Assimilation of cosmic-ray soil moisture observations into an integrated land surface-subsurface model

The coupling of land surface and subsurface models might improve the overall predictive accuracy of hydrological and atmospheric models. In general, predictions with such highly parameterized models are associated with a considerable degree of uncertainty due to the uncertain initial conditions and the poorly known subsurface and vegetation properties. An important variable of such systems is the soil moisture content which influences the partitioning of energy fluxes at the land surface and is highly variable in space and time. Information on soil moisture content is therefore essential for improving the prediction capability of integrated models, e.g., with respect to the estimation of latent and sensible heat fluxes from the land surface, regional water budgets and river discharge, and for constraining the associated uncertainties of these variables. Soil moisture data can be derived from a variety of sensor types which operate at different spatial scales ranging from point measurements like TDR sensors (dm scale) over medium range measurements like cosmic ray probes (ha scale) to large scale measurements like satellite remote sensing products (km scale). Especially cosmic ray soil moisture data are well suited for medium scale distributed hydrological models because their footprint closely matches the typical spatial discretization of such models. We will present first results on the assimilation of cosmic ray soil moisture data into an integrated hydrological model of the Rur catchment (Germany). The integrated model consists of the land surface model CLM and the groundwater model Parflow which are coupled via state and flux variables by the coupling software Oasis-MCT. Soil moisture data for assimilation are available from 10 cosmic ray stations which are distributed over the whole catchment area. These data are assimilated into the subsurface model (Parflow) with the Ensemble Kalman Filter and the value of this assimilation is monitored through cross validation with surface flux measurements and discharge data