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  - 
TY  - JOUR
A1  - Schroeter, Kai
A1  - Kreibich, Heidi
A1  - Vogel, Kristin
A1  - Riggelsen, Carsten
A1  - Scherbaum, Frank
A1  - Merz, Bruno
T1  - How useful are complex flood damage models?
JF  - Water resources research
N2  - We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.
KW  - floods
KW  - damage
KW  - model validation
KW  - Bayesian networks
KW  - regression tree
Y1  - 2014
U6  - https://doi.org/10.1002/2013WR014396
SN  - 0043-1397
SN  - 1944-7973
VL  - 50
IS  - 4
SP  - 3378
EP  - 3395
PB  - American Geophysical Union
CY  - Washington
ER  -