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Groundwater vulnerability assessment in agricultural areas using a modified DRASTIC model

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Abstract

Groundwater contamination is a major concern for groundwater resource managers worldwide. We evaluated groundwater pollution potential by producing a vulnerability map of an aquifer using a modified Depth to water, Net recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity (DRASTIC) model within a Geographic Information System (GIS) environment. The proposed modification which incorporated the use of statistical techniques optimizes the rating function of the DRASTIC model parameters, to obtain a more accurate vulnerability map. The new rates were computed using the relationships between the parameters and point data chloride concentrations in groundwater. The model was applied on Saveh-Nobaran plain in central Iran, and results showed that the coefficient of determination (R 2) between the point data and the relevant vulnerability map increased significantly from 0.52 to 0.78 after modification. As compared to the original DRASTIC model, the modified version produced better vulnerability zonation. Additionally, single-parameter and parameter removal sensitivity analyses were performed to evaluate the relative importance of each DRASTIC parameter. The results from both analyses revealed that the vadose zone is the most sensitive parameter influencing the variability of the aquifers’ vulnerability index. Based on the results, for non-point source pollution in agricultural areas, using the modified DRASTIC model is efficient compared to the original model. The proposed method can be effective for future groundwater assessment and plain-land management where agricultural activities are dominant.

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Acknowledgments

The authors would like to acknowledge University of Tehran, Iran and the Markazi Province Regional Water Authority, Iran, for financially supporting this research project.

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Correspondence to Mahmood Sadat-Noori.

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Sadat-Noori, M., Ebrahimi, K. Groundwater vulnerability assessment in agricultural areas using a modified DRASTIC model. Environ Monit Assess 188, 19 (2016). https://doi.org/10.1007/s10661-015-4915-6

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