# Multi-decadal gravel beach dynamics in the United Kingdom and Ireland: Insights from satellite-derived shorelines

Aikaterini Konstantinou, Tim Scott, Gerd Masselink, Christopher Stokes, Tom Hamilton, Bruno Castelle

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## To cite this version:

Aikaterini Konstantinou, Tim Scott, Gerd Masselink, Christopher Stokes, Tom Hamilton, et al.. Multi-decadal gravel beach dynamics in the United Kingdom and Ireland: Insights from satellite-derived shorelines. Earth Surface Processes and Landforms, 2025, 50 (12), pp.e70160. 10.1002/esp.70160 hal-05378592

HAL Id: hal-05378592

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Submitted on 23 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Multi-decadal gravel beach dynamics in the United Kingdom and Ireland: Insights from satellite-derived shorelines

Aikaterini Konstantinou<fnref n="1" />

Tim Scott<fnref n="1" />

Gerd Masselink<fnref n="1" />

Christopher Stokes<fnref n="2" />

Tom Hamilton<fnref n="1" />

Bruno Castelle<fnref n="3" />

**Correspondence**

Aikaterini Konstantinou, University of
Plymouth, Drake Circus, Plymouth PL4 8AA,
UK.

Email: a.konstantinou@plymouth.ac.uk

**Funding information**

Natural Environment Research Council,
Grant/Award Number: NE/Y50323X1; Met
Office; Agence Nationale de la Recherche,
Grant/Award Numbers: ANR-21-CE01-0015,
ANR-22-EXIR-0004

**Abstract**

Gravel beach systems provide vital protection from coastal flooding and erosion. They are highly dynamic and exhibit complex responses to hydrodynamic forcing over a range of temporal (hourly centennial) and spatial scales (m to km). Yet gravel beach evolution, particularly at interannual to decadal scales, across the spectrum of coastal settings, remains poorly understood. We use four decades of Satellite-Derived Shoreline (SDS) data to explore the morphodynamic behaviour of 45 selected gravel beach systems around the United Kingdom and Ireland. We apply a site-specific SDS extraction methodology and derive shoreline trends along 1554 shore-normal transects. Our findings indicate significant variability in decadal trends between sites ranging from 0.60 m/year retreat to 2.24 m/year progradation, with 36% of sites showing significant long-term trends over the study period. Nesses and spits were by far the most dynamic systems exhibiting the largest changes at transect level (from 4.73 m/year retreat to 10.5 m/year progradation), and the most significant changes in planform shape, while most constrained and unconstrained sites remained stable. We classify the observed behaviours, providing a first inventory of morphodynamic behaviours across different gravel beach systems in the United Kingdom and Ireland. We find that leading regional winter-averaged atmospheric indices provide some insight into planform behaviour over the entire domain, with 16 sites (35.6%) showing at least moderate ($R \ge 0.4$) statistically significant correlations ($p \le 0.05$). Our results provide a deeper understanding of the long-term behaviour of gravel beach systems that can inform more effective coastal management strategies.

**KEYWORDS**

alongshore variability, climate indices, coastal evolution, coastal morphodynamics, coastal resilience, coastal setting, embayment rotation, gravel beach, interannual variability, large-scale coastal behaviour, spit migration

## 1 | INTRODUCTION

Gravel systems act as natural wave barriers, offering vital flood protection to coastal communities, critical infrastructure and sensitive ecosystems (e.g., Masselink & van Heteren, 2014; van Wellen et al., 2000). They are globally ubiquitous, particularly along paraglaciated coasts in middle to high latitudes (e.g., the United Kingdom,

Ireland and Canada) (Forbes et al., 1995) and tectonically active coasts (e.g., New Zealand) (Jennings & Shulmeister, 2002). They are commonly associated with coastlines backed by cliffs or mountains (e.g., Mediterranean) (Simeone et al., 2021), as well as areas close to river mouths where there is an ample supply of material eroded from land-based sources (Carter & Orford, 1993; Forbes & Syvitski, 1994). Many gravel systems are backed by low-lying lands supporting coastal

<footnote n="1">¹University of Plymouth, Plymouth, UK</footnote>

<footnote n="2">²Met Office, Exeter, UK</footnote>

<footnote n="3">³UMR EPOC, CNRS/Université de Bordeaux, Pessac, France</footnote>

<footnote>This is an open access article under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *Earth Surface Processes and Landforms* published by John Wiley & Sons Ltd.</footnote>
communities, sensitive ecosystems (e.g., Gauci et al., 2005; Orford & Jennings, 1998; Walmsley & Davy, 1997; Wiser et al., 2010) or shelter critical infrastructure (e.g., Maddrell, 1996). Gravel beaches possess inherent morphosedimentary characteristics (e.g., higher angle of repose, bed roughness and hydraulic conductivity) that make them highly efficient in dissipating wave energy and naturally resilient to wave action and Sea Level Rise (SLR) (Masselink & van Heteren, 2014; van Wellen et al., 2000). The important function of gravel beaches in conferring coastal risks is widely recognised, and they have often been actively managed to maintain and/or enhance their risk reduction function over the past decades (e.g., Brown et al., 2019; Pye & Blott, 2009; Stéphan et al., 2018). As such, gravel systems represent critical natural capital (Stéphan et al., 2018). Globally, coastal regions are exposed to increased risks driven by a combination of sea-level rise (SLR), reduced sediment supply (Syvitski et al., 2005) and potential increases in storminess (Hinkel et al., 2018; Pollard et al., 2022; Vitousek et al., 2017). Despite an increase in interest in the short-term (storm) response of gravel beaches in recent literature (e.g., Almeida et al., 2015; Casamayor et al., 2022; Matsumoto et al., 2020b; McCall et al., 2015; Polidoro et al., 2018), still little is known about the spatial and temporal variations in their morphology and the primary drivers at longer (O[10-100] years) timescales, which are most relevant to coastal management (Atkinson & Esteves, 2018).

Gravel systems span the complete range of coarse clastic environments (2–256 mm) (Buscombe & Masselink, 2006) and are known variously as gravel, shingle, pebble, cobble or boulder. They occur as a wide array of different landforms (e.g., pocket or embayed beaches, cliff-foot fringing beaches, cuspate forelands or nesses, and spits) and environmental settings and are often considered to be either swash-or drift-aligned depending on wave climate, longshore sediment availability and potential longshore transport conditions (Orford et al., 1996). Swash (drift) alignment is generally associated with shore-normal (oblique) wave approach and low (high) net alongshore sediment input (Orford et al., 2002). Consequently, drift alignment is characterised by down-drift accumulations and distinctive prograded beach ridges, whereas swash-alignment is characterised by swash-aligned beach ridges (Forbes et al., 1995; Orford et al., 2002). These two states are not fixed, and their alignment systems may change over time. For example, swash-aligned systems may disintegrate into drift-aligned sections in a process referred to as cannibalisation as a result of SLR (Orford & Anthony, 2022). However, not all systems can be classified as swash or drift aligned. Those experiencing a bi- or multi-directional wave climate and/or those exposed to opposing longshore transport regimes, for example, tend to display a continuous shoreline adjustment to the disequilibrium and wave approach (Burningham & French, 2017; Dolphin et al., 2011; Wiggins, Scott, Masselink, Russell, & Valiente, 2019).

