Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.
指紋深入研究「Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model」主題。共同形成了獨特的指紋。
- 1 已完成
1/08/19 → 31/07/20