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Updated: May 9, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A new zonation algorithm with parameter estimation using hydraulic head and subsidence observations.

Meijing Zhang¹, Thomas J Burbey, Vitor Dos Santos Nunes

¹Department of Geosciences, Virginia Tech, Blacksburg, VA 24061; +1-540-231-6696; tjburbey@vt.edu.

|August 6, 2013

Summary

This study introduces an automated algorithm to identify groundwater parameter zones, reducing user bias. The method accurately estimates transmissivity and storage coefficients using hydraulic head and subsidence data.

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Watershed Planning within a Quantitative Scenario Analysis Framework

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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

Area of Science:

Hydrogeology and computational modeling.
Geophysical methods for subsurface characterization.

Background:

Groundwater modeling relies on parameter estimation codes like UCODE_2005.
Accurate parameter zone definition is crucial but often unknown.
Interferometric Synthetic Aperture Radar (InSAR) provides valuable subsidence data.

Purpose of the Study:

To develop a discrete adjoint algorithm for automated parameter zonation in groundwater models.
To overcome the limitation of user-defined parameter zones.
To integrate hydraulic head and subsidence data for improved parameter estimation.

Main Methods:

A discrete adjoint algorithm was developed to identify parameter zones.
Simulated drawdown and subsidence data were used for calibration.
UCODE-2005 was employed for final optimal parameter value estimation.

Main Results:

The algorithm successfully identified approximate spatial zonations for transmissivity (T), elastic skeletal specific storage (Sske), and inelastic skeletal specific storage (Sskv).
Estimated zonations closely matched true parameter distributions in a synthetic model.
Automated zonation reduced bias from initial zone distributions.

Conclusions:

The developed algorithm provides a robust method for parameter zonation in groundwater modeling.
Automating zonation improves the accuracy and reliability of parameter estimation.
This approach enhances hydrogeological investigations by reducing reliance on subjective user input.