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Bayesian Chance-Constrained Hydraulic Barrier Design under Geological Structure Uncertainty.

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Geological structure uncertainty significantly impacts groundwater remediation design. A new Bayesian model averaging-chance constrained (BMA-CC) framework reveals traditional methods overestimate reliability, potentially leading to costly, less effective designs.

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Area of Science:

  • Hydrogeology
  • Environmental Engineering
  • Water Resource Management

Background:

  • Geological structure uncertainty is a key factor in groundwater model uncertainty.
  • Previous aquifer remediation designs have not adequately addressed geological structure uncertainty.

Purpose of the Study:

  • To develop and assess a Bayesian model averaging-chance constrained (BMA-CC) framework for evaluating geological structure uncertainty in remediation design.
  • To compare the BMA-CC method with traditional chance-constrained (CC) programming.

Main Methods:

  • Developed three groundwater models with varying hydrostratigraphic architectures to represent geological uncertainty.
  • Applied the BMA-CC framework to design a hydraulic barrier for saltwater intrusion prevention.
  • Compared BMA-CC results with traditional CC programming, which only accounts for parameter uncertainty.

Main Results:

  • Traditional CC programming overestimates the reliability of remediation designs.
  • Achieving over 90% reliability necessitates at least five additional connector wells.
  • The required injection rate may exceed public supply well pumpage, questioning economic viability.

Conclusions:

  • The BMA-CC framework provides a more realistic assessment of remediation design reliability under geological uncertainty.
  • Hydraulic barrier designs may require significant water injection, impacting cost-effectiveness.
  • Reducing reliability can lower injection rates but compromises protection levels.