Salinity-based spatial evaluation of groundwater quality for agricultural use

Groundwater resources along with surface-water meet the needs of urban, industrial, and agricultural sectors, and therefore, their quality should be examined. Water quality indices are useful tools for aquifer management. In this research, the groundwater quality of the Miandoab Plain in the province of West Azerbaijan, northwest Iran, for agricultural purposes was investigated. For this purpose, the concentrations of the ions Mg2+, Ca2+, Na+, HCO3-, SO42-, Cl- and the pH level were measured. The indices effective salinity and potential salinity as well as sodium adsorption ratio and electrical conductivity were analyzed to evaluate the salinity. The geostatistical analysis was performed using the GS(+) software, and the zoning maps of salinity hazard were prepared using ArcGIS. To prepare the maps, electrical conductivity (EC), effective salinity (ES), potential salinity (PS), and sodium adsorption ratio (SAR) as well as Mg2+, Ca2+, Na+, HCO3-, SO42, and Cl- were selected based on the semi-variogram values and cross-validation technique. The Cl- map was considered as the basis for preparing the groundwater quality maps of the region. The results showed that the groundwater quality in the east of the plain is suitable, in the central part can be recommended under constant supervision, and in the west is unsuitable for agriculture. In other words, according to the geography of the plain, the recharge area is the low-risk part of the plain and the salinity hazard increases toward the discharge area. The results can pave the way for the relevant organizations to plan for the agricultural and environmental sectors.

Automated summary

This research investigated the groundwater quality of the Miandoab Plain in northwest Iran for agricultural purposes. The results showed that the groundwater quality in the east of the plain is suitable, while in the central part it can be recommended under constant supervision, and in the west is unsuitable for agriculture. These findings are of great importance for planning in the agricultural and environmental sectors.