In order to integrate the information of weather radar rainfall, rainfall-gauges data and flood stage, this research uses ANN (Artificial Neural Networks) to establish a set of simple and fast flood stage forecasting and combine 1-D gradually-varied unsteady flow with feedback routing. Then a set of river flood forecasting model was developed to provide accurate and detailed flood information for Tanshui Basin in typhoon period. The above will be taken as reference for announcing flood alert, evacuation and actions for response and mitigation before the disaster happens.n present research, Typhoons Fung-Wong, Sinlaku and Jangm in 2008 are taken as the model testing. The result shows that the application of QPE makes the stage forecasting closer to observations in river flood routing model and provides reasonable and accurate results for river flood mitigation.
Subjects
QPESUMS system
rainfall forecasting
flood stage forecasting
Artificial Neural Networks
dynamic routing
initial correction
Type
thesis
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