Non-point source evaluation of groundwater nitrate contamination from agriculture under geologic uncertainty

Abstract

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 occurs through fertilizers and animal waste eventually make their way into the aquifer by infiltrating soil. The goal of this study is to develop an approach for characterization of nitrate concentrations at potential source locations under conditions of geologic uncertainty. A Bayesian framework using the Markov Chain Monte Carlo (MCMC) approach is developed to estimate posterior probability distributions of non-point sources by incorporating nitrate concentration data as well as geologic uncertainties. The proposed approach is tested using hypothetical contamination scenarios and then validated using an application case study in North Carolina. Uncertainty existing in geologic formation (i.e., heterogeneous hydraulic conductivity field) is treated as prior and used in evaluating the likelihood function that measures the match between observed and simulated concentrations. The likelihood function computation involves a numerical model that simulates nitrate transport in groundwater from non-point agricultural sources and predicts nitrate concentrations at observation wells. Effectiveness of the MCMC approach is evaluated through a convergence analysis. Comparison among different sampling algorithms is carried out with respect to MCMC convergence diagnostics and making inference. The Bayesian inference analysis methodology developed in this research will help decision makers and water managers to identify potential areas for source containment and decide if further sampling is required.