Fang et al. (2025) Satellite altimetry reveals intensifying global river water level variability

Identification

• Journal: Nature Communications
• Year: 2025
• Date: 2025-12-19
• Authors: Chenqi Fang, Di Long, Qi Huang, Jean‐François Crétaux, Fabrice Papa, Frédéric Frappart, H. Liu, Colin J. Gleason, Chunhong Hu
• DOI: 10.1038/s41467-025-67682-9

Research Groups

• State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China.
• Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Tsinghua University, Beijing, China.
• School of Water Resources and Environment, China University of Geosciences, Beijing, China.
• Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse (IRD/CNRS/CNES/UT), Toulouse, France.
• Institut de Recherche pour le Développement, Universidade de Brasília, Brasília, Brazil.
• ISPA, UMR1391 INRAE/Bordeaux Sciences Agro, Villenave d’Ornon, France.
• Department of Civil and Environmental Engineering, University of Massachusetts Amherst, USA.
• China Institute of Water Resources and Hydropower Research, Beijing, China.

Short Summary

This study utilizes Sentinel-3 satellite altimetry to establish a global dataset of river water levels across nearly 47,000 virtual stations, revealing that global river seasonality is intensifying while seasonal amplitudes are shrinking due to a surge in extreme hydrological events since 2021.

Objective

• To quantify global river water level (RWL) fluctuations and trends, particularly in ungauged and small-to-medium river systems, and to assess the impact of recent climate variability on seasonal dynamics and extreme events.

Study Configuration

• Spatial Scale: Global coverage across 46,993 virtual stations (VSs) in six continents, including rivers with widths as narrow as 10 m and high-altitude regions exceeding 5,000 m.
• Temporal Scale: September 2016 to 2024.

Methodology and Data

• Models used: Improved Multiple Subwaveform Analysis (IMSA) algorithm for radar waveform retracking; Apportionment Entropy (AE) for seasonality quantification; Robust least squares regression for trend analysis.
• Data sources: Sentinel-3A and Sentinel-3B SAR altimetry; SWOT River Database (SWORD); HydroBASINS and HydroRIVERS; Global Precipitation Climatology Project (GPCP); GISS Surface Temperature Analysis (GISTEMP); GeoDAR (dam/reservoir locations); In-situ gauge data from USGS, GRDC, and the Ministry of Water Resources of China.

Main Results

• Global Fluctuations: The median global RWL fluctuation is 3.76 m, with South America showing the highest median variability (exceeding others by 1.44–2.80 m).
• Wetting and Drying Trends: 51% of stations showed RWL increases (Asia, Africa, Oceania, NW Europe), while 49% showed declines. Significant declines (p < 0.05) were found in 25% of stations, with a median rate of -1.63 cm/yr, concentrated in the Amazon and Mississippi basins.
• Seasonality Shifts: Seasonality is intensifying in 68% of global basins, meaning high water levels are becoming more temporally concentrated.
• Seasonal Amplitude: Global seasonal amplitude is decreasing; maximum RWLs are declining by 0.88 cm/yr, while minimum RWLs are rising by 1.43 cm/yr.
• Extreme Events: A significant surge in extreme RWL events occurred after 2021. 2023 was identified as a record low-stage year globally (drought), while 2024 saw a peak in high-stage years (floods), indicating growing hydrological instability.

Contributions

• Expanded Observational Scope: Provides the most comprehensive global RWL dataset to date, incorporating an order of magnitude more virtual stations than previous studies and extending coverage to small, upstream, and high-altitude rivers.
• Methodological Advancement: Demonstrates the efficacy of the IMSA algorithm in complex riverine environments where traditional altimetry often fails.
• Hydrological Insights: Identifies a global "convergence" of seasonal extremes (lower peaks and higher lows) and a distinct shift in river dynamics post-2021 driven by concurrent drought-flood stressors.

Funding

• National Natural Science Foundation of China (Grant Nos. 52325901 and 42571432).
• CNES-TOSCA SWOT Science Team Project SAMBA.
• Centre National d’Etudes Spatiales (CNES) TOSCA program.

Citation

@article{Fang2025Satellite,
  author = {Fang, Chenqi and Long, Di and Huang, Qi and Crétaux, Jean‐François and Papa, Fabrice and Frappart, Frédéric and Liu, H. and Gleason, Colin J. and Hu, Chunhong},
  title = {Satellite altimetry reveals intensifying global river water level variability},
  journal = {Nature Communications},
  year = {2025},
  doi = {10.1038/s41467-025-67682-9},
  url = {https://doi.org/10.1038/s41467-025-67682-9}
}

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