@article{Buerger2022,
  author    = {B{\"u}rger, Gerd},
  title     = {A conundrum of trends},
  series = {Journal of hydrology},
  volume    = {609},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2022.127745},
  pages     = {3},
  year      = {2022},
  abstract  = {This comment is meant to reiterate two warnings: One applies to the uncritical use of ready-made (openly available) program packages, and one to the estimation of trends in serially correlated time series. Both warnings apply to the recent publication of Lischeid et al. about lake-level trends in Germany.},
  language  = {en}
}
@article{Buerger2018,
  author    = {B{\"u}rger, Gerd},
  title     = {A counterexample to decomposing climate shifts and trends by weather types},
  series = {International Journal of Climatology},
  volume    = {38},
  journal   = {International Journal of Climatology},
  number    = {9},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0899-8418},
  doi       = {10.1002/joc.5519},
  pages     = {3732 -- 3735},
  year      = {2018},
  abstract  = {The literature contains a sizable number of publications where weather types are used to decompose climate shifts or trends into contributions of frequency and mean of those types. They are all based on the product rule, that is, a transformation of a product of sums into a sum of products, the latter providing the decomposition. While there is nothing to argue about the transformation itself, its interpretation as a climate shift or trend decomposition is bound to fail. While the case of a climate shift may be viewed as an incomplete description of a more complex behaviour, trend decomposition indeed produces bogus trends, as demonstrated by a synthetic counterexample with well-defined trends in type frequency and mean. Consequently, decompositions based on that transformation, be it for climate shifts or trends, must not be used.},
  language  = {en}
}
@article{Buerger2019,
  author    = {B{\"u}rger, Gerd},
  title     = {A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil},
  series = {Quarterly Journal of the Royal Meteorological Society},
  volume    = {146},
  journal   = {Quarterly Journal of the Royal Meteorological Society},
  number    = {726},
  publisher = {WILEY-VCH},
  address   = {Weinheim},
  pages     = {14},
  year      = {2019},
  abstract  = {A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.},
  language  = {en}
}
@misc{Buerger2019,
  author    = {B{\"u}rger, Gerd},
  title     = {A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {726},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52383},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-523835},
  pages     = {16},
  year      = {2019},
  abstract  = {A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the Sao Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.},
  language  = {en}
}
@misc{MurawskiBuergerVorogushynetal.2016,
  author    = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {Can local climate variability be explained by weather patterns?},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {525},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41015},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410155},
  pages     = {24},
  year      = {2016},
  abstract  = {To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.},
  language  = {en}
}
@article{MurawskiBuergerVorogushynetal.2016,
  author    = {Murawski, Aline and B{\"u}rger, Gerd and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin},
  series = {Hydrology and earth system sciences : HESS},
  volume    = {20},
  journal   = {Hydrology and earth system sciences : HESS},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-20-4283-2016},
  pages     = {4283 -- 4306},
  year      = {2016},
  abstract  = {To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.},
  language  = {en}
}
@article{DidovetsKrysanovaBuergeretal.2019,
  author    = {Didovets, Iulii and Krysanova, Valentina and B{\"u}rger, Gerd and Snizhko, Sergiy and Balabukh, Vira and Bronstert, Axel},
  title     = {Climate change impact on regional floods in the Carpathian region},
  series = {Journal of hydrology : Regional studies},
  volume    = {22},
  journal   = {Journal of hydrology : Regional studies},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2214-5818},
  doi       = {10.1016/j.ejrh.2019.01.002},
  pages     = {14},
  year      = {2019},
  abstract  = {Study region: Tisza and Prut catchments, originating on the slopes of the Carpathian mountains. Study focus: The study reported here investigates (i) climate change impacts on flood risk in the region, and (ii) uncertainty related to hydrological modelling, downscaling techniques and climate projections. The climate projections used in the study were derived from five GCMs, downscaled either dynamically with RCMs or with the statistical downscaling model XDS. The resulting climate change scenarios were applied to drive the eco-hydrological model SWIM, which was calibrated and validated for the catchments in advance using observed climate and hydrological data. The changes in the 30-year flood hazards and 98 and 95 percentiles of discharge were evaluated for the far future period (2071-2100) in comparison with the reference period (1981-2010). New hydrological insights for the region: The majority of model outputs under RCP 4.5 show a small to strong increase of the 30-year flood level in the Tisza ranging from 4.5\% to 62\%, and moderate increase in the Prut ranging from 11\% to 22\%. The impact results under RCP 8.5 are more uncertain with changes in both directions due to high uncertainties in GCM-RCM climate projections, downscaling methods and the low density of available climate stations.},
