Environmental Earth Sciences

, Volume 74, Issue 7, pp 5639–5652 | Cite as

Understanding factors influencing groundwater levels in hard-rock aquifer systems by using multivariate statistical techniques

  • Deepesh MachiwalEmail author
  • P. K. Singh
Original Article


Spatial and temporal dynamics of groundwater levels provides vital information required for management of fast depleting groundwater resources in hard-rock aquifer systems. This study demonstrates application of multivariate statistical techniques to analyze spatial and temporal variations of a 39-month period (May 2006–July 2009) monthly groundwater levels at 50 monitoring sites and to understand principal factors most influencing the groundwater system in Ahar River catchment of Udaipur district, Rajasthan, India. Box-whisker plots drawn for mean monthly groundwater levels revealed that spatial variation of the groundwater levels was less during rainy season in comparison to that during dry season. The groundwater levels in the aquifer system were found to be largely influenced by rainfall occurrences in the area. Firstly, hierarchical cluster analysis technique was applied to classify 50 monitoring sites into different clusters according to behaviour of the groundwater levels. This resulted into four clusters of the groundwater levels at less than 22 linkage distance. The most (25.29 m) and the least (6.48 m) spatial variability of the groundwater levels were observed for clusters III and I, respectively. Furthermore, principal component analysis (PCA) technique was utilized to understand and identify the most significant variables influencing the groundwater levels in each of the four clusters of the monitoring sites. The first two principal components (PCs) explained 43–55 % of the total variance. Based on the PCA, the significant PCs for clusters I and II were characterized as ‘topography factor’. On the other side, the significant PCs for clusters III and IV were termed as ‘geomorphologic’ and ‘land use’ factors, respectively.


Groundwater level Hierarchical cluster analysis Principal component analysis Spatial and temporal variability 



The authors gratefully acknowledge All India Coordinated Research Project on Groundwater Utilization, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, for providing groundwater level data for the present study. They are also very thankful to three anonymous reviewers for providing their useful suggestions, which improved the quality of the earlier version of this paper.


