@article{GruenthalStromeyerBosseetal.2018,
  author    = {Gr{\"u}nthal, Gottfried and Stromeyer, Dietrich and Bosse, Christian and Cotton, Fabrice and Bindi, Dino},
  title     = {The probabilistic seismic hazard assessment of Germany-version 2016, considering the range of epistemic uncertainties and aleatory variability},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {16},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {10},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-018-0315-y},
  pages     = {4339 -- 4395},
  year      = {2018},
  abstract  = {The basic seismic load parameters for the upcoming national design regulation for DIN EN 1998-1/NA result from the reassessment of the seismic hazard supported by the German Institution for Civil Engineering (DIBt). This 2016 version of the national seismic hazard assessment for Germany is based on a comprehensive involvement of all accessible uncertainties in models and parameters and includes the provision of a rational framework for integrating ranges of epistemic uncertainties and aleatory variabilities in a comprehensive and transparent way. The developed seismic hazard model incorporates significant improvements over previous versions. It is based on updated and extended databases, it includes robust methods to evolve sets of models representing epistemic uncertainties, and a selection of the latest generation of ground motion prediction equations. The new earthquake model is presented here, which consists of a logic tree with 4040 end branches and essential innovations employed for a realistic approach. The output specifications were designed according to the user oriented needs as suggested by two review teams supervising the entire project. Seismic load parameters, for rock conditions of nu(S30) = 800 m/s, are calculated for three hazard levels (10, 5 and 2\% probability of occurrence or exceedance within 50 years) and delivered in the form of uniform hazard spectra, within the spectral period range 0.02-3 s, and seismic hazard maps for peak ground acceleration, spectral response accelerations and for macroseismic intensities. Results are supplied as the mean, the median and the 84th percentile. A broad analysis of resulting uncertainties of calculated seismic load parameters is included. The stability of the hazard maps with respect to previous versions and the cross-border comparison is emphasized.},
  language  = {en}
}
@article{VogelWeiseSchroeteretal.2018,
  author    = {Vogel, Kristin and Weise, Laura and Schr{\"o}ter, Kai and Thieken, Annegret},
  title     = {Identifying Driving Factors in Flood-Damaging Processes Using Graphical Models},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {11},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2018WR022858},
  pages     = {8864 -- 8889},
  year      = {2018},
  abstract  = {Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.},
  language  = {en}
}