Share This Article:

A Monte Carlo-Based Approach for Groundwater Chemistry Inverse Modeling

Abstract Full-Text HTML XML Download Download as PDF (Size:2961KB) PP. 112-120
DOI: 10.4236/ojmh.2014.44011    2,875 Downloads   3,456 Views  


Inverse geochemical modeling of groundwater entails identifying a set of geochemical reactions which can explain observed changes in water chemistry between two samples that are spatially related in some sense, such as two points along a flow pathway. A common inversion approach is to solve a set of simultaneous mass and electron balance equations involving water-rock and oxidation-reduction reactions that are consistent with the changes in concentrations of various aqueous components. However, this mass-balance approach does not test the thermodynamic favorability of the resulting model and provides limited insight into the model uncertainties. In this context, a Monte Carlo-based forward-inverse modeling method is proposed that generates probability distributions for model parameters which best match the observed data using the Metro-polis-Hastings search strategy. The forward model is based on the well-vetted PHREEQC geochemical model. The proposed modeling approach is applied to two test applications, one involving an inverse modeling example supplied with the PHREEQC code that entails groundwater interactions with a granitic rock mineral assemblage, and the other concerning the impact of fuel hydrocarbon bioattenuation on groundwater chemistry. In both examples, the forward-inverse approach is able to approximately reproduce observed water quality changes invoking mass transfer reactions that are all thermodynamically favorable.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

McNab Jr., W. (2014) A Monte Carlo-Based Approach for Groundwater Chemistry Inverse Modeling. Open Journal of Modern Hydrology, 4, 112-120. doi: 10.4236/ojmh.2014.44011.


[1] Zhu, C. and Anderson, G. (2002) Environmental Applications of Geochemical Modeling. Cambridge University Press, Cambridge.
[2] Plummer, L.N., Prestemon, E.C. and Parkhurst, D.L. (1991) An Interactive Code (NETPATH) for Modeling NET Geochemical Reactions along a Flow PATH. USA Geological Survey Water-Resources Investigations Report, 91-4078.
[3] Plummer, L.N., Prestemon, E.C. and Parkhurst, D.L. (1994) An Interactive Code (NETPATH) for Modeling NET Geochemical Reactions along a Flow PATH, Version 2.0. USA Geological Survey Water-Resources Investigations Report, 94-4169.
[4] Parkhurst, D.L. and Appelo, C.A.J. (1999) User’s Guide to PHREEQC (Version 2): A Computer Program for Speciation, Batch-Reaction, One-Dimensional Transport, and Inverse Geochemical Calculations. USA Geological Survey Water-Resources Investigations Report, 99-4259.
[5] Sharif, M.U., Davis, R.K., Steele, K.F., Kim, B., Kresse, T.M. and Fazio, J.A. (2008) Inverse Geochemical Modeling of Groundwater Evolution with Emphasis on Arsenic in the Mississippi River Valley Alluvial Aquifer, Arkansas (USA). Journal of Hydrology, 350, 41-55.
[6] Dhiman, S.D. and Keshari, A.K. (2006) GIS Assisted Inverse Geochemical Modeling for Plausible Phase Transfers in Aquifers. Environmental Geology, 50, 1211-1219.
[7] Toride, N., Leij, F.J. and van Genuchten, M.Th. (1995) The CXTFIT Code for Estimating Transport Parameters from Laboratory or Field Tracer Experiments, Version 2.0. USA Salinity Laboratory, Riverside.
[8] Tang, G., Mayes, M.A., Parker, J.C. and Jardine, P.M. (2010) CXTFIT/Excel—A Modular Adaptable Code for Parameter Estimation, Sensitivity Analysis and Uncertainty Analysis for Laboratory or Field Tracer Experiments. Computers & Geosciences, 36, 1200-1209.
[9] Tonkin, M. and Doherty, J. (2008) Calibration-Constrained Monte Carlo Analysis of Highly-Parameterized Models Using Subspace Techniques. Water Resources Research, 45, 1-17.
[10] Wang, Y. and Van Cappellen, P. (1996) A Multicomponent Reactive Transport Model of Early Diagenesis: Application to Redox Cycling in Coastal Marine Sediments. Geochimica et Cosmochimica Acta, 60, 2993-3014.
[11] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953) Equations of State Calculations by Fast Computing Machines. Journal of Chemical Physics, 21, 1087-1092.
[12] McNab, W.W., Ramirez, A.L. and Johnson, J.W. (2013) Quantifying Reactive Chemistry along an Injected CO2 Flow Path at the Field Scale Using a Monte Carlo Simulation Approach. International Journal of Greenhouse Gas Control, 16, S194-S202.
[13] Garrels, R.M. and Mackenzie, F.T. (1967) Origin of the Chemical Composition of Some Springs and Lakes. In: Stumm, W., Ed., Equilibrium Concepts in Natural Water Systems, Chap. 10, American Chemical Society, Washington DC, 222-242.
[14] Chapelle, F.H. and Lovley, D.R. (1990) Rates of Bacterial Metabolism in Deep Coastal-Plain Aquifers. Applied and Environmental Microbiology, 56, 1865-1874.
[15] Murphy, E.M. and Schramke, J.A. (1998) Estimate of Microbial Rates in Groundwater by Geochemical Modeling Constrained with Stable Isotopes. Geochimica et Cosmochimica Acta, 62, 3395-3406.

comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.