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Future impacts of climate change on inland Ramsar wetlands

Nature Climate Change volume 11, pages 45–51 (2021)Cite this article

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Abstract

The 1971 Ramsar Convention promotes wetland conservation worldwide, yet climate change impacts on wetland extent and associated biodiversity are unclear. Hydrological modelling and soil moisture estimates are used to quantify climate change-driven shifts in wetland area across 1,250 inland Ramsar sites. We estimate that net global wetland area expanded during 1980–2014, but 47% of sites experienced wetland loss. By 2100, a net area loss of at least 6,000 km2 (about 1%) is projected. The number of sites with area loss over 10% will increase by 19–65% under low emissions, 148–243% under high emissions and ~16% with global mean warming of 2 °C relative to 1.5 °C. Sites most vulnerable to shrinkage are located in the Mediterranean, Mexico, Central America and South Africa—all seasonal waterbird migration hotspots. Our findings highlight that climate mitigation is essential for future Ramsar wetlands conservation, in addition to the minimization of human disturbance.

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Fig. 1: Historical change in wetland area and SM across 1,250 inland Ramsar sites.
Fig. 2: Temporal changes in numbers of global inland Ramsar sites with >10% wetland loss/gain through the twenty-first century.
Fig. 3: Spatial distributions of change in wetland area across global inland Ramsar sites by 2100 and the uncertainties.
Fig. 4: Change in absolute wetland area across global inland Ramsar sites and five continents by 2100.
Fig. 5: Wetland loss and waterbird migration.

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Data availability

The shapefile data of the Ramsar inland sites are available from the Ramsar Sites Information Service (https://rsis.ramsar.org/) and the World Database on Protected Areas (http://datasets.wri.org/dataset/64b69c0fb0834351bd6c0ceb3744c5ad). The global gridded topographic index data are available from https://doi.org/10.5285/6b0c4358-2bf3-4924-aa8f-793d468b92be. The CRU TS v.4.01 climate datasets are available from CRU (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.01/). The GLEAM v.3.2a data sets are available at https://www.gleam.eu/. The MSWEP v.2.1 datasets are available at http://www.gloh2o.org/. The JRC surface water products are available at https://global-surface-water.appspot.com/download. The RFW datasets are available from https://doi.pangaea.de/10.1594/PANGAEA.892657. The GLDAS-Noah v.2.0 datasets are available at https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_M_V2.0/summary?keywords=GLDAS. All CMIP5 data can be accessed from the CMIP5 archive (https://esgf-node.llnl.gov/search/cmip5/). The change in wetland area in different projected epochs for each Ramsar site under the four RCP scenarios is publicly available on GitHub (https://github.com/yixixy/Ramsar_wetlands_change)58. Source data are provided with this paper.

Code availability

Computer codes to simulate wetland area by TOPMODEL and to analyse the change in wetland area for Ramsar sites are publicly available on GitHub (https://github.com/yixixy/Ramsar_wetlands_change)58.

References

  1. Creed, I. F. et al. Enhancing protection for vulnerable waters. Nat. Geosci. 10, 809–815 (2017).

    CAS  Google Scholar 

  2. Davidson, N. C. How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshw. Res. 65, 934–941 (2014).

    Google Scholar 

  3. Gong, P. et al. China’s wetland change (1990–2000) determined by remote sensing. Sci. China Earth Sci. 53, 1036–1042 (2010).

    Google Scholar 

  4. Zedler, J. B. & Kercher, S. Wetland resources: status, trends, ecosystem services, and restorability. Annu. Rev. Env. Resour. 30, 39–74 (2005).

    Google Scholar 

  5. Finlayson, M. et al. Millennium Ecosystem Assessment: Ecosystems and Human Well-Being: Wetlands and Water Synthesis (Island Press, 2005).

  6. Keddy, P. A. et al. Wet and wonderful: the world’s largest wetlands are conservation priorities. Bioscience 59, 39–51 (2009).

