@article{RoezerKreibichSchroeteretal.2019,
  author    = {R{\"o}zer, Viktor and Kreibich, Heidi and Schr{\"o}ter, Kai and M{\"u}ller, Meike and Sairam, Nivedita and Doss-Gollin, James and Lall, Upmanu and Merz, Bruno},
  title     = {Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates},
  series = {Earths future},
  volume    = {7},
  journal   = {Earths future},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF001074},
  pages     = {384 -- 394},
  year      = {2019},
  abstract  = {Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90\% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78\% (totalling U.S.\$3.8 billion) compared to commonly used models.},
  language  = {en}
}
@article{SairamSchroeterLuedtkeetal.2019,
  author    = {Sairam, Nivedita and Schr{\"o}ter, Kai and L{\"u}dtke, Stefan and Merz, Bruno and Kreibich, Heidi},
  title     = {Quantifying Flood Vulnerability Reduction via Private Precaution},
  series = {Earth future},
  volume    = {7},
  journal   = {Earth future},
  number    = {3},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF000994},
  pages     = {235 -- 249},
  year      = {2019},
  abstract  = {Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.},
  language  = {en}
}