Spatiotemporal mapping of groundwater quality for irrigation using geostatistical analysis combined with a linear regression method

Abstract

Spatiotemporal assessment of groundwater quality for irrigation is essential for agricultural management. This study was conducted to quantify the spatiotemporal changes of Kerman groundwater irrigation quality during 1999–2010 using geostatistical analysis combined with a new method established based on regression coefficient (RC). Result showed that among the main soluble ions, Na+ and Cl− had the highest concentrations. Except Ca+2, the average concentration of all other soluble ions and also EC were higher than the maximum permissible levels for drinking, however, Na+, SO4−2 and EC showed no limitations for agricultural irrigation. Based on the proposed RC, soluble Na+, Ca+2, Cl−, total cations, total anions and EC have gradually increased over the years. Soluble Cl− with 0.18 meq l−1 y−1 showed averagely the highest value of RC. Also, EC exhibited an increasing trend with an average value of 16.8 µS cm−1 y−1. In contrast, Mg+2, SO4−2 and SAR exhibited negative values of RC, while the value for HCO3− was zero. Based on nugget-to-sill ratios, the groundwater variables had moderate to strong spatial structure. Finally, the spatiotemporal changes of groundwater salinity (EC) and sodicity (SAR) have been mainly controlled by Na+ and Cl−.