Improved solute transport modeling through joint estimation of hydraulic conductivity and dispersivities from tracer and ERT data

Abstract

The hydraulic conductivity (K) has been recognized as one of the controlling parameters that significantly impact the transport behavior in groundwater systems. To improve the predictive ability of a transport model, concentration measurements are commonly used in inverse modeling to characterize the K distribution. In addition to K, the spread of the solute plume also depends on dispersion coefficients. The evolution of the plume morphology can only be satisfactorily reproduced with appropriate dispersivities. Dispersivity values or distributions rely on the resolution of the inferred K distribution. Dispersivities were typically treated as spatially uniform in inverse modeling and jointly estimated with K. Although employing spatially variable dispersivities may hold the potential to better capture the solute transport behavior, it may significantly increase the number of unknown parameters for inverse modeling. Solving such an ill-posed inversion problem is challenging, especially when the amount of intrusive borehole-based measurements is limited. As a non-intrusive, cost-effective, and high sampling density method, time-lapse geophysical technique (e.g., electrical resistivity tomography, ERT) has not yet drawn much attention as a useful source of information for dispersivity estimation. In this study, we developed a joint hydrogeophysical inversion framework to simultaneously estimate spatially variable K and dispersivities by integrating tracer-concentration and ERT-derived potential data. We evaluated the proposed framework by conducting numerical tracer experiments in a highly heterogeneous aquifer. Results show that using limited tracer concentration data alone failed to resolve the fine structure in K field. In this case, there was little difference in the prediction performance of solute transport by considering spatially uniform or variable dispersivities. In contrast, the K distribution was estimated at a significantly higher resolution when integrating tracer-concentration and ERT data, contributing to a remarkable improvement in transport modeling. Powered by the sufficient amount of ERT measurements, jointly estimating spatially variable dispersivities further enhanced the reproduction of tracer plumes and breakthrough curves.