Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Global carbon dioxide efflux from rivers enhanced by high nocturnal emissions


Carbon dioxide (CO2) emissions to the atmosphere from running waters are estimated to be four times greater than the total carbon (C) flux to the oceans. However, these fluxes remain poorly constrained because of substantial spatial and temporal variability in dissolved CO2 concentrations. Using a global compilation of high-frequency CO2 measurements, we demonstrate that nocturnal CO2 emissions are on average 27% (0.9 gC m−2 d−1) greater than those estimated from diurnal concentrations alone. Constraints on light availability due to canopy shading or water colour are the principal controls on observed diel (24 hour) variation, suggesting this nocturnal increase arises from daytime fixation of CO2 by photosynthesis. Because current global estimates of CO2 emissions to the atmosphere from running waters (0.65–1.8 PgC yr−1) rely primarily on discrete measurements of dissolved CO2 obtained during the day, they substantially underestimate the magnitude of this flux. Accounting for night-time CO2 emissions may elevate global estimates from running waters to the atmosphere by 0.20–0.55 PgC yr−1.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Magnitude and bias of diel variation in CO2 emission fluxes from global streams.
Fig. 2: Geographical distribution of diel variation in stream CO2 emission fluxes.
Fig. 3: Seasonal pattern of diel changes in CO2 emission fluxes from streams.
Fig. 4: Night versus day differences in CO2 emission fluxes along the river size and colour continuum.

Similar content being viewed by others

Data availability

Data are freely available at Zenodo ( Data can be explored interactively at:


  1. Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 171–185 (2007).

    Article  Google Scholar 

  2. Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).

    Article  Google Scholar 

  3. Drake, T. W., Raymond, P. A. & Spencer, R. G. M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. (2017).

  4. Lauerwald, R., Laruelle, G. G., Hartmann, J., Ciais, P. & Regnier, P. A. G. Spatial patterns in CO2 evasion from the global river network. Global Biogeochem. Cycles 29, 534–554 (2015).

    Article  Google Scholar 

  5. Borges, A. V. et al. Globally significant greenhouse-gas emissions from African inland waters. Nat. Geosci. 8, 637–642 (2015).

    Article  Google Scholar 

  6. Sawakuchi, H. O. et al. Carbon dioxide emissions along the lower Amazon River. Front. Mar. Sci. 4, 76 (2017).

  7. Hastie, A., Lauerwald, R., Ciais, P. & Regnier, P. Aquatic carbon fluxes dampen the overall variation of net ecosystem productivity in the Amazon basin: an analysis of the interannual variability in the boundless carbon cycle. Glob. Change Biol. 25, 2094–2111 (2019).

    Article  Google Scholar 

  8. Horgby, Å. et al. Unexpected large evasion fluxes of carbon dioxide from turbulent streams draining the world’s mountains. Nat. Commun. 10, 4888 (2019).

  9. Peter, H. et al. Scales and drivers of temporal \(p_{{\mathrm{CO}}_2}\) dynamics in an Alpine stream. J. Geophys. Res. Biogeosci. 119, 1078–1091 (2014).

    Article  Google Scholar 

  10. Rocher-Ros, G., Sponseller, R. A., Bergstr, A., Myrstener, M. & Giesler, R. Stream metabolism controls diel patterns and evasion of CO2 in Arctic streams. Glob. Change Biol. (2020).

  11. Wallin, M. B., Audet, J., Peacock, M., Sahlée, E. & Winterdahl, M. Carbon dioxide dynamics in an agricultural headwater stream driven by hydrology and primary production. Biogeosciences 17, 2487–2498 (2020).

  12. Crawford, J. T., Stanley, E. H., Dornblaser, M. M. & Striegl, R. G. CO2 time series patterns in contrasting headwater streams of North America. Aquat. Sci. 79, 473–486 (2017).

    Article  Google Scholar 

  13. Reiman, J. & Xu, Y. J. Diel variability of \(p_{{\mathrm{CO}}_2}\) and CO2 outgassing from the lower Mississippi River: implications for riverine CO2 outgassing estimation. Water 11, 43 (2018).

    Article  Google Scholar 

  14. Hensley, R. T. & Cohen, M. J. On the emergence of diel solute signals in flowing waters. Water Resour. Res. 52, 759–772 (2016).

    Article  Google Scholar 

  15. Odum, H. T. Primary production in flowing waters. Limnol. Oceanogr. 1, 102–117 (1955).

    Article  Google Scholar 

  16. Johnson, M. S. et al. Direct and continuous measurement of dissolved carbon dioxide in freshwater aquatic systems—method and applications. Ecohydrology 3, 68–78 (2010).

    Google Scholar 

  17. Stets, E. G. et al. Carbonate buffering and metabolic controls on carbon dioxide in rivers. Global Biogeochem. Cycles 31, 663–677 (2017).

    Article  Google Scholar 

  18. Cory, R. M., Ward, C. P., Crump, B. C. & Kling, G. W. Sunlight controls water column processing of carbon in Arctic fresh waters. Science 345, 925–928 (2014).