    Google Scholar 

  7. Global Wetland Outlook: State of the World’s Wetlands and Their Services to People (Ramsar Convention Secretariat, 2018).

  8. Russi, D. et al. The Economics of Ecosystems and Biodiversity for Water and Wetlands (Ramsar Secretariat, 2013).

  9. Moomaw, W. R. et al. Wetlands in a changing climate: science, policy and management. Wetlands 38, 183–205 (2018).

    Google Scholar 

  10. Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).

    Google Scholar 

  11. Davidson, N. C., Fluet-Chouinard, E. & Finlayson, C. M. Global extent and distribution of wetlands: trends and issues. Mar. Freshw. Res. 69, 620–627 (2018).

    Google Scholar 

  12. Dixon, M. J. R. et al. Tracking global change in ecosystem area: the wetland extent trends index. Biol. Conserv. 193, 27–35 (2016).

    Google Scholar 

  13. Sievers, M., Hale, R., Parris, K. M. & Swearer, S. E. Impacts of human-induced environmental change in wetlands on aquatic animals. Biol. Rev. 93, 529–554 (2018).

    Google Scholar 

  14. Prigent, C. et al. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett. 39, L08403 (2012).

    Google Scholar 

  15. Schroeder, R. et al. Development and evaluation of a multi-year fractional surface water data set derived from active/passive microwave remote sensing data. Remote Sens. 7, 16688–16732 (2015).

    Google Scholar 

  16. Čížková, H. et al. Actual state of European wetlands and their possible future in the context of global climate change. Aquat. Sci. 75, 3–26 (2013).

    Google Scholar 

  17. Reis, V. et al. A global assessment of inland wetland conservation status. Bioscience 67, 523–533 (2017).

    Google Scholar 

  18. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).

    Google Scholar 

  19. Comyn-Platt, E. et al. Carbon budgets for 1.5 and 2 °C targets lowered by natural wetland and permafrost feedbacks. Nat. Geosci. 11, 568–573 (2018).

    CAS  Google Scholar 

  20. Gedney, N., Cox, P. M. & Huntingford, C. Climate feedback from wetland methane emissions. Geophys. Res. Lett. https://doi.org/10.1029/2004gl020919 (2004).

  21. Zhang, Z. et al. Emerging role of wetland methane emissions in driving 21st century climate change. Proc. Natl Acad. Sci. USA 114, 9647–9652 (2017).

    CAS  Google Scholar 

  22. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).

    Google Scholar 

  23. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  24. Yang, H. et al. Regional patterns of future runoff changes from Earth system models constrained by observation. Geophys. Res. Lett. 44, 5540–5549 (2017).

    Google Scholar 

  25. Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. Bull. 24, 43–69 (1979).

    Google Scholar 

  26. Stocker, B. D., Spahni, R. & Joos, F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands. Geosci. Model Dev. 7, 3089–3110 (2014).

    Google Scholar 

  27. Decharme, B. & Douville, H. Introduction of a sub-grid hydrology in the ISBA land surface model. Clim. Dynam. 26, 65–78 (2006).

    Google Scholar 

  28. Habets, F. & Saulnier, G. M. Subgrid runoff parameterization. Phys. Chem. Earth Part B 26, 455–459 (2001).

    Google Scholar 

  29. Ringeval, B. et al. Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data. Geosci. Model Dev. 5, 941–962 (2012).

    Google Scholar 

  30. Zhang, Z., Zimmermann, N. E., Kaplan, J. O. & Poulter, B. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties. Biogeosciences 13, 1387–1408 (2016).

    Google Scholar 

  31. Rodell, M. et al. The global land data assimilation system. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    Google Scholar 

  32. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  33. Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S. & Gedney, N. High-resolution global topographic index values for use in large-scale hydrological modelling. Hydrol. Earth Syst. Sci. 19, 91–104 (2015).

