We compare two ensemble Kalman-based methods to estimate the hydraulic conductivity field of an aquifer from data of hydraulic and tracer tomographic experiments: (i) the Ensemble Kalman Filter (EnKF) and (ii) the Kalman Ensemble Generator (KEG). We generated synthetic drawdown and tracer data by simulating two pumping tests, each followed by a tracer test. Parameter updating with the EnKF is performed using the full transient signal. For hydraulic data, we use the standard update scheme of the EnKF with damping, whereas for concentration data, we apply a restart scheme, in which solute transport is resimulated from time zero to the next measurement time after each parameter update. In the KEG, we iteratively assimilate all observations simultaneously, here inverting steady-state heads and mean tracer arrival times. The inversion with the dampened EnKF worked well for the transient pumping-tests, but less for the tracer tests. The KEG produced similar estimates of hydraulic conductivity but at significantly lower costs. We conclude that parameter estimation in well-defined hydraulic tests can be done very efficiently by iterative ensemble Kalman methods, and ambiguity between state and parameter updates can be completely avoided by assimilating temporal moments of concentration data rather than the time series themselves.

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

<p>Estimating states and fluxes of the water cycle with terrestrial system models needs a large amount of input data, including soil and vegetation parameters, resulting in large uncertainties in model predictions. Assimilation of pressure head and/or soil moisture data can better constrain states and parameters of a terrestrial system model. Here we assimilate pressure head data and soil moisture data in a terrestrial system model over the Neckar catchment (13928 km<sup>2</sup>) with a spatial horizontal resolution of 800 m. We use the Terrestrial System Modeling Platform (TSMP), which consists of an atmospheric model component (not used in this work), the Community Land Model version 3.5 (CLM3.5), and the subsurface hydrological model Parflow, coupled by OASIS. TSMP is coupled to the Parallel Data Assimilation Framework (PDAF), which allows the assimilation of land surface and subsurface observations to estimate the model states and parameters. In this work the localized Ensemble Kalman Filter (LEnKF) was used to update hydraulic head, soil moisture and/or saturated hydraulic conductivity by assimilating hydraulic head or in situ soil moisture observations for a period of one year. Ensembles of soil properties, leaf area index and atmospheric forcings were generated. The ensemble of atmospheric forcings considered correlations among four variables, and spatio-temporal correlations of the atmospheric variables using a geostatistical procedure. The characterization of the water table depth and river discharge without data assimilation and for different scenarios of pressure head and soil moisture data assimilation were compared.</p>