Improving Groundwater Model Calibration with Repeat Microgravity Measurements

Published 2022. This article is a U.S. Government work and is in the public domain in the USA.

Bibliographische Detailangaben
Veröffentlicht in:Ground water. - 1979. - 60(2022), 3 vom: 23. Mai, Seite 393-403
1. Verfasser: Kennedy, Jeffrey R (VerfasserIn)
Weitere Verfasser: Wildermuth, Libby, Knight, Jacob E, Larsen, Joshua
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Ground water
Schlagworte:Journal Article
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520 |a Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation-change in storage measured using repeat microgravity-can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5 m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone 
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700 1 |a Knight, Jacob E  |e verfasserin  |4 aut 
700 1 |a Larsen, Joshua  |e verfasserin  |4 aut 
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