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A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection

  • Deepesh MachiwalEmail author
  • Vincent Cloutier
  • Cüneyt Güler
  • Nerantzis Kazakis
Original Article

Abstract

Water quality evaluation is critically important for the protection and sustainable management of groundwater resources, which are variably vulnerable to ever-increasing human-induced physical and chemical pressures (e.g., overexploitation and pollution of aquifers) and to climate change/variability. Preceding studies have applied a variety of tools and techniques, ranging from conventional to modern, for characterization of the groundwater quality worldwide. Recently, geographic information system (GIS) technology has been successfully integrated with the advanced statistical/geostatistical methods, providing improved interpretation capabilities for the assessment of the water quality over different spatial scales. This review intends to examine the current standing of the GIS-integrated statistical/geostatistical methods applied in hydrogeochemical studies. In this paper, we focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment. In addition, we provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe. Then, limitations and research gaps of the past studies are outlined and perspectives of the future research needs are discussed. It is revealed that comprehensive applications of the GIS-integrated advanced statistical methods are generally rare in groundwater quality evaluations. One of the major challenges in future research will be implementing procedures of statistical methods in GIS software to enhance analysis capabilities for both spatial and temporal data (multiple sites/stations and time frames) in a simultaneous manner.

Keywords

Artificial intelligence methods Geostatistical modeling GIS Hydrogeochemistry Multivariate analysis Time series modeling Water quality index 

Notes

Acknowledgements

The authors are grateful to Dr. Olaf Kolditz (Editor-in-Chief) and two anonymous reviewers for their useful suggestions and comments, which helped improve earlier version of this article.

