Seamless Estimation of Hydrometeorological Risk Across Spatial Scales. Issue 5 (30th May 2019)

• Record Type: Journal Article
• Authors: Sieg, Tobias; Vogel, Kristin; Merz, Bruno; Kreibich, Heidi
• Is Part Of: Earth's future. Volume 7:Issue 5(2019)
• Page Start: 574
• Page End: 581
• Publication Date: 2019-05-30
• Subjects: spatial scales -- risk assessment -- hydro‐meteorological hazards -- object‐based damage modeling -- uncertainty -- probabilistic approaches; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2018EF001122
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract : 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 asopenstreetmap.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. Key Points: The proposed method resolves differences between risk assessments at different spatial scales Resulting probability distributions capture uncertainties associated with hazard, exposure, and vulnerability at all scales The object‐based method performs as well as or better than state‐of‐the‐art land use‐based models