Environment, Development and Sustainability

, Volume 21, Issue 2, pp 1013–1034 | Cite as

Identification of groundwater potential zones of the Kumari river basin, India: an RS & GIS based semi-quantitative approach

  • Deb Kumar MaityEmail author
  • Sujit Mandal


Groundwater is envisaged as a valuable common resource. In the present day, groundwater is declining very rapidly due to human intervention. Stress on groundwater in the semiarid locale of West Bengal, especially in Purulia district, is very high due to immense demand and overexploitation. The fundamental goal of the study is to discover potential groundwater zones for the appraisal of groundwater availability in the Kumari river basin, India. Survey of India topographical maps, elevation data (ASTER DEM 30 m), satellite imageries (Landsat 8 and Sentinel-2) and Google Earth images were analyzed using RS-GIS software (ArcGIS 10.3, ERDAS IMAGINE 9.2, MicroImages TNT MIP Pro 2016) to prepare various thematic data layers like altitude, slope angle, drainage density, geomorphology, soil type, geology, land use/land cover, lineament density, distance from rivers and mean annual rainfall. All prepared maps were changed with GIS software utilizing the raster converter apparatus in the raster space. Weighted layer for each thematic data layer was statistically computed by assigning weight values to individual parameters. Class rank was assigned in light of their significance to underground water recharge. Finally, a groundwater potential zone map was prepared utilizing analytical hierarchy process (AHP) and five distinct zones were arranged accordingly. ROC (receiver operating characteristics) curve and groundwater depth map were prepared using the field data to validate the groundwater zonation map of the Kumari river basin.


Groundwater potential zone Analytical hierarchy process (AHP) Linear weightage sum combination method Receiver operating characteristics (ROC) curve 


