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Title: A Bayesian Modeling Approach for Estimation of a Shape-Free Groundwater Age Distribution using Multiple Tracers

The mixing of groundwaters with different ages in aquifers, groundwater age is more appropriately represented by a distribution rather than a scalar number. To infer a groundwater age distribution from environmental tracers, a mathematical form is often assumed for the shape of the distribution and the parameters of the mathematical distribution are estimated using deterministic or stochastic inverse methods. We found that the prescription of the mathematical form limits the exploration of the age distribution to the shapes that can be described by the selected distribution. In this paper, the use of freeform histograms as groundwater age distributions is evaluated. A Bayesian Markov Chain Monte Carlo approach is used to estimate the fraction of groundwater in each histogram bin. This method was able to capture the shape of a hypothetical gamma distribution from the concentrations of four age tracers. The number of bins that can be considered in this approach is limited based on the number of tracers available. The histogram method was also tested on tracer data sets from Holten (The Netherlands; 3H, 3He, 85Kr, 39Ar) and the La Selva Biological Station (Costa-Rica; SF 6, CFCs, 3H, 4He and 14C), and compared to a number of mathematical forms. Accordingmore » to standard Bayesian measures of model goodness, the best mathematical distribution performs better than the histogram distributions in terms of the ability to capture the observed tracer data relative to their complexity. Among the histogram distributions, the four bin histogram performs better in most of the cases. The Monte Carlo simulations showed strong correlations in the posterior estimates of bin contributions, indicating that these bins cannot be well constrained using the available age tracers. The fact that mathematical forms overall perform better than the freeform histogram does not undermine the benefit of the freeform approach, especially for the cases where a larger amount of observed data is available and when the real groundwater distribution is more complex than can be represented by simple mathematical forms.« less
Authors:
 [1] ;  [2] ;  [1] ;  [3]
  1. The Catholic Univ. of America, Washington, DC (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Deltares, Utrecht (Netherlands); Geological Survey of the Netherlands, Utrecht (Netherlands); VU Univ., Amsterdam (Netherlands)
Publication Date:
OSTI Identifier:
1237523
Report Number(s):
LLNL-JRNL--637575
Journal ID: ISSN 0883-2927
Grant/Contract Number:
AC52-07NA27344
Type:
Accepted Manuscript
Journal Name:
Applied Geochemistry
Additional Journal Information:
Journal Volume: 50; Journal ID: ISSN 0883-2927
Publisher:
Elsevier
Research Org:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES