Flood Forecasting Model Using the High-resolution Rainfall Products from QPESUMS
Date Issued
2008
Date
2008
Author(s)
Huang, Peng-Hao
Abstract
Taiwan located in the subtropical area, and the climate often influenced by the monsoon, typhoon and ocean current. The average annual rainfall of 2510 mm approximately concerntrates on the plum rains and typhoon seasons during May to October. In addition, the natural environmental factor of the high terrain and short river course results in severe flood inundation at the downstream area, which causes disastrous losses of life and the economy. This study aims to develop a flood forecasting model by utilizing the meteorological and hydrological information for flood mitigations and emergency resposes.he prediction of rainfall is quite important for development of flood forecasting model, and its accuracy concerns the consequences of river stage simulation. In recent years, with the progress of observation techniques and analysis system, Taiwan finished the island wide Doppler radar network by Central Weather Bureau since 2002, and cooperated with National Severe Storm Laboratory (NSSL) to develop the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system, which has improved the monitoring and prewarning weather system. The study built a rainfall-stage forecasting model using artificial neural networks (ANN), comparing the accuracy of stage forecasting with 1 hr / 1~2hr and 1~3hr ahead leading rainfall. Moreover, the radar grid in the space has higher resolution. For comparison, this study uses Thiessen Polygons Method to divide several control polygon areas for each surface rainfall gauge station, while attempting to link up the radar precipitation to rainfall station indirectly. Then the precipitation in the control polygon area is calculated by the average from the QPESUMS. Because the lack of precise quantitative precipitation forecasting (QPF) information at present, the lead time shifting from quantitative precipitation estimation (QPE) is taken place to calculate the increasing and decreasing rate of the average rainfall from radar observation. After rainfall forecasting estimation, the effects of stage forecasting with / without rainfall forecasting among rainfall-stage forecasting model, initial stage correction for forecasting model, and integrated flood forecasting model with ANN are investigated in this study. With the consideration of the complete data of each station and radar data, the three recent typhoon events are simulated to verify the efficiency of the forecasting model. he results reveal that it’s certainly helpful to improve the accuracy of rainfall-stage forecasting model by inputting the future information of rainfall, especially for the one-hr ahead leading rainfall. It’s feasible to estimate the future information of rainfall by using QPESUMS. With the future rainfall, the forecasting stage profile from flood forecasting model is better agreement to the observational value. For a flash flood or storm, this present model can provide the reliable and satisfactory river-stages forecasting information.
Subjects
Artificial Neural Network
rainfall forecasting
initial stage of forecasting
integrated flood forecasting model with ANN
flood forecasting
SDGs
Type
thesis
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