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The “data-rich but information-poor” syndrome in water quality monitoring

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Abstract

Water quality monitoring conducted routinely over time at fixed sites has been a part of most water quality management efforts for many years. It has been assumed that such monitoring plays a major role in management. However, the lack of routine data analysis, and reporting of information derived from such analysis, points up the fact that the exact nature of the role of routine, fixed-station monitoring is poorly defined.

There is a need to very clearly define this role in the design of such systems if routine monitoring is to efficiently and effectively meet the information expectations placed on it. Design of routine monitoring systems will therefore have to consider not only the where, what, and when of sampling, but also why. A framework for including the “why” of monitoring in the design process is proposed and experience with using the framework in New Zealand is discussed.

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Literature cited

  • Cairns, J., Jr. 1985. Evaluating options for water quality management.Water Recourus Bulletin 21:1–6.

    Google Scholar 

  • Council on Environment Quality. 1980. Final report of the interagency task force on environmental data and monitoring. Available from National Technical Information Service, US Department of Commerce, 5285 Port Royal Road, Springfield, Virginia 22161, 21 March.

    Google Scholar 

  • General Accounting Office. 1981. Better monitoring techniques are needed to assess the quality of rivers and streams. US General Accounting Office Report CED-81-30, Washington, DC, 30 April.

  • Herricks, E. E., D.J. Schaeffer, and J. C. Kapsner. 1985. Complying with NPDES permit limits: when is a violation a violation?Journal of the Water Pollution Control Federation 57:109–115.

    Google Scholar 

  • Hirsch, R. M., J. R. Slack, and R. A. Smith, 1982. Techniques of trend analysis for monthly water quality data.Water Resources Research 18:107–121.

    Google Scholar 

  • Lettenmaier, D. P. 1976. Detection of trends in water quality data records with dependent observations.Water Resources Research 12:1037–1046.

    Google Scholar 

  • Loftis, J. C., and R. C. Ward. 1980. Water quality monitoring: some practical sampling frequency considerations.Environmental Management 4:521–526.

    Google Scholar 

  • Loftis, J. C., R. C. Ward, and G. M. Smillie. 1983. Statistical models for water quality regulation.Journal of the Water Pollution Control Federation 55:1098–1104.

    Google Scholar 

  • National Academy of Sciences. 1977. Analytical studies for the US Environmental Protection Agency, vol. 4: Environmental monitoring. National Academy of Sciences, Washington, DC.

    Google Scholar 

  • Pitts, W. T. 1980. Implementation plan for regional water quality monitoring program. Report no. 36, Larimer-Weld Regional Council of Governments, Loveland, Colorado, Tom Pitts and Associates, Loveland, Colorado.

    Google Scholar 

  • Ponce, S. L. 1980. Statistical methods commonly used in water quality data analysis. Technical Paper WSDG-TP-00001. Watershed Systems Development Group, USDA Forest Service, Fort Collins, Colorado.

    Google Scholar 

  • Sanders, T. G., R. C. Ward, J. C. Loftis, T. D. Steele, D. D. Adrian, and V. Yevjecich. 1983. Design of networks for monitoring water quality. Water Resources Publications, Littleton, Colorado.

    Google Scholar 

  • Ward, R. C. 1979. Regulatory water quality monitoring: a systems perspective.Water Resources Bulletin 15:369–380.

    Google Scholar 

  • Ward, R. C., and J. C. Loftis. 1983. Incorporating the stochastic nature of water quality into management.Journal of the Water Pollution Control Federation 55:408–414.

    Google Scholar 

  • Ward, R. C., and G. B. McBride. 1985. Design of water quality monitoring systems in New Zealand. Miscellaneous Publication. Water and Soil Directorate, Ministry of Works and Development, Wellington, New Zealand (in press).

    Google Scholar 

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Ward, R.C., Loftis, J.C. & McBride, G.B. The “data-rich but information-poor” syndrome in water quality monitoring. Environmental Management 10, 291–297 (1986). https://doi.org/10.1007/BF01867251

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