Environmental Earth Sciences

, 77:786 | Cite as

Integrated geospatial, geostatistical, and remote-sensing approach to estimate groundwater level in North-western India

  • Lakhvinder Kaur
  • Madhuri S. RishiEmail author
Original Article


The depletion of groundwater resources in Northwest India has been extensively studied. The top priority to meet the scarcity of water for irrigation, industrial and domestic purposes is supplemented by groundwater. Geostatistical modelling approach is considered to be beneficial tool for the assessment, evaluation, monitoring, and management of groundwater resources. This study is an attempt to analyze the spatio-temporal variability of groundwater level in semi-arid region of Panipat district, Haryana, India using kriging technique to fill the data gaps. Ordinary kriging was found optimal for the interpolation of groundwater levels. The results revealed that there was not much seasonal variation and also the groundwater flow direction remained almost constant in the study area during the assessment period. Spatial variability analysis showed significant variation in groundwater level and further depicted that the study area had undergone more or less decline in groundwater over the period of time. To validate the observations and results geo spatial and remote sensing techniques including normalised difference vegetation index and impervious surface relationships were worked out. It was further co-related with the rainfall data and the canal network existing in Panipat region. The integrated approach substantiated the observed results with the ground reality and helped in better understanding of the causes of declining groundwater trend in central part of Panipat.


Geostatistical modelling Groundwater Semi-arid region Kriging Normalised difference vegetation index (NDVI) 



Authors are thankful to NRSC for providing training and sharing their research experiences.


