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

Journal Article · Tue Oct 15 04:00:00 UTC 2013 · Applied Geochemistry

DOI: https://doi.org/10.1016/j.apgeochem.2013.10.004 · OSTI ID:1237523

Massoudieh, Arash ^[1]; Visser, Ate ^[2]; Sharifi, Soroosh ^[1]; Broers, Hans Peter ^[3]

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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)

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. According 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.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1237523
• Report Number(s): LLNL-JRNL--637575
• Journal Information: Applied Geochemistry, Journal Name: Applied Geochemistry Vol. 50; ISSN 0883-2927
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English