@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}
}
@article{KawaCucchiRubinetal.2022,
  author    = {Kawa, Nura and Cucchi, Karina and Rubin, Yoram and Attinger, Sabine and Hesse, Falk},
  title     = {Defining Hydrogeological Site Similarity with Hierarchical Agglomerative Clustering},
  series = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association},
  journal   = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0017-467X},
  doi       = {10.1111/gwat.13261},
  pages     = {11},
  year      = {2022},
  abstract  = {Hydrogeological information about an aquifer is difficult and costly to obtain, yet essential for the efficient management of groundwater resources. Transferring information from sampled sites to a specific site of interest can provide information when site-specific data is lacking. Central to this approach is the notion of site similarity, which is necessary for determining relevant sites to include in the data transfer process. In this paper, we present a data-driven method for defining site similarity. We apply this method to selecting groups of similar sites from which to derive prior distributions for the Bayesian estimation of hydraulic conductivity measurements at sites of interest. We conclude that there is now a unique opportunity to combine hydrogeological expertise with data-driven methods to improve the predictive ability of stochastic hydrogeological models.},
  language  = {en}
}