# Storm response of the upper intertidal zone on a gravel beach

E.T. Mendoza, Imen Turki, E. Ojeda, A. Soloy, E. Salameh, C. Lopez-Solano,

Julien Deloffre, N. Lecoq

► To cite this version:

E.T. Mendoza, Imen Turki, E. Ojeda, A. Soloy, E. Salameh, et al.. Storm response of the upper intertidal zone on a gravel beach. International Journal of Applied Earth Observation and Geoinformation, 2025, 144, pp.104945. 10.1016/j.jag.2025.104945. hal-05360997

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Storm response of the upper intertidal zone on a gravel beach☆

E.T. Mendoza<sup>a,b,*</sup>, I. Turki<sup>c</sup>, E. Ojeda<sup>c</sup>, A. Soloy<sup>c</sup>, E. Salameh<sup>c</sup>, C. Lopez-Solano<sup>c</sup>, J. Deloffre<sup>c</sup>, N. Lecoq<sup>c</sup>

ARTICLE INFO

Keywords:

Beach erosion

Remote sensing

Storm cluster

Beach recovery

Resilience

Macrotidal

A B S T R A C T

This paper investigates morphodynamics of a macrotidal gravel beach (Etretat, Normandy, NW France) during two consecutive winter seasons characterized by moderate (2018–2019) and exceptionally energetic (2019–2020) conditions according to a 42-year analysis of hindcasted wave data. Upper intertidal beach changes and shoreline data were derived from a video monitoring system. The results show that storm impacts were strongly influenced by the storm characteristics, the tide phasing, and more importantly, by the pre-existing conditions of the beach before the impact of the storms. A multiple regression including wave height and tide range during the storm event explained 46% of the variance of the volume change due to storm events. Storm clusters often cause beach erosion but can also contribute significantly to beach recovery.

# 1. Introduction

The study of storm impacts is essential in the understanding of beach dynamics. The effect of this impact together with the beach recovery (inter-storm or post-storm recovery) serve as key parameters in determining coastal resilience against changes in physical drivers from short scales, as storms, to large scales, as sea level rise and climate change effects.

The characteristics of wave storms, including their intensity and duration, can vary widely worldwide (Lobeto et al., 2024). Given this variability, studying how these storms evolve, and impact coastal environments is crucial to properly enhance science-based decision-making and conduct effective adaptation strategies.

Recent works have concentrated on studying the impact and the recovery of beaches in response to storm clustering along the coastlines of Northern Europe (Dodet et al., 2019), particularly in areas severely affected by the most extreme storm sequences observed in the past years (Castelle et al., 2015; Masselink et al., 2016b) with widespread impacts on regions such as the Atlantic France, the southwest coasts of England, or the southern North Sea (Angnuureng et al., 2017; Brooks et al., 2017; Burvingt and Castelle, 2023; Wadey et al., 2015). A storm cluster occurs when the interval between two consecutive storms is shorter than the time required for the beach to be recovered from individual storms. Its

impact depends on the specific storm conditions and sediment dynamics (Biausque and Senechal, 2019). The aforementioned works emphasized the urgent need for a deeper understanding of the role of the sequencing of storms, their effects on the beach morphology and the role of wave-tide interaction, including sandy and gravel beaches.

While the dynamics of sandy beaches have been the subject of numerous studies, those of gravel and mixed beaches are scarcer. Despite the lack of reliable global estimates of their total extent, coarse-grained beaches are known to form an important part of many temperate and high-latitude coastlines. Nevertheless, coarse-grained beaches play essential roles in coastal protection, provide important habitats, and support a range of human activities (Jennings and Shulmeister, 2002).