References

  1. Abbasi T, Abbasi SA (2012) Water quality indices. Elsevier, Oxford, UK, p 384Google Scholar
  2. Abbasi S, Mohammadi K, Kholghi MK, Howard K (2013) Aquifer vulnerability assessments using DRASTIC, weights of evidence and the analytic element method. Hydrol Sci J 58(1):186–197Google Scholar
  3. Adeloye AJ, Montaseri M (2002) Preliminary streamflow data analyses prior to water resources planning study. Hydrol Sci J 47(5):679–692Google Scholar
  4. Adhikary PP, Dash CJ, Chandrasekharan H, Rajput TBS, Dubey SK (2012) Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a peri-urban area of Delhi, India. Arab J Geosci 5:1423–1434Google Scholar
  5. Ağca N, Karanlık S, Ödemiş B (2014) Assessment of ammonium, nitrate, phosphate, and heavy metal pollution in groundwater from Amik Plain, southern Turkey. Environ Monit Assess 186(9):5921–5934Google Scholar
  6. Aguilar JB, Orban P, Dassargues A, Brouyère S (2007) Identification of groundwater quality trends in a chalk aquifer threatened by intensive agriculture in Belgium. Hydrogeol J 15:1615–1627Google Scholar
  7. Ahmed S (2006) Application of Geostatistics in Hydrosciences. In: Thangarajan M (ed) Groundwater resource evaluation, augmentation, contamination, restoration, modeling and management. Capital Publishing Company, New Delhi, pp 78–111Google Scholar
  8. Aller L, Bennett T, Lehr JH, Petty RJ (1985) DRASTIC: a standardized system for evaluating ground water potential using hydrogeological settings. In: EPA/600/285/018, Environmental Research Laboratory. US Environmental Protection Agency, AdaGoogle Scholar
  9. Alley WM (1993) Regional ground-water quality. Wiley, New York, p 634Google Scholar
  10. Alther GA (1979) A simplified statistical sequence applied to routine water quality analysis—a case history. Ground Water 17(6):556–561Google Scholar
  11. Antonakos AK, Lambrakis NL (2007) Development and testing of three hybrid methods for assessment of aquifer vulnerability to nitrates, based on the DRASTIC model, an example from NE Korinthia, Greece. J Hydrol 333(2–4):288–304Google Scholar
  12. APHA-AWWA-WEF (2017) Standard methods for the examination of water and wastewater, 23rd edn. American Public Health Association (APHA)-American Water Works Association (AWWA)-Water Environment Federation (WEF), Washington, DCGoogle Scholar
  13. Arslan H (2017) Determination of temporal and spatial variability of groundwater irrigation quality using geostatistical techniques on the coastal aquifer of Çarşamba Plain, Turkey, from 1990 to 2012. Environ Earth Sci 76:38.  https://doi.org/10.1007/s12665-016-6375-x CrossRefGoogle Scholar
  14. ASCE Task Committee (1990a) Review of geostatistics in geohydrology. I: Basic concepts. Journal of Hydraul Eng ASCE 116(5):612,  https://doi.org/10.1061/(ASCE)0733-9429(1990)116:5(612) CrossRefGoogle Scholar
  15. ASCE Task Committee (1990b) Review of geostatistics in geohydrology. II: applications. J Hydraul Eng ASCE 116(5):612.  https://doi.org/10.1061/(ASCE)0733-9429(1990)116:5(633)
  16. ASCE Task Committee (2000) Artificial neural networks in hydrology—I: preliminary concepts. J Hydrol Eng ASCE 5(2):115–123Google Scholar
  17. Ashley RP, Lloyd JW (1978) An example of the use of factor analysis and cluster analysis in groundwater chemistry interpretation. J Hydrol 39:355–364Google Scholar
  18. Assaf H, Saadeh M (2009) Geostatistical assessment of groundwater nitrate contamination with reflection on DRASTIC vulnerability assessment: the case of the Upper Litani Basin, Lebanon. Water Resour Manag 23:775–796Google Scholar
  19. Ayuso SV, Acebes P, López-Archilla AI, Montes C, Guerrero MC (2009) Environmental factors controlling the spatiotemporal distribution of microbial communities in a coastal, sandy aquifer system (Doñana, southwest Spain). Hydrogeol J 17:767–780Google Scholar
  20. Babiker IS, Mohamed MMA, Hiyama T (2007) Assessing groundwater quality using GIS. Water Resour Manag 21:699–715Google Scholar
  21. Backman B, Bodiš D, Lahermo P, Rapant S, Tarvainen T (1998) Application of a groundwater contamination index in Finland and Slovakia. Environ Geol 36(1–2):55–64Google Scholar
  22. Banoeng-Yakubo B, Yidana SM, Emmanuel N, Akabzaa T, Asiedu D (2009) Analysis of groundwater quality using water quality index and conventional graphical methods: the Volta region, Ghana. Environ Earth Sci 59(4):867–879Google Scholar
  23. Barca E, Passarella G (2008) Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation. Environ Monit Assess 137:261–273Google Scholar
  24. Bárdossy A (2011) Interpolation of groundwater quality parameters with some values below the detection limit. Hydrol Earth Syst Sci 15:2763–2775Google Scholar
  25. Bárdossy A, Kundzewicz ZW (1990) Geostatistical methods for detection of outliers in groundwater quality spatial fields. J Hydrol 115:343–359Google Scholar
  26. Berzas JJ, Garcia LF, Rodriguez RC, Martinalvarez PJ (2000) Evolution of the water quality of a managed natural wetland: Tablas de Daimiel National Park (Spain). Water Res 34(12):3161–3170Google Scholar
  27. Bethea RM, Rhinehart RR (1991) Applied engineering statistics. Marcel Dekker, Inc., New YorkGoogle Scholar
  28. Bhuiyan MAH, Bodrud-Doza M, Islam ARMT, Rakib MA, Rahman MS, Ramanathan AL (2016) Assessment of groundwater quality of Lakshimpur district of Bangladesh using water quality indices, geostatistical methods, and multivariate analysis. Environ Earth Sci 75:1020.  https://doi.org/10.1007/s12665-016-5823-y CrossRefGoogle Scholar
  29. Bjerg PL, Christensen TH (1992) Spatial and temporal small-scale variation in groundwater quality of a shallow sandy aquifer. J Hydrol 131:133–149Google Scholar
  30. Boateng TK, Opoku F, Acquaah SO, Akoto O (2016) Groundwater quality assessment using statistical approach and water quality index in Ejisu-Juaben Municipality, Ghana. Environ Earth Sci 75:489.  https://doi.org/10.1007/s12665-015-5105-0 CrossRefGoogle Scholar
  31. Bodrud-Doza Md, Islam ARMT, Ahmed F, Das S, Saha N, Rahman MS (2016) Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh. Water Sci 30:19–40Google Scholar
  32. Bondu R, Cloutier V, Rosa E, Benzaazoua M (2016) A review and evaluation of the impacts of climate change on geogenic arsenic in groundwater from fractured bedrock aquifers. Water Air Soil Pollut 227:296.  https://doi.org/10.1007/s11270-016-2936-6 CrossRefGoogle Scholar
  33. Bondu R, Cloutier V, Rosa E, Benzaazoua M (2017) Mobility and speciation of geogenic arsenic in bedrock groundwater from the Canadian Shield in western Quebec, Canada. Sci Total Environ 574:509–519Google Scholar
  34. Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc Ser B 26(2):211–252Google Scholar
  35. Box GEP, Hunter WG, Hunter JS (1978) Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley Interscience, New YorkGoogle Scholar
  36. Boy-Roura M, Nolan BT, Menció A, Mas-Pla J (2013) Regression model for aquifer vulnerability assessment of nitrate pollution in the Osona region (NE Spain). J Hydrol 505:150–162Google Scholar
  37. Bras RL, Rodriguez-Iturbe I (1985) Random functions and hydrology. Addison-Wesley, ReadingGoogle Scholar
  38. Bronson KF, Malapati A, Booker JD, Scanlon BR, Hudnall WH, Schurbert AM (2009) Residual soil nitrate in irrigated Southern High Plains cotton fields and Ogallala groundwater nitrate. J Soil Water Conserv 64(2):98–104Google Scholar
  39. Brown CE (1998) Applied multivariate statistics in geohydrology and related sciences, 1st edn. Springer, New YorkGoogle Scholar
  40. Buragohain M, Bhuyan B, Sarma HP (2010) Seasonal variations of lead, arsenic, cadmium and aluminium contamination of groundwater in Dhemaji district, Assam, India. Environ Monit Assess 170:345–351Google Scholar
  41. Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, Oxford, 333 ppGoogle Scholar
  42. Busico G, Kazakis N, Colombani N, Mastrocicco M, Voudouris K, Tedesco D (2017) A modified SINTACS method for groundwater vulnerability and pollution risk assessment in highly anthropized regions based on NO3 and SO4 2– concentrations. Sci Total Environ 609:1512–1523Google Scholar
  43. Busico G, Cuoco E, Kazakis N, Colombani N, Mastrocicco M, Tedesco D, Voudouris K (2018) Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace element occurrence in Campania Plain, Southern Italy. Environ Pollut 234:260–269Google Scholar
  44. Cairns SH, Dickson KL, Atkinson SF (1997) An examination of measuring selected water quality trophic indicators with SPOT satellite HRV data. Photogramm Eng Remote Sens 63(3):263–265Google Scholar
  45. Canadian Council of Ministers of the Environment (2001) Canadian water quality guidelines for the protection of aquatic life. CCME Water Quality Index 1.0, Technical Report, in Canadian Environmental Quality Guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg, CanadaGoogle Scholar
  46. Candela L, Olea RA, Custodio E (1988) Lognormal kriging for the assessment of reliability in groundwater quality control observation networks. J Hydrol 103:67–84Google Scholar
  47. Castrignanò A, Giugliarini L, Risaliti R, Martinelli N (2000) Study of spatial relationships among some soil physicochemical properties of a field in central Italy using multivariate geostatistics. Geoderma 97:39–60Google Scholar
