Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone

Reliable estimates of soil water content and groundwater levels are essential in evaluating water availability for plants and as drinking water and thus both subsurface components (vadose zone and groundwater) are commonly monitored. Such measurements can be used for data assimilation in order to improve predictions of numerical subsurface flow models. Within this work, we investigate to what extent measurements from one subsurface component are able to improve predictions in the other one.
For this purpose, we utilize idealized test cases at a subcatchment scale using a Localized Ensemble Kalman Filter to update the water table height and soil moisture at certain depths with measurements taken from a numerical reference model. We do joint, as well as single component updates. We test strongly coupled data assimilation, which implies utilizing correlations between the subsurface components for updating the ensemble and compare it to weakly coupled data assimilation. We also update soil hydraulic parameters and examine the role of their heterogeneity with respect to data assimilation. We run simulations with both a complex 3D model (using TSMP-PDAF) as well as a more simplified and computationally efficient 2.5D model, which consists of multiple 1D vadose-zone columns coupled iteratively with a 2D groundwater-flow model. In idealized settings, such as homogeneous subsurface structures, we find that predictions in one component consistently benefit from updating the other component.