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Wiley InterScience

Ground Water

Ground Water

Volume 44 Issue 6, Pages 792 - 796

Special Issue: Understanding through Modeling

Published Online: 8 Mar 2006

Journal compilation © 2010 National Ground Water Association



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Comparison of Local Grid Refinement Methods for MODFLOW
Steffen Mehl 1 , Mary C. Hill 2 , Stanley A. Leake 3
  2 U.S. Geological Survey, 3215 Marine Street, Boulder, CO 80303; mchill@usgs.gov
  3 U.S. Geological Survey, Tucson, AZ 85719; saleake@usgs.gov
Correspondence to   1 U.S. Geological Survey, 3215 Marine Street, Boulder, CO 80303; (303) 541-3078; fax (303) 447-2505; swmehl@usgs.gov
Copyright 2006 The Author(s) Journal compilation

Abstract

AbstractIntroductionDescription of the methodsComparisonsConclusionsAcknowledgmentsReferences

Many ground water modeling efforts use a finite-difference method to solve the ground water flow equation, and many of these models require a relatively fine-grid discretization to accurately represent the selected process in limited areas of interest. Use of a fine grid over the entire domain can be computationally prohibitive; using a variably spaced grid can lead to cells with a large aspect ratio and refinement in areas where detail is not needed. One solution is to use local-grid refinement (LGR) whereby the grid is only refined in the area of interest. This work reviews some LGR methods and identifies advantages and drawbacks in test cases using MODFLOW-2000. The first test case is two dimensional and heterogeneous; the second is three dimensional and includes interaction with a meandering river. Results include simulations using a uniform fine grid, a variably spaced grid, a traditional method of LGR without feedback, and a new shared node method with feedback. Discrepancies from the solution obtained with the uniform fine grid are investigated. For the models tested, the traditional one-way coupled approaches produced discrepancies in head up to 6.8% and discrepancies in cell-to-cell fluxes up to 7.1%, while the new method has head and cell-to-cell flux discrepancies of 0.089% and 0.14%, respectively. Additional results highlight the accuracy, flexibility, and CPU time trade-off of these methods and demonstrate how the new method can be successfully implemented to model surface water–ground water interactions.


Received May 2004, accepted September 2005.

DIGITAL OBJECT IDENTIFIER (DOI)
10.1111/j.1745-6584.2006.00192.x About DOI

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