  In the scope of assessing aquifer systems in areas where freshwater is
scarce, estimation of transit times is a vital step to quantify the effect of
groundwater abstraction. Transit time distributions of different shapes, mean
residence times, and contributions are used to represent the hydrogeological
conditions in aquifer systems and are typically inferred from measured tracer
concentrations by inverse modeling. In this study, a multi-tracer sampling
campaign was conducted in the Salalah Plain in Southern Oman including CFCs,
SF6, 39Ar, 14C, and 4He. Based on the data of three tracers, a two-component
Dispersion Model (DMmix) and a nonparametric model with six age bins were
assumed and evaluated using Bayesian statistics. In a Markov Chain Monte Carlo
approach, the maximum likelihood parameter estimates and their uncertainties
were determined. Model performance was assessed using Bayes factor and
leave-one-out cross-validation. Both models suggest that the groundwater in the
Salalah Plain is composed of a very young component below 30 yr and a very old
component beyond 1,000 yr, with the nonparametric model performing slightly
better than the DMmix model. All wells except one exhibit reasonable goodness
of fit. Our results support the relevance of Bayesian modeling in hydrology and
the potential of nonparametric models for an adequate representation of aquifer
dynamics.
