@misc{TarasovaMerzKissetal.2019,
  author    = {Tarasova, Larisa and Merz, Ralf and Kiss, Andrea and Basso, Stefano and Bl{\"o}chl, G{\"u}nter and Merz, Bruno and Viglione, Alberto and Pl{\"o}tner, Stefan and Guse, Bj{\"o}rn and Schumann, Andreas and Fischer, Svenja and Ahrens, Bodo and Anwar, Faizan and B{\´a}rdossy, Andr{\´a}s and B{\"u}hler, Philipp and Haberlandt, Uwe and Kreibich, Heidi and Krug, Amelie and Lun, David and M{\"u}ller-Thomy, Hannes and Pidoto, Ross and Primo, Cristina and Seidel, Jochen and Vorogushyn, Sergiy and Wietzke, Luzie},
  title     = {Causative classification of river flood events},
  series = {Wiley Interdisciplinary Reviews : Water},
  volume    = {6},
  journal   = {Wiley Interdisciplinary Reviews : Water},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2049-1948},
  doi       = {10.1002/wat2.1353},
  pages     = {23},
  year      = {2019},
  abstract  = {A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large-scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph-based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space-time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods},
  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{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}
}