Prediction of spatial distribution pattern of groundwater quality indices in Golestan province for agricultural demands using geostatistics and GIS

Nowadays one of the key factors controlling development of sustainable agriculture is unsuitable quality of irrigation water. The use of unsuitable water for agriculture could cause some problems regarding salinity, infiltration and toxicity. The aim of this study is to predict the spatial distribution pattern of groundwater quality indices for agricultural purpose in Golestan province. The spatial variability of EC, TDS, SAR, pH, Na, Cl-, SO42-, Ca2, HCO3-, Na%, ES and PS of water samples obtained from 207 observation wells was investigated. The experimental semivariogram of all parameters was calculated and the best theoretical model was fitted. Ordinary kriging is then used to interpolate the values of investigated parameters at unsampled locations and their spatial distributions were mapped through GIS. The probability maps of exceeding a given critical threshold were produced for some parameters and indices using indicator kriging. The results showed that all water quality parameters have good spatial autocorrelation with spherical structure. Using the generated estimation maps the study area was classified into different water quality zones considering US soil salinity laboratory as well as FAO standard for irrigation. Based on US soil salinity laboratory, 95% of the study area has a moderate water quality (C3S1 and C3S2). Based on FAO limit, 89% of the study area has good and moderate water quality. The area with appropriate water quality is about 2592 sq km mostly contains eastern, southeastern and northwestern parts of the study area. Moreover based on the probability maps of ES and PS, there is a higher probability of salinity risk in central regions as well as some parts in west. The generated estimation and probability maps can help decision-makers and planners to make proper decisions for appropriate use of soil and water resources and development of a sustainable agriculture in the region.