TY  - JOUR
A1  - Kreibich, Heidi
A1  - Botto, Anna
A1  - Merz, Bruno
A1  - Schröter, Kai
T1  - Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO
JF  - Risk analysis
N2  - Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.
KW  - Damage modeling
KW  - multiparameter
KW  - probabilistic
KW  - uncertainty
KW  - validation
Y1  - 2016
U6  - https://doi.org/10.1111/risa.12650
SN  - 0272-4332
SN  - 1539-6924
VL  - 37
IS  - 4
SP  - 774
EP  - 787
PB  - Wiley
CY  - Hoboken
ER  - 
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  -