Abstract
Suspended sediment concentration (SSC) is a key indicator of river ecosystems, influencing aquatic habitat quality, biogeochemical cycling, reservoir sustainability, and the persistence of downstream coastal land. Despite its global significance, long-term assessments of SSC trends have been limited in spatial scope. Leveraging > 88,000,000 satellite-derived SSC estimates, we analyzed trends over a 38-year period (1984–2022) across > 200,000 river segments globally. Our analysis reveals significant SSC trends in one-third of rivers, with 27% exhibiting declines and 7% showing increases. Basins with more widespread declining SSC trends are in temperate and arid regions and the extent of change is primarily associated with dam regulation and forest recovery. In contrast, increasing trends are concentrated in tropical basins, where deforestation and high rainfall causes erosion. These findings highlight the value of a spatially and temporally coherent, global riverine SSC database for documenting and pinpointing environmental change in unmonitored rivers.
Data availability
The Global River Sediment Database (GloRivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the world that are ~ 60 meters wide or greater9 https://doi.org/10.5281/zenodo.15485524. Private Link to Zenodo Land cover data were from European Space Agency (ESA) WorldCover52 https://pure.iiasa.ac.at/id/eprint/18478/. Dam data were collected from Global Reservoir and Dam (GRanD) Database28 https://www.globaldamwatch.org/grand/. Hydrological datasets were from Global Runoff Data Centre (GRDC)54 https://www.researchgate.net/profile/Pete-Falloon/publication/252683891_New_Global_River_Routing_Scheme_in_the_Unified_Model/links/56b05e5e08ae8e37214d7b2a/New-Global-River-Routing-Scheme-in-the-Unified-Model.pdf. Rainfall erosivity data were from Global Rainfall Erosivity Database (GloREDa)15 https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity. Climate classes were referred from Köppen-Geiger climate classification55 https://www.gloh2o.org/koppen/. Lithology data were from Global Lithological Map56 https://doi.pangaea.de/10.1594/PANGAEA.788537. Aridity data were from Global Aridity Index57 https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/5. Rainfall data were collected from Global Rainfall data ERA5 Copernicus Climate58 https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. Elevation data were from MERIT elevation dataset59 http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/.
References
Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12(1), 7–21 (2018).
Syvitski, J. P. M., Vörösmarty, C. J., Kettner, A. J. & Green, P. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308, 376–380 (2005).
Wilkinson, B. H. & McElroy, B. J. The impact of humans on continental erosion and sedimentation. Bull. Geol. Soc. Am. 119, 140–156 (2007).
Vörösmarty, C. J. et al. Anthropogenic sediment retention: Major global impact from registered river impoundments. Glob. Planet. Change 39, 169–190 (2003).
Babiński, Z. Y. The relationship between suspended and bed load transport in river channels. In Proceedings of the International Symposium held at the 7th Scientific Assembly of the International Association of Hydrological Sciences, 182–188 (2005).
Shin, J., Grabowski, R. C. & Holman, I. Indicators of suspended sediment transport dynamics in rivers. Hydrol. Res. 54, 978–994 (2023).
Walling, D. E. & Fang, D. Recent trends in the suspended sediment loads of the world’s rivers. Glob. Planet. Change 39, 111–126 (2003).
Wohl, E. Legacy effects on sediments in river corridors. Earth Sci. Rev. 147, 30–53 (2015).
Prajapati, R., Gardner, J., Pavelsky, T. & Talchabhadel, R. Longitudinal recovery of suspended sediment downstream of large dams in the US. Water Resour. Res. 60 (2024).
Dethier, E. N. et al. A global rise in alluvial mining increases sediment load in tropical rivers. Nature 620, 787–793 (2023).
Gardner, J. et al. Human activities change suspended sediment concentration along rivers. Environ. Res. Lett. 18, 064032 (2023).
Li, J. et al. Recent intensified erosion and massive sediment deposition in Tibetan Plateau rivers. Nat. Commun. 15, (2024).
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Rapid changes to global river suspended sediment flux by humans. Science 376, 1447–1452 (2022).
Sun, X. et al. Changes in global fluvial sediment concentrations and fluxes between 1985 and 2020. Nat. Sustain. 8, 142–151 (2025).
Panagos, P. et al. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Sci. Rep. 7, (2017).
Liu, Y., Zhao, W., Liu, Y. & Pereira, P. Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data. Catena (Amst.) 194, 104768 (2020).
Trabucco, A. & Zomer, R. J. Global aridity index and potential evapotranspiration (ET0) climate database v2. CGIAR Consortium Spat. Inform. https://doi.org/10.6084/m9.figshare.7504448.v5 (2018).
Altenau, E. H. et al. The surface water and ocean topography (SWOT) mission river database (SWORD): A global river network for satellite data products. Water Resour Res 57, e2021WR030054 (2021).
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Toward improved accuracy of remote sensing approaches for quantifying suspended sediment: Implications for suspended-sediment monitoring. J. Geophys. Res. Earth Surf. https://doi.org/10.1029/2019JF005033 (2020).
Balasubramanian, S. V. et al. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters. Remote Sens. Environ. 246, 111768 (2020).
Meyer, H. & Pebesma, E. Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat. Commun. 13, 2208 (2022).
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M. & Nauss, T. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ. Model. Softw. 101, 1–9 (2018).
Prum, P., Gardner, J. & Lucchese, L. Global model for riverine suspended sediment concentration from Landsat. Sci. Rep. (2025).
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).
Roy, D. P., J., J., Mbow, C., Frost, P. & Loveland, T. Accessing free Landsat data via the Internet: Africa’s challenge. Remote Sens. Lett. 1, 111–117 (2010).
