| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 107 === In recent years, due to climate change and extreme weather events, the rainfall intensity has increased. This phenomenon also has the same trend in summer typhoons, which increases the frequency of flooding disasters. At present, the flooding simulation usually sets up a single rainfall station for model simulation. Although this method is faster and simpler to construct, the single rainfall station may not be able to represent the rainfall situation of a whole area. In addition, the drainage method in the metropolitan area usually collects rainwater from the inlet and the side ditch. If the garbage and the fallen leaves accumulate due to heavy rainfall, the water inlet will not reach the designed water collecting effect, which may cause the metropolitan area the product of flooding.
In this study, we investigated the effects of rainfall data from different spatial distributions on flooding simulations in urban areas, using rainfall observation data and radar echo data to simulate flooding. Comparison of single rainfall station, multi-rainfall station Thiessen''s Method rainfall setting, multi-rainfall station Thiessen''s Method Partition rainfall setting, Inverse Distance Weighted rainfall and radar echo rainfall. Through the methods of Accuracy (ACC), Probability of Detection (POD), Threat Score (TS), Precision or Predictive value (PPV), it is estimated that what kind of rainfall setting has better performance for flooding simulation in the urban area. Then set different clogging rates to compare the clogging rate values closer to the real city blockage, and compare the clogging rates with the Taipei city flooding map.
In this study, Jieshang drainage and Hougang drainage basin in Shilin Distrinct, Taipei, is selected as the study area. Two short duration and one long duration rainfall events are used for simulation and analysis. Due to the closeness to real rainfall collection phenomena, the simulated results from Inverse Distance Weighted rainfall setting has a better agreement whether the duration of rainfall event is short or long. And set different clogging rate to get the clogging factor 20% as the closest to the real urban area clogging rate. It is also comparable to the flooding simulation map of Taipei City. The result of setting a 20% clogging rate can make the indicators have better performance.
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