Soil moisture modifies the state of the atmosphere and thus plays a major role in the climate system. Its spatial distribution is strongly modulated by the underlying orography. Yet the vertical transport of soil water and especially the generation of groundwater runoff at the bottom of the soil column are currently treated in a crude way in most atmospheric and climate models. This potentially leads to large biases in near‐surface temperatures during midlatitude summertime conditions, when the soils may dry out. Here we present a new formulation for groundwater and runoff formation. It is based on Richards equation, allows for saturated aquifers, includes a slope‐dependent groundwater discharge, and enables a subgrid‐scale treatment of the underlying orography. The proposed numerical implementation ensures a physically consistent treatment of the water fluxes in the soil column, using ideas from flux‐corrected transport methodologies. An implementation of this formulation into TERRA_ML, the land surface model of the regional climate model of the COnsortium for Small‐scale MOdeling (COSMO) in CLimate Mode (CCLM), is validated both in idealized and real‐case simulations. Idealized simulations demonstrate the important role of the lower boundary condition at the bottom of the soil column and display a physically meaningful recharge and discharge of the saturated zone. Validation against measurements at selected stations shows an improved seasonal evolution of soil water content. Finally, decade‐long climate simulations over Europe exhibit a realistic representation of the groundwater distribution across continental scales and mountainous areas, an improved annual cycle of surface latent heat fluxes, and as a consequence reductions of long‐standing biases in near‐surface temperatures in semiarid regions.

Abstract. Land surface models are important for improving our understanding of the Earth system. They are continuously improving and becoming better in representing the different land surface processes, e.g., the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more data, e.g., from new satellite products and new in situ measurement sites, with increasingly higher quality for a range of important variables of the Earth system. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in recent decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this study, we present the development of the new interface between PDAF and CLM5. This newly implemented coupling integrates the PDAF functionality into CLM5 by modifying the CLM5 ensemble mode to keep changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5-PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5-PDAF system to provide a basis for improved regional to global land surface modeling by enabling the assimilation of globally available observational data.

Abstract. Land surface models (LSM) are an important tool for advancing our knowledge of the Earth system. LSM are constantly improved to represent the various terrestrial processes in more detail. High quality data, freely available from various observation networks, are providing being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSM with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for thirteen forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in-situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point scale measurements from, however for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that state-of-the-art LSM such as CLM5 still suffer from uncertainties in the representation of soil hydrological processes in forests, e.g. deep root water uptake, or highly uncertain vegetation parameters.

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<p>The hydrological observatory for the Rur catchment (2400 km<sup>2</sup>) in Germany is highly equipped including 15 Cosmic Ray Neutron Sensors (CRNS) to measure soil moisture content, 6 eddy covariance stations with measurement of land-atmosphere exchange fluxes and further micrometeorological observations, and additional monitoring stations for river discharge and groundwater levels, amongst others. In addition, 3 intensive research sites at representative locations have been implemented with distributed soil moisture and temperature monitoring. These measurements allow for a better local verification of terrestrial model predictions, and the improvement of model predictions by model-data fusion methods. We did a series of studies on the assimilation of observations from the Rur observatory to improve predictions with the Terrestrial Systems Modelling Platform (TSMP), which models water, energy, carbon and nitrogen cycles of the land surface and subsurface. The data assimilation algorithm was in most cases the Ensemble Kalman Filter, but also the Particle Filter and Markov Chain Monte Carlo were used. Assimilated observations included soil moisture (from FDR-probes, CRNS or remote sensing), groundwater levels and net ecosystem exchange. We found that assimilation improved the characterization of the measured variable, also at verification locations. However, states and fluxes of variables that were not assimilated, such as evapotranspiration, often were not better characterized. The results suggest the importance of the joint assimilation of measurements for different variables, including remotely sensed information and vegetation information.</p>