Abstract
Groundwater is vulnerable to contamination by anthropological activities. Vulnerability mapping is considered as a fundamental aspect of groundwater management. The aim of this study was to estimate aquifer vulnerability by applying the DRASTIC and GOD models in Abarkooh plain, Yazd province, center of Iran. The DRASTIC model uses seven environmental parameters (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity) to characterize the hydrogeological setting and evaluate aquifer vulnerability. GOD is an overlay and index method designed to map groundwater vulnerability over large regions based on three parameters (groundwater confinement, overlying strata, and depth to groundwater). The information layers for models were provided via geographic information system. The overlap techniques were used to provide and produce the vulnerability map of the study areas considering weight coefficients of each layer. Accuracy of the models was evaluated using linear regression between observations values of nitrate and estimated vulnerability to pollution in the measured wells. A significant correlation was observed between measured nitrate and pollution potential evaluated by DRASTIC model (P < 0.01), but no significant correlation was observed for GOD model (P < 0.05). The results showed that the DRASTIC model is better than GOD model to estimate groundwater vulnerability to pollution in the measured wells. For DRASTIC model, the correlation coefficient between vulnerability index and nitrate concentration was 68 % that was substantially higher than 28 % obtained for the GOD model. We can conclude that nitrate concentration should be a suitable parameter to investigate the accuracy of the DRASTIC and GOD models.
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This work was funded through the University of Kahsn in Iran under Grant No. 28400. The authors are grateful to the university for this generous support.
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Ghazavi, R., Ebrahimi, Z. Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models. Int. J. Environ. Sci. Technol. 12, 2909–2918 (2015). https://doi.org/10.1007/s13762-015-0813-2
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DOI: https://doi.org/10.1007/s13762-015-0813-2