Groundwater Depletion, Sustainable Aquifer Management, and Data-Driven Strategies for Long-Term Water Security

Authors

  • Ahmid Chernor Jalloh Conservation Trust Fund, Ministry of the Environment and Climate Change - Sierra Leone Ahmid.jalloh@ctf.gov.sl Author
  • Ansumana Davowah Sierra Leone Agricultural Research Institute (SLARI), ‎Magbosi land water and environment research center (MLWERC) Orcid ID: https://orcid.org/0009-0003-9705-6210 Author
  • Ameer Jan University of Makran. ameerjan@uomp.edu.pk Author

DOI:

https://doi.org/10.63163/jpehss.v4i3.1735

Abstract

Groundwater constitutes over 90% of the world's readily accessible freshwater, supplying drinking water to approximately two billion people and supporting 40% of global agricultural irrigation. However, widespread overexploitation has triggered severe depletion across major aquifer systems worldwide, with global annual groundwater depletion doubling from 126 ± 32 km³/year in 1960 to 283 ± 40 km³/year by 2000. This review synthesizes global groundwater depletion trends, examines severe regional case studies, evaluates sustainable management frameworks, and highlights advanced data-driven strategies for long-term water security. Seven nations India, Pakistan, Iran, the United States, China, Mexico, and Saudi Arabia account for the vast majority of global depletion volumes, driven primarily by subsidized energy policies, uncontrolled tube-well expansion, and water-intensive agricultural practices. The review analyzes irreversible geomechanical impacts, including land subsidence exceeding 35–50 cm/year in Mexico City and permanent aquifer storage loss estimated at ~17 km³/year globally. Supply-side interventions via Managed Aquifer Recharge (MAR) and demand-side controls are evaluated, revealing the "Efficiency Paradox" where irrigation efficiency improvements fail to reduce net basin-scale abstraction due to rebound crop expansion. The review emphasizes emerging data-driven technologies, including GRACE/GRACE-FO satellite gravimetry, InSAR geodesy, machine learning downscaling, and Physics-Informed Neural Networks (PINNs) integrated into subsurface Digital Twins. The synthesis concludes that achieving long-term water security requires polycentric governance aligned with hydrostratigraphic boundaries, enforceable volumetric pumping caps, virtual water trade tracking, and strategically sited MAR infrastructure monitored through physics-constrained artificial intelligence protecting global ecosystems, food production, and human water security for future generations.

Downloads

Published

2026-09-30