
==== Front
Nat Commun
Nat Commun
Nature Communications
2041-1723
Nature Publishing Group UK London

38491010
46608
10.1038/s41467-024-46608-x
Matters Arising
Coastal shoreline change assessments at global scales
http://orcid.org/0000-0002-0205-3814
Warrick Jonathan A. jwarrick@usgs.gov

1
Buscombe Daniel 2
http://orcid.org/0000-0002-9518-1582
Vos Kilian 3
http://orcid.org/0000-0002-4480-740X
Bryan Karin R. 4
Castelle Bruno 5
http://orcid.org/0000-0003-4972-8812
Cooper J. Andrew G. 67
http://orcid.org/0000-0002-1329-7945
Harley Mitch D. 8
http://orcid.org/0000-0003-1778-2187
Jackson Derek W. T. 67
http://orcid.org/0000-0002-3844-2280
Ludka Bonnie C. 9
Masselink Gerd 10
http://orcid.org/0000-0002-6424-2338
Palmsten Margaret L. 11
http://orcid.org/0000-0002-3872-1757
Ruiz de Alegria-Arzaburu Amaia 12
Sénéchal Nadia 5
Sherwood Christopher R. 13
Short Andrew D. 14
Sogut Erdinc 1315
http://orcid.org/0000-0002-0082-8444
Splinter Kristen D. 8
http://orcid.org/0000-0002-4020-5639
Stephenson Wayne J. 16
Syvitski Jaia 17
Young Adam P. 18
1 grid.513147.5 U.S. Geological Survey, Santa Cruz, CA USA
2 Marda Science, contractor to U.S. Geological Survey, Santa Cruz, CA USA
3 grid.502060.1 New South Wales Department of Planning and Environment, Parramatta, NSW Australia
4 https://ror.org/013fsnh78 grid.49481.30 0000 0004 0408 3579 School of Science, University of Waikato, Hamilton, New Zealand
5 grid.462906.f 0000 0004 4659 9485 Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805 Pessac, France
6 https://ror.org/01yp9g959 grid.12641.30 0000 0001 0551 9715 School of Geography and Environmental Sciences, Ulster University, Belfast, UK
7 https://ror.org/04qzfn040 grid.16463.36 0000 0001 0723 4123 School of Agricultural, Earth and Environmental Sciences, University of Kwazulu-Natal, Durban, South Africa
8 https://ror.org/03r8z3t63 grid.1005.4 0000 0004 4902 0432 Water Research Laboratory, University of New South Wales, Manly Vale, NSW Australia
9 Department of Environmental Resources Engineering, Cal Poly Humboldt, Arcata, CA USA
10 https://ror.org/008n7pv89 grid.11201.33 0000 0001 2219 0747 Coastal Processes Research Group, University of Plymouth, Plymouth, UK
11 grid.2865.9 0000000121546924 U.S. Geological Survey, St. Petersburg, FL USA
12 https://ror.org/05xwcq167 grid.412852.8 0000 0001 2192 0509 Institute of Oceanographic Research, Autonomous University of Baja California, Ensenada, México
13 grid.516524.1 U.S. Geological Survey, Woods Hole, Woods Hole, MA USA
14 https://ror.org/0384j8v12 grid.1013.3 0000 0004 1936 834X School of Geosciences, University of Sydney, Camperdown, NSW Australia
15 https://ror.org/03zbnzt98 grid.56466.37 0000 0004 0504 7510 Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA USA
16 https://ror.org/01jmxt844 grid.29980.3a 0000 0004 1936 7830 School of Geography, University of Otago, Dunedin, New Zealand
17 https://ror.org/00924z688 grid.474433.3 0000 0001 2188 4421 Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO USA
18 grid.266100.3 0000 0001 2107 4242 Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA USA
15 3 2024
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15 231624 7 2023
4 3 2024
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Subject terms

Natural hazards
Ocean sciences
Climate sciences
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcarising from R. Almar et al. Nature Communications 10.1038/s41467-023-38742-9 (2023)

During the present era of rapid climate change and sea-level rise, coastal change science is needed at global, regional, and local scales. Essential elements of this science, regardless of scale, include that the methods are defendable and that the results are independently verifiable. The recent contribution by Almar et al.1 does not achieve either of these measures as shown by: (i) the use of an error-prone proxy for coastal shoreline and (ii) analyses that are circular and explain little of the data variance.

