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Updated: Dec 26, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
A simple method to evaluate groundwater vulnerability in urbanizing agricultural regions
E A Ojeda Olivares1, S I Belmonte Jiménez1, S Sandoval Torres1
1Instituto Politécnico Nacional, CIIDIR-Oaxaca, Hornos 1003, Colonia Noche Buena, Santa Cruz Xoxocotlán, CP 71230, Oaxaca, Mexico.
Abstract:
A methodology to evaluate groundwater vulnerability was developed and tested in a case study in the Central Valleys of the state of Oaxaca, Mexico, a region known for intensive agricultural activities and poor water management policies. An analysis was conducted to create and evaluate scenarios reflecting anthropogenic and natural stressors on groundwater using an analytical hierarchy process (AHP) and geographic information systems. Uncertainty in the vulnerability model was assessed using a Monte Carlo analysis. Five indices (abstraction (Abs), pollution (Po), runoff (Ru), groundwater recharge (Re), and marginalization (Ma)) were selected after an evaluation of the effects of population growth, climatology, hydrogeological features, and social marginalization on access to groundwater. Abstraction, pollution, and recharge rates are the main drivers of groundwater vulnerability, accounting for 87% of the vulnerability. The analysis revealed that the proposed model generates consistent results and contains low uncertainty. It also showed that more than 50% of the region's groundwater is moderately, and the vulnerability has become increasingly with abstraction, reduced recharge, and pollution (the most sensitive indices), indicating that groundwater in the Central Valleys is under great stress. Pollution and abstraction of groundwater resources are expected to rise in the more vulnerable areas, which will increase water crises and reduce access to water in rural communities. The approach and the indicators establish a baseline for the management and protection of water resources in developing countries where high-resolution data are lacking.
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