    Google Scholar 

  34. Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    CAS  Google Scholar 

  35. Beck, H. E. et al. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. 21, 589–615 (2017).

    Google Scholar 

  36. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    Google Scholar 

  37. Niu, G., Yang, Z., Dickinson, R. E. & Gulden, L. E. A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res. 110, D21106 (2005).

    Google Scholar 

  38. Lomolino, M. V. Ecology’s most general, yet protean pattern: the species–area relationship. J. Biogeogr. 27, 17–26 (2000).

    Google Scholar 

  39. Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).

    Google Scholar 

  40. New study highlights gaps in protection for migratory birds globally. CMS https://www.cms.int/en/news/new-study-highlights-gaps-protection-migratory-birds-globally (2015).

  41. Lu, X., Zhuang, Q., Liu, Y., Zhou, Y. & Aghakouchak, A. A large-scale methane model by incorporating the surface water transport. J. Geophys. Res. Biogeosci. 121, 1657–1674 (2016).

    Google Scholar 

  42. Tan, Z. & Zhuang, Q. Methane emissions from pan-Arctic lakes during the 21st century: an analysis with process-based models of lake evolution and biogeochemistry. J. Geophys. Res. Biogeosci. 120, 2641–2653 (2015).

    CAS  Google Scholar 

  43. Schuur, E. A. G. et al. Climate Change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    CAS  Google Scholar 

  44. Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013).

    Google Scholar 

  45. Wang, A. & Zeng, X. Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. J. Geophys. Res. Atmos. 117, D05102 (2012).

    Google Scholar 

  46. Bi, H., Ma, J., Zheng, W. & Zeng, J. Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau. J. Geophys. Res. Atmos. 121, 2658–2678 (2016).

    Google Scholar 

  47. Model Output from the Coupled Model Intercomparison Project Phase 5 (WCRP, accessed 12 July 2018); https://esgf-node.llnl.gov/search/cmip5/

  48. Jones, P. W. First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Weather Rev. 127, 2204–2210 (1999).

    Google Scholar 

  49. Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed 12 July 2018); https://datasets.wri.org/dataset/64b69c0fb0834351bd6c0ceb3744c5ad

  50. Herold, M., Van Groenestijn, A., Kooistra, L., Kalogirou, V. & Arino, O. Land Cover CCI, Product User Guide Version 2.0 (ESA, 2015); https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

  51. Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).

    Google Scholar 

  52. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).

    Google Scholar 

  53. Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Change Biol. 23, 3581–3599 (2017).

    Google Scholar 

  54. Xu, J., Morris, P., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).

    Google Scholar 

  55. Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).

    Google Scholar 

  56. Lauerwald, R. et al. ORCHILEAK (revision 3875): a new model branch to simulate carbon transfers along the terrestrial–aquatic continuum of the Amazon basin. Geosci. Model Dev. 10, 3821–3859 (2017).

    CAS  Google Scholar 

  57. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).

    Google Scholar 

  58. Xi, Y., Peng, S., Ciais, P. & Chen, Y. Code and data for simulating future impacts of climate change on inland Ramsar wetlands (Version v1.0.0). Zenodo https://doi.org/10.5281/zenodo.4018712 (2020).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant numbers 41722101, 41830643 and 41671079). P.C. acknowledges support from the ERC Synergy grant ERC-2013-SyG-610028 IMBALANCE-P and the ANR CLAND convergence institute. Y.C. acknowledges support from the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0303).

Author information

Authors and Affiliations

  1. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, and Laboratory for Earth Surface Processes, Peking University, Beijing, China

    Yi Xi & Shushi Peng

  2. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  3. CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China

    Youhua Chen

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Contributions

S.P. conceived and designed the study. Y.X. performed the analysis and created all the figures. S.P., P.C. and Y.X. wrote the manuscript with contribution from Y.C.

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Correspondence to Shushi Peng.

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Peer review information Nature Climate Change thanks Etienne Fluet-Chouinard, Zhenguo Niu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Statistics of numbers and total area of inland Ramsar sites in six continents.