  48. Chachadi AG, Lobo-Ferreira JP (2001) Seawater intrusion vulnerability mapping of aquifers using the GALDIT method. In: Proceedings of the workshop on modelling in hydrogeology, Anna University, Chennai, pp 143–156Google Scholar
  49. Chang K-T (2002) Introduction to geographic information systems. Tata McGraw-Hill Publishing Company Ltd., New Delhi, p 348Google Scholar
  50. Chaudhuri S, Ale S, Delaune P, Rajan N (2012) Spatio-temporal variability in groundwater nitrate concentration in Texas: 1960–2010. J Environ Qual 41:1806–1817Google Scholar
  51. Chen H, Druliner AD (1988) Agricultural chemical contamination of ground water in six areas of the high plains aquifer, Nebraska. National Water Summary 1986—hydrologic events and ground-water quality, water-supply Paper 2325. U.S. Geological Survey, RestonGoogle Scholar
  52. 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):1367–1378Google Scholar
  53. Chen Y, Takara K, Cluckie ID, Smedt FHD (eds) (2004) GIS and remote sensing in hydrology, Water resources and environment. IAHS Publication No. 289. IAHS Press, Wallingford, p 422Google Scholar
  54. Chou CJ (2006) Assessing spatial, temporal, and analytical variation of groundwater chemistry in a large nuclear complex, USA. Environ Monit Assess 119:571–598Google Scholar
  55. Civita M, De Maio M (2004) Assessing and mapping groundwater vulnerability to contamination: the Italian “combined” approach. Geofísica Internacional 43(4):513–532Google Scholar
  56. Clark D (1975) Understanding canonical correlation analysis. Concepts and techniques in modern geography No. 3, geo abstracts. University of East Anglia, NorwichGoogle Scholar
  57. Clarke RT (1998) Stochastic processes for water scientists: development and applications. Wiley, New YorkGoogle Scholar
  58. 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 353:294–313Google Scholar
  59. Cohen DB, Fisher C, Reid ML (1986) Ground-water contamination by toxic substances: a california assessment. In: Garner WY, Honeycutt RC, Nigg HN (eds) Evaluation of pesticides in ground water, ACS Symposium Series 315. American Chemical Society, Washington, DC, pp. 499–529Google Scholar
  60. Collins WD (1923) Graphic representation of water analyses. Ind Eng Chem 15(4):394Google Scholar
  61. Cooley WW, Lohnes PR (1971) Multivariate data analysis. Wiley, New YorkGoogle Scholar
  62. Cooper RM, Istok JD (1988a) Geostatistics applied to groundwater contamination. I: methodology. J Environ Engin ASCE 111(2):270–286Google Scholar
  63. Cooper RM, Istok JD (1988b) Geostatistics applied to groundwater contamination. II. Appl J Environ Eng ASCE 111(2):287–299Google Scholar
  64. Council of Canadian Academies (2009) The sustainable management of groundwater in canada. Report of the expert panel on groundwater. Council of Canadian Academies, OttawaGoogle Scholar
  65. Cryer JD (1986) Time series analysis. PWS Publishers, Duxbury Press, BostonGoogle Scholar
  66. D’Agostino V, Greene EA, Passarella G, Vurro M (1998) Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization. Environ Geol 36(3–4):285–295Google Scholar
  67. Dalton MG, Upchurch SB (1978) Interpretation of hydrochemical facies by factor analysis. Ground Water 16(4):228–233Google Scholar
  68. David M (1977) Geostatistical ore reserve estimation. Elsevier Scientific Publishing Company, New York, p 364Google Scholar
  69. David M, Dagbert M (1975) Lakeview revisited: variograms and correspondence analysis-new tools for the understanding of geochemical data. In: Proceedings of the 5th international geochemical exploration symposium, geochemical exploration, pp 163–181Google Scholar
  70. Davies PJ, Crosbie RS (2018) Mapping the spatial distribution of chloride deposition across Australia. J Hydrol 561:76–88Google Scholar
  71. Davis JC (1986) Statistics and data analysis in geology, 2nd edn. Wiley, New YorkGoogle Scholar
  72. Dawdy DR, Feth JH (1967) Applications of factor analysis in study of chemistry of ground water quality, Mojave River Valley, California. Water Resour Res 3(2):505–510Google Scholar
  73. Dean JD, Huyakorn PS, Donigian AS Jr, Voos KA, Schanz RW, Meeks YJ, Carsel RF (1989) Risk of unsaturated/saturated transport and transformation of chemical concentrations (RUSTIC). Volumes I and II. EPA/600/3–89/048a. United States Environmental Protection Agency, AthensGoogle Scholar
  74. Delhomme JP (1978) Kriging in hydrosciences. Adv Water Resour 1:251–266Google Scholar
  75. Denny SC, Allen DM, Journeay JM (2007) DRASTIC-Fm: a modified vulnerability mapping method for structurally controlled aquifers in the southern Gulf Islands, British Columbia, Canada. Hydrogeol J 15:483–493Google Scholar
  76. Deutsch WJ (1997) Groundwater geochemistry: fundamentals and applications to contamination. CRC Press LLC, Boca Raton, p 221Google Scholar
  77. Dillon R, Goldstein M (1984) multivariate analyses: methods and applications. Wiley, New YorkGoogle Scholar
  78. Dixon B (2005a) Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool. J Appl Geogr 25:327–347Google Scholar
  79. Dixon B (2005b) Applicability of neuro-fuzzy techniques in predicting groundwater vulnerability: a GIS-based sensitivity analysis. J Hydrol 309(1–4):17–38Google Scholar
  80. Dixon B (2009) A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with NO3-N. Hydrogeol J 17:1507–1520Google Scholar
  81. Doerfliger N, Jeannin PY, Zwahlen F (1999) Water vulnerability assessment in karst environments: a new method of defining protection areas using a multi-attribute approach and GIS tools (EPIK method). Environ Geol 39(2):165–176Google Scholar
  82. Dojlido J, Raniszewsk IJ, Woyciechowska J (1994) Water quality index —application for rivers in Vistula river basin in Poland. Water Sci Technol 30:57–64Google Scholar
  83. Dokou Z, Kourgialas NN, Karatzas GP (2015) Assessing groundwater quality in Greece based on spatial and temporal analysis. Environ Monit Assess 187:774.  https://doi.org/10.1007/s10661-015-4998-0 CrossRefGoogle Scholar
  84. Dragon K (2006) Application of factor analysis to study contamination of a semi-confined aquifer (Wielkopolska Buried Valley aquifer, Poland). J Hydrol 331:272–279Google Scholar
  85. Drozdov OA, Shepelevskii AA (1946) The theory of interpolation in a stochastic field of meteorological elements and its application to meteorological maps and network rationalization problems (in Russian). Trudy NIU GUGMS Series, 1(13), Russian Hydrological and Meteorological Service, RussiaGoogle Scholar
  86. Eheart JW, Cieniawski SE, Ranjithan S (1993) Genetic-algorithm-based design of groundwater quality monitoring system. WRC Research Report No. 218, Water Resources Center, University of Illinois at Urbana-Champaign, 205 North Mathews. Avenue Urbana Illinois 61801, p 50Google Scholar
  87. Elçi A, Ayvaz MT (2014) Differential-evolution algorithm based optimization for the site selection of groundwater production wells with the consideration of the vulnerability concept. J Hydrol 511:736–749Google Scholar
  88. El-Fadel M, Tomaszkiewicz M, Adra Y, Sadek S, Najm MA (2014) GIS-based assessment for the development of a groundwater quality index towards sustainable aquifer management. Water Resour Manag 28:3471–3487Google Scholar
  89. El-Shahat MF, Sadek MA, Embaby AA, Salem WM, Mohamed FA (2017) Hydrochemical and multivariate analysis of groundwater quality in the northwest of Sinai, Egypt. J Water Health.  https://doi.org/10.2166/wh.2017.276 CrossRefGoogle Scholar
  90. Enfield CG, Carsel RF, Cohen SZ, Phan T, Walters DM (1982) Approximating pollutant transport to ground water. Ground Water 20(6):711–722Google Scholar
  91. Enwright N, Hudak PF (2009) Spatial distribution of nitrate and related factors in the high plains aquifer. Texas Environ Geol 58:1541–1548Google Scholar
  92. Farnham M, Klaus JS, Ashok KS, Johannesson KH (2000) Deciphering groundwater flow systems in Oasis Valley, Nevada, using trace element chemistry, multivariate statistics, and geographical information system. Math Geol 32(8):943–968Google Scholar
  93. Farnham M, Singh AK, Stetzenbach KJ, Johannesson KH (2002) Treatment of nondetects in multivariate analysis of groundwater geochemistry data. Chemometr Intell Lab Syst 60:265–281Google Scholar
  94. Felmy AR, Girvin DC, Jenne EA (1983) MINTEQ: A Computer Program for Calculating Aqueous Geochemical Equilibria. EPA/600/3–84/032, Pacific Northwest Laboratory, United States Environmental Protection Agency (USEPA), Washington, DCGoogle Scholar
  95. Ferguson G, Gleeson T (2012) Vulnerability of coastal aquifers to groundwater use and climate change. Nat Clim Change 2:342–345Google Scholar
  96. Fijani E, Nadiri AA, Moghaddam AA, Tsai FT-C, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran. J Hydrol 503:89–100Google Scholar
  97. Forina M, Armanino C, Raggio V (2002) Clustering with dendrograms on interpretation variables. Anal Chim Acta 454(1):13–19Google Scholar
  98. Foster SSD (1987) Fundamental concepts in aquifer vulnerability, pollution risk and protection strategy. In: Van Duijvenbooden W, and Waegeningh HG (eds), Vulnerability of soil and groundwater to pollutants. In: TNO committee on hydrological research, the Hague, Proc. Inf., vol 38, pp 69–86Google Scholar
