Assessment of spatial and temporal variations of groundwater quality using WQI during two decades in aquifer of Golestan province

Document Type : Research Paper


1 M.Sc.Water Resource Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Iran

2 Associate Professor of Water Engineering Department, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Iran

3 Assistant Professor of Water Engineering Department, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Iran


Groundwater is of valuable resources that its conservation needs quality and quantity monitoring. Different indices such as WQI (water quality index) are used in order to monitor the groundwater quality. The objective of this study is to assess spatial-temporal changes in WQI in groundwater aquifer of Golestan province including Ghare-Sou, Grgan-Roud and Gorgan gulf basins. The Golestan province was selected as the study area. WQI was calculated based on the quality parameters data of 114 deep wells in the study area, collected during a 21-years period. After zoning WQI in GIS, the spatial-temporal changes of this index were investigated considering the range of changes, the observed minimum and maximum values during the study period. The empirical Bayesian kriging method presented better results in comparison to other methods for WQI zoning in GIS environment. The quality zoning of WQI in the studied area showed that the aquifer is in a good and very good condition and it is only bad in a small part of aquifer located in the eastern shores of the Caspian Sea and the northern part of the study area. In addition, no considerable differences were found between the WQI averages of the two events data sampling during the year. Also, the investigations show a decrease in water quality as closing to the sea shore and to the northern part of the study area. According to the fact that WQI follow the general slope of the region, there was a good spatial structure in the region which caused a better result for empirical Bayesian kriging interpolation method.


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