Here we provide summary information for each of these points:(i) Although there are a number of satellite-derived shoreline techniques that are published with source code and can be applied to detect the drivers of coastal change at the global scale2–5, Almar et al.1 have used a simple and error-prone waterline method. Among the problems with waterline proxies, it is widely shown that they are highly dependent on tidal stage over seasonal, annual, and interannual scales because of the intersection of a sun-synchronous data source and astronomical tidal cycles3,6. For example, tidal stages for the Landsat imagery used and published openly by Vos et al.2 have temporal biases at a range of scales for transects across the globe. Thus, the variability in waterline data is commonly dominated by tidal patterns at a wide range of time scales rather than patterns of coastal change2–7.

The poor quality of waterline measurements can be shown with comparisons with standard techniques. Standard techniques for tracking shoreline position from satellites commonly capture 70–90% of the variance of in situ shoreline measurements (e.g., Fig. S1 in Vos et al.2). In contrast, the waterline measurements of Almar et al.1 captured only 14–37% (mean = 26%) of the variance of shoreline measurements (Fig. S6). The Almar et al.1 method also introduced spurious time-dependent patterns, including 20–50 m of unrealistic shoreline seasonality for Narrabeen Beach, Australia (compare thin lines in Fig. S6i) and a failure to capture the largest accretion event on record, which occurred in 2005 at Torrey Pines, California (compare thick lines in Fig. S6g). Thus, the Almar et al.1 methods fail at characterizing local-scale changes, and they provide no evidence whether these failures improve over regional or global scales.

The Almar et al.1 technique also includes only one transect every 0.5°, or every 55,000 m on average, which grossly undersamples the world’s shorelines. In contrast, standard applications of satellite-based shoreline mapping at regional and global scales is conducted at ~100 m transect spacing2–5,8,9 in recognition that this scale is required to properly sample the great diversity of coastal settings, behaviors, and geomorphic changes10–12. Although space limitations prevent a complete review of the effects of spatial sampling and aliasing for shorelines7, we note that down-sampling of 100-m transect data from Vos et al.2 to 55,000-m intervals results in fundamentally different distributions of the geomorphic change metrics in these data.

Almar et al.1 do allude to problems of their data, which they describe as ‘hydrodynamic variabilities’ that result in an inability to measure the ‘geological’ shoreline (p.6). And yet, Almar et al.1 introduce and summarize their study as relevant to ‘coastal morphological change’ (p.2), ‘shoreline change/evolution’ (p.1-2), and coastal ‘erosion’ (p.6). We argue that if the Almar et al.1. technique is unable to measure the landform (or ‘geological’) shoreline, a result we agree with, then nothing can be concluded about landform change, evolution, or erosion.

(ii) The waterline measurements of Almar et al.1 were shown to have weak positive correlations with independent water-level factors related to sea level, wave energy, and water discharge from rivers, but only with a globally averaged r2 of 0.25 (Fig. 1). That is, a primary finding is that the factors that influence coastal water levels are related (albeit weakly) with the inland position of water on the coastal landscape. We argue that this is a trivial, if not circular, finding. The cross-shore position of the waterline on a beach should be a direct function of the water level. And yet, only ~25% of the variance in Almar et al.’s1 waterline data could be explained by this simple relationship. Furthermore, the globally averaged correlation (r) of an ENSO-based model was 0.43 (Fig. 3a). Thus, only ~18% of the variance in the ‘shoreline’ data was explained by ENSO. In light of this low correlation, it should be recognized that tidal stages are significantly correlated with ENSO13,14, which raises the possibility that a portion of this correlation results from residual tidal effects in the shoreline data, which as noted above commonly dominate sun-synchronous satellite data. In the end, the waterline method captured only ~25% of the variance in actual shorelines, and the regression analyses only captured 18–25% of the variance in the waterline results. Compounding these results by the quadrature-sum method, it is suggested that only ~5% of the variance of actual shorelines would be explained by the ENSO-based regression models, which is contradictory to the primary conclusions of the paper1.

