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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:Tobias SiegORCiDGND, Kristin VogelORCiDGND, Bruno MerzORCiDGND, Heidi KreibichORCiDGND
DOI:https://doi.org/10.1029/2018EF001122
ISSN:2328-4277
Parent Title (English):Earth's Future
Publisher:Wiley-Blackwell
Place of publication:Hoboken, NJ
Document Type:Article
Language:English
Date of first Publication:2019/05/02
Year of Completion:2019
Release Date:2019/09/25
Tag:hydro-meterological hazards; object-based damage modeling; probabilistic approaches; risk assessment; spatial scales; uncertainty
Volume:7
Issue:5
Pagenumber:8
First Page:574
Last Page:581
Funder:Universität Potsdam
Grant Number:PA 2019_40
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publication Way:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung 4.0 International
Notes extern:Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 743