@article{TesselaarBotzenHaeretal.2020,
  author    = {Tesselaar, Max and Botzen, W. J. Wouter and Haer, Toon and Hudson, Paul and Tiggeloven, Timothy and Aerts, Jeroen C. J. H.},
  title     = {Regional inequalities in flood insurance affordability and uptake under climate change},
  series = {Sustainability},
  volume    = {12},
  journal   = {Sustainability},
  number    = {20},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2071-1050},
  doi       = {10.3390/su12208734},
  pages     = {30},
  year      = {2020},
  abstract  = {Flood insurance coverage can enhance financial resilience of households to changing flood risk caused by climate change. However, income inequalities imply that not all households can afford flood insurance. The uptake of flood insurance in voluntary markets may decline when flood risk increases as a result of climate change. This increase in flood risk may cause substantially higher risk-based insurance premiums, reduce the willingness to purchase flood insurance, and worsen problems with the unaffordability of coverage for low-income households. A socio-economic tipping-point can occur when the functioning of a formal flood insurance system is hampered by diminishing demand for coverage. In this study, we examine whether such a tipping-point can occur in Europe for current flood insurance systems under different trends in future flood risk caused by climate and socio-economic change. This analysis gives insights into regional inequalities concerning the ability to continue to use flood insurance as an instrument to adapt to changing flood risk. For this study, we adapt the "Dynamic Integrated Flood and Insurance" (DIFI) model by integrating new flood risk simulations in the model that enable examining impacts from various scenarios of climate and socio-economic change on flood insurance premiums and consumer demand. Our results show rising unaffordability and declining demand for flood insurance across scenarios towards 2080. Under a high climate change scenario, simulations show the occurrence of a socio-economic tipping-point in several regions, where insurance uptake almost disappears. A tipping-point and related inequalities in the ability to use flood insurance as an adaptation instrument can be mitigated by introducing reforms of flood insurance arrangements.},
  language  = {en}
}
@misc{SamprognaMohorThiekenKorup2021,
  author    = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver},
  title     = {Residential flood loss estimated from Bayesian multilevel models},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51774},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517743},
  pages     = {1599 -- 1614},
  year      = {2021},
  abstract  = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.},
  language  = {en}
}
@article{SamprognaMohorThiekenKorup2021,
  author    = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver},
  title     = {Residential flood loss estimated from Bayesian multilevel models},
  series = {Natural Hazards and Earth System Sciences},
  volume    = {21},
  journal   = {Natural Hazards and Earth System Sciences},
  publisher = {European Geophysical Society},
  address   = {Katlenburg-Lindau},
  issn      = {2195-9269},
  doi       = {10.5194/nhess-21-1599-2021},
  pages     = {1599 -- 1614},
  year      = {2021},
  abstract  = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.},
  language  = {en}
}