Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe.

Flood exposure Flood risk Flood vulnerability Hydrodynamic models

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
01 Oct 2020
Historique:
received: 01 04 2020
revised: 25 05 2020
accepted: 04 06 2020
pubmed: 23 6 2020
medline: 23 6 2020
entrez: 23 6 2020
Statut: ppublish

Résumé

Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2-19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.

Identifiants

pubmed: 32569902
pii: S0048-9697(20)33531-2
doi: 10.1016/j.scitotenv.2020.140011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

140011

Informations de copyright

Copyright © 2018. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no conflict of interest.

Auteurs

Dominik Paprotny (D)

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany. Electronic address: paprotny@gfz-potsdam.de.

Heidi Kreibich (H)

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany.

Oswaldo Morales-Nápoles (O)

Delft University of Technology, Department of Hydraulic Engineering, Delft, the Netherlands.

Attilio Castellarin (A)

University of Bologna, DICAM, Water Resources, Bologna, Italy.

Francesca Carisi (F)

University of Bologna, DICAM, Water Resources, Bologna, Italy.

Kai Schröter (K)

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany.

Classifications MeSH