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 - TY - JOUR A1 - Merz, Bruno A1 - Apel, Heiko A1 - Dung Nguyen, Viet-Dung A1 - Falter, Daniela A1 - Guse, Björn A1 - Hundecha, Yeshewatesfa A1 - Kreibich, Heidi A1 - Schröter, Kai A1 - Vorogushyn, Sergiy T1 - From precipitation to damage BT - a coupled model chain for spatially coherent, large-scale flood risk assessment JF - Global flood hazard : applications in modeling, mapping and forecasting N2 - Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale. Y1 - 2018 SN - 978-1-119-21788-6 SN - 978-1-119-21786-2 U6 - https://doi.org/10.1002/9781119217886.ch10 SN - 0065-8448 VL - 233 SP - 169 EP - 183 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Sunyer, M. A. A1 - Hundecha, Yeshewatesfa 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 - Sunyer, M. A. A1 - Hundecha, Yeshewatesfa 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 - JOUR A1 - Nguyen, Viet-Dung A1 - Merz, Bruno A1 - Hundecha, Yeshewatesfa A1 - Haberlandt, Uwe A1 - Vorogushyn, Sergiy T1 - Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany JF - International journal of climatology : a journal of the Royal Meteorological Society N2 - In this work, we present a comprehensive evaluation of a stochastic multi-site, multi-variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km(2). The regional weather generator, which is based on a first-order multi-variate auto-regressive model, is setup using 53-year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi-day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma-Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non-precipitation variables. The weather generator can be used to generate synthetic event footprints for large-scale trans-basin flood risk assessment. KW - correlation KW - extreme KW - flood KW - large‐ scale KW - multi‐ variate KW - weather generator Y1 - 2021 U6 - https://doi.org/10.1002/joc.7107 SN - 0899-8418 SN - 1097-0088 VL - 41 IS - 10 SP - 4933 EP - 4956 PB - Wiley CY - Chichester [u.a.] ER -