Current Science

Yousef Nazzal ¹, Faisal K. Zaidi ², Izrar Ahmed ³, Habes Ghrefat ², Muhammad Naeem ², Nassir S. N. Al-Arifi ², Saeed A. Al-Shaltoni ², Khaled M. Al-Kahtany ²

Affiliations
1 Department of Mathematics and Applied Sciences, College of Arts and Sciences, Abu Dhabi University, PO Box 59911, Abu Dhabi, AE
2 Department of Geology and Geophysics, King Saud University, PO Box 2455, Riyadh 11451, SA
3 Colleges of Engineering, King Saud University, Riyadh 11451, SA

Abstract

Central Saudi Arabia is one of the most arid regions of the world with very little precipitation and extreme climatic conditions. In the absence of available surface water supplies, the non-renewable groundwater resources stored in the Palaeozoic and Mesozoic sedimentary formations form the most important source for irrigation and domestic water requirements. The present study deals with 97 groundwater samples collected from Saq aquifer, which is the major aquifer in the region. The study involves the use of principal component analysis (PCA) and variogram analysis for groundwater quality mapping. PCA helped in establishing a series of factorial variables that summarize all the hydrochemical information. Efforts have been made to identify the spatial development of the principal process acting on groundwater quality by mapping it using factorial variables and ordinary kriging techniques. Two principal components (PCs) were extracted revealing that the chemical characteristics of groundwater in the region were acquired through rock-water interactions and anthropogenic influences. Finally, by applying kriging interpolation technique on the factor distribution values for the two PCs in the area under investigation, the factor distribution maps were prepared. The results concluded that both natural and anthropogenic processes contribute to the groundwater quality, but anthropogenic impacts are more important and may result in further deterioration of groundwater quality if relevant protection methodologies are not adopted.

Keywords

Arid Region, Geostatistics, Groundwater Quality, Kriging, Principal Component Analysis.

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