Gravel systems are more dynamic than their sandy counterparts and can experience rapid and extreme changes in response to storm waves (Matsumoto et al., 2020a; Ruiz de Alegria-Arzaburu & Masselink, 2010) characterised by strongly alongshore variable behaviours (Buosi et al., 2019; Casamayor et al., 2022; Powell, 1990). For example, embayed beaches exposed to bi- or multi-directional wave climate tend to oscillate around an equilibrium position driven by the relative balance between dominant wave directions (Asselot & Brander, 2023; Jaramillo et al., 2020; Wiggins, Scott, Masselink, Russell, & Valiente, 2019), whereas spits tend to be highly dynamic,

capable of migrating at rates exceeding 15 m/year (e.g., Comber et al., 1994). While, in some cases, migration rates are primarily driven by storm events (Stéphan et al., 2018; Suanez et al., 2018), wave climate asymmetry and changes in the incoming wave power balance between dominant directions can control the development of spits and nesses (Ashton & Murray, 2006a; Murray & Ashton, 2013) and the shoreline orientation of embayed beaches (e.g., Wiggins, Scott, Masselink, Russell, & McCarroll, 2019). These studies highlight that the dynamics of gravel system landforms, driven by tidal and wave forcing regimes, vary significantly (Buscombe & Masselink, 2006; Ivamy & Kench, 2006; Jennings & Shulmeister, 2002). Thus, despite their ubiquitous nature and high socioeconomic importance, gravel system dynamics remain poorly understood, particularly at decadal timescales (Burningham & French, 2017). In this context, understanding the long-term resilience outlook of gravel systems is a central requirement for an effective coastal management strategy.

Current theoretical knowledge, which reflects earlier qualitative analysis of gravel system evolution, suggests that at interannual to decadal timescales, gravel beach evolution is a function of geological setting, relative sea level rise and sediment supply conditions, which include elements of swash- and/or drift-alignment (Ashton & Murray, 2006b; Forbes et al., 1995; Orford et al., 2002). In addition, some systems show large alongshore morphological variability (e.g., spits and nesses) that often masks their longer-term signals, underscoring the importance for field measurements to be applied at the appropriate time and spatial scales. Most field studies on decadal-scale gravel beach dynamics tend to concentrate on specific areas (e.g., Bristow et al., 2021; Burningham & French, 2017; Dornbusch, 2021; Wiggins et al., 2020) and often report contradicting findings. Even fewer studies have been conducted at a regional scale, primarily due to a lack of consistent records at sufficient temporal (multi-decadal) scales. The few existing studies rely on a combination of mostly sporadic historical evidence and measured data collected using a variety of survey methods and indicate that the long-term evolution of gravel systems is primarily controlled by sediment delivery and deposition processes, human influences interfering with sediment transport pathways and variability in wave conditions (Burningham & French, 2017; St-Hilaire-Gravel et al., 2012), potentially driven by variability in the prevailing regional climatic signatures (Castelle et al., 2017; Scott et al., 2021). At interannual to decadal timescales, wave variability is the dominant driver of shoreline change and is in turn modulated by variability in large-scale atmospheric (or coupled atmospheric and ocean) signatures (e.g., Boucharel et al., 2021; Castelle et al., 2017; Scott et al., 2021). Understanding the links between atmospheric variability and shoreline dynamics can enhance our understanding of the periodicity of shoreline variability and past shoreline behaviour and improve our shoreline modelling capability, drawing the interest of several studies (e.g., Boucharel et al., 2021; Castelle et al., 2018; Dodet et al., 2010; Masselink et al., 2023). Recent advancements in season-ahead forecast capabilities of atmospheric indices (e.g., Mu et al., 2023; Scaife et al., 2014; Wang et al., 2017) have further increased interest in using climate predictions to inform seasonal prediction of shoreline change (e.g., Hilton et al., 2020). However, previous studies focusing on decadal-scale gravel beach evolution have been spatially (e.g., Casamayor et al., 2022; Dornbusch, 2021; Masselink et al., 2023; Wiggins et al., 2020) and temporally (e.g., Burningham &
French, 2017; Stéphan et al., 2018) limited by the availability of mor-
phological datasets and the difficulty in capturing the spatial variability
gravel systems exhibit with discrete measurements. Although such
datasets are available for several sandy beaches (Castelle et al., 2020;
Ludka et al., 2019; McCarroll et al., 2023; Turner et al., 2016), only
one decadal dataset exists for a gravel system (McCarroll et al., 2023);
therefore, there is a lack of understanding of decadal-scale gravel
beach evolution across a spectrum of coastal settings.

Novel satellite-based shoreline detection tools (e.g., Almeida et al., 2021; Palomar-Vázquez et al., 2023; Sánchez-García et al., 2020; Vos et al., 2019) offer relatively high frequency (O[2–14] days) repeat measurements of the shoreline at large spatial (O[1]km to global) and temporal (O[10]years) scales, representing a powerful tool for investigating gravel beach dynamics over the relevant spatial and temporal scales. Over the last few years, SDS applications have revolutionised our ability to monitor the coastline, becoming common practice among government agencies and coastal researchers (Vitousek, Vos, et al., 2023; Warrick et al., 2022). Yet, despite the broad use of SDS, research has highlighted significant uncertainties associated with specific coastal settings, environmental conditions and image availability (Castelle et al., 2021; Konstantinou et al., 2023), as well as uncertainties relating to atmospheric corrections, image georeferencing and machine vision algorithms (Vitousek, Buscombe, et al., 2023). Despite the inherent uncertainties in SDS, recent studies have demonstrated that SDS are able to reproduce long-term shoreline trends and can provide useful insights into the influence of climate-driven forcing on seasonal to interannual shoreline variability (Castelle et al., 2024; Vos, Harley, et al., 2023; Warrick et al., 2025).

This contribution aims to enhance our understanding of the mor-
phodynamic behaviours of gravel beach systems and the key drivers
of change over decadal timescales. We use CoastSat (Vos et al., 2019)
to examine 40 years of SDS from 45 gravel beach systems around the
British Isles (UK&I; Great Britain, Ireland and the Isle of Man) through
the following objectives: (i) Evaluate the occurrence of gravel beach
typologies around the UK&I in the context of the wave climate;
(ii) establish the long-term shoreline trends of these systems;
(iii) observe and characterise shoreline behaviour at decadal time-
scales; and (iv) investigate relationships between the spatial and tem-
poral shoreline variability and large-scale atmospheric behaviour.
Unlike previous large-scale SDS studies, we apply a site-specific
approach, specifically for gravel beaches in meso-macrotidal environ-
ments, improving SDS reliability (Konstantinou et al., 2023).

2 | STUDY AREA AND SITE SELECTION

The UK&I coastline is over 26,000 km in length and includes a variety
of gravel beach landforms that occur in different geomorphological
settings (May & Hansom, 2003; Sinnot & Devoy, 1992). Over 30% of
beaches in the region comprise of gravel, pebble or cobbles (sediment
grain size between 2 and 400 mm) (Fuller & Randall, 1988) covering
the morphological classification spectrum of gravel systems
(Jennings & Shulmeister, 2002). We identified 153 gravel beaches lon-
ger than 800 m around the UK&I based on literature sources and
visual inspection of satellite and Google Street Map imagery. We
focused on 45 sites where SDS could be derived with relative confi-
dence, and these sites were selected to provide a good representative

distribution of the different system types, as well as cover areas with
varying climate controls on the inshore wave climate as presented by
Scott et al. (2021) (Figure 1). This selection process excluded small,
embayed beaches (<800 m), cliff-foot fringing beaches, sites that com-
prised of a storm gravel berm (higher than the high tide mark) over a
sandy substrate and thin gravel deposits over rocky platforms as these
sites are not considered suitable for SDS applications.

Traditional gravel beach classification approaches focus either on
the spatial (cross-shore and alongshore) distribution of sediment
(Jennings & Shulmeister, 2002), their planform shape (Pethick, 1984)
and their cross-shore profile (Wright & Short, 1983) or their relative
orientation to the prevailing wave incidence (May & Hansom, 2003).
The approach proposed by Pethick (1984) provides an insight into the
processes involved in beach evolution by the nature of their landform
shape, and it can be applied based on satellite imagery alone. For the
purpose of this study, we use a simplified classification approach
designed specifically to support the interpretation of the SDS and pro-
vide the context for the explanation of the observed behaviours. Thus,
we classify beaches by their planform shape following Pethick (1984),
using a simplified approach, into nesses (cuspate landforms), spits,
unconstrained beaches and constrained beaches (i.e., naturally or arti-
ficially embayed beaches).

