@misc{SiegShinkoVogeletal.2019,
  author    = {Sieg, Tobias and Shinko, Thomas and Vogel, Kristin and Mechler, Reinhard and Merz, Bruno and Kreibich, Heidi},
  title     = {Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {708},
  doi       = {10.25932/publishup-42911},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429119},
  pages     = {21},
  year      = {2019},
  abstract  = {Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.},
  language  = {en}
}
@misc{SiegVogelMerzetal.2019,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {743},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43534},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-435341},
  pages     = {574 -- 581},
  year      = {2019},
  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 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.},
  language  = {en}
}
@article{SiegSchinkoVogeletal.2019,
  author    = {Sieg, Tobias and Schinko, Thomas and Vogel, Kristin and Mechler, Reinhard and Merz, Bruno and Kreibich, Heidi},
  title     = {Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification},
  series = {PLoS ONE},
  volume    = {14},
  journal   = {PLoS ONE},
  number    = {4},
  publisher = {Public Library of Science},
  address   = {San Francisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0212932},
  pages     = {21},
  year      = {2019},
  abstract  = {Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.},
  language  = {en}
}
@article{SiegVogelMerzetal.2019,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales},
  series = {Earth's Future},
  volume    = {7},
  journal   = {Earth's Future},
  number    = {5},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken, NJ},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF001122},
  pages     = {574 -- 581},
  year      = {2019},
  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 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.},
  language  = {en}
}
@article{SiegVogelMerzetal.2017,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Tree-based flood damage modeling of companies: Damage processes and model performance},
  series = {Water resources research},
  volume    = {53},
  journal   = {Water resources research},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1002/2017WR020784},
  pages     = {6050 -- 6068},
  year      = {2017},
  abstract  = {Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.},
  language  = {en}
}
@article{SchroeterKreibichVogeletal.2014,
  author    = {Schroeter, Kai and Kreibich, Heidi and Vogel, Kristin and Riggelsen, Carsten and Scherbaum, Frank and Merz, Bruno},
  title     = {How useful are complex flood damage models?},
  series = {Water resources research},
  volume    = {50},
  journal   = {Water resources research},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1002/2013WR014396},
  pages     = {3378 -- 3395},
  year      = {2014},
  abstract  = {We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.},
  language  = {en}
}