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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2019-77
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-77
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Feb 2019

21 Feb 2019

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This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Towards the Development of a Pan-European Stochastic Precipitation Dataset

Lisa-Ann Kautz1, Florian Ehmele1, Patrick Ludwig1, Hilke S. Lentink1, Fanni D. Kelemen1,a, Martin Kadlec2, and Joaquim G. Pinto1 Lisa-Ann Kautz et al.
  • 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 2Impact Forecasting, Aon, Prague, Czech Republic
  • anow at: Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

Abstract. Heavy precipitation leading to widespread river floods are one of the main natural hazards affecting Central Europe. Since extreme precipitation events associated with devastating floods have long return periods, long-term datasets are needed to adequately quantify the frequency and intensity of these events. As long-term observations of precipitation across Europe are rare and not homogeneous in space nor time, they are generally not suitable to run hydrological models. In the present study, a combined approach is presented on how to generate a consistent precipitation dataset based on dynamical downscaling and post-processing statistics. Focus is given to five river catchments in Central Europe: Upper Danube, Elbe, Oder, Rhine, and Vistula. Reanalysis data are dynamically downscaled with a regional climate model and bias corrected towards observations. Empirical quantile mapping was identified as one of the most suitable methods to correct the bias in model precipitation. For most of the top ten precipitation events of large European river catchments, bias correction led to clear improvements towards the raw model data. However, results for Western European rivers (e.g., Rhine) are typically better than for Eastern European rivers (e.g., Vistula), which may also be associated with observational gaps for the latter. Two examples of severe river floods are presented in more detail: the Rhine river flood in winter 1995 and the flood in the Upper Danube and Vistula in June 2009. While the former was already well presented without bias correction, for the latter, bias correction improved underestimated precipitation amounts in the Upper Danube but not in the Vistula catchment. In conclusion, this method can be applied to other extensive datasets towards the development of a Pan-European stochastic precipitation dataset.

Lisa-Ann Kautz et al.

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Lisa-Ann Kautz et al.

Lisa-Ann Kautz et al.

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Short summary
To quantify the flooding risk for Europe it is necessary to run hydrological models. As input for these models, a consistent stochastic precipitation dataset is needed. In the present study, a combined approach is presented on how to generate such a dataset based on dynamical downscaling and subsequent bias correction. Empirical quantile mapping was identified as suitable bias correction method as it led to improvements for specific severe river floods as well as in a climatological perspective.
To quantify the flooding risk for Europe it is necessary to run hydrological models. As input...
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