@incollection{BronstertCrisologoHeistermannetal.2020,
  author    = {Bronstert, Axel and Crisologo, Irene and Heistermann, Maik and {\"O}zt{\"u}rk, Ugur and Vogel, Kristin and Wendi, Dadiyorto},
  title     = {Flash-floods: more often, more severe, more damaging?},
  series = {Climate change, hazards and adaptation options: handling the impacts of a changing climate},
  booktitle = {Climate change, hazards and adaptation options: handling the impacts of a changing climate},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-37425-9},
  issn      = {1610-2010},
  doi       = {10.1007/978-3-030-37425-9_12},
  pages     = {225 -- 244},
  year      = {2020},
  abstract  = {In recent years, urban and rural flash floods in Europe and abroad have gained considerable attention because of their sudden occurrence, severe material damages and even danger to life of inhabitants. This contribution addresses questions about possibly changing environmental conditions which might have altered the occurrence frequencies of such events and their consequences. We analyze the following major fields of environmental changes. Altered high intensity rain storm conditions, as a consequence of regionalwarming; Possibly altered runoff generation conditions in response to high intensity rainfall events; Possibly altered runoff concentration conditions in response to the usage and management of the landscape, such as agricultural, forest practices or rural roads; Effects of engineering measures in the catchment, such as retention basins, check dams, culverts, or river and geomorphological engineering measures. We take the flash-flood in Braunsbach, SW-Germany, as an example, where a particularly concise flash flood event occurred at the end of May 2016. This extreme cascading natural event led to immense damage in this particular village. The event is retrospectively analyzed with regard to meteorology, hydrology, geomorphology and damage to obtain a quantitative assessment of the processes and their development. The results show that it was a very rare rainfall event with extreme intensities, which in combination with catchment properties and altered environmental conditions led to extreme runoff, extreme debris flow and immense damages. Due to the complex and interacting processes, no single flood cause can be identified, since only the interplay of those led to such an event. We have shown that environmental changes are important, but-at least for this case study-even natural weather and hydrologic conditions would still have resulted in an extreme flash flood event.},
  language  = {en}
}
@article{SchoppaSiegVogeletal.2020,
  author    = {Schoppa, Lukas and Sieg, Tobias and Vogel, Kristin and Z{\"o}ller, Gert and Kreibich, Heidi},
  title     = {Probabilistic flood loss models for companies},
  series = {Water resources research},
  volume    = {56},
  journal   = {Water resources research},
  number    = {9},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2020WR027649},
  pages     = {19},
  year      = {2020},
  abstract  = {Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty.},
  language  = {en}
}