@article{DevittNealWageneretal.2021,
  author    = {Devitt, Laura and Neal, Jeffrey and Wagener, Thorsten and Coxon, Gemma},
  title     = {Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models},
  series = {Environmental research letters : ERL / Institute of Physics},
  volume    = {16},
  journal   = {Environmental research letters : ERL / Institute of Physics},
  number    = {6},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/abfac4},
  pages     = {15},
  year      = {2021},
  abstract  = {The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25\% in 68\% and 52\% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25\% at 60\% and 65\% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.},
  language  = {en}
}
@article{MesterWillnerFrieleretal.2021,
  author    = {Mester, Benedikt and Willner, Sven N. and Frieler, Katja and Schewe, Jacob},
  title     = {Evaluation of river flood extent simulated with multiple global hydrological models and climate forcings},
  series = {Environmental research letters : ERL / Institute of Physics},
  volume    = {16},
  journal   = {Environmental research letters : ERL / Institute of Physics},
  number    = {9},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/ac188d},
  pages     = {15},
  year      = {2021},
  abstract  = {Global flood models (GFMs) are increasingly being used to estimate global-scale societal and economic risks of river flooding. Recent validation studies have highlighted substantial differences in performance between GFMs and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent to satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events spread over four continents and various climate zones. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that when flood protection standards are accounted for, many models underestimate flood extent, pointing to deficiencies in their flood frequency distribution. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains.},
  language  = {en}
}