  99. Frans L (2008) Trends of pesticides and nitrate in ground water of the Central Columbia Plateau, Washington, 1993–2003. J Environ Qual 37:273–280Google Scholar
  100. Freeze RA, Cherry JA (1979) Groundwater. Prentice-Hall, Inc., Englewood CliffsGoogle Scholar
  101. Gan Y, Zhao K, Deng Y, Liang X, Ma T, Wang Y (2018) Groundwater flow and hydrogeochemical evolution in the Jianghan Plain, central China. Hydrogeol J 26(5):1609–1623Google Scholar
  102. Gandin LS (1965) Objective analysis of meteorological fields. Israel Program for Scientific Translations, Jerusalem, p 242Google Scholar
  103. Gangadharan R, Nila Rekha P, Vinoth S (2016) Assessment of groundwater vulnerability mapping using AHP method in coastal watershed of shrimp farming area. Arab J Geosci 9: 107.  https://doi.org/10.1007/s12517-015-2230-8 CrossRefGoogle Scholar
  104. Gemitzi A, Petalas C, Tsihrintzis VA, Pisinaras V (2006) Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques. Environ Geol 49:653–673Google Scholar
  105. Gibbs RJ (1970) Mechanisms controlling world water chemistry. Science 170:1088–1090Google Scholar
  106. Giggenbach WF (1988) Geothermal solute equilibria. Derivation of Na-K-Mg-Ca geoindicators. Geochim Cosmochim Acta 52(12):2749–2765Google Scholar
  107. Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New YorkGoogle Scholar
  108. Giles BD, Flocas AA (1984) Air temperature variation in Greece, Part-I: persistence, trend and fluctuations. Int J Climatol 4:531–539Google Scholar
  109. Giri S, Singh G, Gupta SK, Jha VN, Tripathi RM (2010) An evaluation of metal contamination in surface and groundwater around a proposed uranium mining site, Jharkhand, India. Mine Water Environ 29(3):225–234Google Scholar
  110. Gleeson T, VanderSteen J, Sophocleous MA, Taniguchi M, Alley WM, Allen DM, Zhou Y (2010) Groundwater sustainability strategies. Nat Geosci 3:378–379Google Scholar
  111. Gogu RC, Dassargues A (2000) Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ Geol 39(6):549–559Google Scholar
  112. Goldscheider N, Klute M, Sturm S, Hotzl H (2000) The PI method—a GIS-based approach to mapping groundwater vulnerability with special consideration of karst aquifers. Z Angew Geol 46(3):157–166Google Scholar
  113. Gong X, Richman MB (1995) On the application of cluster analysis to growing season precipitation data in North America East of the Rockies. J Clim 8:897–931Google Scholar
  114. Goodchild MF, Parks BO, Steyaert LT (eds) (1993) Environmental modeling with GIS. Oxford University Press, New YorkGoogle Scholar
  115. Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45Google Scholar
  116. Goovaerts P, AvRuskin G, Meliker J, Slotnick M, Jacquez G, Nriagu J (2005) Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resour Res 41:W07013.  https://doi.org/10.1029/2004WR003705 CrossRefGoogle Scholar
  117. Gorgij AD, Kisi O, Moghaddam AA, Taghipour A (2017) Groundwater quality ranking for drinking purposes, using the entropy method and the spatial autocorrelation index. Environ Earth Sci 76:269.  https://doi.org/10.1007/s12665-017-6589-6 CrossRefGoogle Scholar
  118. Güler C, Thyne GD (2004a) Hydrologic and geologic factors controlling surface and groundwater chemistry in Indian Wells-Owens Valley area, southeastern California, USA. J Hydrol 285(1–4):177–198Google Scholar
  119. Güler C, Thyne GD (2004b) Delineation of hydrochemical facies distribution in a regional groundwater system by means of fuzzy c-means clustering. Water Resour Res 40(12):W12503.  https://doi.org/10.1029/2004WR003299 CrossRefGoogle Scholar
  120. Güler C, Thyne GD, McCray JE, Turner AK (2002) Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeol J 10(4):455–474Google Scholar
  121. Güler C, Kurt MA, Alpaslan M, Akbulut C (2012) Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques. J Hydrol 414–415:435–451Google Scholar
  122. Güler C, Thyne GD, Tağa H, Yıldırım Ü (2017) Processes governing alkaline groundwater chemistry within a fractured rock (ophiolitic mélange) aquifer underlying a seasonally inhabited headwater area in the Aladağlar Range (Adana, Turkey). Geofluids.  https://doi.org/10.1155/2017/3153924 (article ID 3153924)CrossRefGoogle Scholar
  123. Gupta A, Kamble T, Machiwal D (2017) Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of northwest India. Environ Earth Sci 76:512.  https://doi.org/10.1007/s12665-017-6814-3 CrossRefGoogle Scholar
  124. Gurdak JJ, McMahon PB, Bruce BW (2012) Vulnerability of groundwater quality to human activity and climate change and variability, high plains aquifer, USA. In: Treidel H, Martin-Bordes JL, Gurdak JJ (eds) Climate Change effects on groundwater resources—a global synthesis of findings and recommendations. Taylor & Francis Group, London, pp 145–168Google Scholar
  125. Gurnell AM, Montgomery DR (eds) (2000) Hydrological applications of GIS. Wiley, Chichester, p 176Google Scholar
  126. Haan CT (1977) Statistical methods in hydrology. Iowa State University Press, IowaGoogle Scholar
  127. Hanh TM, Sthiannopkao P, The Ba S, D. and Kim K-W (2011) Development of water quality indexes to identify pollutants in Vietnam’s surface water. J Environ Eng ASCE 137(4):273–283Google Scholar
  128. Hardy A (1996) On the number of clusters. Comput Stat Data Anal 23:83–96Google Scholar
  129. Hartigan A (1975) Clustering algorithms. Wiley, New YorkGoogle Scholar
  130. Hassan MM, Atkins PJ (2007) Arsenic risk mapping in Bangladesh: A simulation technique of cokriging estimation from regional count data. J Environ Sci Health Part A Toxic/Hazard Substan Environ Engin 42(12):1719–1728Google Scholar
  131. Helsel DR, Frans LM (2006) Regional Kendall test for trend. Environ Sci Technol 40(13):4066–4073Google Scholar
  132. Helstrup T, Jørgensen NO, Banoeng-Yakubo B (2007) Investigation of hydrochemical characteristics of groundwater from the Cretaceous-Eocene limestone aquifer in southern Ghana and southern Togo using hierarchical cluster analysis. Hydrogeol J 15:977–989Google Scholar
  133. Hem JD (1970) Study and interpretation of the chemical characteristics of natural water, 2nd edition, United States Geological Survey Water-Supply Paper 1473, Washington DCGoogle Scholar
  134. Hem JD (1985) Study and Interpretation of the chemical characteristics of natural water. 3rd edition, United States Geological Survey Water-Supply Paper 2254, Washington DCGoogle Scholar
  135. Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306Google Scholar
  136. Hosseini SM, Mahjouri N (2014) Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater. Environ Monit Assess 186:3685–3699Google Scholar
  137. Hotelling H (1936) Relations between two sets of variates. Biometrika 28:312–377Google Scholar
  138. Hoyer BE, Hallberg GR (1991) Ground water vulnerability regions of iowa, special map 11. Iowa Department of Natural Resources, Iowa CityGoogle Scholar
  139. Hudak PF (2000a) Sulfate and chloride concentrations in Texas aquifer. Environ Int 26:55–61Google Scholar
  140. Hudak PF (2000b) Regional trends in nitrate content of Texas groundwater. J Hydrol 228:37–47Google Scholar
  141. Hudak PF (2001) Water hardness and sodium trends in Texas aquifers. Environ Monit Assess 68:177–185Google Scholar
  142. Iskandar I, Koike K (2011) Distinguishing potential sources of arsenic released to groundwater around a fault zone containing a mine site. Environ Earth Sci 63:595–608Google Scholar
  143. Iskandar I, Koike K, Sendjaja P (2012) Identifying groundwater arsenic contamination mechanisms in relation to arsenic concentrations in water and host rocks. Environ Earth Sci 65:2015–2026Google Scholar
  144. Istok JD, Cooper RM (1988) Geostatistics applied to groundwater contamination. III: global estimates. J Environ Engin ASCE 111(2):915–928Google Scholar
  145. Istok JD, Rautman CA (1996) Probabilistic assessment of ground-water contamination: 2. Results of case study. Ground Water 34(6):1050–1064Google Scholar
  146. Istok JD, Smyth JD, Flint FL (1993) Multivariate geostatistical analysis of ground-water contamination: a case history. Ground Water 31(1):63–74Google Scholar
  147. Izenman AJ (2013) Modern multivariate statistical techniques: regression, classification, and manifold learning, 2nd edn. Springer, New YorkGoogle Scholar
  148. Jacobs J, Testa S (2004) Overview of chromium (VI) in the environment: background and history. In: Guertin J, Jacobs J, Avakian C (eds) Chromium (VI) handbook. CRC Press, New York. http://www.engr.uconn.edu/~baholmen/docs/ENVE290W/National%20Chromium%20Files%20From%20Luke/Cr(VI)%20Handbook/L1608_C01.pdf. Accessed 21 June 2017
  149. Jamshidzadeh Z, Barzi MT (2018) Groundwater quality assessment using the potability water quality index (PWQI): a case in the Kashan plain, Central Iran. Environ Earth Sci 77:59.  https://doi.org/10.1007/s12665-018-7237-5 CrossRefGoogle Scholar
  150. Jang C-S (2013) Use of multivariate indicator kriging methods for assessing groundwater contamination extents for irrigation. Environ Monit Assess 185:4049–4061Google Scholar
  151. Jang C-S, Liu C-W, Lu KL, Lin CC (2007) Delimitation of arsenic-contaminated groundwater using risk-based indicator approaches around blackfoot disease hyperendemic areas of southern Taiwan. Environ Monit Assess 134:293–304Google Scholar
