A Framework for Characterizing Component-Wise Nonstationarity in GRACE/GRACE-FO-Derived Terrestrial Water Storage Anomalies

Abstract

Understanding the nonstationarity of GRACE/GRACE-FO-derived terrestrial water storage anomalies (TWSA) is crucial for interpreting component-scale water storage variability, improving TWSA modeling, and supporting evaluation of land-surface and global hydrological models. However, existing studies lack methods for exploring TWSA nonstationarity in sufficient detail. To address this gap, this study introduces a framework comprising four steps: (i) filling TWSA gaps using two independent observation-only methods under an uncertainty-referenced dual-reconstruction strategy; (ii) decomposing TWSA into semi-annual, annual, long-term, and residual components while reducing and diagnosing signal mixing, with the long-term component further separated into trend and interannual–decadal variability (IA-D); (iii) evaluating the extracted components using complementary spectral and time–frequency methods; and (iv) estimating time-varying and average semi-annual amplitude (SAA) and annual amplitude (ANA), while characterizing IA-D in the time and time–frequency domains. The framework integrates non-wavelet-based techniques (Singular Spectrum Analysis, Fast Fourier Transform) and wavelet-based methods (Continuous Wavelet Transform, Multiresolution Analysis of the Maximal Overlap Discrete Wavelet Transform), along with local least-squares fitting, change-point detection, and local-extrema analysis. The Vietnam application demonstrates its ability to characterize component-wise TWSA nonstationarity across diverse hydroclimatic and geographical conditions. SAA fluctuates in all regions, ANA generally weakens with regional exceptions, and IA-D exhibits regionally varying high- and low-value episodes and evolving 2-year and 5-year periodicities. Compared with conventional least-squares fitting, the framework remains broadly consistent with full-record average seasonal amplitude estimates, while revealing time-varying SAA and ANA and providing more reliable average SAA estimates where IA-D variability is much larger than the semi-annual component.