Articles | Volume 21, issue 3
https://doi.org/10.5194/hess-21-1769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-1769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Mar 2017
Research article |
| 27 Mar 2017
A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
Rohini Kumar
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
Matthias Cuntz
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
INRA, Université de Lorraine, UMR1137 Ecologie et Ecophysiologie
Forestières, Champenoux, France
Helmholtz Centre for Environmental Research – UFZ, Department
Computational Hydrosystems, Permoserstraße 15, 04318 Leipzig, Germany
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Saved (final revised paper)
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Short summary
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of hydro-meteorological variables for Germany. Here we describe the derivation of national-scale parameter sets and analyze the uncertainty of the estimated hydrologic variables (focusing on the parametric uncertainty). Our study highlights the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of...
