@misc{KellermannSchoenbergerThieken2016, author = {Kellermann, Patric and Sch{\"o}nberger, Christine and Thieken, Annegret}, title = {Large-scale application of the flood damage model RAilway Infrastructure Loss (RAIL)}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {555}, issn = {1866-8372}, doi = {10.25932/publishup-41191}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-411915}, pages = {15}, year = {2016}, abstract = {Experience has shown that river floods can significantly hamper the reliability of railway networks and cause extensive structural damage and disruption. As a result, the national railway operator in Austria had to cope with financial losses of more than EUR 100 million due to flooding in recent years. Comprehensive information on potential flood risk hot spots as well as on expected flood damage in Austria is therefore needed for strategic flood risk management. In view of this, the flood damage model RAIL (RAilway Infrastructure Loss) was applied to estimate (1) the expected structural flood damage and (2) the resulting repair costs of railway infrastructure due to a 30-, 100- and 300-year flood in the Austrian Mur River catchment. The results were then used to calculate the expected annual damage of the railway subnetwork and subsequently analysed in terms of their sensitivity to key model assumptions. Additionally, the impact of risk aversion on the estimates was investigated, and the overall results were briefly discussed against the background of climate change and possibly resulting changes in flood risk. The findings indicate that the RAIL model is capable of supporting decision-making in risk management by providing comprehensive risk information on the catchment level. It is furthermore demonstrated that an increased risk aversion of the railway operator has a marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies.}, language = {en} } @misc{MetinDungSchroeteretal.2018, author = {Metin, Ayse Duha and Dung, Nguyen Viet and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno}, title = {How do changes along the risk chain affect flood risk?}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1067}, issn = {1866-8372}, doi = {10.25932/publishup-46879}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-468790}, pages = {22}, year = {2018}, abstract = {Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.}, 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} } @misc{StuetzWeberDolleetal.2016, author = {Stuetz, Wolfgang and Weber, Daniela and Doll{\´e}, Martijn E. T. and Jansen, Eug{\`e}ne and Grubeck-Loebenstein, Beatrix and Fiegl, Simone and Toussaint, Olivier and Bernhardt, Juergen and Gonos, Efstathios S. and Franceschi, Claudio and Sikora, Ewa and Moreno-Villanueva, Mar{\´i}a and Breusing, Nicolle and Grune, Tilman and B{\"u}rkle, Alexander}, title = {Plasma carotenoids, tocopherols, and retinol in the age-stratified (35-74 years) general population}, series = {Nutrients}, journal = {Nutrients}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407659}, pages = {17}, year = {2016}, abstract = {Blood micronutrient status may change with age. We analyzed plasma carotenoids, α-/γ-tocopherol, and retinol and their associations with age, demographic characteristics, and dietary habits (assessed by a short food frequency questionnaire) in a cross-sectional study of 2118 women and men (age-stratified from 35 to 74 years) of the general population from six European countries. Higher age was associated with lower lycopene and α-/β-carotene and higher β-cryptoxanthin, lutein, zeaxanthin, α-/γ-tocopherol, and retinol levels. Significant correlations with age were observed for lycopene (r = -0.248), α-tocopherol (r = 0.208), α-carotene (r = -0.112), and β-cryptoxanthin (r = 0.125; all p < 0.001). Age was inversely associated with lycopene (-6.5\% per five-year age increase) and this association remained in the multiple regression model with the significant predictors (covariables) being country, season, cholesterol, gender, smoking status, body mass index (BMI (kg/m2)), and dietary habits. The positive association of α-tocopherol with age remained when all covariates including cholesterol and use of vitamin supplements were included (1.7\% vs. 2.4\% per five-year age increase). The association of higher β-cryptoxanthin with higher age was no longer statistically significant after adjustment for fruit consumption, whereas the inverse association of α-carotene with age remained in the fully adjusted multivariable model (-4.8\% vs. -3.8\% per five-year age increase). We conclude from our study that age is an independent predictor of plasma lycopene, α-tocopherol, and α-carotene.}, language = {en} }