@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{SairamSchroeterRoezeretal.2019,
  author    = {Sairam, Nivedita and Schroeter, Kai and R{\"o}zer, Viktor and Merz, Bruno and Kreibich, Heidi},
  title     = {Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes},
  series = {Water resources research},
  volume    = {55},
  journal   = {Water resources research},
  number    = {10},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2019WR025068},
  pages     = {8223 -- 8237},
  year      = {2019},
  abstract  = {Flood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region- and event-characteristics representing commonly available expert knowledge as group-level predictors within the HBM.},
  language  = {en}
}
@article{RoezerMuellerBubecketal.2016,
  author    = {R{\"o}zer, Viktor and M{\"u}ller, Meike and Bubeck, Philip and Kienzler, Sarah and Thieken, Annegret and Pech, Ina and Schr{\"o}ter, Kai and Buchholz, Oliver and Kreibich, Heidi},
  title     = {Coping with Pluvial Floods by Private Households},
  series = {Water},
  volume    = {8},
  journal   = {Water},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w8070304},
  pages     = {24},
  year      = {2016},
  abstract  = {Pluvial floods have caused severe damage to urban areas in recent years. With a projected increase in extreme precipitation as well as an ongoing urbanization, pluvial flood damage is expected to increase in the future. Therefore, further insights, especially on the adverse consequences of pluvial floods and their mitigation, are needed. To gain more knowledge, empirical damage data from three different pluvial flood events in Germany were collected through computer-aided telephone interviews. Pluvial flood awareness as well as flood experience were found to be low before the respective flood events. The level of private precaution increased considerably after all events, but is mainly focused on measures that are easy to implement. Lower inundation depths, smaller potential losses as compared with fluvial floods, as well as the fact that pluvial flooding may occur everywhere, are expected to cause a shift in damage mitigation from precaution to emergency response. However, an effective implementation of emergency measures was constrained by a low dissemination of early warnings in the study areas. Further improvements of early warning systems including dissemination as well as a rise in pluvial flood preparedness are important to reduce future pluvial flood damage.},
  language  = {en}
}