A probabilistic approach to estimating residential losses from different flood types

Abstract

Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

Development of damage curves for buildings near La Rochelle during storm Xynthia based on insurance claims and hydrodynamic simulations

“…It is known that insurance claims can sometimes be subject to fraud or information distortion. In addition, variables related to the vulnerability of the assets like the construction characteristics, the materials, the quality, and the age of the structures (Paprotny et al, 2021) play important roles in determining whether a particular hydrodynamic is related to damage. This adds a degree of complexity to the analysis.…”

Development of damage curves for buildings near La Rochelle during Storm Xynthia based on insurance claims and hydrodynamic simulations

“…It is well identified that claims are subject to fraud and information distortion. Also variables related with the vulnerability of the assets like the construction characteristics, the materials, the quality and the age of the structures (Paprotny et al, 2021) play an important role in whether for a particular hydrodynamic variable value damage occurs or not. This adds a degree of complexity to the analysis In addition to the sensitivity of results to resolution of the topographic and bathymetric data, the inclusion of thin flood walls via a land survey carried out by the authors also had a significant effect on the damage functions generated.…”

Resilience of art cities to flood risk: A quantitative model based on depth‐idleness correlation

“…The exposure analysis is carried out by intersecting flood maps and the polygonal shapefile of CH for each scenario. Exposure is also the value of the asset affected by the flood, for example, for residential buildings the market value or construction cost (Molinari et al, 2020; Paprotny et al, 2021). The value of CH is hardly monetizable, however the social appreciation and value of a cultural attraction, for example, a museum, a noble palace, a church etc.…”