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Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulation and forecasting by coupling the Karst-Liuxihe model

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Frontiers of Earth Science 2022年第2期16卷 381-400页

作者： Ji LI Daoxian YUAN Yuchuan SUN Jianhong LI Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China Key Laboratory of Karst Dynamics(MNR&Guangxi)Institute of Karst Geology CAGSGuilin 541004China

Long-term rainfall data are crucial for flood simulations and forecasting in karst ***,in karst areas,there is often a lack of suitable precipitation data available to build distributed hydrological models to forecast karst *** precipitation forecasts(QPFs)and estimates(QPEs)could provide rational methods to acquire the available precipitation data for karst ***,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 *** study served two main *** 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 *** 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 *** rainfall forecasting results showed that the precipitation distributions of the 2 weather models were very similar to the observed rainfall ***,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 *** flood simulations achieved based on the postprocessed WRF QPF and PERSIANN-CCS QPEs coupled with the Karst-Liuxihe model were much improved over previous *** 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.

关键词： WRF QPF PERSIANN-CCS QPEs the Karst-Liuxihe model flood simulation and forecasting karst river basin

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