    Article  Google Scholar 

  19. Riml, J., Campeau, A., Bishop, K. & Wallin, M. B. Spectral decomposition reveals new perspectives on CO2 concentration patterns and soil–stream linkages. J. Geophys. Res. Biogeosci. (2019).

  20. Hartmann, J., Lauerwald, R. & Moosdorf, N. A brief overview of the GLObal RIver CHemistry Database, GLORICH. Procedia Earth Planet. Sci. 10, 23–27 (2014).

    Article  Google Scholar 

  21. Hotchkiss, E. R. et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nat. Geosci. 8, 696–699 (2015).

    Article  Google Scholar 

  22. Demars, B. O. L. & Manson, J. R. Temperature dependence of stream aeration coefficients and the effect of water turbulence: a critical review. Water Res. 47, 1–15 (2013).

    Article  Google Scholar 

  23. Koenig, L. E. et al. Emergent productivity regimes of river networks. Limnol. Oceanogr. 4, 173–181 (2019).

    Article  Google Scholar 

  24. Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).

    Article  Google Scholar 

  25. Raymond, P. A. et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ. 2, 41–53 (2012).

    Article  Google Scholar 

  26. Mulholland, P. J. et al. Inter-biome comparison of factors controlling stream metabolism. Freshw. Biol. 46, 1503–1517 (2001).

    Article  Google Scholar 

  27. Roberts, B. J., Mulholland, P. J. & Hill, W. R. Multiple scales of temporal variability in ecosystem metabolism rates: results from 2 years of continuous monitoring in a forested headwater stream. Ecosystems 10, 588–606 (2007).

    Article  Google Scholar 

  28. Vanote, R. L., Minshall, W. G., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).

    Article  Google Scholar 

  29. Finlay, J. C. Stream size and human influences on ecosystem production in river networks. Ecosphere 2, art87 (2011).

    Article  Google Scholar 

  30. Kirk, L., Hensley, R. T., Savoy, P., Heffernan, J. B. & Cohen, M. J. Estimating benthic light regimes improves predictions of primary production and constrains light-use efficiency in streams and rivers. Ecosystems (2020).

  31. Julian, J. P., Doyle, M. W., Powers, S. M., Stanley, E. H. & Riggsbee, J. A. Optical water quality in rivers. Water Resour. Res. 44, W10411 (2008).

  32. Aitkenhead, J. A. & McDowell, W. H. Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales. Global Biogeochem. Cycles 14, 127–138 (2000).

    Article  Google Scholar 

  33. Harrison, J. A., Caraco, N. & Seitzinger, S. P. Global patterns and sources of dissolved organic matter export to the coastal zone: results from a spatially explicit, global model. Global Biogeochem. Cycles 19, GB4S04 (2005).

  34. Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).

    Article  Google Scholar 

  35. Liu, S., Butman, D. E. & Raymond, P. A. Evaluating CO2 calculation error from organic alkalinity and pH measurement error in low ionic strength freshwaters. Limnol. Oceanogr. Methods 18, 606–622 (2020).

  36. Abril, G. et al. Technical Note: Large overestimation of \(p_{{\mathrm{CO}}_2}\) calculated from pH and alkalinity in acidic, organic-rich freshwaters. Biogeosciences 12, 67–78 (2015).

    Article  Google Scholar 

  37. Duvert, C., Butman, D. E., Marx, A., Ribolzi, O. & Hutley, L. B. CO2 evasion along streams driven by groundwater inputs and geomorphic controls. Nat. Geosci. 11, 813–818 (2018).

    Article  Google Scholar 

  38. Rocher‐Ros, G., Sponseller, R. A., Lidberg, W., Mörth, C. & Giesler, R. Landscape process domains drive patterns of CO2 evasion from river networks. Limnol. Oceanogr. Lett. (2019).

  39. Richey, J. E., Melack, J. M., Aufdenkampe, A. K., Ballester, V. M. & Hess, L. L. Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2. Nature 416, 617–620 (2002).

    Article  Google Scholar 

  40. Guth, P. L. Drainage basin morphometry: a global snapshot from the shuttle radar topography mission. Hydrol. Earth Syst. Sci. 15, 2091–2099 (2011).

    Article  Google Scholar 

  41. Schneider, C. L. et al. Carbon dioxide (CO2) fluxes from terrestrial and aquatic environments in a high-altitude tropical catchment. J. Geophys. Res. Biogeosci. 125, e2020JG005844 (2020).

    Article  Google Scholar 

  42. Rocher‐Ros, G. et al. Metabolism overrides photo-oxidation in CO2 dynamics of Arctic permafrost streams. Limnol. Oceanogr. (2020).

  43. Dinsmore, K. J., Billett, M. F. & Dyson, K. E. Temperature and precipitation drive temporal variability in aquatic carbon and GHG concentrations and fluxes in a peatland catchment. Glob. Change Biol. 19, 2133–2148 (2013).

