Salt loads in the Colorado River Basin are a primary water quality concern. Natural groundwater brine discharge to the Dolores River where it passes through the collapsed salt anticline of the Paradox Valley in western Colorado (USA) is a significant source of salt to the Colorado River. An airborne electromagnetic survey of Paradox Valley has provided insights into the three-dimensional distribution of brine in the surficial aquifer. A combination of stochastic and deterministic resistivity inversions was used to interpret the top of the freshwater–brine interface and to qualitatively describe the vertical salinity gradients across the interface. Low-resistivity regions indicative of brine occur near the land surface where brine discharges to the Dolores River and increase in depth several kilometers up-gradient along the axis of the valley. The most conductive parts of the brine plume are found in the areas below and adjacent to the river, suggesting that the brine becomes shallower and more concentrated as it reaches its natural discharge location. A significant freshwater lens overlying the brine west of the Dolores River is spatially correlated to the intermittent West Paradox Creek and agricultural irrigation. Below this lens, the transition from freshwater to brine appears to occur abruptly over a few meters and correlates to available well information. However, away from these regions and particularly with distance from the river, the freshwater–brine interface appears to be more diffuse.

Groundwater pollution source identification (GPSI) provides information about the temporal and spatial distribution of pollution sources and helps decision makers design pollution remediation plans to protect the groundwater environment. The Bayesian approach based on the Markov Chain Monte Carlo (MCMC) approach provides an efficient framework for GPSI. However, MCMC sampling entails multiple model calls to converge to the posterior probability distribution of unknown pollution source parameters and entails a massive computational load if the simulation model is directly called. This study aimed to develop an innovative framework in which an improved MCMC approach was linked to a surrogate model. Sensitivity analysis was incorporated into the MH-MCMC approach, named SAMH-MCMC (sensitivity analysis based Metropolis Hastings-Markov Chain Monte Carlo), to speed up the convergence of the posterior distribution in a novel way to control the search step size. Three computationally inexpensive surrogate models for the simulation model were proposed: support vector regression, Kriging (KRG), and multilayer perceptron, and the most accurate model was chosen. The feasibility and advantages of the developed framework were evaluated and validated through two hypothetical numerical cases with homogenous and heterogeneous media. The proposed approach has strong convergence robustness as it considers the sensitivities of the unknown parameters that characterise groundwater pollution sources and can achieve high identification accuracy. Furthermore, the KRG surrogate model has a higher accuracy than other surrogate models, owing to its linear unbiased estimation characteristic. Overall, the framework developed in this study is a promising solution for identifying groundwater pollution source parameters.