TY  - JOUR
A1  - Wietzke, Luzie M.
A1  - Merz, Bruno
A1  - Gerlitz, Lars
A1  - Kreibich, Heidi
A1  - Guse, Björn
A1  - Castellarin, Attilio
A1  - Vorogushyn, Sergiy
T1  - Comparative analysis of scalar upper tail indicators
JF  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - 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.
KW  - upper tail behaviour
KW  - heavy-tailed distributions
KW  - extremes
KW  - diagnostics
KW  - surprise
Y1  - 2020
U6  - https://doi.org/10.1080/02626667.2020.1769104
SN  - 0262-6667
SN  - 2150-3435
VL  - 65
IS  - 10
SP  - 1625
EP  - 1639
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  - 
TY  - JOUR
A1  - Paprotny, Dominik
A1  - Kreibich, Heidi
A1  - Morales-Napoles, Oswaldo
A1  - Wagenaar, Dennis
A1  - Castellarin, Attilio
A1  - Carisi, Francesca
A1  - Bertin, Xavier
A1  - Merz, Bruno
A1  - Schröter, Kai
T1  - A probabilistic approach to estimating residential losses from different flood types
JF  - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards
N2  - 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.
KW  - fluvial floods
KW  - coastal floods
KW  - pluvial floods
KW  - Bayesian networks
KW  - flood
KW  - damage surveys
Y1  - 2020
U6  - https://doi.org/10.1007/s11069-020-04413-x
SN  - 0921-030X
SN  - 1573-0840
VL  - 105
IS  - 3
SP  - 2569
EP  - 2601
PB  - Springer
CY  - New York
ER  -