To allow the realistic translation of the instantaneous Satellite-
Derived Waterline (SDW) to a standard reference elevation, we fur-
ther separate beaches based on the shape of their cross-shore profile
(Carter & Orford, 1993) into those with a uniform, linear intertidal
profile and those with a composite (non-uniform) intertidal profile. It
should be recognised that this approach inherently includes a certain
level of subjectivity in some cases where beach unit boundaries are
not clear and with regard to the definition of the location (elevation)
of the break in profile slope. The 45 selected study sites include
4 nesses, 4 spits, 17 sections of unconstrained shoreline and
18 beaches that are either naturally or artificially embayed (con-
strained). While constrained and unconstrained beaches are distrib-
uted around the entire study area, gravel nesses and spits are less
common, with the latter only found on the southeast coast of England
(Figure 2a). Based on existing field data and visual inspection of satel-
lite and Google Street Map imagery, the majority of sites have a com-
posite profile, and only two nesses, two unconstrained and five
constrained sites have cross-shore uniform profile.

The tidal regime in the study area is semi-diurnal with the mean
tidal range ranging from 1.7 to 9.8 m (Figure 2c). More than half the
study sites are macrotidal (TR ≥ 4.0 m) with the largest tidal ranges
observed close to the Bristol Channel and in the Irish Sea and the
smallest occurring in the Solent on the south coast where the only
two microtidal (TR ≤ 2.0 m) sites are located. In addition to the varying
geomorphological settings and tidal regimes, the wave climate around
the UK&I coastline varies considerably. Along the northwest coast,
winter wave conditions are very energetic (mean Hs: 3.5 m;Tp: 10.9 s)
and unidirectional primarily from a west-southwest direction. The
wave climate along the south coast is more moderate and strongly bi-
directional (mean Hs: 0.6 m;Tp: 8.0 s) with a southwesterly primary
direction and an easterly secondary direction and becomes even less
energetic towards the east (mean Hs: 0.2 m;Tp: 4.5–6.0 s) with higher
directionality (Figure 2d–f). The more sheltered coasts of E Ireland, W
Wales and NW England located within the semi-enclosed Irish Sea
have varying influences from Atlantic swell waves approaching from
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Centre: map of the study area indicating all identified gravel beaches (white circles with black outline) and selected study sites (black dots). Around the border: examples of selected sites demonstrating the variety of settings, clockwise from top left: South Walney Spit, a relatively sheltered, drift-aligned spit; Sizewell: exposed, unconstrained, swash-aligned coastline exposed to a bidirectional wave approach; Chesil: moderately exposed, swash-aligned, unconstrained coastline; Kessingland: a migrating ness exposed to a high-energy, bidirectional wave climate; Climping: an artificially embayed (constrained), drift-aligned beach; Dungeness: a migrating ness exposed to a moderate, bidirectional wave climate; Newgale: a composite, swash-aligned, constrained beach exposed to an energetic wave climate.</figcaption></figure>

the S-SW and primarily dominated by locally produced wind waves. As a result, wave characteristics around the Irish Sea are heavily influenced by the local shoreline orientation. Scott et al. (2021) also showed that both total and directional winter wave power in the region are strongly correlated to winter-averaged expressions of the leading regional atmospheric indices but with different climate controls driving the nearshore wave climate around the UK&I.

For the purposes of this study, tide data were obtained from the TPXO9-atlas-v5 tidal model (Egbert & Erofeeva, 2002) and generated using TMD3 (Greene et al., 2024). Similarly, wave parameters were obtained from the CMEMS Atlantic-European North West Shelf Wave Physics Reanalysis wave hindcast model (CMEMS, 2023), which provides hourly data on a 1.5-km grid. Data were extracted at near-shore nodes for each site and were shoaled (reverse shoaled) to a consistent depth of 20 m using linear wave theory (Stokes et al., 2021).

In terms of their boundary conditions, constrained and unconstrained sites occupy the full wave-tide spectrum, including wave directionality. In contrast, spits are mostly associated with

unidirectional, low-energy wave climates. Despite commonly being referred to in the literature as features typically found in wave-dominated, micro- to meso-tidal environments, the four nesses included in this dataset occupy the full tidal range and experience relatively low-energy conditions with strong directionality. Details of the site characteristics are provided in Table S1, while the spatial distribution of the different types is shown in Figure 2a. The characteristics of each site are included in Table S1 and Figure S1.

# 3 | METHODS

## 3.1 | Shoreline extraction and uncertainty analysis

### 3.1.1 | Shoreline extraction

We used CoastSat (Vos et al., 2019) to derive instantaneous waterline measurements from all usable Landsat 5 (L5), Landsat 7 (L7), Landsat
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** Morphological and hydrodynamic characterisation of study sites. (a) Morphological classification in terms of beach planform; (b) beach classification in terms of their (intertidal) profile shape; (c) tidal range (TR) as obtained from the TPXO9-atlas-v5 tidal model (Egbert & Erofeeva, 2002) and generated using TMD3 (Greene et al., 2024); (d–f) wave forcing parameters obtained from the CMEMS Atlantic–European North West Shelf–Wave Physics Reanalysis wave hindcast model (CMEMS, 2023) extracted at nearshore nodes and shoaled (reverse shoaled) to 20-m model depth using linear wave theory (Stokes et al., 2021); (g) significant wave height ($H_s$); (h) wave period ($Tp$); and (i) wave directionality indicating the primary wave direction (black), secondary (red) and tertiary (blue).</figcaption></figure>

8 (L8), Landsat 9 (L9) and Sentinel 2 (S2) images between 1984 and June 2023. CoastSat is an open-source, python-based toolbox that can extract the land/water boundary representing the instantaneous Satellite-Derived Waterline (SDW) from pan-sharpened satellite imagery using a combination of supervised and unsupervised image classification and segmentation techniques. A total of 116 081 images were available over the study period across all sites. Of these, 2/3 contained over 50% cloud cover and were removed automatically during pre-processing. However, many of the remaining images contained light cloud and/or haze, cloud and cliff shadow on the shoreline and/or snow cover that clearly compromises shoreline detection. A notable spatial gradient in image availability was observed, with

greater availability in the southeast decreasing towards the northwest, likely reflecting the influence of prevailing weather conditions. These images were removed manually following visual inspection, leaving 13 513 useable images for reliable shoreline detection, corresponding to just over 10% of available images across all sites. It is recognised that the manual image filtering stage is inadvertently open to interpretation error as the distinction between ‘useable’ and ‘not-useable’ images involves subjective decision making.

The overall paucity of data (median: 295 useable images per site and 4 images per site per year) was also characterised by large inter-annual variability as well as significant variability between sites (range between 141 and 533 images per site). Winter data (November to
<figure><img src="image_5.png" /></figure>

**FIGURE 3** Number of useable images available over the study period. (a) All data; (b) images captured over the summer months (May to September); and (c) images available over the winter months (November to March). Data for each site are shown in lighter colour, while the median number of images across all sites is shown in darker colour. The dashed vertical lines correspond to the dates when L8 and S2 became operational (February 2013 and June 2015 respectively), significantly increasing image availability across the board.

March) was particularly scarce (median of all sites: 81; range: 18 to 152 for individual sites), with several sites having no images over multiple years (22 of the 45 sites with no winter images over at least five consecutive years; 21 sites with no winter data over at least 33% of years). Image availability before the introduction of L8 in February 2013 was significantly lower (median: 161 useable images per site and merely 3 images pre site per year on average), while half the sites (22 sites) lacked any winter data. The number of images was dramatically increased following the introduction of S2 in 2015 (median: 986 useable images per site and 21 images per site per year) and less than three sites in any given year having no winter data (Figure 3). Consequently, the sparsity of data greatly restricts the feasibility of temporal averaging (e.g., monthly and seasonal) and hence the type of phenomenon that can be investigated.

