Soil water storage (SWS) is a crucial parameter for vegetation restoration and a variety of hydrological processes in semi-arid regions. Its dynamics reflect the integrated influence of different factors operating at various intensities and scales. In this study, we investigated the scale-specific correlations between SWS and six selected environmental factors along a 1340-m long sampling transect in a semi-arid catchment using multivariate empirical mode decomposition (MEMD). The six factors were bulk density, elevation, clay, silt, sand, and soil organic carbon content. The multivariate data series of the SWS and factors of interest were separated into seven intrinsic mode functions (IMFs) and residue. IMF1, IMF3, and IMF5 were identified as the primary scales according to the percentage of total variance (about 50%) in SWS under different soil moisture conditions. The scale-specific correlations between SWS and the corresponding factors varied with scale but were irrelevant to soil moisture condition. SWS at each IMF and residue could be estimated using the associated scale-specific environmental factors at the same IMF or residue. SWS at the measurement scale was satisfactorily estimated by summarizing all the predicted IMFs and residue, which outperformed measurement-scale SWS estimation based on multivariate stepwise linear regression between SWS and the associated factors. Soil moisture condition could affect the relative importance of the environmental factors in overall SWS estimation. In general, soil properties were the dominant predictor of SWS under different soil moisture conditions. MEMD provides a useful opportunity to deal with scale-specific correlations between various variables.

土壤储水量（SWS）是半干旱地区植被恢复和各种水文过程的关键参数。其动态反映了不同因素以不同强度和规模运作的综合影响。在本研究中，我们使用多元经验模态分解 (MEMD) 调查了半干旱流域中 1340 米长的采样样带上 SWS 与六个选定环境因素之间的特定尺度相关性。这六个因素是容重、海拔、粘土、淤泥、沙子和土壤有机碳含量。SWS 的多变量数据系列和感兴趣的因子被分成七个固有模式函数(IMF) 和残差。根据不同土壤水分条件下SWS的总方差百分比（约50%），确定IMF1、IMF3和IMF5为初级尺度。SWS与相应因子之间的尺度相关性随尺度变化而变化，但与土壤湿度条件无关。每个 IMF 和残留物的 SWS 可以使用相同 IMF 或残留物的相关规模特定环境因素来估计。通过总结所有预测的 IMF 和残差，令人满意地估计了测量尺度的 SWS，其性能优于基于 SWS 与相关因素之间的多元逐步线性回归的测量尺度 SWS 估计。土壤湿度状况可能会影响环境因素在整体 SWS 估算中的相对重要性。一般来说，土壤性质是不同土壤湿度条件下 SWS 的主要预测因素。MEMD 提供了一个有用的机会来处理各种变量之间特定于尺度的相关性。