Groundwater recharge is the main forcing of regional groundwater flow. In traditional partial‐differential‐equation (pde)‐based models that treat aquifers as separate compartments, groundwater recharge needs to be defined as a boundary condition or it is a coupling condition to other compartments. Integrated models that treat the vadose and phreatic zones as a continuum allow for a more sophisticated calculation of subsurface fluxes, as feedbacks between both zones are captured. However, they do not contain an explicit groundwater‐recharge term so it needs to be estimated by post‐processing. Groundwater recharge consists of changes in groundwater storage and of the flux crossing the water table, which can be calculated based on hydraulic gradients. We introduce a method to evaluate the change of groundwater storage by a time‐cumulative water balance over the depth section of water table fluctuations, avoiding the use of a specific yield. We demonstrate the approach first by a simple 1‐D vertical model that does not allow for lateral outflow and illustrates the ambiguity of computing groundwater recharge by different methods. We then apply the approach to a 3‐D model with a complex topography and subsurface structure. The latter example shows that groundwater recharge is highly variable in space and time with notable differences between regional and local estimates. Local heterogeneity of topography or subsurface properties results in complex redistribution patterns of groundwater. In fully integrated models, river‐groundwater exchange flow may severely bias the estimate of groundwater recharge. We, therefore, advise masking out groundwater recharge at river locations.

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<p>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.<br /><span>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.</span></p>

<p>We test the improvement of flux predictions with data assimilation (DA) in a coupled land surface/subsurface model. We present results of DA experiments in an idealized testcase with an extent of 1km x 5km x 50m. Our model considers multiple heterogeneous soil units, different plant functional types and a sophisticated topographical design chosen to induce lateral flow and rivers at specific areas. We use TSMP-PDAF to couple the land-surface model CLM and the subsurface/surface flow model ParFlow with the DA framework PDAF. We use a Localized Ensemble Kalman Filter (LEnKF) with an ensemble of 93 members. We consider uncertainty in the atmosphere, soil properties and initial conditions by different atmospheric forcings, distinct heterogeneous soil parameter distributions and an individual spinup for each ensemble member. The ensemble, which has a horizontal grid resolution of 40m, is updated with virtual measurements from a high resolution (10m) reference model.<br>In the scope of this work, we address the impact of updating different state variables (soil moisture and pressure head) on groundwater recharge, lateral subsurface flow, surface runoff, and evapotranspiration. While surface runoff and evapotranspiration directly depend on pressure head and soil moisture, subsurface flow depends on pressure head gradients. For groundwater recharge, our estimate depends on groundwater storage changes (which can directly be enforced by the updates during DA) as well as subsurface flow. To investigate if DA can directly improve these fluxes, we run multiple experiments with different observation frequencies and localization radii. Further, we investigate if there are improvements in the fluxes during open loop forecasting periods subsequent to DA.</p>