Abstract
Fluvial sediment transport, a key pathway for global biogeochemical cycling, has changed markedly in the Anthropocene. However, disaggregating the compound effects of anthropogenic stresses on fluvial sediment transport at the global scale remains a challenge. Here we map the suspended sediment concentrations for global river channels, based on satellite observations, between 1985 and 2020, and estimate long-term changes in land–ocean sediment transfer. We find significant (P < 0.05) changes in suspended sediment concentrations in 67.8% (3.2 × 105 km) of the examined river channel length, with 43.4% (2.05 × 105 km) displaying a significant increasing trend, driven mainly by rising rainfall erosion and climate warming. Consequently, a global net increase (+0.58 Gt year−1) in land–ocean sediment flux has been observed over the past four decades, despite sediment trapping by recently constructed dams, mostly in Asia. Our study provides a new baseline for source-to-sink fluvial transport in the Anthropocene that can inform global water resource management and delta management and protection.
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Data availability
The satellite-based dataset of global fluvial SSCs (GSED) is available via figshare at https://figshare.com/s/dde3bffd8e12227e2b26 (ref. 62). The field river SSCs can be accessed at https://gemstat.bafg.de/applications/, https://doi.org/10.5066/P9AEWTB9, https://www.mrcmekong.org/, https://hybam.obs-mip.fr and https://www.canada.ca/en/services/environment.html. The GRWL dataset is available via Zenodo at https://doi.org/10.5281/zenodo.1269594 (ref. 63). Source data are provided with this paper.
Code availability
The MATLAB code developed for the river SSC retrieval and statistics is available via figshare at https://figshare.com/s/dde3bffd8e12227e2b26 (ref. 62). The code for the BEAST algorithm is available at https://github.com/zhaokg/Rbeast.
Change history
27 January 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41893-025-01518-8
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Acknowledgements
We thank the United States Geological Survey for providing the global Landsat satellite images, and Google Earth for providing data processing resources. L.F. was supported by the National Natural Science Foundation of China (grant nos. 42425604 and 42321004) and the National Key Research and Development Program of China (grant no. 2022YFC3201802). L.T. was supported by the National Key Research and Development Program of China (grant no. 2023YFB3905304), the National Natural Science Foundation of China (grant nos. 42371336 and 42271354) and LIESMARS Special Research Funding. E.P. was supported by grants from the Ministry of Education, Singapore, under its Academic Research Fund (Tier2: MOE-T2EP50222-0007; Tier3: MOE-T32022-0006), the Earth Observatory of Singapore (EOS) via its funding from the National Research Foundation Singapore, the Singapore Ministry of Education under the Research Centers of Excellence initiative, and the EOS contribution no. 608. C.Z. and X.S. were supported by a grant from the Ningbo Municipal Government.
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X.S. and L.T. contributed to the methodology, data processing and analyses, and writing. L.F. conceptualized the project and contributed to the methodology, funding acquisition, supervision and writing. H.F., D.E.W., L.H., E.P., D.L. and C.Z. participated in interpreting the results and refining the paper.
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Extended data
Extended Data Fig. 1 Development and validation of the SSC retrieval algorithm.
(a) Spatial distributions of satellite-field monitoring matching pairs from common river water bodies (n = 491) and dark-clear waters (n = 237), which were used to develop the SSC retrieval algorithm. Samples from dark-clear waters are primarily located in Northern Europe, especially in Scandinavian rivers, as well as in northern North America. (b) Spectral shapes of satellite reflectance. (c) Validation of the SSC retrieval algorithm. (d) Comparison of long-term trends between Landsat-derived SSC and field-measured SSC, with orange stars in panel (a) indicating the locations of the 66 field sites. (e) The validity of the SSC trends derived using an observational frequency of 8 images per year (that is, the cutoff for reliable Landsat SSC trends used in this study).
Extended Data Fig. 2 Global patterns of satellite-derived fluvial SSC.
(a) The number of valid Landsat observations during 1985-2020 for global river reaches. River reaches with less than 288 observations (that is, 8 observations per year on average) were excluded, providing a mean annual number of observations of 14.7 per year (see histogram within the panel) (b) Long-term annual mean SSC of global rivers.
Extended Data Fig. 3 Satellite images (RGB true colors) showing the effect of dams in trapping fluvial suspended sediment.
The arrow indicates the flow direction and dam ___location.
Extended Data Fig. 4 Dam impoundment time indicated by satellite-derived SSC time series and the BEAST algorithm.
(a) Detected impoundment year (red lines) for three dams: the Wanjiazhai Dam on the Yellow River, China; the Three Gorges Dam on the Yangtze River, China; and the Avrig Dam on the Olt River, Romania. The gray shadings represent the construction periods of these dams. (b) Comparison of BEAST-detected dam impoundment years with the GRanD recorded construction years, where the heights and SSC trapping efficiencies of the dams are demonstrated. (c) The spatial distribution of continuously impounding dams after 1985, with the impoundment year color-coded.
Extended Data Fig. 5 Global patterns of the rainfall erosivity factor (R) and the land cover and management factor (C).
(a-b) Global mean values for R and C between 1985 and 2020; (c-d) Global trends (that is, Sen’s Slope) for R and C over the same period (Mann-Kendall test, P < 0.05).
Extended Data Fig. 6 Climate elasticity models for 40 river basins where SSC is sensitive (P < 0.05) to change in precipitation.
dSSC/SSC and dR/R represent the relative changes in annual SSC and the rainfall erosivity factor over a river basin, compared to their long-term mean values. The linear regression coefficient, namely the climate elasticity (e), represents the proportional SSC increase in response to a 1% increase in rainfall erosivity. The gray-shaded area denotes the 95% confidence interval of the best-fit line. The locations of the river basins are shown in Fig. 3b.
Extended Data Fig. 7 Sensitivity of SSC to temperature change in the high latitudes and the Tibetan Plateau indicated by climate elasticity models.
dSSC/SSC and dT represent the changes in annual SSC and annual mean air temperature, compared to their long-term mean values (see Methods). The linear regression coefficient, namely the climate elasticity (e), represents the percentage change of SSC in response to an increase in air temperature by 1 °C. The gray-shaded area denotes the 95% confidence interval of the best-fit line. The locations of the river basins are shown in Fig. 3b.
Extended Data Fig. 8 Global pattern of river sediment flux (Qs) changes between two periods, 1985-2000 and 2011-2020.
(a) Results from this study, (b) Results of Dethier’s data, and (c) Latitudinal variations of Qs change.
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Sun, X., Tian, L., Fang, H. et al. Changes in global fluvial sediment concentrations and fluxes between 1985 and 2020. Nat Sustain 8, 142–151 (2025). https://doi.org/10.1038/s41893-024-01476-7
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DOI: https://doi.org/10.1038/s41893-024-01476-7
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