Abstract

The response of coastal wetlands to sea-level rise during the twenty-first century remains uncertain. Global-scale projections suggest that between 20 and 90 per cent (for low and high sea-level rise scenarios, respectively) of the present-day coastal wetland area will be lost, which will in turn result in the loss of biodiversity and highly valued ecosystem services1,2,3. These projections do not necessarily take into account all essential geomorphological4,5,6,7 and socio-economic system feedbacks8. Here we present an integrated global modelling approach that considers both the ability of coastal wetlands to build up vertically by sediment accretion, and the accommodation space, namely, the vertical and lateral space available for fine sediments to accumulate and be colonized by wetland vegetation. We use this approach to assess global-scale changes in coastal wetland area in response to global sea-level rise and anthropogenic coastal occupation during the twenty-first century. On the basis of our simulations, we find that, globally, rather than losses, wetland gains of up to 60 per cent of the current area are possible, if more than 37 per cent (our upper estimate for current accommodation space) of coastal wetlands have sufficient accommodation space, and sediment supply remains at present levels. In contrast to previous studies1,2,3, we project that until 2100, the loss of global coastal wetland area will range between 0 and 30 per cent, assuming no further accommodation space in addition to current levels. Our simulations suggest that the resilience of global wetlands is primarily driven by the availability of accommodation space, which is strongly influenced by the building of anthropogenic infrastructure in the coastal zone and such infrastructure is expected to change over the twenty-first century. Rather than being an inevitable consequence of global sea-level rise, our findings indicate that large-scale loss of coastal wetlands might be avoidable, if sufficient additional accommodation space can be created through careful nature-based adaptation solutions to coastal management.

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The data that support the findings of this study are available from the corresponding author upon reasonable request. The Source Data for Fig. 1 and Extended Data Fig. 2 are provided.

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Acknowledgements

This research was financially supported by the Deutsche Forschungsgemeinschaft (DFG) through the Cluster of Excellence 80 ‘The Future Ocean’, funded within the framework of the Excellence Initiative on behalf of the German federal and state governments, the personal research fellowship of M.S. (project number 272052902) and by the Cambridge Coastal Research Unit (Visiting Scholar Programme). Furthermore, this work has partly been supported by the European Union’s Seventh Programme for Research, Technological Development and Demonstration (grant no. 603396, RISES-AM project), the European Union’s Horizon 2020 Research and Innovation Programme (grant no. 642018, GREEN-WIN project), the US National Science Foundation (Coastal SEES 1426981 and NSF CAREER 1654374), Deltares and the UK Natural Environment Research Council. We thank M. Martin for support in editing the calibration data and G. Amable for statistical advice.

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Nature thanks B. Glavovic, J. Woodruff and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Affiliations

  1. Lincoln Centre for Water and Planetary Health, School of Geography, University of Lincoln, Lincoln, UK

    • Mark Schuerch
  2. Cambridge Coastal Research Unit, Department of Geography, University of Cambridge, Cambridge, UK

    • Mark Schuerch
    •  & Tom Spencer
  3. Ecosystem Management Research Group, University of Antwerp, Antwerp, Belgium

    • Stijn Temmerman
  4. Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, VA, USA

    • Matthew L. Kirwan
  5. Institute of Geography, Christian-Albrechts University of Kiel, Kiel, Germany

    • Claudia Wolff
    •  & Athanasios T. Vafeidis
  6. Global Climate Forum, Berlin, Germany

    • Daniel Lincke
    •  & Jochen Hinkel
  7. UN Environment World Conservation Monitoring Centre, Cambridge, UK

    • Chris J. McOwen
  8. Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK

    • Mark D. Pickering
  9. School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria, Australia

    • Ruth Reef
  10. Division of Resource Economics, Thaer-Institute and Berlin Workshop in Institutional Analysis of Social-Ecological Systems (WINS), Humboldt-University, Berlin, Germany

    • Jochen Hinkel
  11. Faculty of Engineering and the Environment, University of Southampton, Southampton, UK

    • Robert J. Nicholls
    •  & Sally Brown

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Contributions

M.S. and T.S. developed the model algorithm. M.S. and D.L. developed the model code. M.S., C.W., C.McO., M.D.P., M.L.K., A.T.V., R.R. and S.B. gathered and produced input data. M.S., S.T. and R.J.N. analysed and interpreted the model simulations. M.S., T.S., S.T. M.L.K. and J.H. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Mark Schuerch.