## 3.1.2 | Definition of Satellite-Derived Shorelines (SDS)

As most beaches are at least partly backed by lagoons or cliffs, a region of interest (ROI) was defined for each site to avoid the inclusion of SDW incorrectly identified along back-barrier lagoon shores or cliff shadows. Shore-normal transects were created within the ROI at 100 m intervals (see also Figure 5) resulting in a total of 1554 transects across the 45 sites and over 480,000 individual data points

(SDW). A tidal correction was applied, as this was shown to be the optimal approach for reflective beaches in meso- to macro-tidal environments (Konstantinou et al., 2023). Tidal data were obtained from the TPXO9-atlas-v5 tidal model (Egbert & Erofeeva, 2002) and generated using TMD3 (Greene et al., 2024). Corrections were applied to each SDW using simple linear translation to the mean sea level elevation (MSL) along each shore-normal transect. The corrected SDW then represented the Satellite-Derived Shoreline (SDS).

Though the application of this method is straightforward, it requires some measure of beachface slope that is implicitly assumed to be uniform across the intertidal area of the beach profile. In the absence of measured data, slope values can potentially be derived from the uncorrected SDS data using frequency-based analysis techniques (Vos et al., 2020). These methods, however, require a sufficiently large number of Landsat images (180 minimum over 36 years and 5 per year) and have been shown to perform poorly in tide-dominated (relative tidal range [RTR] ≥ 10) environments (Vos, Harley, et al., 2023), as well as in high-energy, meso-macro tidal environments of low satellite usability, such as the United Kingdom (Konstantinou et al., 2023). Therefore, SDS-based slope derivation was not considered suitable for this study. As previously discussed, many gravel beaches feature composite cross-shore beach profiles with distinct breaks in the intertidal profile slope (Figure 4), thus negating the assumption of a uniform beachface gradient. Therefore, in this study, we define a minimum water level threshold based on the visual assessment of the satellite images as well as Google Earth imagery, so only waterlines that correspond to the upper reflective (gravel) sections of the beach are included. The beach slope value used was statistically defined as the median slope for gravel beaches (median $tan\beta = 0.15$ for $2 mm \le D_{50} \le 256$) as presented in Bujan et al. (2019). Though this approach includes an element of subjectivity relating to the definition of the location (elevation) of the break in profile slope, it accounts for the morphology of the beach profile, thus representing a positive improvement. An additional, evident limitation relates to the need for model data on tidal elevation, the quality of which varies around the globe (Stammer et al., 2014; Zaron & Elipot, 2021).

### 3.1.3 | Spatial averaging

To enable the characterisation of the observed morphodynamic behaviours, the transect trends were used to define three alongshore sections at each site. For each section and timestep, shoreline position was averaged across all transects in that section if at least 30% of transects had valid SDS data (Figure 5d). Similarly, the long-term trend for each section was calculated as the average trend observed at all transects in that section. Section trends were then used to quantify the difference in observed behaviour between beach sections in relation to the overall beach average (Figure 5d).

### 3.1.4 | Trend analysis

Shoreline data were filtered to remove outliers using a Hampel Identifier (Hampel, 1971), which is a robust outlier filter that can be applied to univariate and multivariate time-series data, and linear trends based
<figure><img src="image_6.png" /><figcaption>**FIGURE 4** Example of a composite beach at Westward Ho!, southwest England (a-c) and pure gravel beach at Blackpool Sands on the south coast of southwest England (d-f). Satellite images taken at low tide (top row) and high tide (middle row) show the intertidal and subaerial extents of the beach. Bottom row: intertidal profiles of the two beaches taken along the red transects indicated on the satellite imagery showing a distinct break in the profile of Westward Ho! (c) and a steep intertidal profile with a uniform slope at Blackpool Sands (d). Grey highlight indicates the intertidal profile area.</figcaption></figure>

on least squares regression were derived along each transect (Figure 5c). The statistical significance of trends was established at the 95% confidence level adjusted based on a two-tailed t test to account for the degrees of freedom of the sample. Where required, *p* values were combined using Fisher's exact test (Fisher, 1992).

### 3.1.5 | Uncertainty analysis of long-term trends

SDS accuracy in microtidal, low- to medium-energy environments is 7–11 m (Vos et al., 2019) but deteriorates rapidly in high-energy environments, dissipative sites, larger tidal range or low satellite usability, and water level corrections are required to reduce errors (Castelle et al., 2021; Konstantinou et al., 2023). In these environments, the level of uncertainty can exceed 100 m (15 m) at dissipative (reflective) sites depending on beach type and translation method used (Konstantinou et al., 2023). Such errors may propagate into derived shoreline trends and should therefore be taken into account.

In addition, Konstantinou et al. (2023) found that the selection of the slope value used for the linear translation of the SDW when applying water level corrections may impact on SDS accuracy (RMSE: 14.8 and 15.3 m when using a slope of 0.09 and 0.19 at a high energy,

meso-macrotidal, gravel beach). To assess the influence of slope on the derived trends, we performed a sensitivity test using the 5th, 50th and 95th percentile slope values for gravel beaches based on Bujan et al. (2019) (0.07, 0.15 and 0.3, respectively). The RMSE corresponding to the maximum difference between trends derived using the three slope values was 0.17 m/year. This uncertainty represents an additional component to the reported measurement (SDW) uncertainty of the method for reflective sites in the United Kingdom (Konstantinou et al., 2023), which can be conservatively estimated at 0.39 m/year. Therefore, using linear error propagation theory, the overall uncertainty associated with the derived trends can be conservatively estimated at ±0.44 m/year and propagated using linear error theory. Though the methods are not directly comparable, this level of uncertainty is similar to that found by Castelle et al. (2024) in relation to SDS trends along the Atlantic coast of Europe derived from annual image composites (0.4 m/year).

## 3.2 | Shoreline variability and atmospheric forcing

Previous work based on in situ data has demonstrated that at local scales, winter-averaged interannual shoreline variability is linked to
<figure><img src="image_7.png" /><figcaption>FIGURE 5 Shoreline extraction methodology illustrated for Kessingland, east coast of England. (a) Raw satellite-derived instantaneous waterlines; (b) 100 m-spaced shore-normal transects along which SDS is determined; (c) long-term transect trends (red: negative; blue: positive); (d) alongshore segmentation of the beach into three sections (S1, C: central section, and S2) denoted by the different colour transects.</figcaption></figure>

large-scale modes of atmospheric variability (e.g., Dodet et al., 2019; Masselink et al., 2023). However, due to the extremely low availability of winter imagery for most sites (Figure 3), here, we use yearly-averaged shoreline data. We derive a shoreline change index (SI) for each year as the z-score normalised change in mean annual shoreline position (Equation 1).

$$SI = \frac{S_i - \bar{S}}{\sigma_s}, \qquad (1)$$

where S is the spatially averaged shoreline position at each time step (i), S̄ is the overall (spatially averaged) mean shoreline position and σs is the standard deviation of the (spatially averaged) shoreline position.

