The study applies geostatistical techniques to the groundwater quality data of December 1994 in parts of Jammu district. Groundwater quality deterioration has led to considerable interest in the design of investigative studies and monitoring programs on the issue.
Various management measures need to take into account the spatial and temporal behavior of groundwater. The spatial dependence between observations can be expressed by semi variance, which is a measure of the average similarity between observations at a given distance apart. One of the prime reasons for obtaining a semivariogram is to use it for estimation of the variable at unsampled locations.
In this study, an application of kriging technique is presented to quantify the spatial variability and interpolate the groundwater quality as measured in the study area. Experimental semivariogram were computed for nine groundwater quality variables and the theoretical models were fitted to each of these.
Various groundwater quality variables (i.e., pH, potassium, magnesium, conductance, sodium, chloride) were found to be spatially auto correlated, whereas, variables such as alkalinity, calcium and hardness were found to be non-auto correlated at the sampling scale. pH was found to be spatially auto correlated upto a distance of 10 km. Magnesium, chlorine and potassium were auto correlated upto a distance of 14 km and conductance upto a distance of 34 km. The semivariogram model obtained from the semi variance analysis was used to estimate observations in the unsampled locations within the area. These values were used for groundwater quality control mapping.
An important application of this investigation is that it gives guidelines for the selection of observation points for studying groundwater quality. The results of the study emphasize that groundwater quality commonly has spatial dependence and that an understanding of it may provide new insights into groundwater quality at the aquifer level.
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