Their response to extreme storms has been examined through various works using different approaches, including in-situ monitoring and numerical modeling (Masselink et al., 2016a; McCarroll et al., 2019; Ruiz de Alegria-Arzaburu and Masselink, 2010). Satellite-derived shoreline data have revealed that these systems exhibit considerable variability over decadal timescales, highlighting the importance of interpreting short-term storm impacts within a longer-term context (Konstantinou et al., 2025). These studies have revealed a series of complexities associated with the internal characteristics of the system, particularly related to the distribution of gravel grain size and their segregation, which define the permeability of the system and its ability to dissipate wave

<footnote><sup>a</sup> Cerema REM, RHITME ResearchTeam, Margny Les Compiègne, France</footnote>

<footnote><sup>b</sup> Univ Caen Normandie, UNICAEN, CNRS, M2C UMR 6143 Caen, France</footnote>

<footnote><sup>c</sup> Univ Rouen Normandie, UNIROUEN, CNRS, M2C UMR 6143, 76000 Rouen, France</footnote>

<footnote>* This article is part of a special issue entitled: 'ESL & Coastal Hazards' published in International Journal of Applied Earth Observation and Geoinformation.</footnote>

<footnote>* Corresponding author at: Cerema, RHITME ResearchTeam, Margny Les Compiègne, France.</footnote>

<footnote>E-mail addresses: ernesto.mendoza-ponce@cerema.fr, ernesto.mendoza-ponce@unicaen.fr (E.T. Mendoza).</footnote>
energy through infiltration and exfiltration processes. These character-
istics undergo continuous changes during high-energy periods, making
it challenging to track the beach evolution, its recovery after storms, as
well as the resilience (Soloy et al., 2022b).

This research addresses the issue of the subsequent beach recovery in gravel systems after individual storms and clustered events with accu- rate and detailed data from comprehensive monitoring of beach volume, shoreline position, and forcing factors that significantly influence the evolution of the beach morphology. Although important parameters as sediment size, permeability, or sediment transport dynamics are not addressed, this work aims to examine fundamental processes that are transferable to similar coarse-grained systems worldwide, as the role of storm sequencing or the effect of the tide phase at the moment of the storm landfall. Specifically, the morphological response and recovery of an upper intertidal section of Etretat beach (on the Normandy coast, NW France) to single and clustered storm events during the winter seasons of 2018/2019 and 2019/2020 is quantified based on Digital Elevation Models (DEMs) derived from video camera data from August 2018 to September 2020.

## 2. Study site

Etretat is a macrotidal embayed gravel beach on the Normandy coast of northern France (Fig. 1). The approximately 1000-m-long pocket beach is intersected by four perpendicular groins and bounded by

limestone cliffs that supply the gravel stock and limit alongshore
transport (Soloy et al., 2024). In the southern sector, a bedrock platform
extends approximately 300 m along the shoreline and is exposed during
low tide (Fig. 1c).

The sediment is mainly gravel (2–10 cm, D50 = 60 mm), underlain
by a subtidal sandy substrate (D50 = 0.8 mm) that only appears on the
beach face after intense successive storms, driving a shift from a pure
gravel beach to a sand-dominated state (Soloy, 2022).

The tidal range in Etretat varies significantly, spanning from 3 m
during neap tides to 9 m during spring tides, creating a macro- to
megatidal environment (Soloy et al., 2021). Wave conditions are char-
acterized by an average significant wave height (Hs) of approximately 1
m and wave periods ranging from 5 to 7 s. According to (Lopez Solano
et al., 2024) high-energy waves predominate during winter (October
through March), whereas low-energy waves are typical during summer
months. The primary wave directions in this area are west and north-
east due to the geographical location and the morphology of the En-
glish Channel. The winters of 2018/2019 and 2019/2020 were marked
by several significant storms, including Storm Gareth, which struck in
March 2019, and Storm Ciara, which made landfall in February 2020.
Both storms caused extensive flooding and power outages in thousands
of homes across northern France.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (a) Location map of France with the Normandy coast highlighted; (b) Location of Etretat Beach within the Normandy region, showing the positions of the SCENES buoy and the ERA5 node; (c) Satellite image of Etretat Beach highlighting the area used for the intertidal volume assessment (yellow polygon), the location of the video survey station (yellow dot) and the beach profile transect (red line). (d) Oblique view of Etretat Beach showing the intertidal zone. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
# 3. Datasets

## 3.1. Hydrodynamic dataset

Wave conditions were characterized using ERA5 reanalysis, validated with in situ buoy measurements.

