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Estimates of Hydraulic Properties from a One-Dimensional Numerical Model of Vertical Aquifer-System Deformation, Lorenzi Site, Las Vegas, Nevada

By Michael T. Pavelko

Report availability: Portable Document Format (PDF).


Land subsidence related to aquifer-system compaction and ground-water withdrawals has been occurring in Las Vegas Valley, Nevada, since the 1930's, and by the late 1980's some areas in the valley had subsided more than 5 feet. Since the late 1980's, seasonal artificial-recharge programs have lessened the effects of summertime pumping on aquifer-system compaction, but the long-term trend of compaction continues in places.

Since 1994, the U.S. Geological Survey has continuously monitored water-level changes in three piezometers and vertical aquifer-system deformation with a borehole extensometer at the Lorenzi site in Las Vegas, Nevada. A one-dimensional, numerical, ground-water flow model of the aquifer system below the Lorenzi site was developed for the period 1901-2000, to estimate aquitard vertical hydraulic conductivity, aquitard inelastic skeletal specific storage, and aquitard and aquifer elastic skeletal specific storage. Aquifer water-level data were used in the model as the aquifer-system stresses that controlled simulated vertical aquifer-system deformation. Nonlinear-regression methods were used to calibrate the model, utilizing estimated and measured aquifer-system deformation data to minimize a weighted least-squares objective function, and estimate optimal property values.

Model results indicate that at the Lorenzi site, aquitard vertical hydraulic conductivity is 3 x 10-6 feet per day, aquitard inelastic skeletal specific storage is 4 x 10-5 per foot, aquitard elastic skeletal specific storage is 5 x 10-6 per foot, and aquifer elastic skeletal specific storage is 3 x 10-7 per foot. Regression statistics indicate that the model and data provided sufficient information to estimate the target properties, the model adequately simulated observed data, and the estimated property values are accurate and unique.

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