  152. Jang C-S, Chen C-F, Liang C-P, Chen J-S (2016) Combining groundwater quality analysis and a numerical flow simulation for spatially establishing utilization strategies for groundwater and surface water in the Pingtung Plain. J Hydrol 533:541–556Google Scholar
  153. Javadi S, Hashemy SM, Mohammadi K, Howard KWF, Neshat A (2017) Classification of aquifer vulnerability using K-means cluster analysis. J Hydrol 549:27–37Google Scholar
  154. Jha MK, Chowdhury A, Chowdary VM, Peiffer S (2007) Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resour Manag 21(2):427–467Google Scholar
  155. Johnson RA, Wichern DW (1992) Applied multivariate statistical analysis, 3rd edn. Prentice-Hall International, Englewood Cliffs, p 642Google Scholar
  156. Jones AL, Smart PL (2005) Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935–1999) as demonstrated by autoregressive modeling. J Hydrol 310:201–215Google Scholar
  157. Journel A (1974) Geostatistics for conditional simulation of orebodies. Econ Geol 69(5):673–687Google Scholar
  158. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, London, p 600Google Scholar
  159. Jovein EB, Hosseini SM (2017) Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging. Environ Monit Assess 189:81.  https://doi.org/10.1007/s10661-017-5795-8 CrossRefGoogle Scholar
  160. Kagan RL (1967) Some problems relative to the interpretation of rainfall data (in Russian). Trudy GGO 208:64–75Google Scholar
  161. Kallio MP, Mujunen SP, Hatzimihalis G, Koutoufides P, Minkkinen P, Wilki PJ, Connor MA (1999) Multivariate data analysis of key pollutants in sewage samples: a case study. Anal Chim Acta 393(1–3):181–191Google Scholar
  162. Kaown D, Hyun Y, Bae G-O, Oh CW, Lee K-K (2012) Evaluation of spatio-temporal trends of groundwater quality in different land uses using Kendall test. Geosci J 16(1):65–75Google Scholar
  163. Karami S, Madani H, Katibeh H, Marj AF (2018) Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches. Appl Water Sci 8:23.  https://doi.org/10.1007/s13201-018-0641-x CrossRefGoogle Scholar
  164. Karanth KR (1987) Ground water assessment: development and management. Tata McGraw-Hill Publishing Company Limited, New Delhi, p 720Google Scholar
  165. Kavouri K, Plagnes V, Tremoulet J, Dorfliger N, Rejiba F, Marchet P (2011) PaPRIKa: a method for estimating karst resource and source vulnerability—application to the Ouysse karst system (southwest France). Hydrogeol J 19:339–353Google Scholar
  166. Kazakis N, Voudouris KS (2015) Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: modifying the DRASTIC method using quantitative parameters. J Hydrol 525:13–25Google Scholar
  167. Kazakis N, Kantiranis N, Voudouris KS, Mitrakas M, Kaprara E, Pavlou A (2015) Geogenic Cr oxidation on the surface of mafic minerals and the hydrogeological conditions influencing hexavalent chromium concentrations in groundwater. Sci Total Environ 514:224–238Google Scholar
  168. Kazakis N, Kantiranis N, Kalaitzidou K, Kaprara Ε, Mitrakas M, Frei R, Vargemezis G, Tsourlos P, Zouboulis A, Filippidis A (2017) Origin of hexavalent chromium in groundwater: the example of Sarigkiol Basin, Northern Greece. Sci Total Environ 593–594:552–566Google Scholar
  169. Kazakis N, Spiliotis M, Voudouris K, Pliakas FK, Papadopoulos B (2018a) A fuzzy multicriteria categorization of the GALDIT method to assess seawater intrusion vulnerability of coastal aquifers. Sci Total Environ 621:552–566Google Scholar
  170. Kazakis N, Chalikakis K, Mazzilli N, Ollivier C, Manakos A, Voudouris K (2018b) Management and research strategies of karst aquifers in Greece: Literature overview and exemplification based on hydrodynamic modelling and vulnerability assessment of a strategic karst aquifer. Sci Total Environ 643:592–609Google Scholar
  171. Kendall MG (1973) Time series. Charles Griffin and Co. Ltd., LondonGoogle Scholar
  172. Ketata M, Gueddari M, Bouhlila R (2012) Use of geographical information system and water quality index to assess groundwater quality in El Khairat deep aquifer (Enfidha, Central East Tunisia). Arab J Geosci 5:1379–1390Google Scholar
  173. Khan HH, Khan A, Ahmed S, Perrin J (2011) GIS-based impact assessment of land-use changes on groundwater quality: study from a rapidly urbanizing region of South India. Environ Earth Sci 63:1289–1302Google Scholar
  174. Kim TH, Chung SY, Park N, Hamm S-Y, Lee SY, Kim B-W (2012) Combined analyses of chemometrics and kriging for identifying groundwater contamination sources and origins at the Masan coastal area in Korea. Environ Earth Sci 67(5):1373–1388Google Scholar
  175. Kissel DE, Bidwell OW, Kientz JF (1982) Leaching classes in Kansas Soils. Bulletin No. 641. Kansas State University, ManhattanGoogle Scholar
  176. Koh E-H, Lee SH, Kaown D, Moon HS, Lee E, Lee K-K, Kang BR (2017) Impacts of land use change and groundwater management on long-term nitrate-nitrogen and chloride trends in groundwater of Jeju Island, Korea. Environ Earth Sci, 76: 176.  https://doi.org/10.1007/s12665-017-6466-3 CrossRefGoogle Scholar
  177. Konikow L, Kendy L (2005) Groundwater depletion: a global problem. Hydrogeol J 13(1):317–320Google Scholar
  178. Kumar S, Machiwal D, Dayal D (2017) Spatial modeling of rainfall trends using satellite datasets and geographic information system. Hydrol Sci J 62(10):1636–1653Google Scholar
  179. Kurumbein WC, Graybill FA (1965) An introduction to statistical models in geology. McGraw-Hill, New YorkGoogle Scholar
  180. Lambrakis N, Antonakos A, Panagopoulos G (2004) The use of multicomponent statistical analysis in hydrogeological environmental research. Water Res 38:1862–1872Google Scholar
  181. Langelier W, Ludwig H (1942) Graphical methods for indicating the mineral character of natural waters. J Am Water Assoc 34:335–352Google Scholar
  182. Leite NK, Stolberg J, da Cruz SP, de Tavela OA, Safanelli JL, Marchini HR, Exterkoetter R, Leite GMC, Krusche AV, Johnson MS (2018) Hydrochemistry of shallow groundwater and springs used for potable supply in Southern Brazil. Environ Earth Sci 77:80.  https://doi.org/10.1007/s12665-018-7254-4 CrossRefGoogle Scholar
  183. Li P, Wu J, Qian H, Lyu X, Liu H (2014) Origin and assessment of groundwater pollution and associated health risk: a case study in an industrial park, northwest China. Environ Geochem Health 36(4):693–712Google Scholar
  184. 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 432–433:26–42Google Scholar
  185. Lo CP, Yeung AKW (2003) Concepts and techniques of geographic information systems. Prentice-Hall of India Pvt. Ltd., New Delhi, p 492Google Scholar
  186. Loftis JC (1996) Trends in groundwater quality. Hydrol Process 10:335–355Google Scholar
  187. Lopez B, Baran N, Bourgine B (2015) An innovative procedure to assess multi-scale temporal trends in groundwater quality: example of the nitrate in the Seine-Normandy basin, France. J Hydrol 522:1–10Google Scholar
  188. Lu L, Kashiwaya K, Koike K (2016) Geostatistics-based regional characterization of groundwater chemistry in a sedimentary rock area with faulted setting. Environ Earth Sci 75:829.  https://doi.org/10.1007/s12665-016-5619-0 CrossRefGoogle Scholar
  189. Lumb A, Sharma TC, Bibeault J-F (2011) A review of genesis and evolution of water quality index (WQI) and some future directions. Water Qual Exposure Health 3:1–14Google Scholar
  190. Machiwal D, Jha MK (2006) Time series analysis of hydrologic data for water resources planning and management: a review. J Hydrol Hydromech 54(3):237–257Google Scholar
  191. Machiwal D, Jha MK (2010) Tools and techniques for water quality interpretation. In: Krantzberg G, Tanik A, Antunes do Carmo JS, Indarto A, Ekdal A (eds) Advances in water quality control. Scientific Research Publishing, Inc., USA, pp 211–252Google Scholar
  192. Machiwal D, Jha MK (2012) Hydrologic time series analysis: theory and practice. Springer, the Netherlands and Capital Publishing Company, New Delhi, p 303Google Scholar
  193. Machiwal D, Jha MK (2014) Role of geographical information system for water quality evaluation. In: Nielson D (ed) Geographic information systems (GIS): techniques, applications and technologies. Nova Science Publishers, New York, USA, pp 217–278Google Scholar
  194. 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 4(A):80–110Google Scholar
  195. Machiwal D, Jha MK, Mal BC (2011) GIS-based assessment and characterization of groundwater quality in a hard-rock hilly terrain of Western India. Environ Monit Assess 174:645–663Google Scholar
  196. Machiwal D, Jha MK, Singh VP, Mohan C (2018) Assessment and mapping of groundwater vulnerability to pollution: current status and challenges. Earth Sci Rev 185:901–927.  https://doi.org/10.1016/j.earscirev.2018.08.009 CrossRefGoogle Scholar
  197. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Le Cam L.M, Neyman J (eds). In: Proceedings of the Fifth Berkeley symposium on mathematical statistics and probability, vol 1, University of California Press, Berkeley, California, pp 281–297Google Scholar
  198. Magesh NS, Chandrasekar N, Elango L (2016) Occurrence and distribution of fluoride in the groundwater of the Tamiraparani River basin, South India: a geostatistical modeling approach. Environ Earth Sci 75:1483.  https://doi.org/10.1007/s12665-016-6293-y CrossRefGoogle Scholar
