Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow

A sequential data assimilation procedure based on the ensemble Kalman filter (EnKF) is introduced and tested for a process-based numerical model of coupled surface and subsurface flow. The model is based on the three-dimensional Richards equation for variably saturated porous media and a diffusion wave approximation for overland and channel flow. A one-dimensional soil column experiment and a three-dimensional tilted v-catchment test case are presented. A preliminary analysis of the assimilation scheme is undertaken for the one-dimensional test case in order to validate the implementation by comparison with published results and to assess the influence of various factors on the filter's performance. The numerical results suggest robustness with respect to the ensemble size and provide useful information for the more complex tilted v-catchment test case. The assimilation frequency and the effects induced by data assimilation on the surface and/or subsurface system states are then evaluated for the v-catchment experiment using synthetic observations of pressure head and streamflow. The results suggest that streamflow prediction can be improved by assimilation of pressure head and streamflow, either individually or in tandem, whereas assimilation of streamflow data alone does not improve the subsurface system state. In terms of the global system state, i.e., surface and subsurface variables, frequent updates are especially beneficial when assimilating both pressure head and streamflow. Furthermore, it is shown that better evaluation of the subsurface volume resulting from assimilation of head data is crucial for improving subsequent surface response.