Evaluating targeted heuristics for vulnerability assessment in flood impact model chains. Issue 4 (7th September 2021)

• Record Type: Journal Article
• Authors: Zischg, Andreas Paul; Röthlisberger, Veronika; Mosimann, Markus; Profico‐Kaltenrieder, Rahel; N. Bresch, David; Fuchs, Sven; Kauzlaric, Martina; Keiler, Margreth
• Is Part Of: Journal of flood risk management. Volume 14:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-07
• Subjects: extreme events -- flood damages -- floodplain -- vulnerability; Flood control -- Periodicals; Floods -- Risk assessment -- Periodicals; Flood damage prevention -- Periodicals; Flood warning systems -- Periodicals; 627.405
• DOI: 10.1111/jfr3.12736
• Languages: English
• ISSNs: 1753-318X

Abstract

Abstract: In flood risk management, the choice of vulnerability functions has a remarkable impact on the overall uncertainty of modelling flood damage. The spatial transferability of empirical vulnerability functions is limited, leading to the need for computation and validation of region‐specific vulnerability functions. In data‐scarce regions however, this option is not feasible. In contrast, the physical processes of flood impact model chains can be developed in these regions because of the availability of global datasets. Here we evaluated the implementation of a synthetic vulnerability function into a flood impact model. The function bases on expert heuristics on a targeted sample of representative buildings (targeted heuristics). We applied the vulnerability function in a meso‐scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region‐specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that targeted heuristics can be a valuable alternative for developing flood impact models in regions without any data or only few data on flood damage.