  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}
}
@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{MurawskiVorogushynBuergeretal.2018,
  author    = {Murawski, Aline and Vorogushyn, Sergiy and B{\"u}rger, Gerd and Gerlitz, Lars and Merz, Bruno},
  title     = {Do changing weather types explain observed climatic trends in the rhine basin?},
  series = {Journal of geophysical of geophysical research-atmosheres},
  volume    = {123},
  journal   = {Journal of geophysical of geophysical research-atmosheres},
  number    = {3},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-897X},
  doi       = {10.1002/2017JD026654},
  pages     = {1562 -- 1584},
  year      = {2018},
  abstract  = {For attributing hydrological changes to anthropogenic climate change, catchment models are driven by climate model output. A widespread approach to bridge the spatial gap between global climate and hydrological catchment models is to use a weather generator conditioned on weather patterns (WPs). This approach assumes that changes in local climate are characterized by between-type changes of patterns. In this study we test this assumption by analyzing a previously developed WP classification for the Rhine basin, which is based on dynamic and thermodynamic variables. We quantify changes in pattern characteristics and associated climatic properties. The amount of between- and within-type changes is investigated by comparing observed trends to trends resulting solely from WP occurrence. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years are analyzed. Increasing frequency is found for some patterns associated with high precipitation, although the trend sign highly depends on the considered period. Trends and interannual variations of WP frequencies are related to the long-term variability of large-scale circulation modes. Long-term WP internal warming is evident for summer patterns and enhanced warming for spring/autumn patterns since the 1970s. Observed trends in temperature and partly in precipitation are mainly associated with frequency changes of specific WPs, but some amount of within-type changes remains. The classification can be used for downscaling of past changes considering this limitation, but the inclusion of thermodynamic variables into the classification impedes the downscaling of future climate projections.},
  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{KoehnReichBuerger2019,
  author    = {K{\"o}hn-Reich, Lisei and B{\"u}rger, Gerd},
  title     = {Dynamical prediction of Indian monsoon},
  series = {International Journal of Climatology},
  volume    = {39},
  journal   = {International Journal of Climatology},
  number    = {8},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0899-8418},
  doi       = {10.1002/joc.6039},
  pages     = {3574 -- 3581},
  year      = {2019},
  abstract  = {Ongoing development of dynamical atmosphere-ocean general circulation models keep expectations high regarding seasonal predictions of Indian monsoon rainfall. This study compares past and present skill of four currently operating forecasting systems, CFSv2 from NCEP, ENSEMBLES, System 4 and the newest SEAS5 from ECMWF, by analysing correlations of respective hindcasts with observed all-India summer rainfall. For the common time period 1982-2005, only ENSEMBLES and CFSv2 give significantly skilful forecasts. It is shown that skill is highly dependent on the chosen time period. Especially the intense El Nino of 1997 seems to degrade the predictions, most notably for SEAS4 and SEAS5 which seem to be linked to El Nino too strongly. We show that by discarding that year, a regime shift in the 1990s is no longer visible. Overall, we observe a convergence of skill towards the present with correlations of about 0.4 for CFSv2 and of 0.6 for System 4 and SEAS5.},
  language  = {en}
}
@misc{Buerger2009,
  author    = {B{\"u}rger, Gerd},
  title     = {Dynamically vs. empirically downscaled medium-range precipitation forecasts},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-44939},
  year      = {2009},
  abstract  = {For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.},
  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{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{BoessenkoolBuergerHeistermann2017,
  author    = {B{\"o}ssenkool, Berry and B{\"u}rger, Gerd and Heistermann, Maik},
  title     = {Effects of sample size on estimation of rainfall extremes at high temperatures},
  series = {Natural hazards and earth system sciences},
  volume    = {17},
  journal   = {Natural hazards and earth system sciences},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-17-1623-2017},
  pages     = {1623 -- 1629},
  year      = {2017},
  abstract  = {High precipitation quantiles tend to rise with temperature, following the so-called Clausius-Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.},
  language  = {en}
}
@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{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}
}
@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}
}
@misc{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, Martin and Kriaučiūnienė, J. and Loukas, A. and Osuch, M. and Y{\"u}cel, I.},
  title     = {Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {512},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-40892},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408920},
  pages     = {21},
  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{Buerger2021,
  author    = {B{\"u}rger, Gerd},
  title     = {Intraseasonal oscillation indices from complex EOFs},
  series = {Journal of climate},
  volume    = {34},
  journal   = {Journal of climate},
  number    = {1},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {0894-8755},
  doi       = {10.1175/JCLI-D-20-0427.1},
  pages     = {107 -- 122},
  year      = {2021},
  abstract  = {Indices of oscillatory behavior are conveniently obtained by projecting the fields in question into a phase space of a few (mostly just two) dimensions; empirical orthogonal functions (EOFs) or other, more dynamical, modes are typically used for the projection. If sufficiently coherent and in quadrature, the projected variables simply describe a rotating vector in the phase space, which then serves as the basis for predictions. Using the boreal summer intraseasonal oscillation (BSISO) as a test case, an alternative procedure is introduced: it augments the original fields with their Hilbert transform (HT) to form a complex series and projects it onto its (single) dominant EOF. The real and imaginary parts of the corresponding complex pattern and index are compared with those of the original (real) EOF. The new index explains slightly less variance of the physical fields than the original, but it is much more coherent, partly from its use of future information by the HT. Because the latter is in the way of real-time monitoring, the index can only be used in cases with predicted physical fields, for which it promises to be superior. By developing a causal approximation of the HT, a real-time variant of the index is obtained whose coherency is comparable to the noncausal version, but with smaller explained variance of the physical fields. In test cases the new index compares well to other indices of BSISO. The potential for using both indices as an alternative is discussed.},
  language  = {en}
}
@misc{RottlerFranckeBuergeretal.2020,
  author    = {Rottler, Erwin and Francke, Till and B{\"u}rger, Gerd and Bronstert, Axel},
  title     = {Long-term changes in central European river discharge for 1869-2016},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {4},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51776},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517763},
  pages     = {22},
  year      = {2020},
  abstract  = {Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions. Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.},
  language  = {en}
}
@article{RottlerFranckeBuergeretal.2020,
  author    = {Rottler, Erwin and Francke, Till and B{\"u}rger, Gerd and Bronstert, Axel},
  title     = {Long-term changes in central European river discharge for 1869-2016},
  series = {Hydrology and Earth System Sciences},
  volume    = {24},
  journal   = {Hydrology and Earth System Sciences},
  number    = {4},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-24-1721-2020},
  pages     = {1721 -- 1740},
  year      = {2020},
  abstract  = {Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions. Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.},
  language  = {en}
}
@article{MtilatilaBronstertBuergeretal.2020,
  author    = {Mtilatila, Lucy Mphatso Ng'ombe and Bronstert, Axel and B{\"u}rger, Gerd and Vormoor, Klaus Josef},
  title     = {Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970-2013)},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {65},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {16},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2020.1837384},
  pages     = {2750 -- 2764},
  year      = {2020},
  abstract  = {The study assesses the variability and trends of both meteorological and hydrological droughts from 1970 to 2013 in Lake Malawi and Shire River basins using the standardized precipitation index (SPI) and standardized precipitation and evaporation index (SPEI) for meteorological droughts and the lake level change index (LLCI) for hydrological droughts. Trends and slopes in droughts and drought drivers are estimated using Mann-Kendall test and Sen's slope, respectively. Results suggest that meteorological droughts are increasing due to a decrease in precipitation which is exacerbated by an increase in temperature (potential evapotranspiration). The hydrological system of Lake Malawi seems to have a >24-month memory towards meteorological conditions, since the 36-month SPEI can predict hydrological droughts 10 months in advance. The study has found the critical lake level that would trigger hydrological drought to be 474.1 m a.s.l. The increase in drought is a concern as this will have serious impacts on water resources and hydropower supply in Malawi.},
  language  = {en}
}
@misc{RottlerBronstertBuergeretal.2021,
  author    = {Rottler, Erwin and Bronstert, Axel and B{\"u}rger, Gerd and Rakovec, Oldrich},
  title     = {Projected changes in Rhine River flood seasonality under global warming},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52296},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-522962},
  pages     = {21},
  year      = {2021},
  abstract  = {Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality in the Rhine River Basin, we analyse changes in streamflow, snowmelt, precipitation, and evapotranspiration at 1.5, 2.0 and 3.0 ◦C global warming levels. The mesoscale Hydrological Model (mHM) forced with an ensemble of climate projection scenarios (five general circulation models under three representative concentration pathways) is used to simulate the present and future climate conditions of both, pluvial and nival hydrological regimes. Our results indicate that the interplay between changes in snowmelt- and rainfall-driven runoff is crucial to understand changes in streamflow maxima in the Rhine River. Climate projections suggest that future changes in flood characteristics in the entire Rhine River are controlled by both, more intense precipitation events and diminishing snow packs. The nature of this interplay defines the type of change in runoff peaks. On the sub-basin level (the Moselle River), more intense rainfall during winter is mostly counterbalanced by reduced snowmelt contribution to the streamflow. In the High Rhine (gauge at Basel), the strongest increases in streamflow maxima show up during winter, when strong increases in liquid precipitation intensity encounter almost unchanged snowmelt-driven runoff. The analysis of snowmelt events suggests that at no point in time during the snowmelt season, a warming climate results in an increase in the risk of snowmelt-driven flooding. We do not find indications of a transient merging of pluvial and nival floods due to climate warming.},
  language  = {en}
}
@article{RottlerBronstertBuergeretal.2021,
  author    = {Rottler, Erwin and Bronstert, Axel and B{\"u}rger, Gerd and Rakovec, Oldrich},
  title     = {Projected changes in Rhine River flood seasonality under global warming},
  series = {Hydrology and earth system sciences : HESS / European Geosciences Union},
  volume    = {25},
  journal   = {Hydrology and earth system sciences : HESS / European Geosciences Union},
  number    = {5},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {1607-7938},
  doi       = {10.5194/hess-25-2353-2021},
  pages     = {2353 -- 2371},
  year      = {2021},
  abstract  = {Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality in the Rhine River Basin, we analyse changes in streamflow, snowmelt, precipitation, and evapotranspiration at 1.5, 2.0 and 3.0 ◦C global warming levels. The mesoscale Hydrological Model (mHM) forced with an ensemble of climate projection scenarios (five general circulation models under three representative concentration pathways) is used to simulate the present and future climate conditions of both, pluvial and nival hydrological regimes. Our results indicate that the interplay between changes in snowmelt- and rainfall-driven runoff is crucial to understand changes in streamflow maxima in the Rhine River. Climate projections suggest that future changes in flood characteristics in the entire Rhine River are controlled by both, more intense precipitation events and diminishing snow packs. The nature of this interplay defines the type of change in runoff peaks. On the sub-basin level (the Moselle River), more intense rainfall during winter is mostly counterbalanced by reduced snowmelt contribution to the streamflow. In the High Rhine (gauge at Basel), the strongest increases in streamflow maxima show up during winter, when strong increases in liquid precipitation intensity encounter almost unchanged snowmelt-driven runoff. The analysis of snowmelt events suggests that at no point in time during the snowmelt season, a warming climate results in an increase in the risk of snowmelt-driven flooding. We do not find indications of a transient merging of pluvial and nival floods due to climate warming.},
  language  = {en}
}
@misc{DelgadoVossBuergeretal.2018,
  author    = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till},
  title     = {Seasonal drought prediction for semiarid northeastern Brazil},
  series = {Hydrology and Earth System Sciences},
  journal   = {Hydrology and Earth System Sciences},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-418461},
  pages     = {16},
  year      = {2018},
  abstract  = {A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Cear{\´a}'s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.},
  language  = {en}
}
@article{DelgadoVossBuergeretal.2018,
  author    = {Delgado, Jos{\´e} Miguel Martins and Voss, Sebastian and B{\"u}rger, Gerd and Vormoor, Klaus Josef and Murawski, Aline and Rodrigues Pereira, Jos{\´e} Marcelo and Martins, Eduardo and Vasconcelos J{\´u}nior, Francisco and Francke, Till},
  title     = {Seasonal drought prediction for semiarid northeastern Brazil},
  series = {Hydrology and Earth System Sciences},
  volume    = {22},
  journal   = {Hydrology and Earth System Sciences},
  number    = {9},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-22-5041-2018},
  pages     = {5041 -- 5056},
  year      = {2018},
  abstract  = {A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Cear{\´a}'s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.},
  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}
}
@article{BuergerPfisterBronstert2019,
  author    = {B{\"u}rger, Gerd and Pfister, A. and Bronstert, Axel},
  title     = {Temperature-Driven Rise in Extreme Sub-Hourly Rainfall},
  series = {Journal of climate},
  volume    = {32},
  journal   = {Journal of climate},
  number    = {22},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {0894-8755},
  doi       = {10.1175/JCLI-D-19-0136.1},
  pages     = {7597 -- 7609},
  year      = {2019},
