基于多源降水融合驱动的WRF-Hydro模型在中小河流洪水预报中的适用性

从提高驱动数据(降水)的质量和时空分辨率出发，评估了基于混合地理加权回归截尾函数(MGWR-BI)多源降水融合算法的有效性，以及融合降水对WRF-Hydro模型计算结果的影响。将融合降水数据用于WRF-Hydro模型中进行子午河流域的洪水预报，并与站点实测降水数据进行比较，结果表明，融合降水的精度高于原始CMORPH卫星降尺度降水，融合降水数据驱动WRF-Hydro模型比CMORPH卫星降尺度降水数据能更好地预报与模拟洪水事件，WRF-Hydro模型具有中小河流洪水预报的潜在优势。

Applicability of WRF-Hydro model based by multi-source precipitation merging in flood forecasting for small and medium-sized watersheds

This study aims to improve the accuracy of flood simulation and forecasting based on improving the quality and spatiotemporal resolution of driven data (precipitation) in small and medium-sized watersheds. The utility of a multi-source precipitation merging method based on the mixed geographically weighted regression and MGWR-BI function was evaluated, and the effect of the merged precipitation to the calculation result of the WRF-Hydro model was studied. The merged precipitation data was used in the WRF-Hydro model for the flood forecasting in the Ziwuhe Basin, and the results were compared to the observation data of station. The comparison results show that the merged precipitation data have substantial higher quality than the downscaling CMORPH satellite data by comparing to the ground observations. Flood events are better captured by the WRF-Hydro model driven by the merged precipitation data than the downscaling CMORPH satellite data for small and medium-sized watersheds. Therefore, the WRF-Hydro model with merged precipitation data has a potential advantage of flood simulation and forecasting for small and medium-sized watersheds.