LAUSR

Abstract Obtaining reliable soil hydraulic properties is essential for correct simulations of soil water content (SWC), which is a key variable in countless applications such as agricultural management, soil remediation, aquifer protection, etc. Soil hydraulic properties can be measured in the laboratory; however, the procedures are laborious and costly, and may provide estimates different from those observed in the field. An alternative approach is to obtain soil hydraulic properties using a soil water flow model in conjunction with SWC monitoring data. The goal of the present study was to analyze the efficiency of obtaining hydraulic properties utilizing data assimilation (DA) based on the Ensemble Kalman Filter method. Two soil textures in homogeneous soil profiles, and four climatic conditions were considered; observations of soil moisture data were synthetically generated using HYDRUS-1D and subsequently perturbed by the application of the conditional multivariate normal distribution. When observed SWC varied in relatively narrow range as a consequence of the forcing imposed by dry climate atmospheric boundary conditions, data assimilation provided sets of properties that led to good Richards model performance, with the RMSE below 0.02 and/or R2 above 0.8 after a period of just 100 days and above 0.98 after a period of three years in all climate/soil conditions. However, the closeness of parameters from DA to the parameters used to generate the synthetic data depended on weather conditions and soil properties. One year was adequate to obtain reliable soil hydraulic properties with data assimilation.