A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty
Matthias Zink, Rohini Kumar, Matthias Cuntz, and Luis Samaniego
Helmholtz Centre for Environmental Research – UFZ, Department Computational Hydrosystems, Permoserstrasse 15, 04318 Leipzig, Germany
Received: 26 Aug 2016 – Accepted for review: 23 Sep 2016 – Discussion started: 26 Sep 2016
Abstract. Long term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture and generated runoff at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed runoff in 222 catchments, which are not used for model calibration. The mean (standard deviation) of the ensemble median NSE estimated for these catchments is 0.68 (0.09) for daily discharge simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.: A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-443, in review, 2016.