@article{FrielerSchaubergerArnethetal.2017,
  author    = {Frieler, Katja and Schauberger, Bernhard and Arneth, Almut and Balkovic, Juraj and Chryssanthacopoulos, James and Deryng, Delphine and Elliott, Joshua and Folberth, Christian and Khabarov, Nikolay and M{\"u}ller, Christoph and Olin, Stefan and Pugh, Thomas A. M. and Schaphoff, Sibyll and Schewe, Jacob and Schmid, Erwin and Warszawski, Lila and Levermann, Anders},
  title     = {Understanding the weather signal in national crop-yield variability},
  series = {Earths future},
  volume    = {5},
  journal   = {Earths future},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2328-4277},
  doi       = {10.1002/2016EF000525},
  pages     = {605 -- 616},
  year      = {2017},
  abstract  = {Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50\% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50\% in seven countries, including the United States. The explained variance exceeds 50\% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.},
  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}
}