# Coastal shoreline change assessments at global scales

Jonathan A. Warrick, Daniel Buscombe, Kilian Vos, Karin R. Bryan, Bruno

Castelle, J. Andrew G. Cooper, Mitch D. Harley, Derek W. T. Jackson,

Bonnie C. Ludka, Gerd Masselink, et al.

► To cite this version:

Jonathan A. Warrick, Daniel Buscombe, Kilian Vos, Karin R. Bryan, Bruno Castelle, et al. Coastal shoreline change assessments at global scales. Nature Communications, 2024, 15 (1), 10.1038/s41467-024-46608-x. http://dx.doi.org/10.1038/s41467-024-46608-x

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# Coastal shoreline change assessments at global scales

Received: 24 July 2023

Accepted: 4 March 2024

Published online: 15 March 2024

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Jonathan A. Warrick Ⓔ<fnref n="1" />, Daniel Buscombe², Kilian Vos Ⓔ³, Karin R. Bryan Ⓔ⁴, Bruno Castelle⁵, J. Andrew G. Cooper Ⓔ⁶,⁷, Mitch D. Harley Ⓔ⁸, Derek W. T. Jackson Ⓔ⁶,⁷, Bonnie C. Ludka Ⓔ⁹, Gerd Masselink¹⁰, Margaret L. Palmsten Ⓔ¹¹, Amaia Ruiz de Alegria-Arzaburu Ⓔ¹², Nadia Sénéchal⁵, Christopher R. Sherwood¹³, Andrew D. Short¹⁴, Erdinc Sogut¹³,¹⁵, Kristen D. Splinter Ⓔ⁸, Wayne J. Stephenson Ⓔ¹⁶, Jaia Syvitski¹⁷ & Adam P. Young¹⁸

ARISING FROM R. Almar et al. *Nature Communications* https://doi.org/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.<fnref n="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.<fnref n="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.<fnref n="1" /> captured only 14–37% (mean = 26%) of the variance of shoreline measurements (Fig. S6). The Almar et al.<fnref n="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.<fnref n="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.<fnref n="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

<footnote n="1">¹U.S. Geological Survey, Santa Cruz, CA, USA. ²Marda Science, contractor to U.S. Geological Survey, Santa Cruz, CA, USA. ³New South Wales Department of Planning and Environment, Parramatta, NSW, Australia. ⁴School of Science, University of Waikato, Hamilton, New Zealand. ⁵Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805 Pessac, France. ⁶School of Geography and Environmental Sciences, Ulster University, Belfast, UK. ⁷School of Agricultural, Earth and Environmental Sciences, University of Kwazulu-Natal, Durban, South Africa. ⁸Water Research Laboratory, University of New South Wales, Manly Vale, NSW, Australia. ⁹Department of Environmental Resources Engineering, Cal Poly Humboldt, Arcata, CA, USA. ¹⁰Coastal Processes Research Group, University of Plymouth, Plymouth, UK. ¹¹U.S. Geological Survey, St. Petersburg, FL, USA. ¹²Institute of Oceanographic Research, Autonomous University of Baja California, Ensenada, México. ¹³U.S. Geological Survey, Woods Hole, Woods Hole, MA, USA. ¹⁴School of Geosciences, University of Sydney, Camperdown, NSW, Australia. ¹⁵Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA. ¹⁶School of Geography, University of Otago, Dunedin, New Zealand. ¹⁷Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA. ¹⁸Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA. ☒e-mail: jwarrick@usgs.gov</footnote>
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/evolu-
tion’ (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 practices², 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 commu-
nities during the modern era of population growth, coastal urbani-
zation, climate change, and sea-level rise. Coastal managers and

citizenry are looking to the scientific community to provide action-
able information at both local and regional scales based on rigor-
ously tested and freely available data. Given the importance of this
science, future efforts to increase the understanding of coastal sys-
tems and carefully reassess the conclusions of Almar et al.¹ will be
needed.

References

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2. 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.* **267**, 1127–734 (2021).

4. Konstantinou, A. et al. Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state. *Mar. Geol.* **462**, 1070–82 (2023).

5. 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, 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, E. H. & Turner, I. L. Shoreline Definition and Detection: A Review. *J. Coast. Res.* **214**, 688–703 (2005).

8. 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).

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* **413**, 108360 (2022).

10. 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* **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.* **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.* **32**, L13805 (2005).

14. Yasuda, I. Impact of the astronomical lunar 18.6-yr tidal cycle on El-Niño and Southern Oscillation. *Sci. Rep.* **8**, 15206 (2018).

**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.
**Competing interests**

The authors declare no competing interests.

**Additional information**

**Correspondence** and requests for materials should be addressed to
Jonathan A. Warrick.

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

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