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Rewetting global wetlands effectively reduces major greenhouse gas emissions

Nature Geoscience volume 15, pages 627–632 (2022)Cite this article

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Abstract

Carbon and nitrogen losses from degraded wetlands and methane emissions from flooded wetlands are both important sources of greenhouse gas emissions. However, the net-exchange dependence on hydrothermal conditions and wetland integrity remains unclear. Using a global-scale in situ database on net greenhouse gas exchanges, we show diverse hydrology-influenced emission patterns in CO2, CH4 and N2O. We find that total CO2-equivalent emissions from wetlands are kept to a minimum when the water table is near the surface. By contrast, greenhouse gas exchange rates peak in flooded and drained conditions. By extrapolating the current trajectory of degradation, we estimate that between 2021 and 2100, wetlands could result in greenhouse gas emissions equivalent to around 408 gigatons of CO2. However, rewetting wetlands could reduce these emissions such that the radiative forcing caused by CH4 and N2O is fully compensated by CO2 uptake. As wetland greenhouse gas budgets are highly sensitive to changes in wetland area, the resulting impact on climate from wetlands will depend on the balance between future degradation and restoration.

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Fig. 1: WTL effects on global wetland NEE and total GHG emissions.
Fig. 2: Nonlinear hydrothermal influence on GHG exchange.
Fig. 3: GHG emissions from degraded wetlands under different scenarios.
Fig. 4: Spatial pattern of the GHG emissions owing to wetland degradation and reduction potential via rewetting wetlands.

Data availability

GLDW dataset is available at http://www.wwfus.org/science/data.cfm. Soilgrids dataset is available at https://soilgrids.org. ECMWF reanalysis climate data are available at https://cds.climate.copernicus.eu/#!/home. FAOSTAT emissions database is available at http://www.fao.org/faostat/en/#data/GT. Atmospheric concentrations data are available at https://ourworldindata.org/atmospheric-concentrations. All GHG data are available in the main text or the supplementary materials. The database of global, in situ, GHG exchange information for wetlands, drawn from 3,704 site-year records, is summarized in Supplementary Data 1. Source data are provided with this paper.

Code availability

The scripts used to generate all the results are MATLAB (R2018a), R-4.1.0 and Python 2.7 based on arcpy. Analysis scripts are available at https://github.com/XiaoBai0417/Multi-greenhouse-gas-assessments.

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Acknowledgements

We thank many individuals for measuring and providing in situ GHG net fluxes from wetlands. This study was supported by the National Natural Science Foundation of China (grant no. 42071022), the start-up fund provided by the Southern University of Science and Technology (grant no. 29/Y01296122) and the High-level Special Funding of the Southern University of Science and Technology (grant no. G02296302). Jin Wu was supported in part by the Innovation and Technology Fund (funding support to State Key Laboratories in Hong Kong of Agrobiotechnology) of the HKSAR, China. D.C. was supported by Swedish National Strategic Research Programs: Biodiversity and Ecosystem Services in a Changing Climate (BECC) and Modelling the Regional and Global Earth system (MERGE). P.C. acknowledges support from the CLAND Convergence Institute 16-CONV-0003.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Junyu Zou, Xin Jiang, Chunmiao Zheng, Jie Wu, Ziyu Lin, Xinyue He, Zuotai Zhang & Zhenzhong Zeng

  2. Faculty of Fisheries Technology and Aquatic Resources, Mae Jo University, Chiang Mai, Thailand

    Alan D. Ziegler

  3. Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    Deliang Chen

  4. Department of Earth and Environmental Science, University of Illinois Chicago, Chicago, IL, USA

    Gavin McNicol

  5. Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France

    Philippe Ciais

  6. The CYPRUS Institute, Nicosia, Cyprus

    Philippe Ciais

  7. EIT Institute for Advanced Study, Ningbo, Zhejiang, China

    Chunmiao Zheng

  8. Department of Geoscience and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Jie Wu

  9. School of Biological Sciences and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China

    Jin Wu & Ziyu Lin

  10. State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, China

    Jin Wu

  11. School of Earth and Environment, University of Leeds, Leeds, UK

    Xinyue He

  12. water@leeds, School of Geography, University of Leeds, Leeds, UK

    Lee E. Brown & Joseph Holden

  13. Singapore Botanic Gardens, National Parks Board, Singapore, Singapore

    Sorain J. Ramchunder

  14. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

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Contributions

J.Z. and Z. Zeng designed the research; J.Z. performed the analysis; J.Z., Z. Zeng and A.D.Z. wrote the draft. J.Z., A.D.Z., D.C., G.M., P.C., X.J., C.Z., Jie Wu, Jin Wu, Z.L., X.H., L.E.B., J.H., Z. Zhang, S.J.R., A.C. and Z. Zeng contributed to the interpretation of the results and the writing of the paper.

