TY - JOUR A1 - Sunyer, M. A. A1 - Hundecha, Y. A1 - Lawrence, D. A1 - Madsen, H. A1 - Willems, Patrick A1 - Martinkova, M. A1 - Vormoor, Klaus Josef A1 - Bürger, Gerd A1 - Hanel, M. A1 - Kriauciuniene, J. A1 - Loukas, A. A1 - Osuch, M. A1 - Yucel, I. T1 - Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe JF - Hydrology and earth system sciences : HESS N2 - 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 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. Y1 - 2015 U6 - https://doi.org/10.5194/hess-19-1827-2015 SN - 1027-5606 SN - 1607-7938 VL - 19 IS - 4 SP - 1827 EP - 1847 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Sunyer, M. A. A1 - Hundecha, Y. A1 - Lawrence, D. A1 - Madsen, H. A1 - Willems, Patrick A1 - Martinkova, M. A1 - Vormoor, Klaus Josef A1 - Bürger, Gerd A1 - Hanel, Martin A1 - Kriaučiūnienė, J. A1 - Loukas, A. A1 - Osuch, M. A1 - Yücel, I. T1 - Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 512 KW - climate-change impacts KW - model output KW - assessing uncertainties KW - multimodel ensemble KW - bias correction KW - simulations KW - scenarios KW - variability KW - basin KW - UK Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-408920 SN - 1866-8372 IS - 512 ER -