The long-term effect of non-point source pollution on groundwater from agricultural practices is a major concern globally. Non-point source pollutants such as nitrate that occur through fertilizers and animal waste eventually make their way into the aquifer by infiltrating soil. The goal of this study is to characterize the probability distributions of non-point source locations and time release history of nitrate contamination into groundwater resources. A Bayesian framework using a Markov Chain Monte Carlo approach (MCMC) is developed to estimate posterior distributions of non-point sources by incorporating groundwater nitrate concentration data as well as geologic and hydrologic uncertainties. Hypothetical scenarios are used to test the approach and then apply it to a basin in North Carolina. The likelihood function computation involves a mechanistic model that simulates nitrate transport in groundwater from non-point agricultural sources and predicts nitrate concentrations at observation wells. Effectiveness of the proposed approach is tested through a convergence analysis of the MCMC algorithm. The Bayesian inference analysis methodology developed in this research will help decision makers and water managers identify potential source containment areas and to decide if further sampling is required.
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