To provide a better explanation of the observed alongshore variable responses, we combine the response of opposing sections of the beach following Wiggins, Scott, Masselink, Russell, & Valiente (2019), but using shoreline position instead of beach volume, and derive a shoreline rotation/migration index (SRI) defined as the difference in SI between opposing sections of the beach as described in (Equation 2):

$$SRI = SI_{S1} - SI_{S2}, \qquad (2)$$

where SI S1 and SI S2 are the shoreline index values for two beach sections. Note that positive (negative) SRI corresponds to clockwise (anticlockwise) rotation or migration. We then compare SI and SRI with three leading winter-averaged (November, December, January, February and March [NDJFM]) atmospheric indices, (North Atlantic Oscillation, NAO; Arctic Oscillation, AO; and Western Europe Pressure Anomaly, WEPA), that have been previously shown to have a strong connection to inshore wave power and directional modality in the study area (Scott et al., 2021). NAO and AO data were acquired

from NOAA (www.cpc.ncep.noaa.gov), and WEPA was derived using the sea level pressure data from the ECA&D project (Tank et al., 2002) and the Irish Meteorological Service (www.met.ie/climate) following Castelle et al. (2017).

# 4 | RESULTS

## 4.1 | Transect-based long-term shoreline trends

Shoreline behaviour across the study sites exhibited great diversity in terms of shoreline variability and trends. Along individual transects, nesses and spits exhibited the largest (40-year) changes in terms of the range of shoreline change (nesses: median: 235.3 m, range: 447.7 m; spits: median: 262.0 m, range: 314.0 m), as well as the highest (within site) alongshore variability (median: nesses = 21 m; spits = 20.6 m), indicating strong alongshore shoreline variability. Constrained and unconstrained beaches showed lower levels of alongshore variability (median: 5.6 m for both), but unconstrained beaches were slightly more mobile compared to constrained beaches in terms of range of shoreline change (unconstrained: median: 86.6 m, range: 175.0 m; constrained: median 74.7 m; range: 133.1 m) (Table S2). Across the dataset, long-term trends obtained at individual transects were also characterised by high variability ranging from -4.73 to +10.50 m/year (Figure 6). Nevertheless, 58% of the transects showed insignificant long-term trends (i.e., either not statistically significant or of absolute magnitude less than the uncertainty limit of ±0.44 m/year) over the study period (Figure 6b). Of those transects that did show significant trends, 40% showed a negative (retreat) trend (mean: -1.31 m/year), and 60% showed a positive (progradation) trend (mean: +1.95 m/year). The overall mean transect
<figure><img src="image_8.png" /><figcaption>FIGURE 6 (a) Examples of UK gravel beach shoreline trends derived from Landsat and Sentinel 2 satellite data between 1984 and June 2023. Positive trends (progradation) are shown in blue, and negative (retreat) trends are shown in red. (b) Shoreline trends derived for all study sites. X-axis tick marks indicate the first transect of each site. (c) Map of the British Isles showing the location of the study sites (black dots). The names and locations of example sites in (a) are indicated in (b) and (c), respectively.</figcaption></figure>

trend is a modest progradation of 0.37 m/year. Nesses and spits exhibited the largest transect trends (maximum statistically significant trend: 10.5 and 5.6 m/year, respectively), as well as the highest along-shore trend variability (median range: 6.93 and 6.36 m/year, respectively). These values are an order of magnitude higher than those observed at constrained and unconstrained beaches (median range: 0.47 and 0.78 m/year, respectively). Examples of the different long-term behaviours exhibited by different beach types included in Figure 6a demonstrate the observed heterogeneity. Figure 6b provides a record of shoreline behaviour at each of the 1554 100-m-spaced transects at 45 gravel beach systems in the study area, providing information on hotspots of shoreline retreat/advance throughout the UK&I.

## 4.2 | Site-based characterisation of morphodynamic behaviours

The morphodynamic behaviour observed at each site was characterised in terms of (i) the overall shoreline trend at each site based on the mean shoreline position across all transects within each site at each timestep (tr) and (ii) the alongshore variability in shoreline behaviour calculated as the trend difference between beach extremities, based on the mean shoreline position across all transects within two beach sections at each timestep (Δtr). Site-averaged shoreline trends vary greatly throughout the UK&I coastline with maximum retreat and progradation rates of −0.59 and +2.24 m/year, respectively. Nevertheless, 27 sites (60%) remained stable or showed insignificant long-term trends (i.e., either not

statistically significant or of absolute magnitude less than the uncertainty limit of ±0.44 m/year) over the study period (Table S2). Only four sites exhibited statistically significant negative (retreat) trends above the uncertainty limit (Westward Ho!: −0.59 m/year; Wellhill Spit: −0.54 m/year; Blakeney: −0.50 m/year; and Beesands: −0.45 m/year). The most dynamic systems were, once again, nesses and spits exhibiting the largest beach-average trends (median tr: 0.71 and 0.56 m/year for nesses and spits, respectively) compared to constrained and unconstrained sites (median tr: 0.32 and 0.12 m/year, respectively).

As discussed above, to investigate the within-site dynamic behaviour, we compare the trends of three alongshore sections defined for each beach as described in Section 3.1.3. This approach allows distinguishing between shoreline trend variability that is spatially coherent indicating a consistent alongshore morphodynamic behaviour (e.g., rotation and migration) and shoreline trend variability that is spatially coherent, signifying mostly uniform cross-shore dominated behaviour. Figure 7a provides examples of the comparison between transect shoreline position and spatially averaged shoreline time-series demonstrating the difference between coherent variability associated with beach rotation at Slapton (Figure 7a) and ness migration Kessingland (Figure 7b). In contrast, trend variability at Chesil Beach is spatially coherent signifying mostly uniform cross-shore dominated behaviour.

The within-site variability in SDS trends observed at some locations, particularly nesses and spits, is indicative of the characteristic migratory behaviour of these systems resulting in dynamic changes of planform shape (e.g., Orford Ness, Kessingland and Wellhill Spit) and/or shoreline realignment (e.g., Slapton, Climping and Budleigh
<figure><img src="image_9.png" /><figcaption>**FIGURE 7** Left: SDS time-series for individual transects (small, coloured dots) and the spatially averaged SDS (black dots) shown for the overall beach and the three beach sections shown in the rightmost panel. Each coloured dot represents the shoreline position along one transect extracted from a single image while the colour in each panel graduates from red (towards S1) to blue (towards S2). The derived linear trends for each beach section are also included (statistically significant: solid line; not statistically significant: dashed line). Examples shown include Slapton Sands (top), Orford Ness (middle) and Chesil Beach (bottom). Numbers in circles indicate the site number corresponding to Figure 6 and Tables S1 and S2.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**FIGURE 8** Comparison of long-term beach trends (x-axis) with the difference in long-term trends between beach extremities (y-axis) across all sites (a). Panel (b) shows a zoomed-in section of panel (a). The shaded areas correspond to the uncertainty limit of 0.44 m/year. Beach types are represented in different colour and shape, and all non-statistically significant trends are shown in grey symbols. Beach codes are provided in Table S2.</figcaption></figure>

Salterton). Such behaviour is often associated with insignificant net beach changes (e.g., Slapton, Orford Ness, Climping and Budleigh Salterton) (refer also to Figure S2). This is evident when comparing the SDS trends of different sections of the beach (Δtr) with the overall beach trends (tr) as illustrated in Figure 8. Nesses and spits once again show the highest Δtr (nesses: median: 2.56 m/year, range: 11.25 m/year; spits: median 1.63 m/year, range: 6.85 m/year). In contrast, unconstrained beaches had the smallest Δtr (median: 0.01 m/year; range: 4.15 m/year), and most (14 out of 17 or 82%) had either insignificant Δtr or within the uncertainty limit (Figure 8).

## 4.3 | Interannual shoreline variability and modes of atmospheric variability

To identify possible links between interannual shoreline variability and the leading modes of atmospheric variability, we compare changes in the normalised mean annual shoreline position expressed through the shoreline index SI and annual alongshore variability in shoreline change expressed through the shoreline rotation index SRI with the winter-averaged (NDJFM) values of leading atmospheric indices controlling inshore wave conditions in the region (Scott et al., 2021). Figure 9 presents correlation values (where found to be statistically significant to at least the 90% confidence level) between shoreline response and atmospheric forcing expressed through the shoreline change index (SI) and the shoreline rotation/migration index (SRI); and the Arctic Oscillation (AO), the North

Atlantic Oscillation (NAO) and the Western Europe Pressure Anomaly (WEPA).