Extended data figures and tables

  1. Extended Data Fig. 1 Map of model performance during model calibration.

    Green lines indicate segments in which the modelled sediment balances match the observed trends in wetland elevation change relative to sea-level rise3,4,19. Red segments indicate model mismatches. The frequency distributions for total suspended matter (TSM) and tidal range (TR) display the distributions of both parameters in matching (green bars) and mismatching segments (red bars), and how they compare to the overall frequency distributions of both parameters (blue bars). The overall frequency distribution only includes coastline segments where coastal wetlands are present. The displayed coastline was generated during the DINAS-COAST FP5-EESD EU project (EVK2-CT-2000-00084).

  2. Extended Data Fig. 2 Global change in coastal wetland area.

    Results for all three SLR scenarios (RCP 2.6, low; RCP 4.5, medium; RCP 8.5, high) and a total of eight different model configurations. These include the upper and lower boundaries of the BAU (5 and 20 people km−2) and the upper boundaries of the NB 1 and NB 2 scenarios (150 and 300 people km−2) as defined in Extended Data Table 2 (solid lines). The dashed lines represent the four hypothetical scenarios, as characterized in Extended Data Table 2: (i) wetland migration only; (ii) sediment accretion only; (iii) maximum resilience; and (iv) no resilience. Source data

  3. Extended Data Fig. 3 Spatial distribution of coastal wetland change.

    a, b, Absolute (a) and relative (b) changes in coastal wetland areas are displayed for a medium SLR scenario (RCP 4.5)), assuming the possibility of wetland inland migration everywhere, but in urban areas with a population density more than 300 people km−2. Population density is subject the population growth throughout the simulation period, following the Shared Socio-Economic Pathway SSP220,68. The displayed coastline was generated during the DINAS-COAST FP5-EESD EU project (EVK2-CT-2000-00084).

  4. Extended Data Fig. 4 Flow diagram representing the overall structure of the global coastal wetland model.

    Input parameters are shown on the left, output parameters are on the right. Net wetland change equals inland wetland gain minus seaward wetland loss.

  5. Extended Data Fig. 5 Schematization of topographic profiles.

    The conversion of upland areas to coastal wetlands (if not inhibited by anthropogenic barriers) and the unconstrained seaward loss of coastal wetlands in response to sea-level rise is shown for an exemplary coastline segment (in western France). Inundation of terrestrial uplands follows the rising mean high water spring (MHWS) level between the time steps t1 and t2 (blue), whereas the unconstrained seaward loss follows the increase in mean sea level (MSL) when neglecting sediment accretion processes (red). To improve the clarity of the figure the actual MHWS level (2.54 m) and MSL rise are exaggerated.

  6. Extended Data Fig. 6 Map of regionalized relative sea-level rise.

    Total relative sea-level rise (in m) for the medium SLR scenario (Extended Data Table 2) during the simulation period, including a delta subsidence rate of 2 mm yr−1 (2010–2100). Black coastlines indicate regions of relative sea-level rise similar to the global mean. The displayed coastline was generated during the DINAS-COAST FP5-EESD EU project (EVK2-CT-2000-00084).

  7. Extended Data Table 1 Performance of calibrated model when compared to field data
  8. Extended Data Table 2 Characteristics of the scenarios
  9. Extended Data Table 3 Model sensitivity to variations in sediment availability
  10. Extended Data Table 4 Model sensitivity to variations in natural and human-induced delta subsidence

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Methods, a Supplementary Discussion and Supplementary references. The Supplementary Methods include extended information on the underlying tidal data, the calibration procedure and detailed information on how the current-day coastal protection level has been calculated as one of key concepts for the definition of the business-as-usual scenario for human adaptation and accommodation space for coastal wetlands. The Extended Discussion section discusses the limitations of this model in detail.

Source data

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