基于降水情景选取的多源土壤湿度同化研究

沙金霞; 石洪源; 仇亚琴; 杜军凯; 郝春沣; 吕向林; 董颢; 毛海睿

doi:10.13928/j.cnki.wrahe.2026.05.010

水利水电技术（中英文） ›› 2026, Vol. 57 ›› Issue (5) : 121 -133. DOI: 10.13928/j.cnki.wrahe.2026.05.010

水文水资源

基于降水情景选取的多源土壤湿度同化研究

沙金霞 ¹ ,
石洪源 ¹^,² ,
仇亚琴 ² ,
杜军凯 ² ,
郝春沣 ² ,
吕向林 ² ,
董颢 ² ,
毛海睿 ²

作者信息 +

Research on multi-source soil moisture assimilation based on precipitation scenario selection

Jinxia SHA ¹ ,
Hongyuan SHI ¹^,² ,
Yaqin QIU ² ,
Junkai DU ² ,
Chunfeng HAO ² ,
Xianglin LYU ² ,
Hao DONG ² ,
Hairui MAO ²

Author information +

文章历史 +

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摘要

【目的】数据同化方法能将实测土壤湿度融入水文模型以提高模拟过程精度。然而当缺乏实测数据验证时，在某些降水条件下同化单一非实测土壤湿度数据可能引入更多的模拟偏差。为此，提出一种基于降水情景选取的多源土壤湿度同化思路。【方法】以洪家塔站点上游流域为例，基于WEP-L模型耦合集合卡尔曼滤波算法构建同化方案。结合流域降水资料划分大雨、中雨、小雨和无雨四种情景，通过相对误差为评价指标评价三组土壤湿度数据在不同降水情景下的同化效果，并基于相应降水情景下同化评价较优数据的策略，构建了优选方案SM4-1。【结果】结果显示，方案SM4-1能够改善不同降水情景下的径流模拟效果，纳什系数提高了0.032,相对误差降低了17.4%,大雨径流系数误差与全年径流系数误差分别较原方案减小了4.06%、17.38%。【结论】结果表明，基于降水情景选取的多源土壤湿度同化方法能够改善对应降雨情景下土壤湿度大小及分布的合理性，具有优化径流模拟效果、改善模型精度的应用潜力。

Abstract

[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.