Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models...Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods.Quantitative precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst areas.Furthermore,coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas.This study served two main purposes.One purpose was to compare the performance of the Weather Research and Forecasting(WRF)QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System(PERSIANN-CCS)QPEs in rainfall forecasting in karst river basins.The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model.The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results.However,the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges,while the quantities forecasted by the PERSIANN-CCS QPEs were smaller.A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models.The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results.In particular,coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting,and a maximum lead time of 96 h is adequate for flood warnings and emergency responses,which is extremely important in flood simulations and forecasting.展开更多
基金This study was supported by the National Science Foundation for Young Scientists of China(No.42101031)Chongqing Natural Science Foundation(No.cstc2021jcyj-msxm0007)+1 种基金the Open Project Program of Guangxi Key Science and Technology Innovation Base on Karst Dynamics(KDL&Guangxi 202009,KDL&Guangxi 202012)the National Natural Science Foundation of China(Grant No.41830648).
文摘Long-term rainfall data are crucial for flood simulations and forecasting in karst regions.However,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst floods.Quantitative precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst areas.Furthermore,coupling a physically based hydrological model with QPFs and QPEs could greatly enhance the performance and extend the lead time of flood forecasting in karst areas.This study served two main purposes.One purpose was to compare the performance of the Weather Research and Forecasting(WRF)QPFs with that of the Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System(PERSIANN-CCS)QPEs in rainfall forecasting in karst river basins.The other purpose was to test the feasibility and effective application of karst flood simulation and forecasting by coupling the WRF and PERSIANN models with the Karst-Liuxihe model.The rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall results.However,the precipitation amounts forecasted by WRF QPF were larger than those measured by the rain gauges,while the quantities forecasted by the PERSIANN-CCS QPEs were smaller.A postprocessing algorithm was proposed in this paper to correct the rainfall estimates produced by the two weather models.The flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous results.In particular,coupling the postprocessed WRF QPF with the Karst-Liuxihe model could greatly extend the lead time of flood forecasting,and a maximum lead time of 96 h is adequate for flood warnings and emergency responses,which is extremely important in flood simulations and forecasting.
