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Article

Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations

1
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, China
3
Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Application, Nanjing 210044, China
4
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
5
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2739; https://doi.org/10.3390/rs17152739 (registering DOI)
Submission received: 13 June 2025 / Revised: 31 July 2025 / Accepted: 5 August 2025 / Published: 7 August 2025

Abstract

The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions.
Keywords: Amazon basin; GNSS and GRACE/GRACE-FO; terrestrial water storage; loading deformation; interannual variations; El Niño/Southern Oscillation Amazon basin; GNSS and GRACE/GRACE-FO; terrestrial water storage; loading deformation; interannual variations; El Niño/Southern Oscillation

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MDPI and ACS Style

He, M.; Chen, T.; Pan, Y.; Zhou, L.; Lv, Y.; Zhao, L. Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations. Remote Sens. 2025, 17, 2739. https://doi.org/10.3390/rs17152739

AMA Style

He M, Chen T, Pan Y, Zhou L, Lv Y, Zhao L. Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations. Remote Sensing. 2025; 17(15):2739. https://doi.org/10.3390/rs17152739

Chicago/Turabian Style

He, Meilin, Tao Chen, Yuanjin Pan, Lv Zhou, Yifei Lv, and Lewen Zhao. 2025. "Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations" Remote Sensing 17, no. 15: 2739. https://doi.org/10.3390/rs17152739

APA Style

He, M., Chen, T., Pan, Y., Zhou, L., Lv, Y., & Zhao, L. (2025). Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations. Remote Sensing, 17(15), 2739. https://doi.org/10.3390/rs17152739

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