Corresponding author

Correspondence to Zhenzhong Zeng.

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Nature Geoscience thanks Scott Bridgham and Debjani for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 The database of global, in-situ, greenhouse gas (GHG) exchange reports for wetlands.

a, GHG data records from global wetlands. NEE, net ecosystem productivity; CH4, methane flux; N2O, nitrous oxide flux. b, Data entry. G, growing season; A, annual; G&A, growing season and annual. c, Year distribution of data source.

Source data

Extended Data Fig. 2 Annual GHG flux values versus water level in wetlands.

a, GHG balance, the nonlinear trend is formed by 103 site-year records reporting exact water levels and complete data including three major GHGs. b, CO2, CH4 and N2O exchange. NEE (c), CH4 (d) and N2O (e) exchange across climate regimes with significant differences. The numbers of records for NEE, CH4 and N2O are 777, 1,247 and 294, respectively. Significant differences in panels a and b are based on the least square method with F-statistic, panels c–e are based on Spearman correlation analysis.

Source data

Extended Data Fig. 3 The water-heat interaction impact on emissions of three greenhouse gases (CO2, CH4 and N2O) and their sum.

a, Frequency distribution of different greenhouse gases with WTL patterns. Note that X axes have been truncated for enhanced readability. b, GHG net exchange with hydrothermal patterns. The area of circles and rings represent the mean and 1.96SEs, respectively. The hollow circles represent absorption.

Extended Data Fig. 4 The relationships of NEE versus the sum of CH4 and N2O (a), and CH4 versus N2O (b).

To make the natural log transformations for visualization, we add the net exchange rates of GHGs to the diverse constants. The units of NEE, CH4 and N2O are t CO2 ha−1 yr−1, kg CH4 ha−1 yr−1 and kg N2O ha−1 yr−1, respectively.

Source data

Extended Data Fig. 5 Greenhouse gas emissions from degraded wetlands in countries (a) and wetland categories (b).

a, The country’s historical emissions. The color of each circle corresponds to the axis of the same color (red/right; blue/left). The size of a circle represents the amount of soil organic carbon stock. b, Emissions from different wetland types under three scenarios. For details see Supplementary Data 1.

Source data

Extended Data Fig. 6 Spatial pattern of the CO2 emissions owing to wetland degradation (a, b) and reduction potential via rewetting wetlands (c).

CO2 emissions under history-derived scenario in 1950–2020 (a) and 2021–2100 (b). c, The reduction potential in 2021–2100 under the scenario of rewetting all degraded wetlands.

Source data

Extended Data Fig. 7 Greenhouse gas emissions from degraded wetlands in three climatic zones under three scenarios.

The sum of three greenhouse gas emissions (a–c) and CO2 emissions alone (d–f). Period I is from 1950 to 2020 and period II is from 2021 to 2100. There are three scenarios in period II: the historical trend scenario (a, d), the scenario rewetting all degraded wetlands (b, e), and the scenario rewetting only high-OCS degraded wetlands (c, f).

Source data

Extended Data Fig. 8 Inter-annual atmospheric GHG concentration changes and emissions from wetlands and other major sources.

a–i Data for CO2 (a–c), CH4 (d–f) and N2O (g–i). Significant correlations exist between the flux of CO2, CH4 and N2O from wetlands and their respective atmospheric concentration growth rates in the past three decades. In addition, the correlations persist when adding other major emission sources to wetlands. Note that atmospheric growth rates of N2O for 1979 and 1982 are excluded.

Source data

Extended Data Table 1 Wetland greenhouse gas (GHG) net fluxes in different climate regimes under various water table levels (WTL)
Full size table
Extended Data Table 2 Wetland characteristics and GHG emissions for each continent
Full size table

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–7 and Table 1.

Supplementary Data 1

Synthetic information of the 504 papers with valid GHG net-flux data collected in this study.

Source data

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Zou, J., Ziegler, A.D., Chen, D. et al. Rewetting global wetlands effectively reduces major greenhouse gas emissions. Nat. Geosci. 15, 627–632 (2022). https://doi.org/10.1038/s41561-022-00989-0

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