@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}
}
@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}
}
@article{DoThiChinhBubeckNguyenVietDungetal.2016,
  author    = {Do Thi Chinh, and Bubeck, Philip and Nguyen Viet Dung, and Kreibich, Heidi},
  title     = {The 2011 flood event in the Mekong Delta: preparedness, response, damage and recovery of private households and small businesses},
  series = {Disasters : the journal of disaster studies, policy and management},
  volume    = {40},
  journal   = {Disasters : the journal of disaster studies, policy and management},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0361-3666},
  doi       = {10.1111/disa.12171},
  pages     = {753 -- 778},
  year      = {2016},
  abstract  = {Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.},
  language  = {en}
}