In contrast to the methods and results of Almar et al.1, there are numerous studies of regional and global-scale shoreline change from satellite data that have included: (i) methods that are consistent with best practices7, and (ii) thorough testing, analysis, and application of shoreline results2–5,8,9,12. Additionally, that corpus clearly shows how ENSO plays a complex role in some regions (e.g., Pacific basin2), while not playing a role in other regions (e.g., Atlantic coast of Europe12).

In summary, we suggest that readers should carefully evaluate these matters and Almar et al.’s general conclusion and headline finding that ENSO is a globally important driver of shoreline change1. We look forward to more rigorous analyses of the trends and causes of coastal change from data that have reasonable uncertainties and are published openly as demonstrated by others2–5,8,9,12. We point toward studies that not only report scientific results, but also provide public-facing data viewers, data repositories, and source codes as good models for getting information to coastal scientists, managers, and citizens2,3. These kinds of information and tools are critical to our understanding of coastal systems and the future of coastal communities during the modern era of population growth, coastal urbanization, climate change, and sea-level rise. Coastal managers and citizenry are looking to the scientific community to provide actionable information at both local and regional scales based on rigorously tested and freely available data. Given the importance of this science, future efforts to increase the understanding of coastal systems and carefully reassess the conclusions of Almar et al.1 will be needed.

Author contributions

J.A.W. led the writing and editing. All others, including D.B., K.V., K.R.B., B.C., A.C., M.D.H., D.W.T.J., B.L., G.M., M.L.P., A.R.d.A.-A., N.S., C.R.S., A.D.S., E.S., K.D.S., W.J.S., J.S. and A.P.Y., provided background information, intellectual contributions, editing, and/or writing of the manuscript.

Peer review

Peer review information

Nature Communications thanks Yongjing Mao and the other, anonymous, reviewer for their contribution to the peer review of this work.

Competing interests

The authors declare no competing interests.

