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    Comprehensive geostatistical based parameter optimization and inverse modeling of North Avra Valley, Arizona

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    Author
    Wolf, Ailco
    Issue Date
    2002
    Advisor
    Yeh, Jim T.C.
    
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    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Geostatistical based optimization was applied to the North A vra Valley ground water model to estimate the transmissivity field and boundary conditions that minimize the difference of the modeled and measured head. The Sequential Self-Calibration (SSC) method was used for the inverse modeling and optimization. SSC is an iterative technique that combines geostatistics with an optimization routine to condition both transmissivity and head fields to measured data. Two calibration methodologies were compared. In the first, the inflow and outflow boundary conditions are adjusted to minimize head residuals, using the uniform geometric mean transmissivity field and the subsequent SSC calibrated transmissivity field is based on those initial boundary conditions. The second method ran the model independent optimization software PEST in series with SSC. This approach calibrates the inflow and outflow boundary conditions and transmissivity field iteratively against the head residuals. As a consequence, the inflow and outflow boundary conditions are optimized against the final geostatistical based transmissivity field used in the model. The serial PEST-SSC calibration method produces consistently better results with respect to head residuals, by an average of 27 .1 percent. The resulting calibrated transmissivity fields of both methods were compared using stochastic error analysis, showing similar results for both methods. A final model run was done employing the PEST-SSC method for a more detailed analysis. This resulted in a relative error ( O'head residuals I head-range) of only 1.5 percent.
    Type
    text
    Thesis-Reproduction (electronic)
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Hydrology and Water Resources
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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