  abstract  = {Estimates of present and future extreme sub-hourly rainfall are derived from a daily spatial followed by a sub-daily temporal downscaling, the latter of which incorporates a novel, and crucial, temperature sensitivity. Specifically, daily global climate fields are spatially downscaled to local temperature T and precipitation P, which are then disaggregated to a temporal resolution of 10 min using a multiplicative random cascade model. The scheme is calibrated and validated with a group of 21 station records of 10-min resolution in Germany. The cascade model is used in the classical (denoted as MC) and in the new T-sensitive (MC+) version, which respects local Clausius-Clapeyron (CC) effects such as CC scaling. Extreme P is positively biased in both MC versions. Observed T sensitivity is absent in MC but well reproduced by MC+. Long-term positive trends in extreme sub-hourly P are generally more pronounced and more significant in MC+ than in MC. In units of 10-min rainfall, observed centennial trends in annual exceedance counts (EC) of P > 5 mm are +29\% and in 3-yr return levels (RL) +27\%. For the RCP4.5-simulated future, higher extremes are projected in both versions MC and MC+: per century, EC increases by 30\% for MC and by 83\% for MC+; the RL rises by 14\% for MC and by 33\% for MC+. Because the projected daily P trends are negligible, the sub-daily signal is mainly driven by local temperature.},
  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{VormoorRosslerBuergeretal.2017,
  author    = {Vormoor, Klaus Josef and Rossler, Ole and B{\"u}rger, Gerd and Bronstert, Axel and Weingartner, Rolf},
  title     = {When timing matters-considering changing temporal structures in runoff response surfaces},
  series = {Climatic change : an interdisciplinary, intern. journal devoted to the description, causes and implications of climatic change},
  volume    = {142},
  journal   = {Climatic change : an interdisciplinary, intern. journal devoted to the description, causes and implications of climatic change},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0165-0009},
  doi       = {10.1007/s10584-017-1940-1},
  pages     = {213 -- 226},
  year      = {2017},
  abstract  = {Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21\% influence), while it is negligible for mean runoff.},
  language  = {en}
}
@article{BuergerPfisterBronstert2021,
  author    = {B{\"u}rger, Gerd and Pfister, Angela and Bronstert, Axel},
  title     = {Zunehmende Starkregenintensit{\"a}ten als Folge der Klimaerw{\"a}rmung},
  series = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder},
  volume    = {65},
  journal   = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder},
  number    = {6},
  publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde},
  address   = {Koblenz},
  issn      = {1439-1783},
  doi       = {10.5675/HyWa_2021.6_1},
  pages     = {262 -- 271},
  year      = {2021},
  abstract  = {Extreme rainfall events of short duration in the range of hours and below are increasingly coming into focus due to the resulting damage from flash floods and also due to their possible intensification by anthropogenic climate change. The current study investigates possible trends in heavy rainfall intensities for stations from Swiss and Austrian alpine regions as well as for the Emscher-Lippe area in North Rhine-Westphalia on the basis of partly very long (> 50 years) and temporally highly resolved time series (<= 15 minutes). It becomes clear that there is an increase in extreme rainfall intensities, which can be well explained by the warming of the regional climate: the analyses of long-term trends in exceedance counts and return levels show considerable uncertainties, but are in the order of 30 \% increase per century. In addition, based on an "average" climate simulation for the 21st century, this paper describes a projection for extreme precipitation intensities at very high temporal resolution for a number of stations in the Emscher-Lippe region. A coupled spatial and temporal "downscaling" is applied, the key innovation of which is the consideration of the dependence of local rainfall intensity on air temperature. This procedure involves two steps: First, large-scale climate fields at daily resolution are statistically linked by regression to station temperature and precipitation values (spatial downscaling). In the second step, these station values are disaggregated to a temporal resolution of 10 minutes using a so-called multiplicative stochastic cascade model (MC) (temporal downscaling). The novel, temperature-sensitive variant additionally considers air temperature as an explanatory variable for precipitation intensities. Thus, the higher atmospheric moisture content expected with warming, which results from the Clausius-Clapeyron (CC) relationship, is included in the temporal downscaling. <br /> For the statistical evaluation of the extreme short-term precipitation, the upper quantiles (99.9 \%), exceedance counts (P > 5mm), and 3-yr return levels of the <= 15-min duration step has been used. Only by adding temperature is the observed temperature observed of the extreme quantiles ("CC scaling") well reproduced. When comparing observed data and present-day simulations of the model cascade, the temperature-sensitive procedure shows consistent results. Compared to trends in recent decades, similar or even larger increases in extreme intensities are projected for the future. This is remarkable in that these appear to be driven primarily by local temperature, as the projected trends in daily precipitation values are negligible for this region.},
  language  = {de}
}