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Satellite altimetry reveals intensifying global river water level variability

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Abstract

River water levels (RWLs) are fundamental to hydrology, water resource management, and disaster mitigation, yet the majority of the world’s rivers remain ungauged. Here, using 46,993 virtual stations from Sentinel-3A/B altimetry (2016‒2024), we present a global assessment of RWL variability. We find a median global fluctuation of 3.76 m, with pronounced spatial patterns: significant RWL declines across Central North/South America and Western Siberia, and increases across Africa, Oceania, Eastern/Southern Asia, and Northwestern/Central Europe. Seasonality is intensifying in 68% of basins, as high RWLs become more temporally concentrated. Maximum RWLs are declining by 0.88 cm/yr, while minimum RWLs are rising by 1.43 cm/yr. This convergence is reducing seasonal amplitude globally, with the most pronounced changes in the Americas and Central Africa. These shifts coincide with a recent surge in extreme RWL events, particularly after 2021, signaling growing hydrological instability amid concurrent droughts and floods. Our findings underscore the urgent need for adaptive water management in response to accelerating climate pressures.

Data availability

Sentinel-3 altimetry data can be downloaded from the Copernicus Data Space Ecosystem at https://dataspace.copernicus.eu/explore-data. Surface Water and Ocean Topography Mission River Database (SWORD) version 15 is available at https://www.swordexplorer.com/. Access to the ancillary data is linked as follows: (1) HydroBASINS: https://data.apps.fao.org/catalog/dataset/7707086d-af3c-41cc-8aa5-323d8609b2d1. (2) In-situ measurements: Water levels and discharge from USGS are available at https://waterdata.usgs.gov/nwis/current/?type=dailystage&group_key=NONE&site_no_name_select=station_nm; Discharge observations from GRDC are accessible via https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Stations. (3) HydroRIVERS: https://www.hydrosheds.org/products/hydrorivers. (4) GeoDAR: https://zenodo.org/records/6163413. (5) Global Precipitation Climatology Project (GPCP): https://climatedataguide.ucar.edu/climate-data/gpcp-monthly-global-precipitation-climatology-project. (6) GISS Surface Temperature Analysis (GISTEMP): https://data.giss.nasa.gov/gistemp/. (7) Connectivity Status Index: https://doi.org/10.6084/m9.figshare.7688801.v1. Process and result files encoded in JSON format79, including the river water level dataset and the validation dataset, are uploaded at https://doi.org/10.5281/zenodo.17130396. A technical documentation detailing the contents of these result files is also available through this link. Source data, recording all stations’ results, are also provided in this paper. Source data are provided with this paper.

Code availability

Water level retrieval from Sentinel-3 altimetry data was performed using MATLAB R2021b, while data postprocessing, including figure plotting, was conducted using Jupyter Notebook (Python 3.10). The code used for processing the results and developing the figures is available at https://github.com/Fangchq/Satellite-rivers/tree/master. The source scripts of our algorithm, available at https://github.com/Fangchq/An-improved-waveform-retracking-method/tree/master, have been slightly tuned to enhance their applicability to global rivers.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 52325901 to D.L. and Grant no. 42571432 to Q.H.). F.P. is supported by the CNES-TOSCA SWOT Science Team Project SAMBA. This work was partially funded by CNES (Centre National d’Etudes Spatiales) under the framework of the TOSCA program. The European Space Agency (ESA) is acknowledged for providing Sentinel-3 altimetry data, while in-situ measurements were made available by the United States Geological Survey (USGS), the Global Runoff Data Center (GRDC), and the Information Center of the Ministry of Water Resources of the People’s Republic of China.

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Author notes

  1. These authors contributed equally: Chenqi Fang, Di Long.

Authors and Affiliations

  1. Department of Hydraulic Engineering, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China

    Chenqi Fang, Di Long & Huaichuan Liu

  2. Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Tsinghua University, Beijing, China

    Chenqi Fang, Di Long & Huaichuan Liu

  3. School of Water Resources and Environment, China University of Geosciences, Beijing, China

    Qi Huang

  4. Université de Toulouse, LEGOS (IRD/CNRS/CNES/UT), Toulouse, France

    Jean-François Cretaux & Fabrice Papa

  5. Institut de Recherche pour le Développement, Universidade de Brasília, Brasília, Brazil

    Fabrice Papa

  6. ISPA, UMR1391 INRAE/Bordeaux Sciences Agro, Villenave d’Ornon, France

    Frédéric Frappart

  7. Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, USA

    Colin J. Gleason

  8. China Institute of Water Resources and Hydropower Research, Beijing, China

    Chunhong Hu

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  1. Chenqi Fang
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  3. Qi Huang
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Contributions

D.L., C.H., and C.F. developed the concept and methodology of this study. C.F., D.L., Q.H., and H.L. performed the data processing and analysis with support from all other authors. D.L., C.F., Q.H., J.F.C., F.P., F.F., H.L., C.J.G., and C.H. discussed the results and improved the writing of this manuscript.

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Correspondence to
Di Long.

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Fang, C., Long, D., Huang, Q. et al. Satellite altimetry reveals intensifying global river water level variability.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67682-9

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