@article{KorupMohrManga2021,
  author    = {Korup, Oliver and Mohr, Christian Heinrich and Manga, Michael M.},
  title     = {Bayesian detection of streamflow response to earthquakes},
  series = {Water resources research : an AGU journal},
  volume    = {57},
  journal   = {Water resources research : an AGU journal},
  number    = {7},
  publisher = {Wiley},
  address   = {Hoboken, NJ},
  issn      = {0043-1397},
  doi       = {10.1029/2020WR028874},
  pages     = {10},
  year      = {2021},
  abstract  = {Detecting whether and how river discharge responds to strong earthquake shaking can be time-consuming and prone to operator bias when checking hydrographs from hundreds of gauging stations. We use Bayesian piecewise regression models to show that up to a fifth of all gauging stations across Chile had their largest change in daily streamflow trend on the day of the M-w 8.8 Maule earthquake in 2010. These stations cluster distinctly in the near field though the number of detected streamflow changes varies with model complexity and length of time window considered. Credible seismic streamflow changes at several stations were the highest detectable in eight months, with an increased variance of discharge surpassing the variance of discharge following rainstorms. We conclude that Bayesian piecewise regression sheds new and unbiased insights on the duration, trend, and variance of streamflow response to strong earthquakes, and on how this response compares to that following rainstorms.},
  language  = {en}
}