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Probabilistic capture zone delineation based on an analytic solution.

Elizabeth Jacobson1, Roko Andricevic, Joseph Morrice

  • 1University of Nevada Las Vegas, Geoscience Department, 89154-4010, USA.

Ground Water
|January 19, 2002
PubMed
Summary
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This study introduces a statistical method to account for uncertainty in wellhead protection area calculations. The new approach improves the accuracy of predicting capture zones by considering variations in hydraulic parameters.

Area of Science:

  • Hydrogeology
  • Environmental Engineering
  • Water Resource Management

Background:

  • Wellhead protection areas are crucial for safeguarding drinking water sources.
  • Simple analytic solutions for capture zone delineation are cost-effective but deterministic.
  • Uncertainty in hydrogeologic parameters can lead to significant errors in capture zone predictions.

Purpose of the Study:

  • To develop a statistical theory for incorporating uncertainty into analytic solutions for well capture zones.
  • To quantify the impact of parameter uncertainty on the reliability of capture zone predictions.
  • To provide a more robust method for designing wellhead protection areas.

Main Methods:

  • Developed a statistical theory to include uncertainty in transmissivity and hydraulic head gradient.

Related Experiment Videos

  • Applied the theory to an analytic solution for ultimate and time-dependent capture zones.
  • Utilized synthetic datasets based on Borden Aquifer data to test the method.

    • Main Results:

    • Uncertainty in time-dependent capture zone length is linked to uncertainty in mean regional flow magnitude.
    • Uncertainty in capture zone width is primarily influenced by uncertainty in regional flow direction.
    • The method allows for capture zone prediction at arbitrary reliability levels.

    Conclusions:

    • The developed statistical approach enhances the reliability of well capture zone predictions.
    • Understanding and quantifying parameter uncertainty is vital for effective wellhead protection.
    • This method offers a practical improvement over deterministic models for municipalities facing high data acquisition costs.