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  • risk assessment  (2)
  • Bayesian networks  (1)
  • 1
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    A probabilistic approach to estimating residential losses from different flood types (2020)
    Paprotny, Dominik ; Kreibich, Heidi ; Morales-Nápoles, Oswaldo ; [et al.]
    Springer Netherlands
    Details
    Publication Date: 2023-06-19
    Description: 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.
    Description: EIT Climate-KIC http://dx.doi.org/10.13039/100013283
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZ (4217)
    Keywords: ddc:551.48 ; Fluvial floods ; Coastal floods ; Pluvial floods ; Bayesian networks ; Flood damage surveys
    Language: English
    Type: doc-type:article
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  • 2
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    Toward an adequate level of detail in flood risk assessments (2023)
    Blackwell Publishing Ltd | Oxford, UK
    Details
    Publication Date: 2024-01-24
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A “sweet spot” between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this “sweet spot.” For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments.〈/p〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: BMBF http://dx.doi.org/10.13039/501100002347
    Description: Federal Environment Agency http://dx.doi.org/10.13039/501100010809
    Description: http://howas21.gfz-potsdam.de/howas21/
    Description: https://www.umwelt.niedersachsen.de/startseite/themen/wasser/hochwasser_amp_kustenschutz/hochwasserrisikomanagement_richtlinie/hochwassergefahren_und_hochwasserrisikokarten/hochwasserkarten-121920.html
    Description: https://download.geofabrik.de/europe/germany.html
    Description: https://emergency.copernicus.eu/mapping/list-of-components/EMSN024
    Description: https://data.jrc.ec.europa.eu/collection/id-0054
    Description: https://oasishub.co/dataset/surface-water-flooding-footprinthurricane-harvey-august-2017-jba
    Description: https://www.wasser.sachsen.de/hochwassergefahrenkarte-11915.html
    Keywords: ddc:551.48 ; decision support ; extreme events ; integrated flood risk management ; risk assessment
    Language: English
    Type: doc-type:article
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  • 3
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    Seamless Estimation of Hydrometeorological Risk Across Spatial Scales (2021)
    Details
    Publication Date: 2021-07-24
    Description: Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe. We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
    Keywords: 551.489 ; spatial scales ; risk assessment ; hydro-meteorological hazards ; object-based damage modeling ; uncertainty ; probabilistic approaches
    Language: English
    Type: article
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