基于DHSVM的遥感土壤水分数据同化对水文过程关键要素的影响OA北大核心CSTPCD

This study aims to investigate the effects of EnKF-based multi-source remote sensing soil moisture data assimilation on key elements of hydrological processes in the context of the Xiangjiang River Basin.SMAP and ASCAT remote sensing soil moisture data were selected for assimilation into the Distributed Hydrology Soil Vegetation Model(DHSVM)using the Ensemble Kalman Filter(EnKF)algorithm.By comparing and analyzing the simulated runoff and soil moisture results of three simula-tion scenarios:the non-assimilated model,ASCAT-DHSVM,and SMAP-DHSVM,the impact of multi-source remote sensing soil moisture data assimilation on hydrological variable simulation was evaluat-ed.The results indicate that within the Xiangjiang River Basin,the ASCAT assimilation scheme outper-forms the SMAP assimilation scheme in both streamflow simulation and soil moisture simulation.In terms of streamflow simulation,the ASCAT assimilation scheme exhibits an overall improvement in Nash-Sutcliffe Efficiency(NSE)(NSE=0.677)compared to the non-assimilation scheme(NSE=0.662),along with a decrease of 1.7 percentage points in BIAS.In terms of soil moisture simulation,compared to the non-assimilation scheme,the ASCAT assimilation scheme shows an overall improvement of 10%in NSE value,a decrease of 4.7 percentage points in BIAS,and a decrease of 12.5%in RMSE.In con-trast,the improvement in the overall simulation performance of the SMAP assimilation scheme is statis-tically insignificant.The results underscore the efficacy of assimilating remote sensing soil moisture da-ta,particularly through the ASCAT assimilation scheme,in enhancing hydrological variable simulation.These findings hold significant implications for water resource management in the Xiangjiang River Ba-sin.