@article{FarragBrillNguyenetal.2022,
  author    = {Farrag, Mostafa and Brill, Fabio Alexander and Nguyen, Viet Dung and Sairam, Nivedita and Schr{\"o}ter, Kai and Kreibich, Heidi and Merz, Bruno and de Bruijn, Karin M. and Vorogushyn, Sergiy},
  title     = {On the role of floodplain storage and hydrodynamic interactions in flood risk estimation},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {67},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {4},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2022.2030058},
  pages     = {508 -- 534},
  year      = {2022},
  abstract  = {Hydrodynamic interactions, i.e. the floodplain storage effects caused by inundations upstream on flood wave propagation, inundation areas, and flood damage downstream, are important but often ignored in large-scale flood risk assessments. Although new methods considering these effects sometimes emerge, they are often limited to a small or meso scale. In this study, we investigate the role of hydrodynamic interactions and floodplain storage on flood hazard and risk in the German part of the Rhine basin. To do so, we compare a new continuous 1D routing scheme within a flood risk model chain to the piece-wise routing scheme, which largely neglects floodplain storage. The results show that floodplain storage is significant, lowers water levels and discharges, and reduces risks by over 50\%. Therefore, for accurate risk assessments, a system approach must be adopted, and floodplain storage and hydrodynamic interactions must carefully be considered.},
  language  = {en}
}
@article{BrillPassuniPinedaEspichanCuyaetal.2020,
  author    = {Brill, Fabio Alexander and Passuni Pineda, Silvia and Espichan Cuya, Bruno and Kreibich, Heidi},
  title     = {A data-mining approach towards damage modelling for El Nino events in Peru},
  series = {Geomatics, natural hazards and risk},
  volume    = {11},
  journal   = {Geomatics, natural hazards and risk},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1947-5705},
  doi       = {10.1080/19475705.2020.1818636},
  pages     = {1966 -- 1990},
  year      = {2020},
  abstract  = {Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.},
  language  = {en}
}
@article{SairamBrillSiegetal.2021,
  author    = {Sairam, Nivedita and Brill, Fabio Alexander and Sieg, Tobias and Farrag, Mostafa and Kellermann, Patric and Viet Dung Nguyen, and L{\"u}dtke, Stefan and Merz, Bruno and Schr{\"o}ter, Kai and Vorogushyn, Sergiy and Kreibich, Heidi},
  title     = {Process-based flood risk assessment for Germany},
  series = {Earth's future / American Geophysical Union},
  volume    = {9},
  journal   = {Earth's future / American Geophysical Union},
  number    = {10},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken, NJ},
  issn      = {2328-4277},
  doi       = {10.1029/2021EF002259},
  pages     = {12},
  year      = {2021},
  abstract  = {Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5\% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60\% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3\% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments.},
  language  = {en}
}