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Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

  • Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of theInformation on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.show moreshow less

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Metadaten
Author:M. A. Sunyer, Y. Hundecha, D. Lawrence, H. Madsen, Patrick WillemsORCiDGND, M. Martinkova, Klaus VormoorGND, Gerd BürgerORCiDGND, Martin HanelORCiD, J. Kriaučiūnienė, A. Loukas, M. Osuch, I. Yücel
URN:urn:nbn:de:kobv:517-opus4-408920
DOI:https://doi.org/10.25932/publishup-40892
ISSN:1866-8372
Parent Title (English):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (512)
Document Type:Postprint
Language:English
Date of first Publication:2019/01/17
Year of Completion:2015
Publishing Institution:Universität Potsdam
Release Date:2019/01/17
Tag:UK; assessing uncertainties; basin; bias correction; climate-change impacts; model output; multimodel ensemble; scenarios; simulations; variability
Issue:512
Pagenumber:21
Source:Hydrology and Earth System Sciences 19 (2015), pp. 1827-1847 DOI: 10.5194/hess-19-1827-2015
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Publication Way:Open Access
Grantor:Copernicus
Licence (German):License LogoCreative Commons - Namensnennung, 4.0 International
Notes extern:Bibliographieeintrag der Originalveröffentlichung/Quelle