Short communication: A model to predict flood loss in mountain areas. (July 2019)

• Record Type: Journal Article
• Authors: Fuchs, Sven; Heiser, Micha; Schlögl, Matthias; Zischg, Andreas; Papathoma-Köhle, Maria; Keiler, Margreth
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 176
• Page End: 180
• Publication Date: 2019-07
• Subjects: Vulnerability -- Elements at risk -- Torrential hazards -- Loss -- Zero-and-one inflated beta regression -- Austria; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.03.026
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Because effects of climate change and an increase in elements at risk, mountain hazard loss increased throughout Europe. Yet, factors influencing loss, i.e. vulnerability, have gained less attention to date. Vulnerability is defined as the degree of loss resulting from the hazard impact on buildings. Recent studies have focused on evaluating vulnerability to dynamic flooding using proxies from case studies and based on empirical ex-post approaches. However, the transferability to other case studies and, therefore, the ability of such models to actually predict future losses is limited. To overcome this gap, we present a beta model based on loss data from the European Alps, which clearly shows that a single vulnerability function is sufficient to predict losses resulting from different types of torrential hazards and to provide probabilities of destruction under specific scenarios. As a result, the curves are transferable and may significantly increase the predictive power of risk analyses. Highlights: We assess physical vulnerability of buildings exposed to torrential hazards. A beta model was used to link process magnitude to the degree of loss. The model can be used for loss prediction considering all damage stages from possible no loss to complete destruction. The model is applicable to dynamic flooding with varying sediment concentration.