TY  - JOUR
A1  - Sieg, Tobias
A1  - Vogel, Kristin
A1  - Merz, Bruno
A1  - Kreibich, Heidi
T1  - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales
JF  - Earth's Future
N2  - 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.
KW  - spatial scales
KW  - risk assessment
KW  - hydro-meterological hazards
KW  - object-based damage modeling
KW  - uncertainty
KW  - probabilistic approaches
Y1  - 2019
U6  - https://doi.org/10.1029/2018EF001122
SN  - 2328-4277
VL  - 7
IS  - 5
SP  - 574
EP  - 581
PB  - Wiley-Blackwell
CY  - Hoboken, NJ
ER  - 
TY  - GEN
A1  - Sieg, Tobias
A1  - Vogel, Kristin
A1  - Merz, Bruno
A1  - Kreibich, Heidi
T1  - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales
T2  - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
N2  - 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 743 
KW  - spatial scales
KW  - risk assessment
KW  - hydro-meterological hazards
KW  - object-based damage modeling
KW  - uncertainty
KW  - probabilistic approaches
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-435341
SN  - 1866-8372
IS  - 743
SP  - 574
EP  - 581
ER  -