Understanding the key factors that influence soil moisture estimation using the unscented weighted ensemble Kalman filter

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 K-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.