Model verification with CRNS proximal sensing in semi-arid regions.

Researchers revealed the potential for improving model estimates of evapotranspiration (ET) and soil water content (SWC) in Europe's water scarce regions. For these semi-arid regions I will improve the meso-scale hydrological model (mHM).

An inter-model comparison of state-of-the-art hydrological simulations helps to discredit mHM model structures that account for insufficient ET and SWC estimates. I plan to advance these model structures, in order to find a soil-vegetation-athmosphere transfer scheme as simple as possible and as complex as necessary. Eventually, mHM should estimate ET and SMC accurately in semi-arid regions with an optimal complex model structure. To winkle out this intermediate complexity I apply Baysian model averaging (BMA). I calibrate and verify the model based on data from cosmic ray neutron sensing (CRNS), a novel non-invasive method for estimating soil moisture at the meso-scale, latent heat fluxes at eddy-covariance sites, and remote sensing products. This data pin down model uncertainty and thous enable an accurate parameterization of advanced model process descriptions.

My work is incorporated in the UFZ Integrated Project T34: Water Scarcity.

Besides my Ph.D. I am actively involved in following research projects:

Conference contributions (first author)