Waves (in situ) — SCENES buoy (49° 28.440'N, 0° 1.932'E) at the mouth of the Seine estuary; curated by DYNECO and operated by IFREMER since 2017. Source: https://dyneco.ifremer.

Waves (reanalysis) — ERA5 global reanalysis produced by ECMWF; data extracted from grid node for Etretat (49° 48.720'N, 0° 2.940'E) and the same position for the Seine estuary buoy (49° 28.440'N, 0° 1.932'E); widely used and validated (Dee et al., 2011; Hersbach et al., 2020). Source: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-data sets/era5.

Sea level (tide gauge) — Le Havre station (49° 28.914'N, 0° 6.359'E), curated within the REFMAR network (https://doi.org/10.171 83/REFMAR#4); data available via SHOM. Source: https://data.shom. fr.

## 3.2. Morphological dataset

Etretat beach is surveyed by a video monitoring system, which provides high-resolution coastal monitoring and consists of three cameras that capture 10-minute time-exposure images every hour during daylight hours with a sampling frequency of 2 Hz and a resolution of 3840 × 2160 pixels. The waterline positions, extracted from the images via the Mask R-CNN method at various moments, are correlated with the corresponding sea levels and used to derive DEMs of the intertidal zone. DEM acquisition is further explained in the following section (section 4.3) as well as in Soloy et al. (2021), where data validation is thoroughly presented.

# 4. Methods

## 4.1. Wave validation, storm wave identification and analysis

Wave data from 1979 to 2021 were obtained from the ERA5 reanalysis at the grid node closest to Etretat Beach, located at a depth of 35 m (49° 48.720'N, 0° 2.940'E). Because the accuracy of ERA5 at this site was not previously established and no in situ wave measurements were available at Etretat, validation of the reanalysis was carried out using the nearest available buoy. Specifically, ERA5 wave data were extracted at the grid point closest to the SCENES buoy in the Seine Bay and compared with the buoy's hourly observations recorded between October 2017 and October 2020. The performance of ERA5 was assessed using standard statistical metrics, including the coefficient of determination (R²), p-value, the root mean squared error (RMSE), and the mean absolute error (MAE). Following this validation, the ERA5 node closest to Etretat Beach was used to identify storm events during the winter seasons of 2018–2019 and 2019–2020.

Storm events were defined following the approach described by Mendoza et al. (2011), based on three criteria: a $H_s$ threshold, a minimum duration, and an independence interval between events. The wave height threshold was defined as the 95th percentile of $H_s$, a value often used to capture the most energetic conditions. The duration threshold was set at 24 h, under the assumption that storms exceeding one day are more likely to generate a measurable coastal response. To distinguish separate events, an independence criterion of 12 h was applied, preventing consecutive peaks from being counted as a single storm. The identified storms were characterized by the maximum significant wave height ($H_s, \max$), the mean wave period ($T_p$), the mean wave direction (degrees relative to north) and the energy content, calculated by the cumulated energy $E_{\text{storm}}$, which corresponds to the integral of the Energy Flux ($E_{\text{flux}}$) defined as:

$$E_{\text{flux}}(t) = \frac{\rho g^2}{64\pi} H_s^2 T$$

$$E_{\text{storm}} = \int_{\text{stormstart}}^{\text{stormend}} E_{\text{flux}}(t) dt$$

where $\rho$ is the sea water density (1025 kg/m³), $g$ is the acceleration of gravity (9.81 m/s²), $H_s$ is the significant wave height in meters, and $T$ is the wave period in seconds.

