In this study the ensemble Kalman filter (EnKF) is implemented in a detailed catchment-scale hydrological model that couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on a digital elevation data for surface water dynamics. In data assimilation, the Kalman filter (KF) updates the system state based on the relative magnitudes of the covariances of both the observations and the model state estimate. EnKF has been demonstrated to be a valid alternative to KF for nonlinear filtering problems, and is based on the approximation of the conditional probability densities of interest using a finite number of randomly generated model trajectories. We describe the implementation of EnKF for our coupled groundwater--surface water model, and will examine issues of robustness and computational efficiency, important for such a detailed numerical model characterized by strong nonlinearities in the pressure--moisture and pressure--conductivity relationships and by complex interactions across the land surface boundary. The implementation is tested for a synthetic soil moisture profile retrieval experiment described in Entekhabi et al. (IEEE Trans. Geosci. Remote Sensing, 1994). In this column experiment surface observations are assimilated to retrieve the true moisture profile starting from a poor estimate of the initial moisture state of the system.

Ensemble Kalman Filter Data Assimilation for a Coupled Model of Surface and Subsurface Flow

CAMPORESE, MATTEO;PUTTI, MARIO;SALANDIN, PAOLO
2006

Abstract

In this study the ensemble Kalman filter (EnKF) is implemented in a detailed catchment-scale hydrological model that couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on a digital elevation data for surface water dynamics. In data assimilation, the Kalman filter (KF) updates the system state based on the relative magnitudes of the covariances of both the observations and the model state estimate. EnKF has been demonstrated to be a valid alternative to KF for nonlinear filtering problems, and is based on the approximation of the conditional probability densities of interest using a finite number of randomly generated model trajectories. We describe the implementation of EnKF for our coupled groundwater--surface water model, and will examine issues of robustness and computational efficiency, important for such a detailed numerical model characterized by strong nonlinearities in the pressure--moisture and pressure--conductivity relationships and by complex interactions across the land surface boundary. The implementation is tested for a synthetic soil moisture profile retrieval experiment described in Entekhabi et al. (IEEE Trans. Geosci. Remote Sensing, 1994). In this column experiment surface observations are assimilated to retrieve the true moisture profile starting from a poor estimate of the initial moisture state of the system.
2006
Eos Trans. AGU, Fall Meet. Suppl.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2431157
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