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Sandy coastlines under threat of erosion

Nature Climate Change volume 10, pages 260–263 (2020)Cite this article

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Matters Arising to this article was published on 27 October 2020

Abstract

Sandy beaches occupy more than one-third of the global coastline1 and have high socioeconomic value related to recreation, tourism and ecosystem services2. Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones3. However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological4,5, geological6 and anthropogenic factors1,7. A substantial proportion of the world’s sandy coastline is already eroding1,7, a situation that could be exacerbated by climate change8,9. Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world’s sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures.

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Fig. 1: Projected long-term shoreline changes.
Fig. 2: Projected median long-term shoreline change under RCP 8.5 by the year 2100 (dxshore,LT), for the 26 IPCC SREX subregions and the worldwide average.
Fig. 3: Per country percentage of sandy beach coastline projected to experience critical erosion.

Data availability

The models and datasets presented are part of the integrated risk assessment tool LISCoAsT (Large scale Integrated Sea-level and Coastal Assessment Tool) developed by the Joint Research Centre of the European Commission. The dataset is available through the LISCoAsT repository of the JRC data collection: http://data.europa.eu/89h/18eb5f19-b916-454f-b2f5-88881931587e.

Code availability

The code that supported the findings of this study is available from the corresponding author upon reasonable request.

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Acknowledgements

R.R. is supported by the AXA Research fund and the Deltares Strategic Research Programme ‘Coastal and Offshore Engineering’. P.A. is supported by the EU Horizon 2020 Programme for Research and Innovation under grant no. 776613 (EUCP: European Climate Prediction system). T.P. was funded by the research group RNM-328 of the Andalusian Research Plan (PAI) and the Portuguese Science and Technology Foundation (FCT) through grant no. UID/MAR/00350/2013 attributed to CIMA of the University of Algarve. The authors are grateful to A. Giardino and A. van Dongeren for providing helpful comments on the manuscript and the methodology, and E. Voukouvalas for contributing to the generation of the storm surge dataset.

Author information

Authors and Affiliations

  1. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Michalis I. Vousdoukas, Lorenzo Mentaschi & Luc Feyen

  2. Department of Water Science and Engineering, IHE Delft Institute for Water Education, Delft, the Netherlands

    Roshanka Ranasinghe

  3. Water Engineering and Management, Faculty of Engineering Technology, University of Twente, Enschede, the Netherlands

    Roshanka Ranasinghe & Panagiotis Athanasiou

  4. Harbour, Coastal and Offshore Engineering, Deltares, Delft, the Netherlands

    Roshanka Ranasinghe, Panagiotis Athanasiou & Arjen Luijendijk

  5. Department of Applied Physics, CASEM, University of Cadiz, Cadiz, Spain

    Theocharis A. Plomaritis

  6. CIMA, University of Algarve, Campus de Gambelas, Faro, Portugal

    Theocharis A. Plomaritis

  7. Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands

    Arjen Luijendijk

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  1. Michalis I. Vousdoukas
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Contributions

M.I.V, R.R. and L.F. jointly conceived the study. M.I.V. and L.M. produced the storm surge and wave projections. L.M. produced the ambient shoreline change data and developed the extreme value statistical analysis methodology. M.I.V. and T.A.P. produced the storm erosion and SLR retreat projections. P.A. produced the global beach slope dataset. A.L. produced the global sandy beach presence dataset. M.I.V. analysed the data and prepared the manuscript, with all authors discussing results and implications and commenting on the manuscript at all stages.

Corresponding author

Correspondence to Michalis I. Vousdoukas.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Patrick Barnard, Mark Davidson 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 Geographical regions considered in the present analysis.

Geographical regions considered in the present analysis, based on the IPCC SREX report and limited to those that contain ice-free sandy coastlines.

Extended Data Fig. 2 Projected long-term shoreline change due to SLR-driven retreat (R) alone, by the year 2050 and 2100 under RCP4.5 and RCP8.5.

Projected long-term shoreline change due to SLR-driven retreat (R) alone, by the year 2050 (a,c) and 2100 (b,d) under RCP4.5 (a-b) and RCP8.5 (c-d). Values represent the median change and positive/negative values express accretion/erosion in m, relative to 2010. The global average median change is shown in the inset text for each case, along with the 5th-95th percentile range.

Extended Data Fig. 3 Projected long-term shoreline change driven due to the ambient shoreline change rate (AC) alone, by the year 2050 and 2100.

