@misc{BubeckAertsdeMoeletal.2016, author = {Bubeck, Philip and Aerts, Jeroen C. J. H. and de Moel, Hans and Kreibich, Heidi}, title = {Preface}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {609}, issn = {1866-8372}, doi = {10.25932/publishup-41238}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412387}, pages = {6}, year = {2016}, abstract = {kein abstract}, language = {en} } @misc{PrahlRybskiBoettleetal.2016, author = {Prahl, Boris F. and Rybski, Diego and Boettle, Markus and Kropp, J{\"u}rgen}, title = {Damage functions for climate-related hazards}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {534}, issn = {1866-8372}, doi = {10.25932/publishup-41018}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410184}, pages = {15}, year = {2016}, abstract = {Most climate change impacts manifest in the form of natural hazards. Damage assessment typically relies on damage functions that translate the magnitude of extreme events to a quantifiable damage. In practice, the availability of damage functions is limited due to a lack of data sources and a lack of understanding of damage processes. The study of the characteristics of damage functions for different hazards could strengthen the theoretical foundation of damage functions and support their development and validation. Accordingly, we investigate analogies of damage functions for coastal flooding and for wind storms and identify a unified approach. This approach has general applicability for granular portfolios and may also be applied, for example, to heat-related mortality. Moreover, the unification enables the transfer of methodology between hazards and a consistent treatment of uncertainty. This is demonstrated by a sensitivity analysis on the basis of two simple case studies (for coastal flood and storm damage). The analysis reveals the relevance of the various uncertainty sources at varying hazard magnitude and on both the microscale and the macroscale level. Main findings are the dominance of uncertainty from the hazard magnitude and the persistent behaviour of intrinsic uncertainties on both scale levels. Our results shed light on the general role of uncertainties and provide useful insight for the application of the unified approach.}, language = {en} } @misc{KellermannSchoebelKundelaetal.2015, author = {Kellermann, Patric and Sch{\"o}bel, A. and Kundela, G. and Thieken, Annegret}, title = {Estimating flood damage to railway infrastructure}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {504}, issn = {1866-8372}, doi = {10.25932/publishup-40842}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408429}, pages = {12}, year = {2015}, abstract = {Models for estimating flood losses to infrastructure are rare and their reliability is seldom investigated although infrastructure losses might contribute considerably to the overall flood losses. In this paper, an empirical modelling approach for estimating direct structural flood damage to railway infrastructure and associated financial losses is presented. Via a combination of event data, i.e. photo-documented damage on the Northern Railway in Lower Austria caused by the March River flood in 2006, and simulated flood characteristics, i.e. water levels, flow velocities and combinations thereof, the correlations between physical flood impact parameters and damage occurred to the railway track were investigated and subsequently rendered into a damage model. After calibrating the loss estimation using recorded repair costs of the Austrian Federal Railways, the model was applied to three synthetic scenarios with return periods of 30, 100 and 300 years of March River flooding. Finally, the model results are compared to depth-damage-curve-based approaches for the infrastructure sector obtained from the Rhine Atlas damage model and the Damage Scanner model. The results of this case study indicate a good performance of our two-stage model approach. However, due to a lack of independent event and damage data, the model could not yet be validated. Future research in natural risk should focus on the development of event and damage documentation procedures to overcome this significant hurdle in flood damage modelling.}, language = {en} } @misc{PrahlRybskiBurghoffetal.2015, author = {Prahl, Boris F. and Rybski, Diego and Burghoff, Olaf and Kropp, J{\"u}rgen}, title = {Comparison of storm damage functions and their performance}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {492}, issn = {1866-8372}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-408119}, pages = {20}, year = {2015}, abstract = {Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties for both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic wind-damage relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two sources of wind gust data, direct observations by the German Weather Service and ERA-Interim reanalysis data. While the choice of gust data has little impact on the evaluation of German storm loss, spatially resolved coefficients of variation reveal dependence between model and data choice. The comparison shows that the probabilistic models by Heneka et al. (2006) and Prahl et al. (2012) both provide accurate loss predictions for moderate to extreme losses, with generally small coefficients of variation. We favour the latter model in terms of model applicability. Application of the versatile deterministic model by Klawa and Ulbrich (2003) should be restricted to extreme loss, for which it shows the least bias and errors comparable to the probabilistic model by Prahl et al. (2012).}, language = {en} }