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 - TY - JOUR A1 - Hundecha, Yeshewatesfa A1 - Sunyer, Maria A. A1 - Lawrence, Deborah A1 - Madsen, Henrik A1 - Willems, Patrick A1 - Bürger, Gerd A1 - Kriauciuniene, Jurate A1 - Loukas, Athanasios A1 - Martinkova, Marta A1 - Osuch, Marzena A1 - Vasiliades, Lampros A1 - von Christierson, Birgitte A1 - Vormoor, Klaus Josef A1 - Yuecel, Ismail T1 - Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe JF - Journal of hydrology N2 - The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km(2) in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35-60% of the total variance. (C) 2016 Elsevier B.V. All rights reserved. KW - Flooding KW - Statistical downscaling KW - Regional climate models KW - Climate change KW - Europe Y1 - 2016 U6 - https://doi.org/10.1016/j.jhydrol.2016.08.033 SN - 0022-1694 SN - 1879-2707 VL - 541 SP - 1273 EP - 1286 PB - Elsevier CY - Amsterdam ER -