Projected long-term shoreline change driven due to the ambient shoreline change rate (AC) alone, by the year 2050 (a) and 2100 (b). Values represent the median change and positive/negative values express accretion/erosion in m, relative to 2010. The global average median change is shown in the inset text for each case, along with the 5th-95th percentile range.

Extended Data Fig. 4 Projected change in 100-year episodic beach erosion for the year 2050 and 2100 under RCP4.5 and RCP8.5.

Projected change in 100-year episodic beach erosion for the year 2050 (a,c) and 2100 (b,d) under RCP4.5 (a-b) and RCP8.5 (c-d). Values represent the median change and positive/negative values express less/more erosion (m), relative to 2010. The global average median change is shown in the inset text for each case, along with the 5th-95th percentile range.

Extended Data Fig. 5 Projected median long-term shoreline change under RCP4.5 by the year 2050 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average.

Projected median long-term shoreline change under RCP4.5 by the year 2050 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average (horizontal bar plot; positive/negative values express accretion/erosion in m). Shoreline change is considered to be the result of SLR retreat (R) and ambient shoreline change trends (AC). Pie plots show the relative contributions of R and AC to the projected median dxshore,LT, with transparent patches expressing accretive trends. Vertical bar plots show the relative contributions of R and AC, as well as that of RCPs, to the total uncertainty in projected median dxshore,LT.

Extended Data Fig. 6 Projected median long-term shoreline change under RCP8.5 by the year 2050 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average.

Projected median long-term shoreline change under RCP8.5 by the year 2050 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average (horizontal bar plot; positive/negative values express accretion/erosion in m). Shoreline change is considered to be the result of SLR retreat (R) and ambient shoreline change trends (AC). Pie plots show the relative contributions of R and AC to the projected median dxshore,LT, with transparent patches expressing accretive trends. Vertical bar plots show the relative contributions of R and AC, as well as that of RCPs, to the total uncertainty in projected median dxshore,LT.

Extended Data Fig. 7 Projected median long-term shoreline change under RCP4.5 by the year 2100 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average.

Projected median long-term shoreline change under RCP4.5 by the year 2100 (dxshore,LT), for the 26 IPCC SREX sub-regions and the worldwide average (horizontal bar plot; positive/negative values express accretion/erosion in m). Shoreline change is considered to be the result of SLR retreat (R) and ambient shoreline change trends (AC). Pie plots show the relative contributions of R and AC to the projected median dxshore,LT, with transparent patches expressing accretive trends. Vertical bar plots show the relative contributions of R and AC, as well as that of RCPs, to the total uncertainty in projected median dxshore,LT.

Extended Data Fig. 8 Percentage length of sandy beach shoreline that is projected to retreat by more than 50, 100 and 200 m per IPCC SREX sub-region.

Bar plots showing, per IPCC SREX sub-region, the percentage length of sandy beach shoreline that is projected to retreat by more than 50 (blue), 100 (yellow) and 200 m (red), by 2050 (a,c) and 2100 (b,d), under RCP4.5 (a-b) and RCP8.5 (c-d) relative to 2010. Transparent colour patches indicate the 5th-95th quantile range and solid rectangles show the median value. For the region abbreviations, please see Extended Data Fig. 1.

Extended Data Fig. 9 Length of sandy beach shoreline that is projected to retreat by more than 50, 100 and 200 m per IPCC SREX sub-region.

Bar plots showing, per IPCC SREX sub-region, the length (in km) of sandy beach shoreline that is projected to retreat by more than 50 (blue), 100 (yellow) and 200 m (red), by 2050 (a,c) and 2100 (b,d), under RCP4.5 (a-b) and RCP8.5 (c-d) relative to 2010. Transparent colour patches indicate the 5th-95th quantile range and solid rectangles show the median value. For the region abbreviations, please see Supplementary Figs. 2 and 5.

Extended Data Fig. 10 Per country length of sandy beach shoreline that is projected to retreat by more than 100 m.

Per country length of sandy beach coastline which is projected to retreat by more than 100 m by 2050 (a,c) and 2100 (b,d), under RCP4.5 (a-b) and RCP8.5 (c-d). Values are based on the median long-term shoreline change, relative to 2010.

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Supplementary Information

Supplementary Fig. 1 and Tables 1–4.

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Vousdoukas, M.I., Ranasinghe, R., Mentaschi, L. et al. Sandy coastlines under threat of erosion. Nat. Clim. Chang. 10, 260–263 (2020). https://doi.org/10.1038/s41558-020-0697-0

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