Four Decades of Research at the MADE Site and Beyond: Advancing the Understanding and Modeling of Solute Transport in Heterogeneous Media

Abstract

Spatial heterogeneity, manifested as strong variability in hydraulic conductivity ( K ) within geological formations, exerts a fundamental control on groundwater flow and solute transport. Because K can vary over multiple orders of magnitude in complex three‐dimensional patterns, predicting contaminant plume migration remains a long‐standing challenge, particularly where high‐resolution aquifer characterization is infeasible. The Macrodispersion Experiment (MADE) site in Columbus, Mississippi, is among the most intensively studied heterogeneous aquifers worldwide and has played a pivotal role in advancing transport theory over the past four decades. This review synthesizes experimental and modeling studies at the MADE site, highlighting how its uniquely rich data set, including thousands of K measurements, multilevel tracer tests, and detailed geophysical surveys, enabled rigorous evaluation of the classical advection‐dispersion equation (ADE) and revealed its limitations in reproducing observed non‐Fickian transport behavior. These findings catalyzed the development of alternative modeling frameworks, including dual‐domain mass‐transfer models, continuous‐time random walk formulations, fractional ADE (fADE) approaches, and hydrofacies‐based simulations. Collectively, these frameworks account for preferential flow, facilitate parameter upscaling, and enable non‐Gaussian representations of transport in highly heterogeneous systems. To place the MADE site in a broader context, we integrate insights from other landmark field experiments, including Borden, Cape Cod, Twin Lake, and Hanford, identifying common lessons on the physical origins of anomalous transport and the persistent challenges of predictive modeling in highly heterogeneous aquifers. Finally, we discuss emerging opportunities for machine learning and artificial intelligence to enhance subsurface characterization and strengthen transport prediction for sustainable groundwater management.