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

1. Almar R Influence of El Niño on the variability of global shoreline position Nat. Commun. 2023 14 3133 10.1038/s41467-023-38742-9 37308517
Almar, R. et al. Influence of El Niño on the variability of global shoreline position. Nat. Commun. 14, 3133 (2023).37308517
2. Vos K Harley MD Turner IL Splinter KD Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation Nat. Geosci. 2023 16 140 146 10.1038/s41561-022-01117-8
Vos, K., Harley, M. D., Turner, I. L. & Splinter, K. D. Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. Nat. Geosci. 16, 140–146 (2023).
3. Bishop-Taylor R Nanson R Sagar S Lymburner L Mapping Australia’s dynamic coastline at mean sea level using three decades of Landsat imagery Remote Sens. Environ. 2021 267 112734 10.1016/j.rse.2021.112734
Bishop-Taylor, R., Nanson, R., Sagar, S. & Lymburner, L. Mapping Australia’s dynamic coastline at mean sea level using three decades of Landsat imagery. Remote Sens. Environ. 267, 112734 (2021).
4. Konstantinou A Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state Mar. Geol. 2023 462 107082 10.1016/j.margeo.2023.107082
Konstantinou, A. et al. Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state. Mar. Geol. 462, 107082 (2023).
5. Mao Y Harris DL Xie Z Phinn S Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine ISPRS J. Photogramm. Remote Sens. 2021 181 385 399 10.1016/j.isprsjprs.2021.09.021
Mao, Y., Harris, D. L., Xie, Z. & Phinn, S. Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 181, 385–399 (2021).
6. Eleveld MA Van Der Wal D Van Kessel T Estuarine suspended particulate matter concentrations from sun-synchronous satellite remote sensing: Tidal and meteorological effects and biases Remote Sens. Environ. 2014 143 204 215 10.1016/j.rse.2013.12.019
Eleveld, M. A., Van Der Wal, D. & Van Kessel, T. Estuarine suspended particulate matter concentrations from sun-synchronous satellite remote sensing: Tidal and meteorological effects and biases. Remote Sens. Environ. 143, 204–215 (2014).
7. Boak EH Turner IL Shoreline Definition and Detection: A Review J. Coast. Res. 2005 214 688 703 10.2112/03-0071.1
Boak, E. H. & Turner, I. L. Shoreline Definition and Detection: A Review. J. Coast. Res. 214, 688–703 (2005).
8. Mentaschi L Vousdoukas MI Pekel J-F Voukouvalas E Feyen L Global long-term observations of coastal erosion and accretion Sci. Rep. 2018 8 12876 10.1038/s41598-018-30904-w 30150698
Mentaschi, L., Vousdoukas, M. I., Pekel, J.-F., Voukouvalas, E. & Feyen, L. Global long-term observations of coastal erosion and accretion. Sci. Rep. 8, 12876 (2018).30150698
9. Castelle B Ritz A Marieu V Nicolae Lerma A Vandenhove M Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery Geomorphology 2022 413 108360 10.1016/j.geomorph.2022.108360
Castelle, B., Ritz, A., Marieu, V., Nicolae Lerma, A. & Vandenhove, M. Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery. Geomorphology 413, 108360 (2022).
10. Harley MD Turner IL Short AD New insights into embayed beach rotation: The importance of wave exposure and cross-shore processes J. Geophys. Res. Earth Surf. 2015 120 1470 1484 10.1002/2014JF003390
Harley, M. D., Turner, I. L. & Short, A. D. New insights into embayed beach rotation: The importance of wave exposure and cross-shore processes. J. Geophys. Res. Earth Surf. 120, 1470–1484 (2015).
11. Burvingt O Masselink G Russell P Scott T Classification of beach response to extreme storms Geomorphology 2017 295 722 737 10.1016/j.geomorph.2017.07.022
Burvingt, O., Masselink, G., Russell, P. & Scott, T. Classification of beach response to extreme storms. Geomorphology 295, 722–737 (2017).
12. Masselink G Castelle B Scott T Konstantinou A Role of Atmospheric Indices in Describing Shoreline Variability Along the Atlantic Coast of Europe Geophys. Res. Lett. 2023 50 e2023GL106019 10.1029/2023GL106019
Masselink, G., Castelle, B., Scott, T. & Konstantinou, A. Role of Atmospheric Indices in Describing Shoreline Variability Along the Atlantic Coast of Europe. Geophys. Res. Lett. 50, e2023GL106019 (2023).
13. Gurubaran S Interannual variability of diurnal tide in the tropical mesopause region: A signature of the El Nino-Southern Oscillation (ENSO) Geophys. Res. Lett. 2005 32 L13805 10.1029/2005GL022928
Gurubaran, S. Interannual variability of diurnal tide in the tropical mesopause region: A signature of the El Nino-Southern Oscillation (ENSO). Geophys. Res. Lett. 32, L13805 (2005).
14. Yasuda I Impact of the astronomical lunar 18.6-yr tidal cycle on El-Niño and Southern Oscillation Sci. Rep. 2018 8 15206 10.1038/s41598-018-33526-4 30315185
Yasuda, I. Impact of the astronomical lunar 18.6-yr tidal cycle on El-Niño and Southern Oscillation. Sci. Rep. 8, 15206 (2018).30315185