  1. Acharya, T., Nag, S. K., & Mallik, S. B. (2012). Hydraulic significance of fracture correlated lineaments in precambrian rocks in Purulia District, West Bengal. Journal of the Geological Society of India, 80, 723–730.Google Scholar
  2. Al-Abadi, A. (2015). Groundwater potential mapping at northeastern Wasit and Missan governorates, Iraq using a data-driven weights of evidence technique in framework of GIS. Environmental Earth Sciences, 74(2), 1109–1124.Google Scholar
  3. Bandyopadhyay, S., Srivastava, S., Jha, M., Hegde, V., & Jayaraman, V. (2007). Harnessing earth observation (EO) capabilities in hydrogeology: An Indian perspective. Hydrogeology Journal, 15(1), 155–158.Google Scholar
  4. Cetin, M. (2015a). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187(10), 640.Google Scholar
  5. Cetin, M. (2015b). Evaluation of the sustainable tourism potential of a protected area for landscape planning: A case study of the ancient city of Pompeipolis in Kastamonu. International Journal of Sustainable Development and World Ecology, 22(6), 490–495.Google Scholar
  6. Cetin, M. (2015c). Using GIS analysis to assess urban green space in terms of accessibility: Case study in Kutahya. International Journal of Sustainable Development and World Ecology, 22(5), 420–424.Google Scholar
  7. Cetin, M., & Sevik, H. (2016). Evaluating the recreation potential of Ilgaz Mountain National Park in Turkey. Environmental Monitoring and Assessment, 188(1), 52.Google Scholar
  8. Chowdhury, A., Jha, M. K., & Chowdary, V. M. (2010). Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district, West Bengal, using RS, GIS and MCDM techniques. Environment Earth Sciences, 5, 1209–1222.Google Scholar
  9. Corsini, A., Cervi, F., & Ronchetti, F. (2009). Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology, 111(1–2), 79–87.Google Scholar
  10. Dar, I., Sankar, K., & Dar, M. (2010). Remote sensing technology and geographic information system modeling: An integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin. Journal of Hydrology, 394(3–4), 285–295.Google Scholar
  11. Das, R. T. & Pal, S. (2016). Delineation of potential ground water-bearing zones in the Barind tract of West Bengal, India. Environment, Development and Sustainability, pp. 1–25.
  12. Edet, A. E., Okereke, C. S., Teme, S. C., & Esu, E. O. (1998). Application of remote sensing data to groundwater exploration: A case study of the Cross River State, south eastern Nigeria. Hydrogeology Journal, 6(3), 394–404.Google Scholar
  13. Ghayoumian, J., Mohseni Saravi, M., Feiznia, S., Nouri, B., & Malekian, A. (2007). Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in a coastal aquifer in southern Iran. Journal of Asian Earth Sciences, 30(2), 364–374.Google Scholar
  14. Ghosh, P., Bandyopadhyay, S., & Jana, N. (2015). Mapping of groundwater potential zones in hard rock terrain using geoinformatics: A case of Kumari watershed in western part of West Bengal. Modeling Earth Systems and Environment, 2(1), 1. Scholar
  15. Ghosh, P. & Jana, N. (2017). Groundwater potentiality of the Kumari River Basin in drought-prone Purulia upland, Eastern India: A combined approach using quantitative geomorphology and GIS. Sustainable Water Resources Management. Scholar
  16. Hassan, S., & Mahmud-ul-islam, S. (2013). Drought vulnerability assessment in the high Barind tract of Bangladesh using MODIS NDVI and land surface temperature (LST) imageries. International Journal of Science and Research, 26, 2319–7064.Google Scholar
  17. Hoque, M. S., & Burgess, A. W. G. (2012). 14C dating of deep groundwater in the Bengal Aquifer System, Bangladesh: Implications for aquifer anisotropy, recharge sources and sustainability. Journal of Hydrology, 34, 209–220.Google Scholar
  18. Horton, R. E. (1932). Drainage-basin characteristics. Eos Transactions AGU, 13, 350–361.Google Scholar
  19. Horton, R. E. (1945). Erosional development of streams and their drainage density: Hydrophysical approach to quantitative geomorphology. Geological Society of America Bulletin, 56, 275–370.Google Scholar
  20. Jha, M., & Chowdary, V. (2006). Challenges of using remote sensing and GIS in developing nations. Hydrogeology Journal, 15(1), 197–200.Google Scholar
  21. Jha, M. K., Chowdary, V. M., & Chowdhury, A. (2010). Groundwater assessment in salboni block, west Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeology Journal, 18, 1713–1728.Google Scholar
  22. Kundu, C. (2004). Appraisal of water resources in the Kumari Basin. Geographical Review of India, 66(3), 254–263.Google Scholar
  23. Le Page, M., Berjamy, B., Fakir, Y., Bourgin, F., Jarlan, L., Abourida, A., et al. (2012). An Integrated DSS for groundwater management based on remote sensing. The case of a semi-arid aquifer in Morocco. Water Resources Management, 26(11), 3209–3230.Google Scholar
  24. Lee, S., Song, K. Y., Kim, Y., & Park, I. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeology Journal, 20(8), 1511–1527.Google Scholar
  25. Magesh, N., Chandrasekar, N., & Soundranayagam, J. (2012). Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geoscience Frontiers, 3(2), 189–196.Google Scholar
  26. Manap, M. A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W. N. A., & Ramli, M. F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geoscience, 7, 711–724.Google Scholar
  27. Manap, M., Sulaiman, W., Ramli, M., Pradhan, B., & Surip, N. (2013). A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arabian Journal of Geosciences, 6(5), 1621–1637.Google Scholar
  28. Mogaji, K., Lim, H., & Abdullah, K. (2014). Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model. Arabian Journal of Geosciences, 8(5), 3235–3258.Google Scholar
  29. Moghaddam, D., Rezaei, M., Pourghasemi, H., Pourtaghie, Z., & Pradhan, B. (2013). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran. Arabian Journal of Geosciences, 8(2), 913–929.Google Scholar
  30. Murthy, K. (2000). Ground water potential in a semi-arid region of Andhra Pradesh—A geographical information system approach. International Journal of Remote Sensing, 21(9), 1867–1884.Google Scholar
  31. Nag, S. K. (2005). Application of lineament density and hydrogeomorphology to delineate groundwater potential zones of Bagmundi Block in Purulia District, West Bengal. Journal of the Indian Society of Remote Sensing, 33(4), 521–529.Google Scholar
  32. Nag, S. (2016). Delineation of groundwater potential zones in hard rock terrain in Kashipur block, Purulia District, West Bengal, using geospatial techniques. International Journal of Waste Resources, 06(01), 1–7. Scholar
  33. Naghibi, S., Pourghasemi, H., Pourtaghi, Z., & Rezaei, A. (2014). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1), 171–186.Google Scholar
  34. Nampak, H., Pradhan, B., & Manap, M. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.Google Scholar
  35. Negnevitsky, M. (2002). Artificial intelligence: A guide to intelligent systems. Harlow: Pearson.Google Scholar
  36. Oh, H. J., Kim, Y. S., Choi, J. K., Park, E., & Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399, 158–172.Google Scholar
  37. Ozdemir, A. (2011). Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the sultan mountains (Aksehir, Turkey). Journal of Hydrology, 405(1), 123–136.Google Scholar
  38. Pitz, C. F. (2016). Predicted impacts of climate change on groundwater resources of Washington State (pp. 1–25). Washington: Environmental Assessment Program Washington State Department of Ecology Olympia.Google Scholar
  39. Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63, 965–996.Google Scholar
  40. Pourtaghi, Z., & Pourghasemi, H. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643–662.Google Scholar
  41. Pradhan, B., Singh, R., & Buchroithner, M. (2006). Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Advances in Space Research, 37(4), 698–709.Google Scholar
  42. Rahman, M., & Mahbub, A. Q. M. (2012). Lithological study and mapping of Barind Tract using borehole log data with GIS: In the context of Tanore Upazila. Earth and Environmental Science, 4, 349–357.Google Scholar
  43. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281.Google Scholar
  44. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  45. Saaty, T. L. (1990). An exposition of the AHP in reply to the paper “Remarks on the analytic hierarchy process’’. Management Science, 36(3), 259.Google Scholar
  46. Saaty, T. L. (1996). Decision making with dependence and feedback, the analytic network process. Pittsburgh: RWS Publications.Google Scholar
  47. Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). Pittsburgh: Rws Publications.Google Scholar
  48. Saha, S. (2017). Groundwater potential mapping using analytical hierarchical process: A study on Md. Bazar Block of Birbhum District, West Bengal. Spatial Information Research, 25(4), 615–626.Google Scholar
  49. Shahid, S., Nath, S., & Roy, J. (2000). Groundwater potential modelling in a soft rock area using a GIS. International Journal of Remote Sensing, 21(9), 1919–1924.Google Scholar
  50. Sharma, R. (2009). Cratons of the Indian shield. In Cratons and fold belts of India. Lecture Notes in Earth Sciences (Vol 127, pp. 41–115). Berlin, Heidelberg: Springer.
  51. Tiwari, A., & Rai, B. (1996). Hydromorphological mapping for ground-water prospecting using landsat-MSS images—A case study of Part of Dhanbad District, Bihar. Journal of the Indian Society of Remote Sensing, 24, 281–285.Google Scholar
  52. Vaux, H. (2011). Groundwater under stress: The importance of management. Environmental Earth Science, 62, 19–23.Google Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of GeographyUniversity of Gour BangaMaldaIndia

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