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\begin{document}

\title{Quantifying Subsurface Parameter and Transport Uncertainty Using
Surrogate Modeling and Environmental Tracers}



\author[1]{Nicholas Thiros}%
\author[2]{W. Gardner}%
\author[2]{Marco Maneta}%
\author[2]{Douglas Brinkerhoff}%
\affil[1]{University of Montana Missoula}%
\affil[2]{University of Montana}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
We combine physics-based groundwater reactive transport modeling with
machine learning techniques to quantify hydrogeologic model and solute
transport predictive uncertainties. We train an artificial neural
network (ANN) on a dataset of groundwater hydraulic heads and
\textsuperscript{3}H concentrations generated using a high-fidelity
groundwater reactive transport model. Using the trained ANN as a
surrogate model to reproduce the input-output response of the
high-fidelity reactive transport model, we quantify the posterior
distributions of hydrogeologic parameters and hydraulic forcing
conditions using Markov-chain Monte Carlo (MCMC) calibration against
field observations of groundwater hydraulic heads and
\textsuperscript{3}H concentrations. We demonstrate the methodology with
a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute
transport at a contaminated site in Wyoming, USA. Our results show that
including \textsuperscript{3}H observations in the calibration dataset
reduced the uncertainty in the estimated permeability field and
infiltration rates, compared to calibration against hydraulic heads
alone. However, predictive uncertainty quantification shows that CFC-12
transport predictions conditioned to the parameter posterior
distributions cannot reproduce the field measurements. We found that
calibrating the model to hydraulic head and \textsuperscript{3}H
observations results in groundwater mean ages that are too large to
explain the observed CFC-12 concentrations. The coupling of the
physics-based reactive transport model with the machine learning
surrogate model allows us to efficiently quantify model parameter and
predictive uncertainties, which is typically computationally intractable
using reactive transport models alone.%
\end{abstract}%



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