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

Abstract

[1] 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.

Multivariate hydrological data assimilation of soil moisture and groundwater head

“…This indicates that the unrealistic inter-variable and cross-variable correlations may exist in the model ensemble. In a similar study, Camporese et al (2009b) showed the EnKF assimilation of surface soil moisture can actually improve the saturated zone and assimilation of groundwater head can also improve surface soil moisture, where the saturated and unsaturated zones are based on solving the 3-D Richards equation for the entire subsurface.…”

“…Xie andment Tool (SWAT), with updating of multiple states and parameters including runoff, soil moisture and evapotranspiration. Camporese et al (2009b) used EnKF in the CATHY (CATchment HYdrology) model with coupled surface and subsurface flow, to assimilate groundwater head and stream discharge. Rasmussen et al (2015) assimilated the same variables using the ensemble transform Kalman filter (ETKF) with the MIKE SHE model.…”

Improving the representation of hydrologic processes in Earth System Models

“…With this method, the water table simulated by the saturated zone is consistent with the soil moisture profile. This class of methods can dynamically describe return flow, regional groundwater circulation, groundwater discharge, and saturation excess runoff [see also Camporese et al, 2009;Camporese et al, 2010].…”

Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes

“…Moreover, it is still not clear how the ensemble size should scale with the system dimension to achieve adequate estimates (Reichle et al, 2002a;Camporese et al, 2009). In case of linear systems, the solution of EnKF converges to that of SKF as the ensemble size increases (Evensen, 2003).…”

“…It has become a popular choice for data assimilation in traditional hydrological applications like the estimation of streamflow (Moradkhani et al, 2005;Clark et al, 2008;Weerts and El Serafy, 2006;Xie and Zhang, 2010), land surface energy fluxes (Dunne and Entekhabi, 2006;Pipunic et al, 2008) and soil moisture (Reichle and Koster, 2003;Reichle et al, 2007;De Lannoy et al, 2007). It has also been applied in subsurface models based on the numerical solver of the Richards equation (Das and Mohanty, 2006;Huang et al, 2007;Camporese et al, 2009).…”