    Article  Google Scholar 

  44. Lynch, J. K., Beatty, C. M., Seidel, M. P., Jungst, L. J. & DeGrandpre, M. D. Controls of riverine CO2 over an annual cycle determined using direct, high temporal resolution \(p_{{\mathrm{CO}}_2}\) measurements. J. Geophys. Res. Biogeosci. 115, G03016 (2010).

  45. Teodoru, C. R. et al. Dynamics of greenhouse gases (CO2, CH4, N2O) along the Zambezi River and major tributaries, and their importance in the riverine carbon budget. Biogeosciences 12, 2431–2453 (2015).

    Article  Google Scholar 

  46. Borges, A. V. et al. Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial–wetland connectivity. Biogeosciences 16, 3801–3834 (2019).

    Article  Google Scholar 

  47. Le, T. P. Q. et al. CO2 partial pressure and CO2 emission along the lower Red River (Vietnam). Biogeosciences 15, 4799–4814 (2018).

    Article  Google Scholar 

  48. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).

    Article  Google Scholar 

  49. Ulseth, A. J. et al. Distinct air–water gas exchange regimes in low- and high-energy streams. Nat. Geosci. 12, 259–263 (2019).

    Article  Google Scholar 

  50. Lapierre, J.-F., Guillemette, F., Berggren, M. & del Giorgio, P. A. Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems. Nat. Commun. 4, 2972 (2013).

    Article  Google Scholar 

Download references


We thank S. Blackburn, J. Crawford, the Krycklan Catchment study and the Swedish Infrastructure for Ecosystem Science for sharing data used in this study. This study was largely supported by a Formas grant to R.A.S. Datasets provided by the StreamPULSE Network were funded by the National Science Foundation Macrosystems program (NSF Grant EF-1442439). D.A.R.-I. acknowledges support from the National Science Foundation (Grant EAR-1847331).

Author information

Authors and Affiliations



L.G.-G., G.R.-R. and R.A.S designed the study and wrote the paper with input from M.J.C. L.G.-G. and G.R.-R. compiled, processed and analysed the data. Å.H. provided remote sensing estimates. All authors contributed with data and commented on the earlier versions of this manuscript.

Corresponding authors

Correspondence to Lluís Gómez-Gener or Gerard Rocher-Ros.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Alberto Borges, Pierre Regnier and Jun Xu for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Geographical and size distribution of the dataset.

a) Global distribution of the stream and river sites colored by canopy cover category. b) Distribution and relationship between catchment area and median annual discharge, colored by canopy cover category. Symbols indicate the origin of the data (see Supplementary Table 1 and Supplementary Table 5 for more information).

Extended Data Fig. 2 Drivers of night-day differences of CO2 emissions from streams.

Structural equation model (SEM) representing connections between reach-scale physical and biological parameters contributing to the relative night-day variation in summertime CO2 emissions (%). The SEM consisted of two dependent levels of factor interaction or metamodels. Metamodel 1 assessed the influence of kCO2 and stream water pCO2 on night-day differences of CO2 emissions. Metamodel 2 assessed relationships between environmental variables and diel changes in stream water pCO2. Blue arrows represent statistically significant effects (p < 0.05). Numbers adjacent to arrows are the standardized effect sizes of each relationship. Arrow width is proportional to the effect size. SEM goodness of fit was evaluated based on variance explained by each of the two models (r2). A summary of statistical outputs from the SEM model is provided in Supplementary Table 4. Reach-scale properties for each site used in the SEM model are presented in Supplementary Table 2.

Extended Data Fig. 3 Effect of water colour on the night-day differences in riverine CO2 emission fluxes.

Comparison of night-day differences in CO2 emission fluxes averaged by watercourse and grouped by canopy level and dissolved organic carbon concentration (DOC; mg L−1) level (lower than 10 mg L−1, between 10 and 20 mg L−1, and higher than 20 mg L−1). Box plots display the 25th, 50th, and 75th percentiles whiskers display minimum and maximum values.

Extended Data Fig. 4 Distribution of dissolved organic carbon (DOC) in the GLORICH dataset and in this study.

Inset table shows a selection of summary statistics. In the GLORICH database12, 92.6 and 98.5 % of the samples were below 10 and 20 mg L−1 respectively. 15 observations from the GLORICH database (out of 6,771) had DOC > 50 mg L−1 and are not represented in the density plot for better visualization (max. value 839 mg L−1).

Extended Data Fig. 5 Distribution of stream canopy cover and DOC concentrations by biome.

Panel a shows the canopy cover distribution for each biome (note that canopy category can only be 0, 1 or 2). Panel b represents the ranges in DOC for each biome.

Supplementary information

Supplementary Information

Supplementary methods, Figs. 1–3 and Tables 1–5.

Supplementary Tables

Supplementary Tables 1–5 as an excel file.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez-Gener, L., Rocher-Ros, G., Battin, T. et al. Global carbon dioxide efflux from rivers enhanced by high nocturnal emissions. Nat. Geosci. 14, 289–294 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene