Integrated Groundwater Modeling to Support Sustainable and Equitable Water Management in a Data-Scarce Volcanic Aquifer, Central Mexico

Groundwater resources in the Global South are increasingly stressed by rapid urbanization, agricultural expansion, and climate variability, yet effective management is often constrained by limited monitoring infrastructure and fragmented datasets. This study presents an integrated groundwater resource assessment of the Toluca Valley aquifer (central Mexico), a complex, multilayer volcanic system that supplies water to municipal, industrial, and agricultural users while also supporting inter-basin transfers to Mexico City.

A multilayer numerical groundwater flow model was developed using MODFLOW and implemented in the Groundwater Modeling System (GMS) to simulate aquifer dynamics from 1981 to 2017. The model integrates heterogeneous geological and hydrogeological information, long-term groundwater-level observations from multi-piezometer networks, climate-driven recharge estimates, and spatially distributed abstraction data. Monthly transient simulations were calibrated using a combination of manual and automated parameter estimation, explicitly addressing parameter uncertainty arising from sparse subsurface data. Model validation was strengthened through comparison of MODFLOW-simulated river leakage with independently derived SWAT baseflow estimates, providing cross-model consistency for surface–groundwater interactions.

Results indicate a progressive transition from near-balanced groundwater conditions in the 1980s to persistent storage deficits exceeding 70 million m³/year by 2017, driven primarily by increased abstraction that outpaces recharge. The strongest groundwater declines occur in high-elevation recharge zones, highlighting inequities in resource depletion between recharge areas and demand centers.

By combining accessible public datasets, integrated modeling, and uncertainty-aware calibration, this work demonstrates an inclusive and transferable approach to groundwater characterization in data-scarce regions. The results support evidence-based, locally grounded strategies for sustainable and climate-resilient groundwater governance.