Assessment of Groundwaters Quality for Industrial and Agricultural Applications Using Consecutive Gaussian Simulation

The geostatistical method is one of the most advanced techniques to assess and survey groundwater quality.  Using unsuitable water results in some problems such as soil salinity, reduction of soil permeability, decrease in water absorption by plant roots, diminution of crop productivity, or even menace agricultural production. Accordingly, in this study, the groundwater quality in the study's area for agricultural uses as well as industrial applications by using common diagrams and components related to the classification of water quality was investigated. ISATIS and Surfur software were used to illustrate changes in quality characteristics in the study's area graphically and Consecutive Gaussian simulations to analyze spatial connection among variables and estimate some of the quality indexes such as PH, RSC, Na%, SAR, and Ec. After data normalization, the variogram was calculated, and a sufficient model was selected to fit the experimental variogram based on the lowest SSR Error. Afterward, Consecutive Gaussian simulation in 100 samples in the study's area was examined using the fitted model based on the experimental model, and the maps resulting through simulation in different thresholds were provided. The results showed that the majority of samples have fairly suitable quality in terms of SAR, and in general, they are appropriate for agricultural uses. Percentage risk of Sodium in agricultural water in the study's area was also classified into the appropriate class. Except for four samples whose spatial connection of the forgoing cases have been interpolated and the interpolated probability maps have been provided, the water quality for industrial uses is susceptible to sedimentation in all statutes.