How useful are complex flood damage models?

• Published in: Water resources research 2014-04, Vol.50 (4), p.3378-3395
• Main Authors: Schröter, Kai, Kreibich, Heidi, Vogel, Kristin, Riggelsen, Carsten, Scherbaum, Frank, Merz, Bruno
• Format: Article
• Language: English
• Subjects: Bayesian networks; damage; Flood control; Flood damage; floods; Historic floods; model validation; regression tree; Residential areas; Variables; Water depth
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2013WR014396

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. Key Points Increased complexity improves the predictive capability of flood damage models Model approach seems more important than using additional variables Bayesian network‐based predictions show superior precision and reliability