  199. Mair A, El-Kadi AI (2013) Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. J Contam Hydrol 153:1–23Google Scholar
  200. Malapati A, Bronson KF, Booker JD, Hudnall WH, Schubert AM (2011) Soil profile sulfate in irrigated Southern High Plains cotton fields and Ogallala aquifer. J Soil Water Conserv 66(5):287–294Google Scholar
  201. Margat J (1968) Groundwater vulnerability maps. Conception-estimation-mapping. EEC Institut Europeen de l’ Eau, Paris (in French) Google Scholar
  202. Marko K, Al-Amri NS, Elfeki AMM (2014) Geostatistical analysis using GIS for mapping groundwater quality: case study in the recharge area of Wadi Usfan, western Saudi Arabia. Arab J Geosci 7(12):5239–5252Google Scholar
  203. Masoud AA, Koike K, Mashaly HA, Gergis F (2016) Spatio-temporal trends and change factors of groundwater quality in an arid area with peat rich aquifers: Emergence of water environmental problems in Tanta District, Egypt. J Arid Environ 124:360–376Google Scholar
  204. Masoud AA, El-Horiny MM, Atwia MG, Gemail KS, Koike K (2018) Assessment of groundwater and soil quality degradation using multivariate and geostatistical analyses, Dakhla Oasis, Egypt. J Afr Earth Sci 142:64–81Google Scholar
  205. Matheron G (1965) Lee Variables Regionalisées et leur Estimation. Masson, Paris, p 306Google Scholar
  206. Matheron G (1973) The intrinsic random functions and their applications. Adv Appl Prob 5:439–468Google Scholar
  207. McBride GB (2005) Using statistical methods for water quality management: issues, problems and solutions. Wiley, New YorkGoogle Scholar
  208. McCuen RH (2003) Modeling hydrologic change: statistical methods. Lewis Publishers, CRC Press LLC, FloridaGoogle Scholar
  209. Melloul A, Collin M (1992) The ‘principal components’ statistical method as a complementary approach to geochemical methods in water quality factor identification; Application to the Coastal Plain aquifer of Israel. J Hydrol 140:49–73Google Scholar
  210. Melloul AJ, Collin M (1998) A proposed index for aquifer water quality assessment: the case of Israel’s Sharon region. J Environ Manag 54:131–142Google Scholar
  211. Mendizabal I, Baggelaar PK, Stuyfzand PJ (2012) Hydrochemical trends for public supply well fields in The Netherlands (1898–2008), natural backgrounds and upscaling to groundwater bodies. J Hydrol 450–451:279–292Google Scholar
  212. Mendoza GA, Martins H (2006) Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. For Ecol Manag 230:1–22Google Scholar
  213. Meng SX, Maynard JB (2001) Use of statistical analysis to formulate conceptual models of geochemical behavior: Water chemical data from the Botucatu aquifer in São Paulo state, Brazil. J Hydrol 250:78–97Google Scholar
  214. Michalak AM, Kitanidis PK (2004) Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling. Water Resour Res 40:W08302.  https://doi.org/10.1029/2004WR003214 CrossRefGoogle Scholar
  215. Mirzaei R, Sakizadeh M (2016) Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environ Sci Pollut Res 23(3):2758–2769Google Scholar
  216. Mohammadi K, Niknam R, Majd VJ (2009) Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran-Karaj aquifer. Iran Environ Geol 58:437–446Google Scholar
  217. Molina M, Aburto FN, Calderan RL, Cazanga M, Escudey M (2009) Trace element composition of selected fertilizers used in Chile: phosphorus fertilizers as a source of long-term soil contamination. Soil Sediment Contam 18:497–511Google Scholar
  218. Moore JS (1988) SEEPPAGE: a system for early evaluation of pollution potential of agricultural ground water environments. Geology Technical Note 5 (Revision 1), US Department of Agriculture, Soil Conservation Service, Washington, DCGoogle Scholar
  219. Mouser PJ, Hession WC, Rizzo DM, Gotelli NJ (2005) Hydrology and geostatistics of a Vermont, USA Kettlehole Peatland. J Hydrol 301:250–266Google Scholar
  220. Myers DE, Begovich CL, Butz TR, Kane VE (1982) Variogram models for regional groundwater geochemical data. Math Geol 14(6):629–644Google Scholar
  221. Nadiri AA, Sedghi Z, Khatibi R, Gharekhani M (2017) Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Sci Total Environ 593–594:75–90Google Scholar
  222. Narany TS, Ramli MF, Aris AZ, Sulaiman WNA, Fakharian K (2014) Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol-Babol Plain, Iran. Environ Monit Assess 186:5797–5815Google Scholar
  223. Nas B, Berktay A (2010) Groundwater quality mapping in urban groundwater using GIS. Environ Monit Assess 160:215–227Google Scholar
  224. Nasiri F, Maqsood I, Huang G, Fuller N (2007) Water quality index: A fuzzy river-pollution decision support expert system. J Water Resour Plan Manag ASCE 133(2):95–105Google Scholar
  225. National Research Council (1993) Groundwater vulnerability assessment, contaminant potential under conditions of uncertainty. National Academy Press, Washington DCGoogle Scholar
  226. Nematollahi MJ, Ebrahimi P, Razmara M, Ghasemi A (2016) Hydrogeochemical investigations and groundwater quality assessment of Torbat-Zaveh plain, Khorasan Razavi, Iran. Environ Monit Assess 188:2.  https://doi.org/10.1007/s10661-015-4968-6 CrossRefGoogle Scholar
  227. Niu B, Loáiciga HA, Wang Z, Zhan FB, Hong S (2014) Twenty years of global groundwater research: a science citation index expanded-based bibliometric survey (1993–2012). J Hydrol 519:966–975Google Scholar
  228. Nobre RCM, Rotunno Filho OC, Mansur WJ, Nobre MMM, Cosenza CAN (2007) Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool. J Contam Hydrol 94:277–292Google Scholar
  229. Noshadi M, Ghafourian A (2016) Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran). Environ Monit Assess 188:419.  https://doi.org/10.1007/s10661-016-5412-2 CrossRefGoogle Scholar
  230. Nriagu JO, Nieboer E (1988) Chromium in the natural and human environments. Wiley-Interscience, New YorkGoogle Scholar
  231. Nshagali BG, Nouck PN, Meli’i JL, Arétouyap Z, Manguelle-Dicoum E (2015) High iron concentration and pH change detected using statistics and geostatistics in crystalline basement equatorial region. Environ Earth Sci 73(11):7135–7145Google Scholar
  232. Ochsenkühn M, Kontoyannakos J, Ochsenkühn-Petropulu M (1997) A new approach to a hydrochemical study of groundwater flow. J Hydrol 194:64–75Google Scholar
  233. Oleson SG, Carr JR (1990) Correspondence analysis of water quality data: Implications for fauna deaths at Stillwater Lakes, Neveda. Math Geol 22:665–698Google Scholar
  234. Otto M (1998) Multivariate methods. In: Kellner R, Mermet JM, Otto M, Widmer HM (eds) Analytical chemistry. Wiley-VCH, Weinheim, p 916Google Scholar
  235. Pacheco FAL (1998) Finding the number of natural clusters in groundwater data sets using the concept of equivalence class. Comput Geosci 24(1):7–15Google Scholar
  236. Pacheco FAL, Pires LMGR, Santos RMB, Sanches Fernandes LF (2015) Factor weighting in DRASTIC modeling. Sci Total Environ 505:474–486Google Scholar
  237. Panagopoulos G, Antonakos A, Lambrakis N (2006) Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS. Hydrogeol J 14:894–911Google Scholar
  238. Paradis D, Vigneault H, Lefebvre R, Savard MM, Ballard J-M, Qian B (2016) Groundwater nitrate concentration evolution under climate change and agricultural adaptation scenarios: Prince Edward Island, Canada. Earth Syst Dyn 7(1):183–202Google Scholar
  239. Park S-C, Yun S-T, Chae G-T, Yoo I-S, Shin K-S, Heo C-H, Lee S-K (2005) Regional hydrochemical study on salinization of coastal aquifers, western coastal area of South Korea. J Hydrol 313:182–194Google Scholar
  240. Parkhurst DL, Thorstenson DC, Plummer LN (1980) PHREEQE: a computer program for geochemical calculations. Water resources investigations report 80–96, United States Geological Survey, NTIS Technical Report, PB81-167801, Springfield, Virginia 22161, p 210Google Scholar
  241. Parkhurst DL, Plummer LN, Thorstenson DC (1982) BALANCE: a computer program for calculating mass transfer for geochemical reactions in ground water. Water resources investigations Report 82–14, United States Geological Survey, NTIS Technical Report, PB82-255902, Springfield, Virginia 22161, p 27Google Scholar
  242. Passarella G, Vurro M, D’Agostino V, Giuliano G, Barcelona MJ (2002) A probabilistic methodology to assess the risk of groundwater quality degradation. Environ Monit Assess 79:57–74Google Scholar
  243. Pebesma EJ, de Kwaadsteniet JW (1997) Mapping groundwater quality in the Netherlands. J Hydrol 200:364–386Google Scholar
  244. Pételet-Giraud E, Dörfliger N, Crochet P (2000) RISKE: method d’évaluation multicritère de la vulnérabilité des aquifers karstiques. Application aux systèmes des Fontanilles et Cent-Fonts (Hérault, Sud de la France) [Risk: methodology for multicriteria evaluation of the vulnerability of karst aquifers. Application to systems Fontanilles and Cent-Fonts Fontanilles (Herault, southern France]. Hydrogéologie 4:71–88Google Scholar
  245. Petrişor A-I, Ianoş I, Iurea D, Văidianu MN (2012) Applications of principal component analysis integrated with GIS. Proc Environ Sci 14:247–256Google Scholar
  246. Pettyjohn WA, Savoca M, Self D (1991) Regional assessment of aquifer vulnerability and sensitivity in the conterminous United States. Report EPA-600/2–91/043, United States Environmental Protection Agency, Ada, OklahomaGoogle Scholar