Correlations with winter-averaged climate indices were generally weak and/or not statistically significant and did not show clear spatial patterns. Despite using yearly shorelines to increase data availability, 25% of sites had more than 20 data points across the 40-year study period. Even so, several sites showed strong, statistically significant correlations with the leading teleconnection patterns. Figure 10 includes some examples of the relationships between SI and SRI with AO, NAO and WEPA for four selected sites.

For example, at Slapton Sands (Figure 10a), we found weak but statistically significant correlations between SI and AO ($R = -0.34, p = 0.04$) and between SRI and NAO ($R = -0.36, p = 0.02$) indicating positive (clockwise) beach rotation with higher NAO, concurring with the findings of Masselink et al. (2023) based on long-term measured shoreline data. Similarly, at Chesil, an open stretch of coastline with a southwesterly orientation, a moderate but significant correlation was found between SI and WEPA ($R = -0.4, p = 0.02$), which is linked to increased wave power at near-shore-normal incidence. Wellhill Spit on the northeast coast of the United Kingdom showed the strongest relationships between shoreline response and atmospheric forcing (SI/AO: $R = -0.84, p = 0.00$; SRI/WEPA: $R = +0.79, p = 0.01$) in general agreement with local climate controls (Scott et al., 2021). On the east coast of England, cross-shore shoreline response at both Orford Ness (Figure 10c) and Kessingland (Figure 10d) was found to be correlated with AO ($R = -0.45, p = 0.00$ and $R = -0.35, p = 0.04$), respectively.
<figure><img src="image_11.png" /><figcaption>FIGURE 9 Relationship between annual shoreline change expressed through the shoreline index (SI; left column) and shoreline rotation index (SRI; right column) with the winter averaged (NDJFM) AO (a, b), NAO (c, d) and WEPA (e, f). Only correlations (Pearson) significant to at least the 90% level are shown, while those significant to the 95% level are outlined in black.</figcaption></figure>

5 | DISCUSSION

5.1 | Transect-based long-term trends

Our findings indicate that the majority of transects show insignificant long-term trends (Figure 6b) and only 16% show significant negative trends, implying that chronic shoreline retreat is a highly localised problem centred on a relatively small number of hotspots around the

UK&I. Most hotspots are associated with nesses and spits (e.g., sites 18: Dungeness, 21: Orford Ness; 23: Kessingland; and 26: Wellhill Spit in Figure 6a), mainly located on the southeast and east coast of England. These sites are also associated with some of the largest positive (prograding) trends, suggesting these systems are undergoing continuous changes in planform shape. Overall, nesses and spits were found to be by far the most dynamic systems exhibiting the largest transect trends (10.50 and 5.56 m/year, respectively) and the highest
<figure><img src="image_12.png" /><figcaption>**FIGURE 10** Examples of the relationship between shoreline change (annual) expressed through the shoreline index (SI; right column row) and shoreline rotation index (SRI; middle column) with the winter averaged (NDJFM) NAO, WEPA and AO. Data points are coloured by year (1985–2023). Pearson correlation coefficients (R) and *p* values are shown in bold font for statistically significant correlations (*p* ≤ 0.05). The rightmost column shows the SDW acquired for each site, with colour corresponding to the year of acquisition.</figcaption></figure>

alongshore variability (6.93 and 6.36 m/year, respectively) within each
site. High alongshore variability was also observed in several other
systems, primarily constrained beaches such as Beesands, Slapton

Sands and Blackpool Sands in southwest England. Previous studies
based on in situ, shorter term (2006–2019) measurements demon-
strated that these three embayments are part of a larger system that
<figure><img src="image_13.png" /><figcaption>**FIGURE 11** Transect trends for (a) Beesands, Slapton Sands and Blackpool Sands, all sub-embayments of the larger Start Bay embayment, showing consistent long-term shoreline retreat (red) of the southwest end of Beesands growing weaker at the northern end of the beach, a rotational behaviour at Slapton with the southern end eroding and the northern end accreting, and consistent accretion at Blackpool demonstrating full embayment rotation at Start Bay. (b) Transect trends at Seaford small but statistically significant positive trends; (c, d) shorelines fronting the two nuclear power plants at Dungeness (c) and Sizewell (d) indicated by the black boxes, showing evidence of shoreline stabilisation.</figcaption></figure>

has been rotating clockwise with a north-eastward dominant littoral drift (Figure 11a) (Wiggins, Scott, Masselink, Russell, & McCarroll, 2019). Evidence of these linked behaviours also highlights the critical importance of considering whole systems in our interpretation of observations for understanding not only the morphodynamic behaviour of systems but also the sediment budgets and transport pathway.

While the dominance of positive trends found here aligns with the findings of Castelle et al. (2024) and Luijendijk et al. (2018), it is in seeming contradiction with the broader findings of Mentaschi et al. (2018) and Vousdoukas et al. (2020) who indicate the prevalence of erosion of sedimentary coastlines along the western Atlantic European coastline. Notwithstanding the possibility of an inherent positive bias, it is worth noting that prograding behaviour tends to receive little attention while retreating behaviours increasingly draw the focus of the media and research alike and raise awareness of issues resulting from coastal erosion (e.g., Vousdoukas et al., 2020). Characteristically, a keyword search for publications that included ‘coastal’ or ‘shoreline’ or ‘beach’ and ‘erosion’ or ‘retreat’ in Scopus

(04/05/2025) returned 12 892 results, while a search for ‘coastal’ or ‘shoreline’ of ‘beach’ and ‘propagation’ or ‘advance’ only returned 2842 results, suggesting there is a clear bias towards reporting erosion events compared to accretion. A possible explanation for the relative lack of negative trends might be that many sites are managed and although records of management efforts exist, details and/or complete records of the timing of interventions and estimations of sediment volumes being added to the systems or moved around do not exist. Examples of such sites include Seaford, where the local coastal management plan includes bi-annual maintenance works (Figure 11b), and the coastline directly fronting the Dungeness (Figure 11c) and Sizewell (Figure 11d) nuclear power plants where evidence of local shoreline stabilisation can be clearly seen. The absence of dominant negative shoreline trends in the data can also be related to significant onshore sediment transport from the shelf of glacially or para-glacially derived sediment. Although this process has been instrumental in the formation of several gravel barrier systems (e.g., Slapton Sands and Chesil), there is no clear evidence that this onshore transport persists to the present day.
<figure><img src="image_14.png" /><figcaption>**FIGURE 12** Classification of observed behavioural modes according to beach type. Numbers shown in black circles indicate the total number of study sites of that typology and numbers shown next to the schematics indicate the number of sites showing each observed behaviour.</figcaption></figure>

Another possibility is that trend uncertainties exceed those estimated here (±0.44 m/year). Indeed, Vos, Splinter, et al. (2023) compared the performance of five shoreline detection algorithms at four different sites and found differences in trends derived from in situ data and SDS obtained using CoastSat that exceeded 2 m/year at individual transects and showed large variability between sites. These examples highlight the possibility of large uncertainties in SDS-derived trends arising from errors inherent to satellite remote sensing (e.g., geolocation uncertainty, atmospheric interference and sensor-specific errors) that can be both temporally and spatially variable (Choate et al., 2021; Languille et al., 2015; Storey et al., 2014).