  1. Aboufirassi M, Miguel AM (1983) Kriging of water levels in the Souss aquifer, Morocco. Math Geosci 15(4):537–551Google Scholar
  2. Ahmed S (2007) Application of geostatistics in hydrosciences. In: Thangarajan M (ed) Groundwater. Springer, Amsterdam, pp 78–111CrossRefGoogle Scholar
  3. Ali A, Javed S, Ullah S, Fatima SH, Zaidi F, Khan MS (2018) Bayesian spatial analysis and prediction of groundwater contamination in Jhelum city (Pakistan). Environ Earth Sci 77(3):87CrossRefGoogle Scholar
  4. Bachmaier M, Backes M (2008) Variogram or semivariogram? Understanding the variances in a variogram. Precis Agric 9(3):173–175CrossRefGoogle Scholar
  5. Barnes KB, Morgan J, Roberge M (2001) Impervious surfaces and the quality of natural and built environments. Department of Geography and Environmental Planning, Towson University, BaltimoreGoogle Scholar
  6. Burgess TM, Webster R (1980) Optimal interpolation and isarithmic mapping of soil properties. Eur J Soil Sci 31(2):333–341CrossRefGoogle Scholar
  7. Caers J (2005) Petroleum geostatistics. Society of Petroleum Engineers, RichardsonGoogle Scholar
  8. Cambardella CA, Moorman TB, Parkin TB, Karlen DL, Novak JM, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58(5):1501–1511CrossRefGoogle Scholar
  9. Caruso C, Quarta F (1998) Interpolation methods comparison. Comput Math Appl 35(12):109–126CrossRefGoogle Scholar
  10. Chen Y, Ye Z, Shen Y (2011) Desiccation of the Tarim River, Xinjiang, China, and mitigation strategy. Quat Int 244(2):264–271CrossRefGoogle Scholar
  11. Chithra SV, Nair MH, Amarnath A, Anjana NS (2015) Impacts of impervious surfaces on the environment. Int J Eng Sci Invent 4(5):27–31Google Scholar
  12. ESRİ (2013) The principals of geostatistical analysis vol 54. Accessed 22 Feb 2014
  13. Gorai AK, Kumar S (2013) Spatial distribution analysis of groundwater quality index using GIS: a case study of Ranchi Municipal Corporation (RMC) area. Geoinf Geostat Overv 1(2):1–11, CrossRefGoogle Scholar
  14. Guo Z, Liu H (2005) Eco-depth of groundwater table for natural vegetation in inland basin, northwestern China. J Arid Land Res Environ 19(3):5Google Scholar
  15. Hinge G, Surampalli RY, Goyal MK (2018) Prediction of soil organic carbon stock using digital mapping approach in humid India. Environ Earth Sci 77(5):172CrossRefGoogle Scholar
  16. Hofer B, Papadakis E, Mäs S (2017) Coupling knowledge with GIS operations: the benefits of extended operation descriptions. ISPRS Int J Geoinf 6(2):40CrossRefGoogle Scholar
  17. Hutchinson MF (1995) Interpolating mean rainfall using thin plate smoothing splines. Int J Geogr Inf Sci 9(4):385–403CrossRefGoogle Scholar
  18. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  19. Jinglei W, Jingsheng S, Jiyang Z, Junfu X (2004) Crop water requirement isoline based on GIS and geostatistics. Trans Chin Soc Agric Eng 5:010Google Scholar
  20. Jolly WM, Graham JM, Michaelis A, Nemani R, Running SW (2005) A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales. J Environ Model Softw 20:873–882CrossRefGoogle Scholar
  21. Kholghi M, Hosseini SM (2009) Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environ Model Asses 14(6):729CrossRefGoogle Scholar
  22. Kitanidis PK (1997) Introduction to geostatistics. University Press, CambridgeCrossRefGoogle Scholar
  23. Kumar V, Remadevi V (2006) Kriging of groundwater levels—a case study. Josh 6(1):81–94Google Scholar
  24. Ma TS, Sophocleous M, Yu YS (1999) Geostatistical applications in ground-water modeling in south-central Kansas. J Hydrol Eng 4(1):57–64CrossRefGoogle Scholar
  25. Mateu J (2015) Spatial and spatio-temporal geostatistical modeling and kriging, vol 998. Wiley, New YorkGoogle Scholar
  26. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92(4):211–225CrossRefGoogle Scholar
  27. Naoum S, Tsanis IK (2004) Ranking spatial interpolation techniques using a GIS-based DSS. Global Nest J 6(1):1–20Google Scholar
  28. Nikroo L, Kompani-Zare M, Sepaskhah AR, Shamsi SRF (2010) Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran. Environ Monit Assess 166(1–4):387–407CrossRefGoogle Scholar
  29. Oliver MA (1991) Disjunctive kriging: an aid to making decisions on environmental matters. Area 23:19–24Google Scholar
  30. Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Sci 4(3):313–332CrossRefGoogle Scholar
  31. Rishi MS (2008) Evaluation of groundwater resource of Faridabad district. Envoinformatics, Haryana, p 47Google Scholar
  32. Singh O, Kasana A (2017) GIS-based spatial and temporal investigation of groundwater level fluctuations under rice–wheat ecosystem over Haryana. J Geol Soc India 89(5):554–562CrossRefGoogle Scholar
  33. Siska PP, Hung IK (2001) Assessment of kriging accuracy in the GIS environment. In: 21st annual ESRI international conference, San DiegoGoogle Scholar
  34. Statistical abstract (2011–2015) Accessed 05 Sep 2018
  35. Sun Y, Kang S, Li F, Zhang L (2009) Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environ Model Softw 24(10):1163–1170CrossRefGoogle Scholar
  36. Vaziri V, Hamidi JK, Sayadi AR (2018) An integrated GIS-based approach for geohazards risk assessment in coal mines. Environ Earth Sci 77(1):29CrossRefGoogle Scholar
  37. Virdee TS, Kottegoda NT (1984) A brief review of kriging and its application to optimal interpolation and observation well selection. Hydrol Sci J 29(4):367–387CrossRefGoogle Scholar
  38. Webster R, Oliver MA (2007) Geostatistics for environmental scientists. Wiley, New YorkCrossRefGoogle Scholar
  39. Wu C, Wu J, Luo Y, Zhang H, Teng Y, DeGloria SD (2011) Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation. Environ Earth Sci 63(5):1093–1110CrossRefGoogle Scholar
  40. Zhou Y, Wenninger J, Yang Z, Yin L, Huang J, Hou L, Uhlenbrook S (2013) Groundwater–surface water interactions, vegetation dependencies and implications for water resources management in the semi-arid Hailiutu River catchment, China—a synthesis. Hydrol Earth Syst Sci 17(7):2435–2447CrossRefGoogle Scholar
  41. Zhu L, Gong H, Dai Z, Xu T, Su X (2015) An integrated assessment of the impact of precipitation and groundwater on vegetation growth in arid and semiarid areas. Environ Earth Sci 74(6):5009–5021CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Environment StudiesPanjab UniversityChandigarhIndia

Personalised recommendations