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Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment

Nature Geoscience volume 17, pages 896–904 (2024)Cite this article

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Abstract

Rivers are a key component of the global carbon cycle. They receive vast quantities of terrestrial carbon, of which a large fraction is ultimately exported to the coastal ocean. Our review of previously published assessments reveals that substantial uncertainties remain with regard to the spatial distribution and speciation of the carbon export. Accurate quantification of the relative contributions of dissolved, particulate, organic and inorganic carbon to the total amounts is, however, of crucial importance for the coupling between the terrestrial and marine carbon cycles. Breaking down existing spatially explicit assessments over large river basins, we find a disagreement in flux estimates that exceeds two orders of magnitude for more than half of the basins. Using machine-learning techniques in combination with a multi-model ensemble and an updated database of observations, we overcome the inconsistencies in existing assessments and narrow down uncertainties in riverine carbon exports. Our revised assessment yields a global riverine export of 1.02 ± 0.22 (2σ) PgC yr−1. This carbon flux is partitioned into 0.52 ± 0.17, 0.30 ± 0.14, 0.18 ± 0.04 and 0.03 ± 0.02 PgC yr−1 of dissolved inorganic, dissolved organic, particulate organic and particulate inorganic carbon, respectively. We estimate the carbon contribution through groundwater export to be minor (0.016 PgC yr−1). Our assessment suggests an underestimation of the land-to-ocean carbon flux by 0.24 PgC yr−1 by the Intergovernmental Panel on Climate Change (IPCC) and calls for a revision of the oceanic carbon budget.

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Fig. 1: Discrepancies in global and continental riverine carbon export from different estimates.
Fig. 2: Evaluation of different riverine carbon export estimates by observations.
Fig. 3: Ensembles of global river carbon export through observations and multi-models.
Fig. 4: High-resolution global river carbon export.

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Data availability

All processed and generated data are accessible through the figshare repository at https://doi.org/10.6084/m9.figshare.24883290 (ref. 96).

Code availability

All machine-learning models were constructed using the mlr3 (https://doi.org/10.32614/CRAN.package.mlr3) and DALEX (https://doi.org/10.32614/CRAN.package.DALEX) packages in R.

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Acknowledgements

We thank the RECCAP2 Scientific Committee and the Global Carbon Project that initiated this group effort (https://www.globalcarbonproject.org/reccap/index.htm). P.R. received financial support from BELSPO through the project ReCAP (which is part of the Belgian research programme FedTwin), from the European Union’s Horizon 2020 research and innovation programme ESM 2025–Earth System Models for the Future (grant no. 101003536) project, and from the European Space Agency (ESA) project Climate-space RECCAP2: Global land carbon budget and its attribution to regional drivers. M. Liu, Q. Zhang., C.X. and Yangmingkai Li received funding from the National Natural Science Foundation of China (42476127, 41821005 and 41977311). M. Liu is also supported by the Fundamental Research Funds for the Central Universities (7100604309). Q. Zhang. acknowledges support from Beijing Natural Science Foundation (8244068), China Postdoctoral Science Foundation (2022M720005) and the High-Performance Computing Platform of Peking University. P.A.R. and S.C. were supported by the National Science Foundation (1340749 and 1561082). P.A.R. also acknowledges funding from a DOE grant (award no. DE-SC0024709). R.L. acknowledges funding from French state aid, managed by ANR under the ‘Investissements d’avenir’ programme with the reference ANR-16-CONV-0003 (‘Cland’) and under the ‘France 2030’ programme with the reference ANR-22-PEXF-0009 (PEPR ‘FairCarboN’—project ‘DEEP-C’). G.T.-M., J.J.M. and J.W. received funding from the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW), and from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 847504. C.P. was supported by the Joint Fund for Regional Innovation and Development of the National Natural Science Foundation (U22A20570) and the Science and Technology Innovation Program of Hunan Province (2022RC4027). F.L. was funded by the Swiss National Science Foundation (PZ00P2_216442) and by the European Union’s Horizon 2020 research and innovation programme through grant agreement no. 01003687 for the PROVIDE project. H.T. acknowledges funding support from the National Science Foundation (grant no. 1903722), USDA CBG (grant no. TENX12899) and the US Department of the Treasury in cooperation with the State of Alabama Department of Conservation and Natural Resources (grant no. DISL-MESC-ALCOE-06).

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Authors and Affiliations

  1. Ministry of Education Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Maodian Liu, Qianru Zhang, Changhao Xiao & Yangmingkai Li

  2. School of the Environment, Yale University, New Haven, CT, USA

    Maodian Liu & Peter A. Raymond

  3. Institute of Carbon Neutrality, Peking University, Beijing, China

    Maodian Liu

  4. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Palaiseau, France

    Ronny Lauerwald

  5. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Qianru Zhang

  6. Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands

    Gerrit Trapp-Müller, Jack J. Middelburg, Alexander F. Bouwman, Arthur H. W. Beusen, Junjie Wang & Wim J. van Hoek

