Seamless Estimation of Hydrometeorological Risk Across Spatial Scales

  • 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, fromHydrometeorological 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.show moreshow less

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Metadaten
Author details:Tobias SiegORCiDGND, Kristin VogelORCiDGND, Bruno MerzORCiDGND, Heidi KreibichORCiDGND
URN:urn:nbn:de:kobv:517-opus4-435341
DOI:https://doi.org/10.25932/publishup-43534
ISSN:1866-8372
Title of parent work (English):Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
Publication series (Volume number):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (743)
Publication type:Postprint
Language:English
Date of first publication:2019/09/25
Publication year:2019
Publishing institution:Universität Potsdam
Release date:2019/09/25
Tag:hydro-meterological hazards; object-based damage modeling; probabilistic approaches; risk assessment; spatial scales; uncertainty
Issue:743
Number of pages:8
First page:574
Last Page:581
Source:Earth’s Future 7 (2019) 5, S. 574–581 DOI: 10.1029/2018EF001122
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
DDC classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer review:Referiert
Publishing method:Open Access
Institution name at the time of the publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
License (German):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
External remark:Bibliographieeintrag der Originalveröffentlichung/Quelle
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