  247. Piper AM (1944) A graphical procedure in the geochemical interpretation of water analysis. Am Geophysi Union Trans 25:914–928Google Scholar
  248. Pique A, Grandia F, Canals A (2010) Processes releasing arsenic to groundwater in the Caldes de Malavella geothermal area, NE Spain. Water Res 44:5618–5630Google Scholar
  249. Pirkle FL, Howell JA, Wecksung GW, Duran BS, Stablein NK (1984) An example of cluster analysis applied to a large geologic data set: aerial radiometric data from Copper Mountain, Wyoming. Math Geol 16(5):479–498Google Scholar
  250. Plummer LN, Prestemon EC, Parkhurst DL (1991) An Interactive Code (NETPATH) for Modeling Net Geochemical Reactions along a Flow Path. Water-Resources Investigations Report 94-4078, United States Geological Survey, Reston, Virginia 22092, p 227Google Scholar
  251. Postma D, Larsen F, Thai NT, Pham TKT, Jakobsen R, Nhan PQ, Long TV, Viet PH, Murray AS (2012) Groundwater arsenic concentrations in Vietnam controlled by sediment age. Nat Geosci 5(9):656–661Google Scholar
  252. Prati L, Pavanello R, Pesarin F (1971) Assessment of surface water quality by a single index of pollution. Water Res 5:741–751Google Scholar
  253. Rainwater FH, Thatcher LL (1960) Methods for Collection and Analysis of Water Samples. 1st edition, United States Geological Survey Water-Supply Paper 1454, Washington, DCGoogle Scholar
  254. Ramakrishnaiah CR, Sadashivaiah C, Ranganna G (2009) Assessment of water quality index for the groundwater in Tumkur taluk, Karnataka state, India. E J Chem 6(2):523–530Google Scholar
  255. Ramesh S, Sumukaran N, Murugesan AG, Rajan MP (2010) An innovative approach of drinking water quality index—a case study from southern Tamil Nadu, India. Ecol Ind 10:857–868Google Scholar
  256. Ramos Leal JA, Barrón Romero LE, Sandoval Montes I (2004) Combined use of aquifer contamination risk maps and contamination indexes in the design of water quality monitoring networks in Mexico. Geofísica Internacional 43(4):641–650Google Scholar
  257. Rao SN, Rao SP, Varma D (2013) Spatial variations of groundwater vulnerability using cluster analysis. J Geol Soc India 81:685–697Google Scholar
  258. Rautman CA, Istok JD (1996) Probabilistic assessment of ground-water contamination: 1. Geostatistical framework. Ground Water 34(5):899–909Google Scholar
  259. Reghunath R, Murthy TRS, Raghavan BR (2002) The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res 36:2437–2442Google Scholar
  260. Ribeiro L, Macedo ME (1995) Application of multivariate statistics, trend- and cluster analysis to groundwater quality in the Tejo and Sado aquifer. In: Proceedings of the Prague conference on groundwater quality: remediation and protection. IAHS Publication No. 225, Prague, pp 39–47Google Scholar
  261. Riley JA, Steinhorst RK, Winter GV, Williams RE (1990) Statistical analysis of the hydrochemistry of ground waters in Columbia River basalts. J Hydrol 119:245–262Google Scholar
  262. Rouhani S, Hall TJ (1988) Geostatistical schemes for groundwater sampling. J Hydrol 103:85–102Google Scholar
  263. Rupert MG (2001) Calibration of the DRASTIC ground water vulnerability mapping method. Ground Water 39(4):625–630Google Scholar
  264. Sacha L, Fleming D, Wysocki H (1987) Survey of pesticides in selected areas having vulnerable ground waters in Washington State. EPA/910/9–87/169, United States Environmental Protection Agency, Region X, Seattle, WashingtonGoogle Scholar
  265. Sadat-Noori SM, Ebrahimi K, Liaghat AM (2014) Groundwater quality assessment using the water quality index and GIS in Saveh-Nobaran aquifer, Iran. Environ Earth Sci 71:3827–3843Google Scholar
  266. Saeedi M, Abessi O, Sharifi F, Meraji H (2010) Development of groundwater quality index. Environ Monit Assess 163:327–335Google Scholar
  267. Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modeling of hydrologic time series. Water Resources Publications, LittletonGoogle Scholar
  268. Saleh A, Al-Ruwaih F, Shehata M (1999) Hydrogeochemical processes operating within the main aquifers of Kuwait. J Arid Environ 42:195–209Google Scholar
  269. Salman AS, Zaidi FK, Hussein MT (2015) Evaluation of groundwater quality in northern Saudi Arabia using multivariate analysis and stochastic statistics. Environ Earth Sci 74(12):7769–7782Google Scholar
  270. Sânchez-Martos F, Jiménez-Espinosa R, Pulido-Bosch A (2001) Mapping groundwater quality variables using PCA and geostatistics: a case study of Bajo Andarax, southeastern Spain. Hydrol Sci J 46(2):227–242Google Scholar
  271. Sara MN, Gibbons R (1991) Organization and analysis of water quality data. In: Nielsen DM (ed) Practical handbook of ground-water monitoring. Lewis Publishers, Michigan, pp 541–588Google Scholar
  272. Scanlon BR, Reedy RC, Bronson KF (2008) Impacts of landuse change on nitrogen cycling archived in semiarid unsaturated zone nitrate profiles, Southern High Plains, Texas. Environ Sci Technol 42(20):7566–7572Google Scholar
  273. Scanlon BR, Reedy RC, Gates JB (2010) Effects of irrigated agroecosystems: 1. Quantity of soil water and groundwater in the Southern High Plains, Texas. Water Resour Res.  https://doi.org/10.1029/2009WR008427 CrossRefGoogle Scholar
  274. Schaefer JA, Mayor SJ (2007) Geostatistics reveal the scale of habitat selection. Ecol Model 209(2–4):401–406Google Scholar
  275. Schoeller H (1955) Geochimie des eaux souterraines. Rev de l’Inst Francais du Petrole Paris 10(3):181–213Google Scholar
  276. Sener E, Davraz A (2013) Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: The case of Egirdir Lake basin (Isparta, Turkey). Hydrogeol J 21:701–714Google Scholar
  277. Şener Ş, Şener E, Davraz A (2017) Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci Total Environ 584–585:131–144Google Scholar
  278. Sethy SN, Syed TH, Kumar A (2017) Evaluation of groundwater quality in parts of the Southern Gangetic Plain using water quality indices. Environ Earth Sci 76:116.  https://doi.org/10.1007/s12665-017-6434-y CrossRefGoogle Scholar
  279. Shahin M, Van Oorschot HJL, De Lange SJ (1993) Statistical analysis in water resources engineering. A.A. Balkema, RotterdamGoogle Scholar
  280. Shirazi SM, Imran HM, Akib S (2012) GIS-based DRASTIC method for groundwater vulnerability assessment: a review. J Risk Res 15(8):991–1011Google Scholar
  281. Siebert S, Burke J, Faures J, Frenken K, Hoogeveen J, Döll P, Portmann T (2010) Groundwater use for irrigation—a global inventory. Hydrol Earth Syst Sci 14:1863–1880Google Scholar
  282. Skidmore AK, Bijer W, Schmidt K, Kumar L, K (1997) Use of remote sensing and GIS for sustainable land management. ITC J 3(4):302–315Google Scholar
  283. Smith DG (1990) A better water quality indexing system for rivers and streams. Water Res 24(10):1237–1244Google Scholar
  284. Sneath PHA, Sokal RR (1973) Numerical taxonomy. W.H. Freeman and Co., San FranciscoGoogle Scholar
  285. Snodgrass MF, Kitanidis PK (1997) A geostatistical approach to contaminant source identification. Water Resour Res 33(4):537–546Google Scholar
  286. Soltan ME (1999) Evaluation of groundwater quality in Dakhla Oasis (Egyptian Western Desert). Environ Monit Assess 57:157–168Google Scholar
  287. Sorichetta A, Masetti M, Ballabio C, Sterlacchini S (2012) Aquifer nitrate vulnerability assessment using positive and negative weights of evidence methods, Milan Italy. Comput Geosci 48:199–210Google Scholar
  288. Sprague LA, Lorenz DL (2009) Regional nutrient trends in streams and rivers of the United States, 1993–2003. Environ Sci Technol 43(10):3430–3435Google Scholar
  289. Stafford DB (ed) (1991) Civil engineering applications of remote sensing and geographic information systems. ASCE, New YorkGoogle Scholar
  290. Stamatis G, Parpodis K, Filintas A, Zagana E (2011) Groundwater quality, nitrate pollution and irrigation environmental management in the Neogene sediments of an agricultural region in central Thessaly (Greece). Environ Earth Sci 64:1081–1105Google Scholar
  291. Stambuk-Giljanovic N (1999) Water quality evaluation by index in Dalmatia. Water Res 33(16):3423–3440Google Scholar
  292. Steenhuis TS, Pacenka S, Porter KS (1987) MOUSE: a management model for evaluation ground water contamination from diffuse surface sources aided by computer graphics. Appl Agric Res 2:277–289Google Scholar
  293. Steinhorst RK, Williams RE (1985) Discrimination of groundwater sources using cluster analysis, MANOVA, canonical analysis and discriminant analysis. Water Resour Res 21(8):1149–1156Google Scholar
  294. Steube C, Richter S, Griebler C (2009) First attempts towards an integrative concept for the ecological assessment of groundwater ecosystems. Hydrogeol J 17(1):23–35Google Scholar
  295. Stiff HA Jr. (1951) The interpretation of chemical water analysis by means of patterns. J Pet Technol 3(10):15–17Google Scholar
  296. Stigter TY, Ribeiro L, Carvalho Dill AMM (2006) Application of a groundwater quality index as an assessment and communication tool in agroenvironmental policies: two Portuguese case studies. J Hydrol 327:578–591Google Scholar
  297. Stoner JD (1978) Water-Quality Indices for Specific Water Uses. Geological Survey Circular 770, Washington, DCGoogle Scholar
