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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-428
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-428
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Aug 2018

Research article | 15 Aug 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

Do climate-informed extreme value statistics improve the estimation of flood probabilities in Europe?

Eva Steirou1, Lars Gerlitz1, Heiko Apel1, Xun Sun2,3, and Bruno Merz1,4 Eva Steirou et al.
  • 1Section Hydrology, GFZ German Research Center for Geosciences, Potsdam, 14473, Germany
  • 2Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, 200241, Shanghai, China
  • 3Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
  • 4Institute of Earth and Environmental Science, University of Potsdam, Potsdam, 14476, Germany

Abstract. The link between streamflow extremes and climatology has been widely studied during the last decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed Generalized Extreme Value distribution (GEV) to about 600 streamflow records in Europe for each of the standard seasons, i.e. to winter, spring, summer and autumn maxima, and compare it with the classical GEV with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), the East Atlantic / West Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the Polar-Eurasian pattern (POL).

It is found that for a high percentage of stations the climate-informed model is preferred to the classical model, a result that provides evidence towards an improvement of the estimation of flood probabilities. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV for 44% of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as Northwest Scandinavia and the British Isles, variations of the climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years. Plots of extreme streamflow with a probability of exceedance of 0.01 indicate that the deviation between the classical and climate-informed analysis concerns single years but can also persist for longer time periods.

Eva Steirou et al.
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Status: final response (author comments only)
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Eva Steirou et al.
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