Accurate quantification of soil moisture contributes significantly to an understanding of land surface processes. In-situ observable soil moisture data are often sparsely distributed, and model performance is influenced by many factors. In this study, 14 numerical experimental schemes about the effects of uncertainties in multiple factors (soil property, time step, assimilation interval, precipitation, soil layer thickness and initial value) on soil moisture estimation were evaluated based on the unscented weighted ensemble Kalman filter (UWEnKF) and a one-dimensional vertical water flow model at the ELBARA field site in the Maqu monitoring network in the upper reaches of the Yellow River, China. The experiments showed that soil properties had little effect on model parameters (e.g., saturated soil moisture content, saturated soil hydraulic conductivity, saturated soil matric potential) in either the horizontal or vertical direction using the model numerical solving scheme adopted, and thus had little effect on soil moisture estimation. Using only the observed ${\mathrm{K}}_{\text{sat}}$may lead to better soil moisture predictions. Reducing the simulation time step has limited impact on soil moisture estimation. The effects of precipitation on soil moisture estimations varied due to overestimation or underestimation of soil moisture content in different soil layers, and differences in soil layer thicknesses led to uncertainty in soil moisture estimation. The model accurately predicted the change trend of soil moisture if the initial values were reasonable. UWEnKF performed well in terms of improving soil moisture estimations despite the uncertainty of many factors in data assimilation system, and performed better with high assimilation frequency (i.e., small assimilation interval). Thus, UWEnKF is an effective and practical technique for soil moisture assimilation whatever the uncertainty of multiple factors is.

土壤水分的准确量化对于了解地表过程具有重要意义。原位可观测土壤水分数据往往分布稀疏，模型性能受多种因素影响。本研究基于无味加权集合卡尔曼滤波器，对14个数值试验方案进行了多因素（土壤性质、时间步长、同化间隔、降水量、土层厚度和初始值）的不确定性对土壤水分估计的影响（ UWEnKF)和黄河上游玛曲监测网ELBARA现场的一维垂直水流模型。实验表明，土壤特性对模型参数（例如，饱和土壤含水量、饱和土壤导水率、饱和土壤基质势）在水平或垂直方向使用采用的模型数值求解方案，因此对土壤水分估计影响很小。仅使用观察到的${\mathrm{钾}}_{\text{坐}}$可能会导致更好的土壤水分预测。减少模拟时间步长对土壤水分估计的影响有限。由于不同土层土壤水分含量的高估或低估，降水对土壤水分估计的影响各不相同，土壤层厚度的差异导致土壤水分估计的不确定性。如果初始值合理，该模型可以准确预测土壤水分的变化趋势。尽管数据同化系统中存在许多因素的不确定性，UWEnKF 在改进土壤水分估计方面表现良好，并且在同化频率高（即同化间隔小）下表现更好。因此，无论多因素的不确定性如何，UWEnKF 都是一种有效且实用的土壤水分同化技术。