## 5.2 | Site-based behavioural modes

The wide range of observed morphodynamic behaviours can be classified in groups relating to morphological setting as summarised in (Figure 12). Both constrained and unconstrained sites showed combinations of cross-shore and alongshore uniform or variable behaviours mostly shared between the two groups, often resulting in negligible long-term trends. Where these beaches showed significant trends, these were dominated by progradation and rotational behaviours. Nesses and spits, however, only exhibited alongshore variable behaviours associated with net long-term trends, and none remained stable over the study period. Nesses and spits also exhibited high spatial variability and average trends that were considerably lower than local (transect level) trends. These landforms, particularly nesses, were mostly found along the south and southeast coast of Britain, where longshore littoral drift dominates (Burningham & French, 2014; Carter et al., 2004). Consequently, this section of the UK&I shoreline is by far the most dynamic even though it is not the most energetic in terms of hydrodynamic forcing as shown in Figure 2c-f.

Unconstrained coastlines displayed the greatest variability in morphodynamic behaviours including alongshore uniform and alongshore

variable modes, such as uniform progradation (e.g., Seaford), rotation with no long-term trend (e.g., North Beach Greastones), alongshore variable progradation (e.g., Greystones), alongshore variable retreat (e.g., Westward Ho!), rotation with advance (e.g., Great Yarmouth) and alongshore uniform advance (e.g., Seaford) (Figure 12). Similarly, constrained beaches also displayed a wide variety of both alongshore-uniform and alongshore variable behaviours including rotation with no long-term trend (e.g., Slapton Sands), rotation with retreat (e.g., Beesands), rotation with advance (e.g., Lancing) and alongshore uniform advance (e.g., Blackpool Sands), although once again, most trends were within the uncertainty limit. However, most constrained and unconstrained sites were stable, while all nesses and spits showed strong, alongshore variable behaviours characterised by alongshore beach migration and most were also associated with net beach trends (O[1] m/year).

## 5.3 | Comparison with field data

Decadal scale SDS-based trends for gravel ness and spit systems found here are comparable to trends reported in the literature (1.0 to over 15.0 m/year). For example, at Cape Charles, Yorke, Canada, St-Hilaire-Gravel et al. (2015) report historical shoreline change rates of the gravel ness ranging between -0.33 and +0.77 m/year while Renaud (2000) reported varying historical retreat rates of the eastern shoreface of the Pespébiac ness (Gulf of St Lawrence, southwest Canada) between 1870 and 2006 of -1.56 to 4.87 m/year, whereas Xhardé et al. (2011) recorded erosion rates reaching -6.7 m/year between 2003 and 2006 at the same site. Similarly, Clemmensen et al. (2011) found that the northwestern shoreline of the Flakket gravel ness in the Kattegat Sea retreated at a rate of 10 m/year, while the north-eastern shoreface advanced at a rate reaching 17.5 m/year. Stéphan et al. (2018) estimated the average roll-over rate at the Sillon de Talbert, North Britany, France gravel spit at 2.15 m/year with a maximum rate
of 4.42 m/year, over the period 2002–2017, observing that retreat (roll-over) was partially variable and episodic, and mostly occurring during the most energetic winters (2007/2008 and 2013/2014). Similar migration rates have been reported for sandy nesses such as at Bec du Perroquet, Nouvelle-Aquitaine in France (north section: –5.6 m/year, mid-bay section: to 3.47 m/year between 1947 and 2009) (Hesp et al., 2016), and of salient features along the Cape Jaffa Marina estate, South Australia (–6.4 to +3.7 m/year) (DaSilva et al., 2021). Similarly, Blanco-Chao et al. (2020) found that the sandy spit in Ria Arousa, Spain, eroded at a rate of –6.6 m/year (1980–2025).

Comparisons with field data and published work generally show good agreement, with a few exceptions. For example, Burningham and French (2017) reported retreat of the central section of Orford Ness at a rate of (O[4.0–5.0]m/year) and progradation of the adjacent sections by (O[2.0–4.0] m/year) between 1990 and 2015. SDS-based trends derived here show negative trends of –1.64 m/year and positive trends between 0.5 and 2.0 m/year for the two sections covering similar areas over the same period. The same authors report progradation rates of 5.0–15.0 m/year at Kessingland (referred to therein as Benacre Ness), which compares well with the positive SDS-based trend of 6.4 m/year found here. Similar comparisons with trends derived from the quasi-biannual data collected by the UK National Network of Regional Coastal Monitoring Programmes (www.coastalmonitoring.org) at Slapton Sands, Chesil, Orford Ness, Kessingland and Dungeness were largely within the uncertainty limits of the method, indicating that the ±0.44 m/year uncertainty level of SDS-derived trends considered here is a realistic, though conservative, representation of the uncertainty of the method for this type of beach in the UK environment and that SDS-derived trends are able to reproduce observed trends within this limitation (Table S3).

## 5.4 | Interannual shoreline change and modes of atmospheric variability

Most studies addressing possible links between atmospheric forcing and shoreline variability focus on values averaged over the winter months (DJF or NDJF) when both forcing and shoreline change signals are much stronger (e.g., Castelle et al., 2024; Masselink et al., 2023; Vos, Harley, et al., 2023). The lack of consistent data over the winter periods, especially before the introduction of S2 in 2015, precluded the use of winter-averaged shorelines in this study. Thus, we use annual shoreline data to address the relationship between annual shoreline change and climate indices averaged over the most energetic half of the year (NDJFM) in the region over the entire time series (1984–2023). Unavoidably, our yearly SDS data include subsequent spring, summer and early autumn periods, thus diluting the winter response signal and potentially weakening relationships with forcing as demonstrated by Masselink et al. (2023). Despite the resulting dampening of shoreline change signals, several sites show statistically significant relationships with key teleconnection patterns controlling the inshore climate in the study area (Scott et al., 2021). Our findings here indicate that AO is to be more closely linked to cross-shore shoreline variability (Figure 9), whereas NAO shows skill in explaining alongshore shoreline variability and changes in planform shape (Figure 9b). Most correlations with both AO and NAO are generally weak ($R < 0.4$), but they do extend across the entire UK&I. While AO

and NAO are highly correlated (e.g., Simpkins, 2021), Scott et al. (2021) showed that NAO has a stronger control on wave power directionality, which might suggest a closer link with alongshore sediment transport and changes in beach planform shape as shown by our results. Significant though weak correlations were also found between WEPA and shoreline variability at three sites in the south of the UK&I, where WEPA has been shown to have a significant connection with westerly wave power and directionality at these latitudes (Castelle et al., 2017; Scott et al., 2021).

Despite several sites experiencing significant long-term changes in planform shape over the study period (Kessingland, Wellhill Spit, Dungeness, Orford Ness, South Walney Reserve, Great Yarmouth, Shingle Street; Figure 8), Wellhill Spit and Kessingland were the only ones that showed strong and moderate correlations between atmospheric variability and variability in planform shape, respectively (Wellhill Spit: SRI/WEPA: $R = +0.79$, $p = 0.01$; Kessingland: SRI/WEPA: $R = +0.40$, $p = 0.02$). In our analysis, winter-averaged atmospheric indices were not correlated to the large changes in planform shape at the rest of these very dynamic sites (Figure S3). Strong correlations do not necessarily explain any cause-effect mechanisms, but, where strong relationships do exist, they can prove useful for informing coastal management decisions on early interventions in sensitive areas given our increasing ability to produce atmospheric index forecasts on seasonal to decadal timescales (Athanasiadis et al., 2017; Dunstone et al., 2016; Patrizio et al., 2025; Smith et al., 2019). Future research focusing on the expansion of shoreline prediction models based on climate-index-informed forcing inputs to incorporate index predictions (e.g., Antolínez et al., 2019; Hilton et al., 2020) can provide the necessary tools for leveraging the observed relationships between shoreline variability and climate indices. Such advancements would provide actionable metrics for coastal management purposes on intra-annual to decadal timescales.