Storm thresholds and the storm identification was done for a 42-year period to be able to contextualize the two studied winter periods: 2018/ 2019 and 2019/2020. From August 2018 to October 2020 individual storms were identified and analyzed to determine whether successive storms formed clusters. A storm cluster was defined as two or more storms occurring in succession, with calm periods of less than 5 days between events (Biausque and Senechal, 2019).

## 4.2. Tide ranges observed during storms

Tide-gauge data were analyzed together with the wave parameters to determine at which moment of the tidal cycle the storm occurred. This comparison provides context for the observed morphological changes, as the tidal level at the time of maximum wave energy influences the reach of wave action on the beach profile.

In this study, the term tide refers to the tide recorded by the Le Havre gauge, located in a protected site in Le Havre harbor. No direct measurements of tidal currents were available for Etretat; therefore, the analysis focuses on the interaction between wave forcing and tidal water levels.

## 4.3. Beach morphology assessment

Changes in the upper intertidal beach topography and volume were assessed using DEMs. Each DEM was generated from a series of 15-minute long-exposure images collected continuously over a full semi-diurnal tidal cycle (~12.5 h), yielding up to 50 waterlines per cycle in optimal conditions. The DEMs were interpolated on a 1 m × 1 m grid. The area covered by the DEMs varies with tidal conditions; to ensure comparability, the analysis was restricted to a continuously visible fixed polygon (182 m × 24 m) in the upper intertidal beach, which extends 23 m from the landward limit of the beach, as shown by the yellow polygon in Fig. 1.

Volume changes in the intertidal topography were analyzed, with particular emphasis on the periods before and after storms. To calculate the upper intertidal beach volume within this area, DEMs were georeferenced using the RGF93 / Lambert-93 projection for horizontal positioning, and elevations were referred to the French national vertical datum (IGN69 / NGF-IGN). In this system, the z = 0 m isoline corresponds to the regional hydrographic zero. This reference surface within the fixed polygonal area, and changes between surveys were quantified by differencing successive DEM-derived volumes.

The accuracy of the DEMs used in this study has been previously validated by comparison with differential Global Navigation Satellite System measurements, yielding a vertical RMSE of 0.22 m at Etretat ($R^2$ = 0.99). Sensitivity tests further showed no significant increase in error with distance from the camera (Soloy et al., 2021).

To analyze shoreline position changes, the z = 0 m isoline was considered as the shoreline position.

# 5. Results

## 5.1. Storm wave characterization

### 5.1.1. Wave validation

The comparison between the observed and modeled wave data in the
Seine Bay showed a moderately strong and statistically significant correlation ($R^2 = 0.80$, p-value < 0.001) with an RMSE of 0.24 m, and a MAE of 0.15 m, meaning the typical deviation from actual values is small. The negligible bias further indicates that the model does a good job at predicting general wave conditions but, extreme wave values are underestimated, highlighting a limitation during storms, but still demonstrating the reliability of using modeled wave data for these types of studies (Lopez Solano et al., 2022; Lopez Solano et al., 2024).

### 5.1.2. Storm wave identification

A long-term storminess assessment based on 42 years of wave data (1979–2021) was conducted to properly define storm threshold parameters and to contextualize the two winter seasons analyzed in this study in terms of total storm energy per season and the number of storms (Fig. 2). The results reveal a relatively strong correlation between the number of storms and their total energy content during the first two decades (1979–1999). However, this relationship appears to shift after 2000, with winters characterized by a higher number of storms, sometimes exhibiting lower total energy than seasons with fewer but more intense events. This shift is accompanied by a slight decrease in the number of events per winter. A Wilcoxon rank-sum test confirms that the distributions of storm counts differ significantly (p < 0.05) between the first and second halves of the time series. In contrast, no significant long-term trend is found in total storm energy.

