Including geophysical data in ground water model inverse calibration.

Area of Science:

• Hydrogeology
• Geophysics
• Nonlinear Regression Analysis

Background:

• Groundwater flow models are crucial for water resource management.
• Accurate estimation of hydraulic conductivity is essential for reliable model predictions.
• Traditional model calibration relies solely on hydrological data, often limiting parameter accuracy.

Purpose of the Study:

• To develop and test a nonlinear regression method for estimating groundwater flow model parameters.
• To integrate geophysical observations with hydrological data for improved calibration.
• To assess the impact of geophysical data on the accuracy of hydraulic conductivity and head fields.

Main Methods:

• Developed a nonlinear regression approach to estimate hydraulic conductivity field parameters.
• Incorporated observed geophysical properties functionally related to hydraulic conductivity.
• Introduced a fidelity factor (sigma(r)^2) to quantify confidence in the functional relationship.
• Validated the methodology using synthetic groundwater flow models.

Main Results:

• Geophysical observations significantly increase the number of estimable hydraulic conductivity parameters.
• Improved accuracy in estimated hydraulic conductivity and simulated hydraulic head fields.
• The benefit depends on the quantity, location, and uncertainty of geophysical data.
• Sensitivity to the fidelity factor is low unless geophysical data uncertainty is high.

Conclusions:

• Integrating geophysical data enhances groundwater model calibration beyond traditional hydrological observations.
• The method provides a robust framework for parameter estimation when geophysical data is available.
• Careful consideration of geophysical data uncertainty is crucial for optimal model performance.