What Is The Optimal Approach To Calibrating A MODFLOW Model To Accurately Simulate The Complex Interactions Between A Fractured Bedrock Aquifer And A Confining Layer Comprising Of Low-permeability Till, In A Region With A High Degree Of Spatial Variability In Recharge Rates And Aquifer Properties?

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Calibrating a MODFLOW model for a complex aquifer system involving a fractured bedrock aquifer and a confining layer of low-permeability till, with high spatial variability in recharge and aquifer properties, requires a structured and comprehensive approach. Here's a detailed, step-by-step plan:

1. Data Collection and Analysis

  • Geological Data: Gather maps and reports to understand the distribution of till and bedrock, and identify fractures.
  • Geophysical Surveys: Use electrical resistivity tomography or ground-penetrating radar to map subsurface features.
  • Hydraulic Testing: Analyze pumping and slug tests to determine hydraulic conductivity and storativity.
  • Water Levels: Collect data from monitoring wells for calibration targets.
  • Recharge Data: Use remote sensing and precipitation data to estimate spatially variable recharge.
  • Water Quality: Collect chemical and isotopic data to infer flow paths.

2. Model Conceptualization

  • Layer Discretization: Model till and bedrock as separate layers with distinct properties.
  • Dual-Porosity Model: Use MODFLOW's dual-porosity option for fractured bedrock to simulate fracture and matrix interactions.
  • Spatial Variability: Implement pilot points for hydraulic conductivity zonation and use geostatistical methods for interpolation.

3. Model Setup

  • Grid Discretization: Choose a grid size balancing detail and computational efficiency, such as 100x100 meters.
  • Boundary Conditions: Apply constant head boundaries for edges near water bodies, a recharge boundary on top, and no-flow at the bottom.
  • Stress Periods: Design stress periods to capture transient conditions, including varying recharge and pumping rates.

4. Parameter Estimation and Calibration

  • Automatic Calibration: Employ tools like PEST or UCODE for parameter estimation.
  • Sensitivity Analysis: Conduct analysis to focus on key parameters like hydraulic conductivity and specific storage.
  • Multi-Objective Calibration: Use water levels, flow rates, and water quality data for comprehensive calibration.
  • Regularization Techniques: Apply Tikhonov regularization to stabilize the inverse problem.

5. Uncertainty Assessment

  • Monte Carlo Simulations: Quantify uncertainty in model predictions.
  • Sensitivity Analysis: Post-calibration analysis to identify parameters with high uncertainty.

6. Model Testing and Validation

  • Independent Data: Validate the model with an independent dataset.
  • Predictive Uncertainty: Use methods like Bayesian Model Averaging to assess prediction uncertainty.

7. Documentation and Reporting

  • Comprehensive Documentation: Detail data sources, model setup, calibration, and results.
  • Assumptions and Limitations: Clearly outline model assumptions and limitations.

8. Computational Considerations

  • Parallel Processing: Utilize high-performance computing to handle complex simulations efficiently.

By following this structured approach, the MODFLOW model can be effectively calibrated to simulate the complex interactions in the aquifer system, providing reliable predictions for management and decision-making.