  298. Stumpp C, Żurek A, Wachniew P, Gargini A, Gemitzi A, Filippini M, Witczak S (2016) A decision tree tool supporting the assessment of groundwater vulnerability. Environ Earth Sci 75:1057.  https://doi.org/10.1007/s12665-016-5859-z CrossRefGoogle Scholar
  299. Subbarao C, Subbarao NV, Chandu SN (1996) Characterization of groundwater contamination using factor analysis. Environ Geol 28(4):175–180Google Scholar
  300. Suk H, Lee KK (1999) Characterization of a ground water hydrochemical system through multivariate analysis: clustering into ground water zones. Ground Water 37(3):358–366Google Scholar
  301. Sullivan T, Gao Y (2017) Development of a new P3 (Probability, Protection, and Precipitation) method for vulnerability, hazard, and risk intensity index assessments in karst watersheds. J Hydrol 549:428–451Google Scholar
  302. Sun AY (2007) A robust geostatistical approach to contaminant source identification. Water Resour Res 43:W02418.  https://doi.org/10.1029/2006WR005106 CrossRefGoogle Scholar
  303. Sutadian AD, Muttil N, Yilmaz AG, Perera BJC (2016) Development of river water quality indices—a review. Environ Monit Assess 188:58.  https://doi.org/10.1007/s10661-015-5050-0 CrossRefGoogle Scholar
  304. Taylor CH, Loftis JC (1989) Testing for trend in lake and ground water quality time series. J Am Water Resour Assoc 25(4):715–726Google Scholar
  305. Tennant CB, White ML (1959) Study of the distribution of some geochemical data. Econ Geol 54:1281–1290Google Scholar
  306. Teso RR, Younglove T, Peterson MR, Sheeks DL, Gallavan RE (1988) Soil taxonomy and surveys: classification of areal sensitivity to pesticide contamination of ground water. J Soil Water Conserv 43(4):348–352Google Scholar
  307. Thirumalaivasan D, Karmegam M, Venugopal K (2003) AHP-DRASTIC: software for specific aquifer vulnerability assessment using DRASTIC model and GIS. Environ Model Softw 18(7):645–656Google Scholar
  308. Thurstone LL (1931) Multiple factor analysis. Psychol Rev 38:406–427Google Scholar
  309. Thyne G, Güler C, Poeter E (2004) Sequential analysis of hydrochemical data for watershed characterization. Ground Water 42(5):711–723Google Scholar
  310. Tryon RC (1939) Cluster analysis. Edwards Brothers, Ann ArborGoogle Scholar
  311. United Nations Environment Programme (2007) Global drinking water quality index development and sensitivity analysis report. United Nations Environment Programme, Global Environment Monitoring System/Water ProgrammeGoogle Scholar
  312. USEPA (1996) Guidance for data quality assessment: practical methods for data analysis. Quality Assurance Division, EPA QA/G-9, version QA96, United States Environmental Protection Agency (USEPA), Washington, DCGoogle Scholar
  313. 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, DCGoogle Scholar
  314. USSL (1954) Diagnosis and improvement of saline and alkaline soils. United States Salinity Laboratory (USSL). In: Richards LA (ed) Hand Book 60. United States Department of Agriculture (USDA), Washington DC, USA, p 159Google Scholar
  315. Usunoff EJ, Guzman-Guzman A (1989) Multivariate analysis in hydrochemistry: an example of the use of factor and correspondence analysis. Ground Water 27(1):27–34Google Scholar
  316. Vadiati M, Asghari-Moghaddam A, Nakhaei M, Adamowski J, Akbarzadeh AH (2016) A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices. J Environ Manag 184:255–270Google Scholar
  317. Van Stempvoort D, Evert L, Wassenaar L (1992) Aquifer Vulnerability Index: a GIS compatible method for groundwater vulnerability mapping. Can Water Resour J 18:25–37Google Scholar
  318. Vasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Rajiv Ganthi R, Chidambaram S, Anandhan P, Manivannan R, Vasudevan S (2010) Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India. Environ Monit Assess 171(1–4):595–609Google Scholar
  319. Venkatramanan S, Chung SY, Kim TH, Kim B-W, Selvam S (2016) Geostatistical techniques to evaluate groundwater contamination and its sources in Miryang City. Korea Environ Earth Sci 75:994.  https://doi.org/10.1007/s12665-016-5813-0 CrossRefGoogle Scholar
  320. Visser A, Dubus I, Broers HP, Brouyère S, Korcz M, Orban P, Goderniaux P, Batlle-Aguilar J, Surdyk N, Amraoui N, Job H, Pinault JL, Bierkens M (2009) Comparison of methods for the detection and extrapolation of trends in groundwater quality. J Environ Monit 11:2030–2043Google Scholar
  321. von der Heide C, Böttcher J, Deurer M, Weymann D, Well R, Duijnisveld WHM (2008) Spatial variability of N2O concentrations and of denitrification-related factors in the surficial groundwater of a catchment in Northern Germany. J Hydrol 360:230–241Google Scholar
  322. Voudouris K, Polemio M, Kazakis N, Sifaleras A (2010) An agricultural decision support system for optimal land use regarding groundwater vulnerability. Int J Inf Syst Soc Change 1(4):66–79Google Scholar
  323. Vrba J, Zaporozec A (1994) Guidebook on mapping groundwater vulnerability. International contributions to hydrogeology, vol 16. International Association of Hydrogeologists, HannoverGoogle Scholar
  324. Wachniew P, Zurek AJ, Stumpp C, Gemitzi A, Gargini A, Filippini M, Rozanski K, Meeks J, Kværner J, Witczak S (2016) Toward operational methods for the assessment of intrinsic groundwater vulnerability: a review. Crit Rev Environ Sci Technol 46(9):827–884Google Scholar
  325. Wagenet RJ, Hutson JL (1987) LEACHM: a finite-difference model for simulating water, salt, and pesticide movement in the Plant root zone, continuum 2. New York State Resources Institute, Cornell University, IthacaGoogle Scholar
  326. WHO (2017) Guidelines for drinking-water quality: fourth edition incorporating the first addendum. World Health Organization, Geneva (license: CC BY-NC-SA 3.0 IGO) Google Scholar
  327. Wilcox LV (1955) Classification and use of irrigation water. Circular 696. United States Department of Agriculture (USDA), Washington, DCGoogle Scholar
  328. Williams RE (1982) Statistical identification of hydraulic connections between the surface of a mountain and internal mineralized sources. Ground Water 20(4):466–478Google Scholar
  329. World Bank (2010) Deep wells and prudence: towards pragmatic action for addressing groundwater overexploitation in India. World Bank Report No. 51676, Washington, DCGoogle Scholar
  330. Wunderlin DA, del Pilar DM, Valeria AM, Fabiana PS, Cecilia HA, de los Angeles BM (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River Basin (Córdoba-Argentina). Water Res 35(12):2881–2894Google Scholar
  331. Wurl J, Mendez-Rodriguez L, Acosta-Vargas B (2014) Arsenic content in groundwater from the southern part of the San Antonio-El Triunfo mining district, Baja California Sur, Mexico. J Hydrol 518:447–459Google Scholar
  332. WWAP (World Water Assessment Programme) (2009) United Nations World Water Development Report 3, water in a changing world, United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, 2009. http://unesdoc.unesco.org/images/0021/002156/215644e.pdf. Accessed 9 May 2017
  333. WWAP (World Water Assessment Programme) (2012) United Nations World Water Development Report 4, Managing Water Under Uncertainty and Risk, United Nations Educational, Scientific and Cultural Organization (UNESCO), Paris, 2012. http://unesdoc.unesco.org/images/0021/002156/215644e.pdf. Accessed 9 May 2017
  334. Yazdanpanah N (2016) Spatiotemporal mapping of groundwater quality for irrigation using geostatistical analysis combined with a linear regression method. Model Earth Syst Environ 2:18.  https://doi.org/10.1007/s40808-015-0071-9 CrossRefGoogle Scholar
  335. Yevjevich VM (1972) Stochastic processes in hydrology. Water Resources Publications, Fort CollinsGoogle Scholar
  336. Yu WH, Harvey CM, Harvey CF (2003) Arsenic in groundwater in Bangladesh: a geostatistical and epidemiological framework for evaluating health effects and potential remedies. Water Resour Res 39(6):1146.  https://doi.org/10.1029/2002WR001327 CrossRefGoogle Scholar
  337. Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16:1807–1829Google Scholar
  338. Zakhem BA, Hafez R (2015) Heavy metal pollution index for groundwater quality assessment in Damascus Oasis, Syria. Environ Earth Sci 73(10):6591–6600Google Scholar
  339. Zaporozec A (1972) Graphical interpretation of water-quality data. Ground Water 10(2):32–43Google Scholar
  340. Zwahlen F (ed) (2004) Vulnerability and risk mapping for the protection of carbonate (Karst) aquifers, final report (COST Action 620). European Commission, Directorate-General XII science, research and development, Report EUR 20912, Brussels, p 297Google Scholar

Copyright information

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

Authors and Affiliations

  • Deepesh Machiwal
    • 1
    Email author
  • Vincent Cloutier
    • 2
  • Cüneyt Güler
    • 3
  • Nerantzis Kazakis
    • 4
  1. 1.ICAR-Central Arid Zone Research InstituteRegional Research StationBhujIndia
  2. 2.Groundwater Research Group, Institut de Recherche en Mines et en EnvironnementUniversité du Québec en Abitibi-TémiscamingueAmosCanada
  3. 3.Mersin Üniversitesi, Çiftlikköy Kampüsü, Jeoloji Mühendisliği BölümüMersinTurkey
  4. 4.Laboratory of Engineering Geology and Hydrogeology, School of GeologyAristotle University of ThessalonikiThessaloníkiGreece

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