Within this framework, the 2018/2019 and 2019/2020 winters exhibited contrasting storm regimes. The 2018/2019 winter was relatively moderate, with 7 storms and a total storm energy of $2.5 \times 10^6$ W/m, values close to the long-term mean. In contrast, the 2019/2020 season was exceptionally energetic, producing 13 storms and nearly double the storm energy ($4.4 \times 10^6$ W/m), the second highest in the 42-year record.

### 5.1.3. Storms winter 2018/2019 and 2019/2020

Etretat node wave heights reveal a distinct pattern of wave seasonality from October 2018 to October 2020 (Fig. 3). During the winter months, wave conditions exhibit higher heights ($H_s > 1.5$ m) with periods of approximately six seconds (not shown). These conditions

typically persist from October to March. On the other hand, during the summer months (April to October), wave heights are lower ($H_s < 1$ m), and the periods are slightly shorter (5 s).

The storm events identified over the study period are summarized in Table 1. For this specific study, all the extreme events but one occurred between December and March.

When contrasting the most powerful storms of each winter, the 2018/2019 season featured a storm named Gareth (Storm 7) with a maximum significant wave height ($H_{s,max}$) of 4.5 m, lasting for 4.5 days (108 hrs) and having an energy content of $7.6 \times 10^5$ W/m. In contrast, the 2019/2020 storm named Ciara (Storm 13) reached a $H_{s,max}$ of 6.1 m, with a shorter duration of 3 days (74 hrs), and yet exhibited a higher energy content ($10 \times 10^5$ W/m). In terms of storm clustering (depicted in blue shaded areas in Fig. 3 and detailed in Table 1 –in bold–), the 2018/2019 winter exhibited a single 2-storm cluster (from now on, Storm Cluster 1), comprising Storms 6 and 7. The 2019/2020 winter exhibited three storm clusters: Storm Cluster 2 (formed by Storms 8 and 9), Storm Cluster 3 (formed by Storms 13 to 17), and Storm Cluster 4 (formed by Storms 18 and 19).

### 5.2. Coastal response and beach morphological changes

A total of 588 shoreline positions and 77 DEMs were considered for the study (from August 2018 to September 2020), corresponding to an average of 3 DEMs per month (more frequent during winter season, aiming to have at least a pre- and post-storm evaluation of beach volume).

Examining the volume change, a similar pattern emerges at the beginning of both winters (Fig. 4a). The impact of the firsts storms of each season in a relaxed system (i.e., with a profile adapted to the summer moderate wave conditions), caused large volume losses. The upper intertidal volume losses attained during the firsts events of each season were comparable to the volume losses caused by the most energetic storms of each season (Storm Gareth –Storm Cluster 1, and Storm Ciara –Storm Event 13).

The main differences can be seen at the end of both winter seasons. During the 2018–2019 winter season, no storms were recorded after

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Total energy content of the winter storms and the number of storms per winter season. The red shaded area corresponds to the 2018/2019 and the 2019/2020 studied period. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Significant wave height data from September 2018 to September 2020. The dots correspond to identified storms also marked with dashed vertical lines. The blue semi-transparent areas correspond to the four identified Storm Clusters. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<caption>**Table 1**</caption>

Storm identification (Id), initial date of each extreme event (Date), and the main storm characteristics: maximum significant wave height ($H_{s,max}$), peak period ($T_p$), wave direction (Dir), duration of the storm event (Dur) and energy content (E). **The bold horizontal line separates the two winter periods.** Bolded events indicate that they are part of a storm cluster.

