@article{MenzelBronstertBuergeretal.2000, author = {Menzel, Lucas and Bronstert, Axel and B{\"u}rger, Gerd and Krysanova, Valentina}, title = {Environmental change scenarios and flood responses in the Elbe catchment (Germany)}, year = {2000}, language = {en} } @article{MenzelNiehoffBuergeretal.2002, author = {Menzel, Lucas and Niehoff, Daniel and B{\"u}rger, Gerd and Bronstert, Axel}, title = {Climate change impacts on river flooding : a modelling study of three meso-scale catchments}, year = {2002}, language = {en} } @article{BronstertNiehoffBuerger2002, author = {Bronstert, Axel and Niehoff, Daniel and B{\"u}rger, Gerd}, title = {Effects of climate and land-use change on storm runoff generation : present knowledge and modelling capabilities}, year = {2002}, language = {en} } @article{BuergerReusserKneis2009, author = {B{\"u}rger, Gerd and Reusser, Dominik and Kneis, David}, title = {Early flood warnings from empirical (expanded) downscaling of the full ECMWF Ensemble Prediction System}, issn = {0043-1397}, doi = {10.1029/2009wr007779}, year = {2009}, language = {en} } @article{BuergerSobieCannonetal.2013, author = {B{\"u}rger, Gerd and Sobie, S. R. and Cannon, A. J. and Werner, A. T. and Murdock, T. Q.}, title = {Downscaling extremes an intercomparison of multiple methods for future climate}, series = {Journal of climate}, volume = {26}, journal = {Journal of climate}, number = {10}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0894-8755}, doi = {10.1175/JCLI-D-12-00249.1}, pages = {3429 -- 3449}, year = {2013}, abstract = {This study follows up on a previous downscaling intercomparison for present climate. Using a larger set of eight methods the authors downscale atmospheric fields representing present (1981-2000) and future (2046-65) conditions, as simulated by six global climate models following three emission scenarios. Local extremes were studied at 20 locations in British Columbia as measured by the same set of 27 indices, ClimDEX, as in the precursor study. Present and future simulations give 2 x 3 x 6 x 8 x 20 x 27 = 155 520 index climatologies whose analysis in terms of mean change and variation is the purpose of this study. The mean change generally reinforces what is to be expected in a warmer climate: that extreme cold events become less frequent and extreme warm events become more frequent, and that there are signs of more frequent precipitation extremes. There is considerable variation, however, about this tendency, caused by the influence of scenario, climate model, downscaling method, and location. This is analyzed using standard statistical techniques such as analysis of variance and multidimensional scaling, along with an assessment of the influence of each modeling component on the overall variation of the simulated change. It is found that downscaling generally has the strongest influence, followed by climate model; location and scenario have only a minor influence. The influence of downscaling could be traced back in part to various issues related to the methods, such as the quality of simulated variability or the dependence on predictors. Using only methods validated in the precursor study considerably reduced the influence of downscaling, underpinning the general need for method verification.}, language = {en} } @article{DoblerBuergerStoetter2013, author = {Dobler, C. and B{\"u}rger, Gerd and St{\"o}tter, J.}, title = {Simulating future precipitation extremes in a complex Alpine catchment}, series = {Natural hazards and earth system sciences}, volume = {13}, journal = {Natural hazards and earth system sciences}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1561-8633}, doi = {10.5194/nhess-13-263-2013}, pages = {263 -- 277}, year = {2013}, abstract = {The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.}, language = {en} } @unpublished{Buerger2014, author = {B{\"u}rger, Gerd}, title = {Comment on "Bias correction, quantile mapping, and downscaling: revisiting the inflation issue"}, series = {Journal of climate}, volume = {27}, journal = {Journal of climate}, number = {4}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0894-8755}, doi = {10.1175/JCLI-D-13-00184.1}, pages = {1819 -- 1820}, year = {2014}, abstract = {In a recent paper, Maraun describes the adverse effects of quantile mapping on downscaling. He argues that when large-scale GCM variables are rescaled directly to small-scale fields or even station data, genuine small-scale covariability is lost and replaced by uniform variability inherited from the larger scales. This leads to a misrepresentation mainly of areal means and long-term trends. This comment acknowledges the former point, although the argument is relatively old, but disagrees with the latter, showing that grid-size long-term trends can be different from local trends. Finally, because it is partly incorrectly addressed, some clarification is added regarding the inflation issue, stressing that neither randomization nor inflation is free of unverified assumptions.}, language = {en} } @article{BuergerHeistermannBronstert2014, author = {B{\"u}rger, Gerd and Heistermann, Maik and Bronstert, Axel}, title = {Towards subdaily rainfall disaggregation via Clausius-Clapeyron}, series = {Journal of hydrometeorology}, volume = {15}, journal = {Journal of hydrometeorology}, number = {3}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {1525-755X}, doi = {10.1175/JHM-D-13-0161.1}, pages = {1303 -- 1311}, year = {2014}, abstract = {Two lines of research are combined in this study: first, the development of tools for the temporal disaggregation of precipitation, and second, some newer results on the exponential scaling of heavy short-term precipitation with temperature, roughly following the Clausius-Clapeyron (CC) relation. Having no extra temperature dependence, the traditional disaggregation schemes are shown to lack the crucial CC-type temperature dependence. The authors introduce a proof-of-concept adjustment of an existing disaggregation tool, the multiplicative cascade model of Olsson, and show that, in principal, it is possible to include temperature dependence in the disaggregation step, resulting in a fairly realistic temperature dependence of the CC type. They conclude by outlining the main calibration steps necessary to develop a full-fledged CC disaggregation scheme and discuss possible applications.}, language = {en} } @article{SunyerHundechaLawrenceetal.2015, author = {Sunyer, M. A. and Hundecha, Y. and Lawrence, D. and Madsen, H. and Willems, Patrick and Martinkova, M. and Vormoor, Klaus Josef and B{\"u}rger, Gerd and Hanel, M. and Kriauciuniene, J. and Loukas, A. and Osuch, M. and Yucel, I.}, title = {Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe}, series = {Hydrology and earth system sciences : HESS}, volume = {19}, journal = {Hydrology and earth system sciences : HESS}, number = {4}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-19-1827-2015}, pages = {1827 -- 1847}, year = {2015}, abstract = {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.}, language = {en} } @article{HundechaSunyerLawrenceetal.2016, author = {Hundecha, Yeshewatesfa and Sunyer, Maria A. and Lawrence, Deborah and Madsen, Henrik and Willems, Patrick and B{\"u}rger, Gerd and Kriauciuniene, Jurate and Loukas, Athanasios and Martinkova, Marta and Osuch, Marzena and Vasiliades, Lampros and von Christierson, Birgitte and Vormoor, Klaus Josef and Yuecel, Ismail}, title = {Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe}, series = {Journal of hydrology}, volume = {541}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.08.033}, pages = {1273 -- 1286}, year = {2016}, abstract = {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.}, language = {en} }