@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{KreibichBottoMerzetal.2016, author = {Kreibich, Heidi and Botto, Anna and Merz, Bruno and Schr{\"o}ter, Kai}, title = {Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO}, series = {Risk analysis}, volume = {37}, journal = {Risk analysis}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {0272-4332}, doi = {10.1111/risa.12650}, pages = {774 -- 787}, year = {2016}, abstract = {Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.}, language = {en} } @article{MerzVietDungNguyenVorogushyn2016, author = {Merz, Bruno and Viet Dung Nguyen, and Vorogushyn, Sergiy}, title = {Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist?}, series = {Journal of hydrology}, volume = {541}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.07.041}, pages = {824 -- 838}, year = {2016}, abstract = {The repeated occurrence of exceptional floods within a few years, such as the Rhine floods in 1993 and 1995 and the Elbe and Danube floods in 2002 and 2013, suggests that floods in Central Europe may be organized in flood-rich and flood-poor periods. This hypothesis is studied by testing the significance of temporal clustering in flood occurrence (peak-over-threshold) time series for 68 catchments across Germany for the period 1932-2005. To assess the robustness of the results, different methods are used: Firstly, the index of dispersion, which quantifies the departure from a homogeneous Poisson process, is investigated. Further, the time-variation of the flood occurrence rate is derived by non-parametric kernel implementation and the significance of clustering is evaluated via parametric and non-parametric tests. Although the methods give consistent overall results, the specific results differ considerably. Hence, we recommend applying different methods when investigating flood clustering. For flood estimation and risk management, it is of relevance to understand whether clustering changes with flood severity and time scale. To this end, clustering is assessed for different thresholds and time scales. It is found that the majority of catchments show temporal clustering at the 5\% significance level for low thresholds and time scales of one to a few years. However, clustering decreases substantially with increasing threshold and time scale. We hypothesize that flood clustering in Germany is mainly caused by catchment memory effects along with intra- to inter-annual climate variability, and that decadal climate variability plays a minor role. (C) 2016 Elsevier B.V. All rights reserved.}, language = {en} }