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Environmental Health - Toxic Substances


U.S. Geological Survey Toxic Substances Hydrology Program--Proceedings of the Technical Meeting, Colorado Springs, Colorado, September 20-24, 1993, Water-Resources Investigations Report 94-4015

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Solute-Transport Parameter Estimation for an Injection Experiment at Pinal Creek, Arizona


Brian J. Wagner (U.S. Geological Survey, Menlo Park, Calif.) and Judson W. Harvey (U.S. Geological Survey, Menlo Park, Calif.)


Parameter estimation is an important step in the development of contaminant-transport simulation models. In this paper, we demonstrate an inverse modeling methodology for estimating the transport parameters that characterize the migration, attenuation, and redistribution of contaminants. The inverse model proceeds in two stages: In stage one, a finite difference solute-transport simulation model is coupled with nonlinear least-squares regression to identify the model parameter values that "best" reproduce the measured solute concentrations. In stage two, solute-transport simulation is combined with first-order uncertainty analysis to quantify parameter-estimate uncertainty. The methodology is widely applicable to transport models for ground-water and stream systems; here we demonstrate the application to a stream system. Using data from an injection experiment at Pinal Creek, Arizona, the inverse model is applied to analyze parameters for a one-dimensional solute-transport model with advection, dispersion, lateral inflow, and transient storage. The parameters estimated by the inverse model are dispersion coefficient, stream cross-sectional area, storage-zone cross-sectional area, and stream-storage exchange coefficient. The parameter estimates and associated uncertainties support the interpretation that the transient-storage mechanism is active in Pinal Creek. A discussion of concentration sensitivity to these four estimated parameters is presented to explain how analysis of uncertainties in parameter estimates can be used to identify sampling strategies for reliable parameter estimation.

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