Articles | Volume 22, issue 1
https://doi.org/10.5194/nhess-22-245-2022
https://doi.org/10.5194/nhess-22-245-2022
Research article
 | 
31 Jan 2022
Research article |  | 31 Jan 2022

About the return period of a catastrophe

Mathias Raschke

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Albrecher, H., Araujo-Acuna, J., and Beirlant, J.: Tempered Pareto-type modelling using Weibull distributions, ASTIN Bull., 51, 509–538, https://doi.org/10.1017/asb.2020.43, 2021. 
Asadi, P., Engelke, S., and Davison, A. C.: Extremes on river networks, Ann. Appl. Stat., 9, 2023–2050, 2015. 
Beirlant, J., Goegebeur, Y., Teugels, J., and Segers, J.: Statistics of Extremes – Theory and Application, in: Book Series: Wiley Series in Probability and Statistics, John Wiley & Sons, ISBN 978-0-471-97647-9, 2004. 
Blanchet, J. and Davison, A. C.: Spatial Modelling of extreme snow depth, Ann. Appl. Stat., 5, 1699–1725, 2011. 
Bonazzi, A., Cusack, S., Mitas, C., and Jewson, S.: The spatial structure of European wind storms as characterized by bivariate extreme-value Copulas, Nat. Hazards Earth Syst. Sci., 12, 1769–1782, https://doi.org/10.5194/nhess-12-1769-2012, 2012. 
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We develop the combined return period to stochastically measure hazard and catastrophe events. This is used to estimate a risk curve by stochastic scaling of historical events and averaging corresponding risk parameters in combination with a vulnerability model. We apply the method to extratropical cyclones over Germany and estimate the risk for insured losses. The results are strongly influenced by assumptions about spatial dependence.
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