  1. Allen DM, Mackie DC, Wei M (2004) Groundwater and climate change: a sensitivity analysis for the grand forks aquifer, southern British Columbia. Can Hydrogeol J 12(3):270–290Google Scholar
  2. CGWB (2011) Dynamic Ground Water Resources of India (as on 31 March 2009). Central Ground Water Board (CGWB), Ministry of Water Resources, Government of India, New Delhi, p 243Google Scholar
  3. Chen L, Feng Q (2013) Geostatistical analysis of temporal and spatial variations in groundwater levels and quality in the Minqin oasis. Northwest China Environ Earth Sci 70(3):1367–1378CrossRefGoogle Scholar
  4. Cloutier V, Lefebvre R, Therrien R, Savard MM (2008) Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system. J Hydrol 353(3–4):294–313CrossRefGoogle Scholar
  5. Demirel Z, Güler C (2006) Hydrogeochemical evolution of groundwater in a Mediterranean coastal aquifer, Mersin–Erdemli basin (Turkey). Environ Geol 49:477–487CrossRefGoogle Scholar
  6. Dillon R, Goldstein M (1984) Multivariate analyses: methods and applications. Wiley, New YorkGoogle Scholar
  7. Elsheikh AE (2015) Mitigation of groundwater level deterioration of the Nubian Sandstone aquifer in Farafra Oasis, Western Desert, Egypt. Environ Earth Sci. doi: 10.1007/s12665-015-4236-7 Google Scholar
  8. Eltahir EAB, Yeh PJF (1999) On the asymmetric response of aquifer water level to floods and droughts in Illinois. Water Resour Res 35(4):1199–1217CrossRefGoogle Scholar
  9. Fu C, Zhang W, Zhang S, Su X, Lin X (2014) Identifying key hydrochemical processes in a confined aquifer of an arid basin using multivariate statistical analysis and inverse modeling. Environ Earth Sci 72(1):299–310CrossRefGoogle Scholar
  10. Güler C, Thyne GD (2004) Hydrologic and geologic factors controlling surface and groundwater chemistry in Indian Wells-Owens Valley area, southeastern California, USA. J Hydrol 285:177–198CrossRefGoogle Scholar
  11. Gunawardhana LN, Kazama S (2012) Statistical and numerical analyses of the influence of climate variability on aquifer water levels and groundwater temperatures: the impacts of climate change on aquifer thermal regimes. Global Planet Change 86–87:66–78CrossRefGoogle Scholar
  12. Jeong CH (2001) Effect of land use and urbanization on hydrochemistry and contamination of groundwater from Taejon area, Korea. J Hydrol 253:194–210CrossRefGoogle Scholar
  13. Kaiser HF (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187–200CrossRefGoogle Scholar
  14. Kumar R, Singh RD, Sharma KD (2005) Water resources of India. Curr Sci 89(5):794–811Google Scholar
  15. Lin CY, Abdullah MH, Praveena SM, Yahaya AHB, Musta B (2012) Delineation of temporal variability and governing factors influencing the spatial variability of shallow groundwater chemistry in a tropical sedimentary island. J Hydrol 432–433:26–42CrossRefGoogle Scholar
  16. Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of groundwater quality in a black foot disease area in Taiwan. Sci the Total Environ 313:77–89CrossRefGoogle Scholar
  17. Loaiciga HA, Maidment DR, Valdes JB (2000) Climate change impacts in a regional karst aquifer, TX, USA. J Hydrol 227:173–194CrossRefGoogle Scholar
  18. Machiwal D, Jha MK (2012) Hydrologic time series analysis: theory and practice. Capital Publishing Company, New Delhi, India and Springer, The Netherlands, p 303Google Scholar
  19. Machiwal D, Jha MK (2014) Characterizing rainfall-groundwater dynamics in a hard-rock aquifer system using time series, geographic information system and geostatistical modelling. Hydrol Process 28(5):2824–2843CrossRefGoogle Scholar
  20. Machiwal D, Jha MK (2015) Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. J Hydrol Reg Stud. doi: 10.1016/j.ejrh.2014.11.005 Google Scholar
  21. Machiwal D, Srivastava SK, Jain S (2010) Estimation of sediment yield and selection of suitable sites for soil conservation measures in Ahar River basin of Udaipur, Rajasthan using RS and GIS techniques. J Indian Soc Rem Sens 38(4):696–707CrossRefGoogle Scholar
  22. Machiwal D, Mishra A, Jha MK, Sharma A, Sisodia SS (2012) Modeling short-term spatial and temporal variability of groundwater level using geostatistics and GIS. Nat Resour Res 21(1):117–136CrossRefGoogle Scholar
  23. Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge- and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision making techniques on hard-rock terrain of Ahar catchment, Rajasthan, India. Environ Earth Sci 73:1871–1892CrossRefGoogle Scholar
  24. Mall RK, Gupta A, Singh R, Singh RS, Rathore LS (2006) Water resources and climatic change: an Indian perspective. Curr Sci 90(12):1610–1626Google Scholar
  25. Moukana JA, Koike K (2008) Geostatistical model for correlating declining groundwater levels with changes in land cover detected from analyses of satellite images. Comput Geosci 34:1527–1540CrossRefGoogle Scholar
  26. Nolan BT, Healy RW, Taber PE, Perkins K, Hitt KJ, Wolock DM (2007) Factors influencing ground-water recharge in the eastern United States. J Hydrol 332:187–205CrossRefGoogle Scholar
  27. Otto M (1998) Multivariate methods. In: Kellner R, Mermet JM, Otto M, Widmer HM (eds) Analytical chemistry. Wiley, Weinheim, p 916Google Scholar
  28. Page RM, Lischeid G, Epting J, Huggenberger P (2012) Principal component analysis of time series for identifying indicator variables for riverine groundwater extraction management. J Hydrol 432–433:137–144CrossRefGoogle Scholar
  29. Sahoo S, Jha MK (2015) On the statistical forecasting of groundwater levels in unconfined aquifer systems. Environ Earth Sci 73(7):3119–3136CrossRefGoogle Scholar
  30. Seeboonruang U (2014) An application of time-lag regression technique for assessment of groundwater fluctuations in a regulated river basin: a case study in Northeastern Thailand. Environ Earth Sci. doi: 10.1007/s12665-014-3872-7 Google Scholar
  31. Selle B, Schwientek M, Lischeid G (2013) Understanding processes governing water quality in catchments using principal component scores. J Hydrol 486:31–38CrossRefGoogle Scholar
  32. Singh S (2002) Water Management in Rural and Urban Areas. Agrotech Publishing Academy, Udaipur, India, p 192Google Scholar
  33. StatSoft, Inc. (2004) STATISTICA (data analysis software system), version 6.
  34. Tabari H, Nikbakht J, Some’e BS (2012) Investigation of groundwater level fluctuations in the north of Iran. Environmental Earth Sciences 66(1):231–243CrossRefGoogle Scholar
  35. Tukey JW (1977) Exploratory data analysis. Addison-Wesley, BostonGoogle Scholar
  36. USEPA (1998) Guidance for data quality assessment: Practical methods for data analysis. Quality assurance division, EPA QA/G-9, version QA97, United States Environmental Protection Agency (USEPA), Washington, pp 2.3-3–2.3-5Google Scholar
  37. USGS (2004) Shuttle Radar Topography Mission, 1 Arc Second scene SRTM_u03_n008e004, Unfilled Unfinished 2.0. Global Land Cover Facility, University of Maryland, College Park, MarylandGoogle Scholar
  38. Valdes D, Dupont J-P, Laignel B, Ogier S, Leboulanger T, Mahler BJ (2007) A spatial analysis of structural controls on Karst groundwater geochemistry at a regional scale. J Hydrol 340:244–255CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.SWE DepartmentCollege of Technology and EngineeringUdaipurIndia
  2. 2.ICAR - Central Arid Zone Research Institute, Regional Research StationBhujIndia

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