Towards a new generation of high-resolution meteorological input data for small-scale hydrologic modeling

Current and future challenges of hydrologic sciences are to accurately predict and assess climate-driven impacts on water resources for the relevant scales of planning. However, process-based small-scale hydrologic modeling is data demanding and large uncertainties exist in data-sparse areas. The aim of our study was to test the applicability of the COSMO-DE analysis data (COSMO-DE-A) for hydrologic modeling. COSMO-DE-A data are a new meteorological data set with high temporal and spatial resolution that originates from the German Weather Service data assimilation system using the COSMO-DE weather prediction model. We collected field parameters in a small (10 km2) mountainous catchment in the Upper Middle Rhine Valley (west Germany) to parameterize the static boundary conditions of the hydrologic model CATFLOW. We evaluated error components of hourly COSMO-DE-A fields in comparison to interpolated hourly meteorological station data (i.e. reference data), applied two bias-correction methods for precipitation (i.e. linear correction method and quantile–quantile mapping technique), calibrated (in a 36 ha large subcatchment) and tested (in another 48 ha large subcatchment) the CATFLOW model using the reference data for input, and compared stream flow predictions using uncorrected and bias-corrected COSMO-DE-A data for input. Moreover, we compared soil moisture and latent heat flux from COSMO-DE-A with values simulated by CATFLOW. Relative error between COSMO-DE-A precipitation and interpolated precipitation is ca. 50%. Other climatic variables from COSMO-DE-A are almost unbiased, though errors for global radiation and temperature are autocorrelated. Nash and Sutcliffe efficiencies accounted for 0.83 and 0.33 for the simulated vs. observed stream flow of the calibration and test catchment, respectively. The use of uncorrected COSMO-DE-A precipitation leads to poor performances of the hydrologic model. In contrast, if either of both bias-correction methods is applied to COSMO-DE-A precipitation, predicted hydrographs and soil moisture are almost the same as if interpolated reference data is used. Soil moisture and latent heat flux are simulated consistently by the independent models COSMO-DE-A and CATFLOW. We conclude that COSMO-DE-A data are suitable for hydrologic modeling of longer periods (e.g. seasons), provided that bias correction of precipitation is done prior to model application. Further research is required to test the regional and temporal stability of bias-correction terms.