Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data

Area of Science:

• Hydrology
• Remote Sensing
• Geophysics

Background:

• Groundwater storage variations are critical for water resource management.
• Satellite gravimetry, like GRACE, offers a method to monitor terrestrial water storage.
• Data assimilation models like GLDAS provide essential components of the water cycle.

Purpose of the Study:

• To develop and validate a scheme for estimating groundwater storage variations in Iran.
• To utilize Gravity Recovery and Climate Experiments (GRACE) observations and Global Land Data Assimilation Systems (GLDAS) model outputs.
• To refine GRACE data using advanced signal processing techniques for improved accuracy.

Main Methods:

• Combining GRACE-derived terrestrial water storage variations with GLDAS soil moisture, snow water equivalent, and canopy water storage.
• Applying wavelet analysis and Gaussian filtering to refine GRACE data and mitigate stripping errors.
• Validating the estimated groundwater storage variations against pisometric well data near Urmia Lake.

Main Results:

• The wavelet analysis approach demonstrated superior signal localization and improved the signal-to-noise ratio in GRACE data.
• The integrated approach successfully estimated groundwater storage variations across Iran.
• The refined estimates showed favorable agreement with in-situ pisometric well measurements.

Conclusions:

• Wavelet analysis is an effective method for enhancing GRACE data quality in hydrological studies.
• The presented methodology provides a reliable approach for estimating groundwater storage variations in data-scarce regions.
• Accurate groundwater monitoring is essential for sustainable water management in Iran.