Numbers and total area of inland Ramsar sites in six continents including Africa, Asia, Europe, Latin America, North America, and Oceania.

Extended Data Fig. 2 Diagram of workflow for simulating wetland dynamics across global inland Ramsar sites.

Workflow for simulating historical (1980–2014) and future (1980–2100) wetland dynamics based on soil moisture from GLDAS Noah v2.0 and CMIP5 climate models under four RCP scenarios.

Extended Data Fig. 3 Evaluation of the TOPMODEL-based hydrological model.

Spatial patterns of long-term maximum wetland fraction from RFW (a) and historical simulation with GLDAS-Noah v2.0 (b), as well as RMSE (the minimum root-mean-square-error) between RFW and the simulation over global wetlands (c) and inland Ramsar sites (d).

Extended Data Fig. 4 Same as Fig. 2, but for temporal changes in numbers of global inland Ramsar sites with wetland loss/gain of five different cutoffs.

a, c, e, g, and i show temporal changes in numbers of sites with wetland loss of 5–10%, 10–20%, 20–30%, 30–50%, and over 50% respectively through the 21st century under RCP2.6 and RCP8.5, while b, d, f, h, and j are for numbers of sites with wetland gain of 5–10%, 10–20%, 20–30%, 30–50%, and over 50% respectively.

Extended Data Fig. 5 Distributions of projected numbers of Ramsar sites with wetland gain/loss of five different cutoffs by the end of this century.

a–d show the distributions of projected numbers of Ramsar sites with wetland gain/loss of 5%, 10%, 20%, 30%, and 50% by the end of this century under four RCP scenarios, respectively. The wider parts of distributions signify that relatively more models project that number of sites will change.

Extended Data Fig. 6 Same as Fig. 2, but for temporal changes in numbers of inland Ramsar sites with >10% wetland loss/gain for five continents.

a, c, e, g, and i show temporal changes in numbers of sites with >10% wetland loss in five continents including Africa, Asia, Europe, Latin America, and North America through the 21st century under RCP2.6, RCP8.5, and a global warming of 1.5 °C and 2 °C, while b, d, f, h, and j are for numbers of sites with >10% wetland gain in the five continents respectively.

Extended Data Fig. 7 Wetland risk boundaries across global inland Ramsar sites by 2100.

a, c, e, g, and i show multi-model mean percentage of sites shrinking by over 10% for nine criteria by the end of this century in five continents respectively under RCP2.6, and b, d, f, h, and j are for RCP8.5. C1-C9 represent nine criteria for Ramsar Site designation (Supplementary Table 1). The central angle of each criterion is determined according to the ratio of sites meeting nine criteria. The width of each annulus for each criterion is determined according to the ratio of sites with a loss of 10–20%, 20–30%, 30–50%, or more than 50% to all sites meeting this criterion. The dark grey boundary indicates the threshold of 20% of all inland sites meeting this criterion with over 10% loss.

Extended Data Fig. 8 Projected CMIP5 ensemble mean change in wetland area for global all wetlands.

a-d indicate projected CMIP5 ensemble mean change in wetland area for global all wetlands for 2081–2100 relative to the reference period 1981–2000 under RCP2.6 (13 models), RCP4.5 (18 models), RCP6.0 (9 models), and RCP8.5 (19 models) respectively.

Extended Data Fig. 9 An example of calculating the change of Ramsar wetland.

a, Spatial location of Shengjin Lake National Nature Reserve. b, Spatial pattern of CTI (Compound Topographic Index) for the site and the shapefile of the site and its buffer. c, d, Annual maximum wetland area change by 2100 from FGOALS-g2 model extracted with sub-grid CTI distribution under RCP2.6 and RCP8.5.

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Supplementary Discussion 1 and 2, Tables 1–4 and Figs. 1–13.

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Xi, Y., Peng, S., Ciais, P. et al. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Chang. 11, 45–51 (2021). https://doi.org/10.1038/s41558-020-00942-2

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