TY  - JOUR
A1  - Cucchi, Karma
A1  - Hesse, Falk
A1  - Kawa, Nura
A1  - Wang, Changhong
A1  - Rubin, Yoram
T1  - Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology
JF  - Advances in water resources
N2  - 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.
KW  - Data assimilation
KW  - Data fusion
KW  - Bayesian hierarchical model
KW  - Informative prior
KW  - Databases
Y1  - 2019
U6  - https://doi.org/10.1016/j.advwatres.2019.02.003
SN  - 0309-1708
SN  - 1872-9657
VL  - 126
SP  - 65
EP  - 78
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Kawa, Nura
A1  - Cucchi, Karina
A1  - Rubin, Yoram
A1  - Attinger, Sabine
A1  - Hesse, Falk
T1  - Defining Hydrogeological Site Similarity with Hierarchical Agglomerative Clustering
JF  - Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association
N2  - 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.
Y1  - 2022
U6  - https://doi.org/10.1111/gwat.13261
SN  - 0017-467X
SN  - 1745-6584
PB  - Wiley
CY  - Hoboken
ER  -