@article{WietzkeMerzGerlitzetal.2020,
  author    = {Wietzke, Luzie M. and Merz, Bruno and Gerlitz, Lars and Kreibich, Heidi and Guse, Bj{\"o}rn and Castellarin, Attilio and Vorogushyn, Sergiy},
  title     = {Comparative analysis of scalar upper tail indicators},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {65},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {10},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2020.1769104},
  pages     = {1625 -- 1639},
  year      = {2020},
  abstract  = {Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution.},
  language  = {en}
}
@article{PaprotnyKreibichMoralesNapolesetal.2020,
  author    = {Paprotny, Dominik and Kreibich, Heidi and Morales-Napoles, Oswaldo and Wagenaar, Dennis and Castellarin, Attilio and Carisi, Francesca and Bertin, Xavier and Merz, Bruno and Schr{\"o}ter, Kai},
  title     = {A probabilistic approach to estimating residential losses from different flood types},
  series = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards},
  volume    = {105},
  journal   = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards},
  number    = {3},
  publisher = {Springer},
  address   = {New York},
  issn      = {0921-030X},
  doi       = {10.1007/s11069-020-04413-x},
  pages     = {2569 -- 2601},
  year      = {2020},
  abstract  = {Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model's ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.},
  language  = {en}
}