<table><thead><tr><th>Id</th><th>Date</th><th>H<sub>s,max</sub> (m)</th><th>T<sub>p</sub> (s)</th><th>Dir (°)</th><th>Dur (h)</th><th>E (W/m) (x10<sup>5</sup>)</th></tr></thead><tbody><tr><td>1</td><td>2018/12/7</td><td>5.1</td><td>7.8</td><td>271</td><td>55</td><td>6.29</td></tr><tr><td>2</td><td>2018/12/21</td><td>3.7</td><td>6.9</td><td>259</td><td>25</td><td>1.57</td></tr><tr><td>3</td><td>2019/1/27</td><td>4.1</td><td>7</td><td>284</td><td>26</td><td>2.00</td></tr><tr><td>4</td><td>2019/2/8</td><td>3.9</td><td>7.2</td><td>245</td><td>28</td><td>2.15</td></tr><tr><td>5</td><td>2019/3/3</td><td>3.7</td><td>7.1</td><td>250</td><td>37</td><td>2.13</td></tr><tr><td><b>6</b></td><td>2019/3/10</td><td><b>5.3</b></td><td><b>7.5</b></td><td><b>275</b></td><td><b>31</b></td><td><b>3.28</b></td></tr><tr><td><b>7</b></td><td>2019/3/12</td><td><b>4.5</b></td><td><b>7.1</b></td><td><b>262</b></td><td><b>108</b></td><td><b>7.63</b></td></tr><tr><td><b>8</b></td><td>2019/12/8</td><td><b>5.4</b></td><td><b>7.5</b></td><td><b>273</b></td><td><b>34</b></td><td><b>3.55</b></td></tr><tr><td><b>9</b></td><td>2019/12/13</td><td><b>4.8</b></td><td><b>7.7</b></td><td><b>264</b></td><td><b>63</b></td><td><b>5.77</b></td></tr><tr><td>10</td><td>2019/12/22</td><td>3.6</td><td>7.7</td><td>269</td><td>29</td><td>1.80</td></tr><tr><td>11</td><td>2020/1/13</td><td>4.4</td><td>7.2</td><td>225</td><td>37</td><td>2.81</td></tr><tr><td>12</td><td>2020/1/28</td><td>3.9</td><td>7.3</td><td>269</td><td>27</td><td>2.01</td></tr><tr><td><b>13</b></td><td>2020/2/9</td><td><b>6.1</b></td><td><b>8.1</b></td><td><b>260</b></td><td><b>74</b></td><td><b>9.90</b></td></tr><tr><td><b>14</b></td><td>2020/2/15</td><td><b>4.7</b></td><td><b>7.6</b></td><td><b>245</b></td><td><b>63</b></td><td><b>5.43</b></td></tr><tr><td><b>15</b></td><td>2020/2/22</td><td><b>4.1</b></td><td><b>7</b></td><td><b>255</b></td><td><b>35</b></td><td><b>2.25</b></td></tr><tr><td><b>16</b></td><td>2020/2/25</td><td><b>3.9</b></td><td><b>7.2</b></td><td><b>279</b></td><td><b>25</b></td><td><b>1.88</b></td></tr><tr><td><b>17</b></td><td>2020/2/29</td><td><b>3.9</b></td><td><b>7.3</b></td><td><b>250</b></td><td><b>27</b></td><td><b>2.15</b></td></tr><tr><td><b>18</b></td><td>2020/3/10</td><td><b>3</b></td><td><b>6.8</b></td><td><b>256</b></td><td><b>29</b></td><td><b>1.59</b></td></tr><tr><td><b>19</b></td><td>2020/3/12</td><td><b>3.6</b></td><td><b>7.2</b></td><td><b>265</b></td><td><b>30</b></td><td><b>1.97</b></td></tr><tr><td>20</td><td>2020/3/28</td><td>3.7</td><td>6.5</td><td>34</td><td>34</td><td>2.42</td></tr></tbody></table>

Storm 7 (the most intense storm of that season in terms of energy content). Although there were some fluctuations in beach volume, after the month of July an overall erosion trend persisted, preventing the beach volume from fully recovering before the start of the following winter season. In contrast, the 2019–2020 winter season ended with a series of less energetic storms following the impact of Storm 13 (the most intense storm of the study period in terms of energy content). These conditions facilitated a gradual recovery of the beach volume, which reached the

initial level of the study by July 2020 (Fig. 4a).