Traditional process-based geomorphology is mostly focused on comparisons between forcing events and the resultant geomorphic responses, generally assuming a linear ‘cause and effect’ relationship. This approach assumes that landforms are the sum of all on-going processes (Harrison, 2001). Evidence indicates that geomorphic systems are complex, non-linear systems, whose behaviour cannot always be fully explained as a sum of its component parts (Forbes et al., 1995; Murray et al., 2016; Phillips, 1995). A growing body of literature suggests that the evolution of geomorphic systems requires consideration of the internal dynamics of the system and self-organising behaviour (French, Payo, et al., 2016; Hird et al., 2021; Murray et al., 2014; Ruggiero et al., 2016; van den Berg et al., 2014; von Elverfeldt et al., 2016). Early studies of gravel systems have highlighted that adjacent systems, of similar geomorphological context, exposed to the same regional forcing conditions can show contrasting behaviours (Forbes et al., 1995; Forbes et al., 1997). Furthermore, unlike long-term shoreline changes, small-scale shoreline changes (surf-zone and storm scales) do not accumulate over time, suggesting that processes between scales are decoupled (Forbes et al., 1995; Lazarus et al., 2011; Murray et al., 2016; Werner & Fink, 1993). This suggests that there is limited propagation of cause and effect from small to large scale and that response to external forcing can be influenced by self-organisation and internal dynamics. Forbes et al. (1995) strongly advocated that self-organisation is a key control on the sedimentology, morphodynamics and long-term gravel beach evolution. Indeed,
several studies have demonstrated that shoreline evolution in the
mesoscale, including rapid, large-scale changes and cyclical behaviour,
can occur independently of external drivers (e.g., Cooper et al., 2007;
French, Burningham, et al., 2016). From this point of view, the weak
links between shoreline change and atmospheric forcing found here
are perhaps not surprising.

Observed linked behaviours highlight the critical importance of
considering whole systems in our interpretation of observations for
understanding not only the morphodynamic behaviour of systems but
also the sediment budgets and regional transport pathways. Indeed,
although there is increasing evidence that atmospheric and climatic
variability is one of the main drivers of decadal shoreline change
(e.g., Castelle et al., 2024; Splinter et al., 2012; Warrick et al., 2025;
Wiggins et al., 2020), there are also clear indications that other fac-
tors, including geologic controls (Cooper et al., 2018; Jackson
et al., 2005), human development in the coastal zone (e.g., Brown
et al., 2017; Hapke et al., 2013; Kinsela et al., 2022), coastal manage-
ment interventions (Burningham & French, 2017) and internal system
dynamics and self-organisation (e.g., Forbes et al., 1995; Murray
et al., 2014) play a key role in shoreline evolution, the relative impor-
tance of which is difficult to unravel and is highly site-specific.

6 | LIMITATIONS

The use of a fixed slope, though unavoidable in areas where quantita-
tive data are lacking and satellite usability is low, limiting the use of
frequency-based methods (Vos et al., 2020), may introduce errors in
SDS. We limit these uncertainties by accounting for the morphology
of composite beaches (Section 3.1.2) and including the uncertainty
derived from a sensitivity analysis using data from all transects
(Section 3.1.5). However, in the absence of quantitative data, variabil-
ity in beach slope and long-term trends in slope and their influence on
SDS accuracy and trends remain unresolved. Therefore, advancing
environment-specific, reliable, remote sensing methods for monitoring
grain size and beach slope will further improve our confidence in SDS.

Geolocation accuracy of satellite images also represents a singular
source of error (O[10 m]) and one that varies spatially and temporally
around the globe (e.g., Choate et al., 2021). While geolocation accu-
racy represents an obvious area of improvement, ongoing research
suggests this may lead to minimal gain in SDS accuracy (<0.5 m)
(Mikkelsen et al., in review). Comparisons performed by Vos, Splinter,
et al. (2023) suggest that algorithmic performance is far more impor-
tant. It is clear that there remains an urgent need for a better under-
standing of the influence of uncertainties relating to computer vision,
shoreline extraction algorithms, environmental conditions, variability
in beach state and uncertainties inherent to satellite remote sensing
(e.g., image geolocation accuracy, sensor-specific characteristics and
biases, and atmospheric influence) in different environments (Castelle
et al., 2021; Konstantinou et al., 2023; Vitousek, Buscombe,
et al., 2023; Vos, Splinter, et al., 2023).

7 | CONCLUSIONS

Our findings represent a significant contribution to the understanding
of the decadal scale morphodynamics of gravel beach systems in the

UK&I and present a first nation-wide inventory of morphodynamic
behaviours across 45 different gravel beach systems in the
United Kingdom and Ireland. Results indicate significant variability in
long-term trends between sites (transects), with 60% of sites (58% of
transects) remaining stable or showing insignificant long-term trends
over the study period. The importance of local shoreline behaviour,
which can be critical from a coastal management perspective, is
highlighted by the presence of hotspots of shoreline retreat at sites
that show insignificant overall (beach-averaged) trends. By far, the
most dynamic systems were nesses and spits exhibiting the largest
shoreline changes at transect level (O[100]m) over the last four
decades (1984–2023). These systems were also associated with the
highest retreat and advance transect trends (−4.73 and 10.5 m/year,
respectively) and exhibited the highest within-site alongshore variabil-
ity. In contrast, constrained and unconstrained sites showed largely
insignificant long-term trends but displayed a range of spatially uni-
form and spatially variable morphodynamic behaviours, dominated by
rotation and propagation. Multiannual fluctuations in winter-average
atmospheric forcing were correlated with annual shoreline response
(R ≥ 0.4, *p* ≤ 0.05) at 16 sites (35.6%) gravel beach sites throughout
the UK&I. These findings provide novel insights into decadal shoreline
evolution at local to regional spatial scales, enhancing our understand-
ing of past shoreline behaviour and predictive capacity for future
changes, critical for effective coastal management and long-term
adaptation strategies.

AUTHOR CONTRIBUTIONS

**Aikaterini Konstantinou:** Conceptualisation; methodology; writing; provision of data; data analysis; review and editing. **Gerd Masselink:** Conceptualisation; methodology; review and editing. **Tim Scott:** Conceptualisation; methodology; review and editing. **Bruno Castelle:** Methodology; review and editing. **Tom Hamilton:** Data analysis; review and editing.

ACKNOWLEDGEMENTS

AK was supported by a PhD studentship awarded by CMAR with
additional support provided by the University of Plymouth. TH was
supported by a PhD studentship awarded by the University of Plym-
outh. GM and TS were supported by the UK Natural Environment
Research Council (Grant NE/Y50323X1; #gravelbeach project). CS
was founded by the Met Office, United Kingdom, and BC was
founded by Agence Nationale de la Recherche (ANR) Grant Numbers
ANR-21-CE01-0015 (Project SHORMOSAT) and ANR-22-EXIR-0004
(IRICOT/PEPR IRIMA). EOF-based climate indices are publicly avail-
able for the period 1980–2025 (National Oceanic and Atmospheric
Administration [NOAA] Climate Prediction Centre; www.cpc.ncep.
noaa.gov). The Western Europe Pressure Anomaly (WEPA) climate
index (1943–2023) developed by Castelle et al. (2017) was provided
by BC. Wave data are publicly available through the Copernicus
Marine and Service (https://marine.copernicus.eu/). The survey data
were collected by the South Coast Coastal Monitoring (SWCM) and
South East Coastal Monitoring (SECM) and are made available
through the National Network of Regional Coastal Monitoring
Programmes (www.coastalmonitoring.org).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.
## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are archived in zenodo (https://zenodo.org) and are openly available at https://doi.org/10.5281/zenodo.16884492.

## PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES

No material from other sources is reproduced in this manuscript.

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Konstantinou, A., Scott, T., Masselink, G., Stokes, C., Hamilton, T. & Castelle, B. (2025) Multi-decadal gravel beach dynamics in the United Kingdom and Ireland: Insights from satellite-derived shorelines. *Earth Surface Processes and Landforms*, 50(12), e70160. Available from: https://doi.org/10.1002/esp.70160