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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-2019-40
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-40
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 12 Mar 2019

Submitted as: research article | 12 Mar 2019

Review status
A revised version of this preprint is currently under review for the journal HESS.

Uncovering the shortcomings of a weather typing based statistical downscaling method

Els Van Uytven1, Jan De Niel1, and Patrick Willems1,2 Els Van Uytven et al.
  • 1Hydraulics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Leuven, Belgium
  • 2Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium

Abstract. In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.

Els Van Uytven et al.

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Els Van Uytven et al.

Els Van Uytven et al.

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Short summary
In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology.
In recent years many methods for statistical downscaling of the climate model outputs have been...
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