Open Access
September 2010 Predicting vertical connectivity within an aquifer system
Laura Guadagnini, Alberto Guadagnini, Dave Higdon, Margaret Short, Daniel M. Tartakovsky
Bayesian Anal. 5(3): 557-581 (September 2010). DOI: 10.1214/10-BA522

Abstract

The subsurface environment beneath the Municipality of Bologna, Italy, is comprised of a series of alluvial deposits which constitute large and productive aquifer systems. These are separated from the shallow, free surface aquifer by a low permeability barrier called aquitard Alpha. The upper aquifer contains water that shows relevant contamination from industrial pollutants. The deep aquifers are relatively pristine and provide about 80\% of all groundwater used for drinking and industrial purposes in the area of Bologna. Hence, it is imperative that planners understand where along aquitard Alpha there exists potential direct connection between the upper and the deep aquifers, which could lead to contamination of the city's key water supply well fields.

In order to better assess the existence of preferential flow paths between these aquifer systems, we carry out a statistical analysis in which the aquitard is represented as a bivariate spatial process, accounting for dependence between the two spatial components. The first process models its effective thickness. The second process is binary, modeling the presence or absence of direct vertical connections between the aquifers. This map is then cross referenced with other forms of data regarding the hydrology of the region.

Citation

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Laura Guadagnini. Alberto Guadagnini. Dave Higdon. Margaret Short. Daniel M. Tartakovsky. "Predicting vertical connectivity within an aquifer system." Bayesian Anal. 5 (3) 557 - 581, September 2010. https://doi.org/10.1214/10-BA522

Information

Published: September 2010
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62418
MathSciNet: MR2719667
Digital Object Identifier: 10.1214/10-BA522

Keywords: Gaussian process , Markov chain Monte Carlo , spatial model , Subjective likelihood

Rights: Copyright © 2010 International Society for Bayesian Analysis

Vol.5 • No. 3 • September 2010
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