A GIS-based EPIK Model for Assessing Aquifer Vulnerability in Irbid Governorate, North Jordan
Journal: Jordan Journal of Civil Engineering (JJCE) (Vol.2, No. 3)Publication Date: 2008-07-01
Authors : Muheeb Awawdeh; Awad Nawafleh;
Page : 267-278
Keywords : Vulnerability; Groundwater; EPIK; GIS; Irbid.;
Abstract
Vulnerability assessment has become an important element for wise resource management and land use planning. This work examined the sensitivity of karstic aquifers, in the area of Irbid governorate northwest Jordan, to surface contamination by applying the EPIK method, and then compared this susceptibility evaluation to water-chemistry data collected from wells and springs within the area. An additional objective was to demonstrate the combined use of the EPIK and geographical information system (GIS) as an effective method for groundwater pollution risk assessment. Using this method of investigation, the pollution susceptibility map classifies 3% of the study area as having low pollution susceptibility, 63% as having moderate pollution susceptibility, 33% as having high pollution susceptibility, and 1% as having very high pollution susceptibility. The northern and southern parts of the study area were dominated by very high and high vulnerability classes; while the eastern and western parts were characterized by moderate vulnerability classes. When comparing these modeling results to water-chemistry data from wells within the study area, two samples with the highest concentration of major cations and anions are found within regions of high pollution potential, and many wells with high nitrate contamination are found within regions of moderate pollution potential. This confirms the usefulness of the predictive modeling approach for assessing aquifer pollution susceptibility. Excessive concentrations of chemicals in water samples are explained by the intensive agricultural activities and wastewater contamination. The GIS technique has provided efficient environment for analyses and high capabilities of handling large spatial data.
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