  7. Australian Institute of Marine Science, Indian Ocean Marine Research Centre, Crawley, Western Australia, Australia

    Kay L. Davis

  8. Department of Biogeochemistry and Geology, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany

    Kay L. Davis & Nils Moosdorf

  9. Institute for Geosciences, Kiel University, Kiel, Germany

    Nils Moosdorf

  10. PBL-Netherlands Environmental Assessment Agency, The Hague, The Netherlands

    Arthur H. W. Beusen

  11. School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, China

    Changhui Peng

  12. Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, Quebec, Canada

    Changhui Peng

  13. Institute of Geography, University of Bern, Bern, Switzerland

    Fabrice Lacroix

  14. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Fabrice Lacroix

  15. Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA

    Hanqin Tian

  16. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Mingxu Li

  17. College of Geography and Remote Sensing, Hohai University, Nanjing, Jiangsu, China

    Qiuan Zhu

  18. Department of Geography and the Environment, University of Alabama, Tuscaloosa, AL, USA

    Sagy Cohen

  19. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Ya Li

  20. School of Geographic Sciences, East China Normal University, Shanghai, China

    Yuanzhi Yao

  21. Biogeochemistry and Modeling of the Earth System (BGEOSYS), Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Brussels, Belgium

    Pierre Regnier

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  1. Maodian Liu
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Contributions

P.R., R.L. and P.A.R. designed the research. M. Liu, Q. Zhang. and Yangmingkai Li performed the research. G.T.-M. contributed new PIC estimates. K.L.D. and N.M. contributed new groundwater DOC and DIC estimates. A.F.B., A.H.W.B., C.P., F.L., H.T., J.W., M. Li, Q. Zhu, S.C., W.J.v.H., Ya Li and Y.Y. provided modelling data. C.X., G.T.-M. and Q. Zhang. contributed observation data. M. Liu, P.R., R.L., P.A.R. and Q. Zhang. wrote the manuscript. F.L., G.T.-M., J.J.M. and J.W. provided important suggestions and revisions during the writing. All authors revised and completed the paper.

Corresponding author

Correspondence to Maodian Liu.

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The authors declare no competing interests.

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Nature Geoscience thanks Clark Blake, Robert Hilton, Michael Shields and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Global riverine dissolved organic carbon (DOC) export from the previously published estimates reported in this study6,25,26,27,29,33.

Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 2 Global riverine particulate organic carbon (POC) export from the previously published and updated (Ludwig et al.; Galy et al.) estimates reported in this study3,6,24,25,26,27,29,33.

Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 3 Global riverine dissolved inorganic carbon (DIC) export from the previously published estimates reported in this study6,26,27,33,35,49.

Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 4 Global riverine particulate inorganic carbon (PIC) export from the previously published and updated (GloRiSe) estimates reported in this study19.

Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 5 Updated global riverine particulate organic carbon (POC) export based on Galy et al. and Ludwig et al3,24,25.

Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 6 Continental and watershed-scale discrepancies in global riverine carbon export from the different estimates reported in Extended Data Fig. 1–3.

a. Discrepancies at the continental scale. b. Discrepancies at the watershed scale. Relative maximum variation - maximum variation divided by mean values. DOC - dissolved organic carbon. POC - particulate organic carbon. DIC - dissolved inorganic carbon. Source map of watersheds adapted with permission from ref. 39, Wiley.

Extended Data Fig. 7 Variations in carbon flux estimates reported in Extended Data Fig. 1–3 as a function of their mean carbon fluxes.

Upper panel: relative standard deviation. Lower panel: relative maximum variation. Data are compared at watershed levels. DOC - dissolved organic carbon. POC - particulate organic carbon. DIC - dissolved inorganic carbon.

Extended Data Fig. 8 Location of measurements of riverine water column carbon concentration gathered in our new river carbon dataset.

The number of sampling sites per region is also reported, using the Regional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) segmentation of the global land mass. Data sources are provided in Supplementary Table 3. a. DOC - dissolved organic carbon. b. POC - particulate organic carbon. c. DIC - dissolved inorganic carbon. d. PIC - particulate inorganic carbon. The continental boundaries are defined in Extended Data Fig. 10.

Extended Data Fig. 9 Evaluation of individual model and MLA-based model ensemble performances against observations.

Top: assessment based on total export fluxes of carbon. Bottom: assessment based on area-normalized (MB, NMB, RMSE, and IOA) or size- normalized (MSA and SSPB) fluxes of carbon. Arithmetic mean - mean values of all previous and updated estimates for each C species. MLA-based weighting - model ensembles using four independent supervised MLAs. R2 - determination coefficient between model estimates and observations. MB - mean bias (unit: Tg yr-1 per observed watershed and Mg km-2 yr-1 per observed watershed for the top panel and bottom panel, respectively). NMB - normalized mean bias (unit: %). RMSE - root mean square error (unit: %). IOA - index of agreement (unit: %). MSA - median symmetric accuracy (unit: %). SSPB - symmetric signed percentage bias (unit: %). DOC - dissolved organic carbon. POC - particulate organic carbon. DIC - dissolved inorganic carbon. E1 to E4 - empirical models 1 to 4. P1 to P5 - process-based models 1 to 5.

Extended Data Fig. 10 Definitions of the boundaries of continents according to RECCAP2. Note the finer segmentation of Asia.

RECCAP2 - Regional Carbon Cycle Assessment and Processes Phase.

Supplementary information

Supplementary Information

Supplementary Texts 1–10, Figs. 1–8, Tables 1–10, Data 1–9 and References 1–198.

Supplementary Data 1–9

This file includes Supplementary Data 1–9, including all data analysed, processed and generated for this Article. It also contains the datasets used to produce all the figures presented within.

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Liu, M., Raymond, P.A., Lauerwald, R. et al. Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment. Nat. Geosci. 17, 896–904 (2024). https://doi.org/10.1038/s41561-024-01524-z

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