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Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data
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Abstract
In this study, a scheme is presented to estimate groundwater storage variations in Iran. The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (
GRACE
) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (
GLDAS
) model including soil moisture, snow water equivalent, and total canopy water storage. To do so, the sums of
GLDAS
outputs are subtracted from terrestrial water storage variations determined by
GRACE
observations. Because of stripping errors in the
GRACE
data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the
GRACE
data. It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations. To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.
Title: Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using
GRACE
Data
Description:
Abstract
In this study, a scheme is presented to estimate groundwater storage variations in Iran.
The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (
GRACE
) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (
GLDAS
) model including soil moisture, snow water equivalent, and total canopy water storage.
To do so, the sums of
GLDAS
outputs are subtracted from terrestrial water storage variations determined by
GRACE
observations.
Because of stripping errors in the
GRACE
data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the
GRACE
data.
It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations.
To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.
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