@article{CucchiHesseKawaetal.2019,
  author    = {Cucchi, Karma and Hesse, Falk and Kawa, Nura and Wang, Changhong and Rubin, Yoram},
  title     = {Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology},
  series = {Advances in water resources},
  volume    = {126},
  journal   = {Advances in water resources},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2019.02.003},
  pages     = {65 -- 78},
  year      = {2019},
  abstract  = {Stochastic modeling is a common practice for modeling uncertainty in hydrogeology. In stochastic modeling, aquifer properties are characterized by their probability density functions (PDFs). The Bayesian approach for inverse modeling is often used to assimilate information from field measurements collected at a site into properties' posterior PDFs. This necessitates the definition of a prior PDF, characterizing the knowledge of hydrological properties before undertaking any investigation at the site, and usually coming from previous studies at similar sites. In this paper, we introduce a Bayesian hierarchical algorithm capable of assimilating various information-like point measurements, bounds and moments-into a single, informative PDF that we call ex-situ prior. This informative PDF summarizes the ex-situ information available about a hydrogeological parameter at a site of interest, which can then be used as a prior PDF in future studies at the site. We demonstrate the behavior of the algorithm on several synthetic case studies, compare it to other methods described in the literature, and illustrate the approach by applying it to a public open-access hydrogeological dataset.},
  language  = {en}
}