Measuring the Change in Water Table with Gravity Methods - a Controlled Experiment

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Gravity changes linearly with the change in soil water content. With the GRACE satellite mission the interest for ground-based gravity methods in hydrology has gained new attention. Time-lapse gravity data have the potential to constrain hydrological model parameters in a calibration scheme. The greatest potential is seen for specific yield. The gravity signal from hydrology is small (10^-8 m/s^2 level) and the application of ground-based methods is mainly limited by the sensitivity of available instruments. In order to demonstrate the ability of the Scintrex CG-5 gravity meter to detect a change in water content, a controlled experiment was set up in 30 m by 20 m basin. The water table was lowered 0.69 m within 1½ hours and the corresponding gravity signal measured using two different approaches: a time series measurements at one location and a gravity network measurement including four points. Both where in agreement with the calculated maximum theoretical gravity change of 27*10^-8 m/s^2. Uncertainties on the change in gravity in the network measurements where 4*10^-8 m/s^2 (one standard deviation). This corresponds to an infinite horizontal slab of water with a thickness of 0.1 m. The time series data showed a similar uncertainty before the drawdown, but showed a higher noise level during drawdown due to an unstable setup. A series of corrections must be applied to both time series and network data. We find that network measurements are superior to time series measurements due to a simpler and more flexible measurement- and data processing procedure.
Original languageEnglish
Title of host publicationAGU Fall Meeting Abstracts
PublisherAmerican Geophysical Union
Publication date2009
Publication statusPublished - 2009
EventAmerican Geophysical Union Fall Meeting 2009 - San Francisco, United States
Duration: 14 Dec 200918 Dec 2009


ConferenceAmerican Geophysical Union Fall Meeting 2009
CountryUnited States
CitySan Francisco
Internet address

ID: 6552697