4.7 Article

A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe

期刊

WATER RESOURCES RESEARCH
卷 55, 期 12, 页码 10616-10635

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2019WR026213

关键词

Probabilistic; flood loss model; Europe; flood risk

资金

  1. European Union's Horizon 2020 research and innovation program, through the IMPREX project [641811]
  2. Climate Knowledge and Innovation Community (Climate-KIC) by the European Institute of Innovation and Technology (EIT)
  3. European Union's Horizon 2020 research and innovation program H2020 Insurance [730381]
  4. Climate-KIC through project SAFERPLACES - Improved assessment of pluvial, fluvial and coastal flood hazards andrisks in European cities as a mean to build safer and resilientcommunities [TC2018B_4.7.3-SAFERPL_P430-1A KAVA2 4.7.3]
  5. German Research Network Natural Disasters (German Ministry of Education and Research (BMBF)) [01SFR9969/5]
  6. German Research Centre for Geosciences GFZ
  7. Deutsche Ruckversicherung AG
  8. H2020 Societal Challenges Programme [641811, 730381] Funding Source: H2020 Societal Challenges Programme

向作者/读者索取更多资源

In view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth-damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN-FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN-FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended.

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