Frasson, R. P. et al. Global relationships between river width, slope, catchment area, meander wavelength, sinuosity, and discharge. Geophys. Res. Lett. 46, 3252–3262 (2019).
Lin, T.-Y., Chen, B., Zhang, Z., Xiao, Y. & Wang, P. Horton’s law of stream widths in China and its association with climate. J. Hydrol. Reg. Stud. 48, 101471 (2023).
East, A. E. et al. Measuring and attributing sedimentary and geomorphic responses to modern climate change: Challenges and opportunities. Earths Future 10, (2022).
Schattman, R. E., Niles, M. T. & Aitken, H. M. Water use governance in a temperate region: Implications for agricultural climate change adaptation in the Northeastern United States. Ambio 50, 942–955 (2021).
Lazurko, A. et al. Assessing sand dams for contributions to local water security and drought resilience in the semi-arid eastern Shashe catchment, Zimbabwe. Reg. Environ. Change 24, 36 (2024).
Moragoda, N. et al. Modeling and analysis of sediment trapping efficiency of large dams using remote sensing. Water Resour. Res. https://doi.org/10.1029/2022WR033296 (2023).
Li, L. et al. Global trends in water and sediment fluxes of the world’s large rivers. Sci. Bull. (Beijing). 65, 62–69 (2020).
Lehner, B. et al. Global Reservoir and Dam (GRanD) Database. http://sedac.ciesin.columbia.edu/pfs/grand.html (2011).
Narayanan, A., Cohen, S. & Gardner, J. R. Riverine sediment response to deforestation in the Amazon basin. Earth Surf. Dyn. 12, 581–599 (2024).
Jautzy, T., Maltais, M. & Buffin-Bélanger, T. Interannual evolution of hydrosedimentary connectivity induced by forest cover change in a snow-dominated mountainous catchment. Land Degrad. Dev. 32, 2318–2335 (2021).
Li, Q. et al. Forest cover change and water yield in large forested watersheds: A global synthetic assessment. Ecohydrology https://doi.org/10.1002/eco.1838 (2017).
Juracek, K. E. & Fitzpatrick, F. A. Geomorphic responses of fluvial systems to climate change: A habitat perspective. River Res. Appl. 38, 757–775 (2022).
Schmidt, J. C. & Wilcock, P. R. Metrics for assessing the downstream effects of dams. Water Resour. Res. 44, 4404 (2008).
Claverie, M., Vermote, E. F., Franch, B. & Masek, J. G. Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products. Remote Sens. Environ. 169, 390–403 (2015).
Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).
Gardner, J. R. et al. The color of rivers. Geophys. Res. Lett. 48, (2021).
Jones, J. W. Improved automated detection of subpixel-scale inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sensing 11, 374 (2019).
Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).
Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).
Topp, S. N. et al. Shifting patterns of summer lake color phenology in over 26,000 US lakes. Water Resour. Res. https://doi.org/10.1029/2020WR029123 (2021).
National Water Quality Monitoring Council. National Water Quality Monitoring Council Total suspended solids [Dataset]. (2022).
European Environment Agency. European Environment Agency Waterbase. https://www.eea.europa.eu/en/datahub/datahubitem-view/208518d1-ffe3-4981-9cae-13264cd9c32c (2022).
HYBAM. HYdro-geochemistry of the AMazonian Basin. https://hybam.obs-mip.fr (2022).
Brazilian Water Agency. Brazilian hydrological data. https://www.snirh.gov.br/hidroweb/serieshistoricas (2022).
UN Environment Programme. Global Freshw. Qual. Database https://gemstat.org/ (2022).
Hartmann, J., Lauerwald, R. & Moosdorf, N. A brief overview of the GLObal RIver Chemistry Database, GLORICH. Procedia Earth Planet. Sci. 10, 23–27 (2014).
Cao, Z. et al. Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019. Int. J. Appl. Earth Obs. Geoinf. 106, 102642 (2022).
Ghatkar, J. G., Singh, R. K. & Shanmugam, P. Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model. Int. J. Remote Sens. 40, 9412–9438 (2019).
Zhang, Y. et al. Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods. GIScience & Remote Sensing 59, 1367–1383 (2022).
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245 (1945).
Kendall, M. G. Rank Correlation Methods. (1948).
Zanaga, D. et al. ESA WorldCover 10 m 2021 v200. https://pure.iiasa.ac.at/id/eprint/18478/ (2022).
Grill, G. et al. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales. Environmental Research Letters https://doi.org/10.1088/1748-9326/10/1/015001 (2015).
Falloon, P., Betts, R. & Bunton, C. New Global River Routing Scheme in the Unified Model http://hydro.iis.u (2007).
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018).
Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: A representation of rock properties at the earth surface. Geochem. Geophys. Geosyst. 13, (2012).
Hersbach, H. et al. ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (2023).
Yamazaki, D. et al. MERIT Hydro: A high-resolution global hydrography map based on latest topography dataset. Water Resour. Res. 55, 5053–5073 (2019).
Acknowledgements
Funding to Rajaram Prajapati from NASA-Earth Science New Investigator Program Grant 80NSSC21K0921. Funding to John Gardner from NSF-EAR Postdoctoral Fellowship Grant 1806983.
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R.P. and J.G. conceived the study and designed the methodology. R.P. collected and analyzed the data and prepared the figures. R.P. wrote the original draft of the manuscript. All authors reviewed, edited, and approved the final manuscript.
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Prajapati, R., Gardner, J. & Prum, P. Extent of sediment concentration trends associated with climate and human factors across global rivers. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47267-2
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DOI: https://doi.org/10.1038/s41598-026-47267-2