Fig. 4b illustrates the variations in the shoreline position. The alternating warm (red-yellow) and cold (blue-green) colors represent periods of beach advancement and retreat, respectively. The rapid retreat of the entire shoreline position associated with certain storm events is clearly discernible, for instance, at the beginning of each winter period or as a response to event 6 or 7. However, the most clear patterns are the almost continuous presence along time of an advanced part of the beach in the northern side of the beach, this is associated to the presence of a dike; and the extreme effect of the 2019/2020 winter season, which lead to a retreated beach that persisted the entire season.

The two distinct winter periods are hereafter analyzed separately to gain a more precise understanding of the prevailing conditions. By isolating each period, we can better assess the impact of successive storm events and their interaction with tidal conditions—both of which play a critical role in shaping the beach’s morphological evolution. This focused analysis allows for a clearer distinction of short-term changes driven by storm activity versus longer-term seasonal trends.

### 5.2.1. Beach morphology assessment winter 2018–2019

We defined the September 26, 2018 measurement as the initial reference volume (stable conditions) and hypothesize a seasonal trajectory: decline through winter, recovery thereafter. At this time, the intertidal beach volume was estimated at 20 000 m³ (indicated by the left vertical red solid line in Fig. 5a) and by the corresponding initial beach profile shown by the red solid line in Fig. 5d). This baseline served as the reference for evaluating subsequent changes, allowing to determine whether the beach underwent net erosion or accretion over the study period. During the 2018/2019 winter season, Storm 1 impacted during a spring tide, causing a marked decrease in the beach volume of approximately –4 700 m³, affecting mostly the southern side of the beach (Fig. 4b, Fig. 5b) the beach profile indicates overall erosion and flattening, with the upper part of the beach lowered by about 1.5 m (Fig. 5d). As the season progressed, Storms 2 through 5 caused slight volume fluctuations.
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** (a) Wave height, tide level and beach volume data from October 2018 to October 2020; (b) Time-space diagram of the shoreline position related to an arbitrary reference line. The y-axis refers to the beach alongshore location; warm colors are related to advances in the shoreline location and cold colors to retreats (color bar values in meters). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

In a strict sense of wave storm identification, Storms 6 and 7 were classified as two events because they were separated by 13 h, slightly exceeding the conventional 12-hour threshold. However, from a meteorological perspective, both events were associated with the same storm system, named Gareth. When considered as a storm cluster, Gareth encompasses both events 6 and 7. The system resulted in the loss of -3 000 m³ of sediment during the initial hours of the storm (Event 6), coinciding with a spring tide. During Event 7, which was much more energetic, no additional reduction in the intertidal beach volume was detected compared with Event 6. This can be explained by the fact that the peak of Storm 7 impacted during low tide, limiting wave action on the upper foreshore, and that the beach profile had already been substantially modified by Storm 6, resulting in a more dissipative state. Nevertheless, by the end of the storm cluster, the final profile displayed the greatest erosion of the sequence, particularly in the upper intertidal zone, accompanied by the deposition in the lower area (Fig. 5d). As illustrated in Fig. 5a, after mid-March, the wave conditions remained subdued, and no further storms were identified.

Considering the initial and final volume during the winter period (red lines in Fig. 5a), the overall outcome was a net loss of volume (-3 400 m³), with a net predominant cross-shore movement of sediment from the upper to the lower part of the intertidal beach (Fig. 5c and 5d).

After these events, moderate energy conditions induced the gradual recovery of the beach volume in the following months until mid-July. As the summer season progressed, the volume fluctuations showed a slight decreasing trend that reached approximately 15 000 m³ by the beginning of the 2019–2020 winter season.

