Environmental Monitoring and Assessment

, Volume 185, Issue 5, pp 4049–4061

Use of multivariate indicator kriging methods for assessing groundwater contamination extents for irrigation



Multivariate geostatistical approaches have been applied extensively in characterizing risks and uncertainty of pollutant concentrations exceeding anthropogenic regulatory limits. Spatially delineating an extent of contamination potential is considerably critical for regional groundwater resources protection and utilization. This study used multivariate indicator kriging (MVIK) to determine spatial patterns of contamination extents in groundwater for irrigation and made a predicted comparison between two types of MVIK, including MVIK of multiplying indicator variables (MVIK-M) and of averaging indicator variables (MVIK-A). A cross-validation procedure was adopted to examine the performance of predicted errors, and various probability thresholds used to calculate ratios of declared pollution area to total area were explored for the two MVIK methods. The assessed results reveal that the northern and central aquifers have excellent groundwater quality for irrigation use. Results obtained through a cross-validation procedure indicate that MVIK-M is more robust than MVIK-A. Furthermore, a low ratio of declared pollution area to total area in MVIK-A may result in an unrealistic and unreliable probability used to determine extents of pollutants. Therefore, this study suggests using MVIK-M to probabilistically determine extents of pollutants in groundwater.


Multivariate indicator kriging (MVIK) Groundwater Cross-validation Irrigation Uncertainty 


