Assessment and projection of high and low precipitation extremes simulated by COSMO-CLM4.8 for four small river catchments in Hesse, Germany

Huebener, H.; Stoffels, Benno; Brahmer, G.

doi:10.1127/metz/2016/0708

Cited by 1 publication

(1 citation statement)

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“…Hereafter, the abbreviation ERAI-CCLM (ERA20C-CCLM) is used for the CCLM run in which ERA-Interim (ERA-20C) data were used as initial and boundary conditions. CCLM has already proved its skill to simulate extreme precipitation associated with major river floods in Europe (e.g., Kelemen et al, 2016;Huebener et al, 2016). 3 www.cosmo-model.org 4 www.euro-cordex.net…”

Section: Dynamical Downscalingmentioning

confidence: 99%

Towards the Development of a Pan-European Stochastic Precipitation Dataset

Kautz

Ehmele

Ludwig

et al. 2019

Preprint

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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.

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Section: Dynamical Downscalingmentioning

confidence: 99%