基于降水情景选取的多源土壤湿度同化研究OA

[Objective]Data assimilation method can integrate observed soil moisture into hydrological models to enhance the accuracy of the simulation process.However,in the absence of observed data for validation,assimilating a single source of unobserved soil moisture under certain precipitation conditions may introduce greater simulation deviations.To address this,a multi-source soil moisture assimilation approach based on precipitation scenario selection is proposed.[Methods]Taking the upstream watershed of Hongjiata station as an example,an assimilation scheme was constructed by coupling the WEP-L model with the Ensemble Kalman Filter algorithm.Four precipitation scenarios-heavy rain,moderate rain,light rain,and no rain-were classified using watershed precipitation data.The assimilation performance of three soil moisture datasets was evaluated under different precipitation scenarios using relative error as the evaluation indicator.Based on the strategy of assimilating the better-performing data under the corresponding precipitation scenarios,an optimized scheme(SM4-1)was developed.[Results]The results showed that scheme SM4-1 improved runoff simulation performance across different precipitation scenarios,with the Nash-Sutcliffe efficiency coefficient increasing by 0.032 and the relative error decreasing by 17.4％.The errors in runoff coefficients for heavy rainfall events and annual periods were reduced by 4.06％and 17.38％,respectively,compared to the original scheme.[Conclusion]The results indicate that the proposed multi-source soil moisture assimilation method based on precipitation scenario selection enhances both the magnitude and spatial distribution of soil moisture under corresponding rainfall conditions.This approach demonstrates potential for optimizing runoff simulation performance and enhancing overall model accuracy.