  1. AERC (Agriculture Engineering Research Center). (2008). Analysis and evaluation of the groundwater quality survey in Taiwan areas (p. 487). Taiwan: Water Resources Agency, Ministry of Economic Affairs, Executive Yuan.Google Scholar
  2. Castrignanò, A., Goovaerts, P., Lulli, L., & Bragato, G. (2000). A geostatistical approach to estimate probability of occurrence of Tuber melanosporum in relation to some soil properties. Geoderma, 98, 95–113.CrossRefGoogle Scholar
  3. Chilès, J. P., & Delfiner, P. (1999). Geostatistics: modeling spatial uncertainty (pp. 283–287). New York: Wiley.CrossRefGoogle Scholar
  4. Chu, H. J., Lin, Y. P., Jang, C. S., & Chang, T. K. (2010). Delineating the hazard zone of multiple soil pollutants by multivariate indicator kriging and conditioned Latin hypercube sampling. Geoderma, 158(3–4), 242–251.CrossRefGoogle Scholar
  5. Deutsch, C. V. (2002). Geostatistical reservoir modeling (pp. 124–152). New York: Oxford University Press.Google Scholar
  6. Deutsch, C. V., & Journel, A. G. (1998). GSLIB: Geostatistical Software Library and User’s Guide (2nd ed.). New York: Oxford University Press.Google Scholar
  7. Diodato, N., & Ceccarelli, M. (2004). Multivariate indicator kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands. Ecological Indicators, 4(3), 177–187.CrossRefGoogle Scholar
  8. Eaton, F. M. (1950). Significance of carbonates in irrigation water. Soil Science, 69, 123–133.CrossRefGoogle Scholar
  9. Goovaerts, P. (1997). Geostatistics for natural resources evaluation (pp. 259–368). New York: Oxford University Press.Google Scholar
  10. 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 Resources Research, 41. doi:10.1029/2004WR003705.
  11. Halvorson, J. J., Smith, J. L., & Papendick, R. I. (1996). Integration of multiple soil parameters to evaluate soil quality: a field example. Biology and Fertility of Soils, 21(3), 207–214.CrossRefGoogle Scholar
  12. Jang, C. S., & Chen, J. S. (2009). Probabilistic assessment of groundwater mixing with surface water for agricultural utilization. Journal of Hydrology, 376(1–2), 188–199.CrossRefGoogle Scholar
  13. Jang, C. S., Liu, C. W., Lu, K. L., & Lin, C. C. (2007). Delimitation of arsenic-contaminated groundwater using risk-based indicator approaches around blackfoot disease hyperendemic areas of southern Taiwan. Environmental Monitoring and Assessment, 134, 293–304.CrossRefGoogle Scholar
  14. Jang, C. S., Chen, S. K., & Lin, C. C. (2008). Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan. Hydrological Processes, 22(22), 4477–4489.CrossRefGoogle Scholar
  15. Jang, C. S., Liou, Y. T., & Liang, C. P. (2010). Probabilistically determining roles of groundwater used in aquacultural fishponds. Journal of Hydrology, 388, 491–500.CrossRefGoogle Scholar
  16. Juang, K. W., & Lee, D. Y. (1998). Simple indicator kriging for estimating the probability of incorrectly delineating hazardous areas in a contaminated site. Environmental Science & Technology, 32, 2487–2493.CrossRefGoogle Scholar
  17. Juang, K. W., & Lee, D. Y. (2000). Comparison of three nonparametric kriging methods for delineating heavy-metal contaminated soils. Journal of Environmental Quality, 29, 197–205.CrossRefGoogle Scholar
  18. Juang, K. W., Chen, Y. S., & Lee, D. Y. (2004). Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution, 127, 229–238.CrossRefGoogle Scholar
  19. Lee, J. J., Liu, C. W., Jang, C. S., & Liang, C. P. (2008). Zonal management of multi-purpose use of water from arsenic-affected aquifers by using a multi-variable indicator kriging approach. Journal of Hydrology, 359(3–4), 260–273.CrossRefGoogle Scholar
  20. Lin, Y. P., Cheng, B. Y., Shyu, G. S., & Chang, T. K. (2010). Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan. Environmental Pollution, 158(1), 235–244.CrossRefGoogle Scholar
  21. Liu, C. W., Jang, C. S., & Liao, C. M. (2004). Evaluation of arsenic contamination potential using indicator kriging in the Yun-Lin aquifer (Taiwan). Science of the Total Environment, 321, 173–188.CrossRefGoogle Scholar
  22. Liu, C. W., Wang, S. W., Jang, C. S., & Lin, K. H. (2006). Occurrence of arsenic in groundwater of the Choshui river alluvial fan, Taiwan. Journal of Environmental Quality, 35, 68–75.CrossRefGoogle Scholar
  23. Oyedele, D. J., Amusan, A. A., & Obi, A. O. (1996). The use of multiple-variable indicator kriging technique for assessment of the suitability of an acid soil for maize. Tropical Agriculture, 73(4), 259–263.Google Scholar
  24. Saisana, M., Dubois, G., Chaloulakou, A., & Spyrellis, N. (2004). Classification criteria and probability risk maps: limitations and perspectives. Environmental Science & Technology, 38, 1275–1281.CrossRefGoogle Scholar
  25. Sawyer, C. N., & McCarty, P. L. (1978). Chemistry for Environmental Engineering (3rd ed., pp. 371–373). New York: McGraw-Hill.Google Scholar
  26. Smith, J.L., & Halvorson, J.J. (2011). Field scale studies on the spatial variability of soil quality indicators in Washington State, USA. Applied and Environmental Soil Science, 2011, Article ID 198737. doi:10.1155/2011/198737.
  27. Smith, J. L., Halvorson, J. J., & Papendick, R. I. (1993). Using multiple-variable indicator kriging for evaluating soil quality. Soil Science Society of America Journal, 57, 743–749.CrossRefGoogle Scholar
  28. Taiwan CGS (Central Geological Survey). (2002). Hydrogeological survey report of Pingtung Plain, Taiwan (pp. 97–142). Taiwan: Central Geological Survey, Ministry of Economic Affairs, Executive Yuan.Google Scholar
  29. Taiwan Sugar Company. (2005). Groundwater quality monitoring and analysis for Taiwan Groundwater Monitoring Network (2/2) (pp. 10-1–10-74). Taiwan: Water Resources Agency, Ministry of Economic Affairs, Executive Yuan.Google Scholar
  30. Ting, C. S., Zhou, Y., de Vries, J. J., & Simmers, I. (1998). Development of a preliminary ground water flow model for water resources management in the Pingtung plain, Taiwan. Ground Water, 35(6), 20–36.CrossRefGoogle Scholar
  31. USSL (1954). Diagnosis and improvement of saline and alkali soils. USDA Hand Book, 60, (pp. 147). Washington DC.Google Scholar
  32. Van Meirvenne, M., & Goovaerts, P. (2001). Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold. Geoderma, 102, 75–100.CrossRefGoogle Scholar
  33. Wang, S. W., Liu, C. W., & Jang, C. S. (2007). Factors responsible for high arsenic concentrations in two groundwater catchments at Taiwan. Applied Geochemistry, 22, 460–476.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Leisure and Recreation ManagementKainan UniversityTaoyuanTaiwan

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