Combined spatial and Kalman filter estimation of optimal soil hydraulic properties

A method for determining optimal parameters for a field-scale hydraulic con ductivity function is presented and tested on soil moisture and matric pote ntial data measured at several locations in a field drainage experiment. Th e change in moisture content over time at the individual locations is model ed using Richards' equation, and an optimization for the hydraulic conducti vity parameters is performed using a merit function derived from the Kalman filter, which allows consideration of measurement and process noise. The s patial correlation among the different measurement points is explicitly tak en into account using the covariance between points in the calculation of t he process noise covariance matrix. It is shown that the standard deviation of the effective hydraulic conductivity function estimated by the Kalman f ilter method applied to all measurements is significantly less than the sta ndard deviations estimated by simple averaging of the parameters derived us ing other methods applied to the individual point moisture time series.