SPATIAL VARIABILITY OF GROUND WATER QUALITY FOR IRRIGATION OF MANSA DISTRICT, PUNJAB

CHANDER SHEKHAR¹, KAPIL ROHILLA²*, PARDEEP KUMAR³, PARVEEN SIHAG⁴, ANIL SOOD^5
1Haryana Space Applications Centre (HARSAC), CCS Haryana Agriculture University, Hisar, 125004, Haryana, India
²Haryana Space Applications Centre (HARSAC), CCS Haryana Agriculture University, Hisar, 125004, Haryana, India
³Haryana Space Applications Centre (HARSAC), CCS Haryana Agriculture University, Hisar, 125004, Haryana, India
⁴Haryana Space Applications Centre (HARSAC), CCS Haryana Agriculture University, Hisar, 125004, Haryana, India
⁵Punjab Remote Sensing Centre (PRSC), Ludhiana, 141004, Punjab, India
* Corresponding Author : rohilla21@gmail.com

Received : 12-01-2020     Accepted : 27-01-2020     Published : 30-01-2020
Volume : 12     Issue : 2       Pages : 9448 - 9450
Int J Agr Sci 12.2 (2020):9448-9450

Keywords : Geostatistical interpolation techniques, Water quality mapping, Spatial variability, Irrigation water
Academic Editor : Horo Aniketa, Dr Vijayachandra Reddy S, Dr H. V. Pandya, Dr Hemangi Mehta, Dr S. G. Savalia
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Punjab Remote Sensing Centre (PRSC), Ludhiana, 141004, Punjab, India
Author Contribution : All authors equally contributed

In the present study, suitable geostatistical approach is used for mapping different water quality parameters and generate water quality map for Mansa district, Punjab. Georeferenced ground water samples were collected and analysed for different quality parameters i.e. pH, Electrical Conductivity (EC), Carbonate and Bicarbonate (CO32-, HCO3-), Chloride (Cl-), Calcium + Magnesium (Ca2++Mg2+) (Total Hardness), Sodium (Na+), Residual Sodium Carbonate (RSC), Potassium (K+) and SAR. Different geostatistical approaches such as ordinary kriging, simple kriging, Radial Basis Functions (RBF) and Inverse Distance Weighting (IDW) were compared on the basis of root mean square error to select the best technique for a particular parameter. EC and RSC variability maps were integrated to generate water quality map in GIS environment. Water quality maps were broadly categorized as Good, Marginal and Poor on the basis of EC and RSC values. Change detection was conducted using previous year water quality map and water quality generated in this study.

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