Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems

Area of Science:

• Hydrogeology
• Environmental Science
• Water Resource Management

Background:

• Groundwater model predictions face uncertainty from input data limitations.
• Monitoring field data is crucial for assessing model performance and reducing prediction uncertainty.
• High data collection costs necessitate optimizing the number and placement of observation wells.

Purpose of the Study:

• To develop a methodology for optimizing the number and spatial/depth locations of new observation wells.
• To effectively measure multiple hydrogeological parameters and reduce prediction uncertainty for saltwater intrusion (SWI).
• To identify optimal sampling locations for head and salinity data in coastal aquifers.

Main Methods:

• Incorporation of Bayesian model averaging and genetic algorithms.
• Application of linear data-worth analysis for a 3D location search.
• Evaluation of the methodology on a heterogeneous coastal aquifer with limited data experiencing SWI.

Main Results:

• The proposed methodology effectively optimizes the number and location of new observation wells.
• Optimal observation well placement varied based on specific design constraints and cost ratios.
• The study successfully identified effective locations for sampling head and salinity data.

Conclusions:

• The developed methodology aids in reducing uncertainties associated with predicting multiple variables in groundwater systems.
• Cost-benefit analysis, considering monitoring program start-up and well installation costs, is vital for optimal design.
• This approach provides a framework for cost-effective groundwater monitoring and improved predictive accuracy.