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Environmental Management

, Volume 19, Issue 3, pp 383–392 | Cite as

Evaluating the effects of spatial monitoring policy on groundwater quality portrayal

  • Sheryl Luzzadder-Beach
Research
  • 44 Downloads

Abstract

What size sample is sufficient for spatially sampling ambient groundwater quality? Water quality data are only as spatially accurate as the geographic sampling strategies used to collect them. This research used sequential sampling and regression analysis to evaluate groundwater quality spatial sampling policy changes proposed by California's Department of Water Resources. Iterative or sequential sampling of a hypothetical groundwater basin's water quality produced data sets from sample sizes ranging from 2.8% to 95% coverage of available point sample sites. Contour maps based on these sample data sets were compared to an original (control), mapped hypothetical data set, to determine at which point map information content and pattern portrayal are not improved by increasing sample sizes. Comparing series of contour maps of ground water quality concentration is a common means of evaluating the geographic extent of groundwater quality change. Comparisons included visual inspection of contout maps and statistical tests on digital versions of these map files, including correlation and regression products. This research demonstrated that, down to about 15% sample site coverage, there is no difference between contour maps produced from the different sampling strategies and the contout map of the original data set.

Key Words

Spatial sampling Groundwater quality monitoring Regression analysis Contour mapping Water resources geography 

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Literature Cited

  1. Beach, S. L. 1987. Ground water sampling strategies for a water resources geographic information system. Pages 14-1–14-22 in D. Brown and P. Gersmehl (eds.), File structure design and data specifications for water resources geographic information systems. Water Resources Research Center, University of Minnesota St. Paul, Minnesota, Special Report No. 10.Google Scholar
  2. Beach, S. L., R. F. Clawson, and L. R. Gibson, 1986a. Alturas Basin Ground Water Quality Study. California Department of Water Resources Report. Sacramento, California. January 1986.Google Scholar
  3. Beach, S. L., R. F. Clawson, and L. R. Gibson. 1986b. Surprise Valley Ground Water Quality Study. California Department of Water Resources Report. Sacramento, California. January 1986.Google Scholar
  4. Brown D. A., P. G. Gersmehl, and K. A. Anderson. 1987. File structure and cell size consideration for a water resources GIS. Chapter 2in D. Brown and P. Gersmehl (eds.), File structure design and data specifications for water resources geographic information systems. Water Resources Research Center, University of Minnesota. St. Paul, Minnesota. Special Report No. 10.Google Scholar
  5. CDWR (California Department of Water Resources). 1986. Water resources inventory study memo. Unpublished, typewritten memo. Water Resources Inventory Study Task Force, December 1986. Sacramento, California.Google Scholar
  6. Carrera, J., E. Usinoff, and F. Szidarovszky. 1984. A method for optimal observation network design for ground water management.Journal of Hydrology 73: 147–163.CrossRefGoogle Scholar
  7. Choquette, A., and B. Katz. 1989. Grid based ground water sampling: Lessons learned from an extensive regional network for 1,2-dibromoethane (EDB) in Florida. Pages 79–86in S. Rangone (ed.), Regional characteristics of water quality: Proceedings of the International Association of Hydrological Sciences Baltimore Symposium, May 1989. IAHS Publ. No. 182.Google Scholar
  8. Cliff, A. D. 1970. Computing the spatial correspondence between geographical patterns.Transactions, Institute of British Geographers 50: 143–154.CrossRefGoogle Scholar
  9. Cohen, D. K., and S. A. Smiriglio. 1987. Putting the ground water monitoring pieces together.Water/Engineering & Management 134: 24–26.Google Scholar
  10. Costanza, R., S. O. Funtowicz, and J. R. Ravetz. 1992. Assessing and communicating data quality in policy-relevant research.Environmental Management 16: 121–131.CrossRefGoogle Scholar
  11. France, R. 1992. Use of sequential sampling of amphipod abundance to classify the biotic integrity of acid-sensitive lakes.Environmental Management 16: 157–166.CrossRefGoogle Scholar
  12. Hsueh, Y., and R. Rajagopal. 1988. Modeling ground water quality sampling decisions.Ground Water monitoring Review 8: 121–134.Google Scholar
  13. Leahy, P. P. 1992. Consistent data on water quality—it's long overdue.Geotimes 37(12):5.Google Scholar
  14. Loaiciga, H. 1989. An optimization approach for ground water quality monitoring network design.Water Resources Research 25: 1771–1782.Google Scholar
  15. Loftis, J., J. Harris, and R. Montgomery. 1987. Detecting changes in ground water quality at regulated facilities.Ground Water Monitoring Review 7:8–22.Google Scholar
  16. McMillan, J. R. 1987. Antelope Ground Water Study. California Department of Water Resources Report. April 1987, 67 pp.Google Scholar
  17. Meyer, P., and E. D. Brill, Jr. 1988. A method for locating wells in a ground water monitoring network under conditions of uncertainty.Water Resources Research 24:1277–1282.CrossRefGoogle Scholar
  18. Montgomery, R., J. Loftis and J. Harris. 1987. Statistical characteristics of ground-water quality variables,Ground Water 25:176–193.CrossRefGoogle Scholar
  19. Nelson, J. D., and R. C. Ward. 1981. Statistical considerations and sampling techniques for ground-water quality monitoring.Ground Water 19:617–625.CrossRefGoogle Scholar
  20. Pfannkuch, H. O. 1982. Problems of network design to detect unanticipated contamination.Ground Water Monitoring Review 2:67–76.Google Scholar
  21. Rajagopal, R. 1986. The effect of sampling frequency on ground water quality characterization.Ground Water Monitoring Review 6:65–73.Google Scholar
  22. Rajagopal, R. 1987. Large data based and regional ground water quality assessments—an Iowa case study.Ground Water 25:415–426.CrossRefGoogle Scholar
  23. Rouhani, S. 1985. Variance reduction analysis.Water Resources Research 21:837–846.Google Scholar
  24. Rouhani, S. 1986. Comparative study of ground-water mapping techniques.Ground Water 24:207–216.CrossRefGoogle Scholar
  25. Schweitzer, G. E., and S. C. Black. 1985. Monitoring statistics: An important tool for ground water and soil studies.Environmental Science and Technology 19:1026–1030.CrossRefGoogle Scholar
  26. Showalter, P. 1985. Developing objectives for the ground water quality monitoring network of the Salinas River drainage basin.Ground Water Monitoring Review 5:37–45.Google Scholar
  27. Sokal, R. R., and P. H. A. Sneath, 1973. Numerical taxonomy. W. H. Freeman and Co., San Francisco, 573 pp.Google Scholar
  28. Tobin, G. A., and R. Rajagopal. 1990. Expert opinion and ground water quality: The case of agricultural drainage wells.Journal of Soil and Water Conservation 45:336–341.Google Scholar
  29. Unwin, D. 1981. Introductory spatial analysis. Methuen & Co., New York, 212 pp.Google Scholar
  30. Veneziano, D., and P. K. Kitanidis. 1992. Sequential sampling to contour an uncertain function.Mathematical Geology 14:387–404.CrossRefGoogle Scholar
  31. Ward, R. C. 1981. Ground water quality monitoring—what information is to be obtained?Ground Water 19:130–132.CrossRefGoogle Scholar
  32. Ward, R. C., J. C. Loftis, and G. B. McBride, 1986. The “data-rich but information poor” syndrome in water quality monitoring.Environmental Management 10:291–297.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1995

Authors and Affiliations

  • Sheryl Luzzadder-Beach
    • 1
  1. 1.Department of Geography and Earth Systems ScienceGeorge Mason UniversityFairfaxUSA

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