Evaluating groundwater quality for irrigated agriculture: spatio-temporal investigations using GIS and geostatistics in Punjab, Pakistan

Abstract

Groundwater is cornerstone of global food production, and its quality plays a vital role in crop development. Success of agriculture in Pakistan relies heavily on groundwater because of reduced surface water supplies and rainfalls. The current study was conducted to assess the spatio-temporal quality of groundwater in the Lower Chenab Canal (LCC) east area with emphasis on characterizing groundwater quality zones for irrigation applications. Groundwater samples from 289 observation wells were collected and chemically analyzed. A geostatistical approach using kriging interpolation was applied to express spatial variation and minimize estimation error in data. Three water quality parameters, electrical conductivity (EC), sodium absorption ratio (SAR), and residual sodium carbonate (RSC), were utilized to develop groundwater quality maps for two seasons including pre-monsoon and post-monsoon. To describe the spatial autocorrelation, suitable semivariogram models were identified and cross validated. The exponential model gave the best results for EC and RSC, whereas the spherical model was found to be the best fit for SAR. The overlay analysis was performed to create composite water quality maps according to which 40% LCC area is showing good groundwater quality for irrigation, 49% is exhibiting marginal quality, and 10% unsuitable during pre-monsoon and post-monsoon seasons. Groundwater quality is 100% good for Sultanpur irrigation subdivision, and only 3.75% is good for Paccadala subdivision. The results of the study may provide guidelines for the planners/policy makers to devise area-specific strategies for sustainable groundwater use.