### 5.2.2. Beach morphology assessment winter 2019–2020

The winter 2019–2020 season commenced with a volume of 15 000 m³ as a not fully recovered beach compared with the initial state in September 26, 2018. The impact of Storm Cluster 2 (the first storms of the season) coincided with a spring tide that caused a steep loss of beach volume of approximately 4 400 m³ (Fig. 6a). The data available pre and post Storm 10 do not allow us to thoroughly discern the effect of this event; however, it can be concluded from the data that Storm 10 or the

posterior hydrodynamic conditions were responsible for an increase in the intertidal beach volume.

The volume showed an analogous response to Storms 11 and 12, with mild decreases in the upper intertidal beach volume followed by a recovery in the posterior days. Storm Cluster 3 began in February and consisted of 5 storm events (Storms 13 through 17) that resulted in 20 days of high-energy wave conditions. Storm 13 is the most energetic storm occurring during the study period, an extratropical storm named Ciara by the Met Office, with the highest $H_s$ and energy content of both winter seasons. It made landfall during a spring tide, causing a loss of 3 400 m³ predominantly in the upper central region of the beach and change of the elevation in the upper part of the foreshore of -1 m. Following Ciara, exposure of the sandy substrate was visible at the upper intertidal beach within the monitored area; however, DEM differencing indicates that this sand behaved as an essentially immobile basal layer during the subsequent, moderate-energy events, so the observed volume change primarily reflects gravel redistribution (Fig. 6b and 6e) and reaching the lowest beach volume observed during the study.

In contrast, the subsequent storms (14 through 17) produced a continuous augmentation of the intertidal volume, recovering the beach profile from pre-Ciara storm conditions (Fig. 6c, 6e). Storm Cluster 4 (comprising Storms 18 and 19) impacted the beach during spring tide. The addition of the effect of the two events forming the cluster resulted in severe erosion; the profile recovered before the mild loss caused by Storm 20.

Considering the initial and final volumes over the winter period (Fig. 6a), there was a net volume loss of -4 200 m³. Contrasting with the previous winter season outcome (Fig. 5c), the erosion was not constant in the alongshore direction. The southern section of the beach shows sediment accumulation in the upper intertidal, while the erosion was predominant in the northern section (Fig. 6d).

After the winter period, the beach consistently accreted until reaching a peak in volume of 19 800 m³ was observed in mid-June, followed by a series of volume fluctuations within the vicinity of 20 000 m³ (Fig. 4a).
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** (a) Wave height, tide, and intertidal beach volume data from September 2018 to April 2019. Storm event initial and final dates are marked by vertical lines; (b) Difference in volume due to Storm 1 –blue solid-dashed lines in Fig. 5a (05–12-2018 to 10–12-2018); (c) Difference in volume between the initial and final control dates of the winter season (26–09-2018 and 17–03-2019) – red solid and dashed lines in Fig. 5a-; (d) beach profiles with the corresponding colors in Fig. 5a. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** (a) Wave height, tide and intertidal beach volume data from December 2019 to April 2020. Storm event initial and final dates are marked by vertical green and blue lines; (b) Volume change before (08-02-2020) and after (14-02-2020) Storm 13 -blue solid and dotted lines-; (c) Volume change corresponding to storms 14-17 (14 February 2018 - 13 June 2020), indicated by the vertical blue dash-dotted line and the light green line.; (d) The difference in volume between the initial and final control dates of the winter season (12-01-2019 and 15-04-2020) -red solid and dashed lines in 6a-; (e) Beach profiles with the corresponding colors in Fig. 6a. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
# 6. Discussion

## 6.1. Storm wave characterization

The comparison with ERA5 wave data confirms that extreme values in the Seine Bay are underestimated, consistent with global assessments of hindcast model data (Fanti et al., 2023; Lobeto et al., 2024; Morim et al., 2023). Nevertheless, the dataset remains sufficiently reliable for identifying storm events in the study area (Lopez Solano et al., 2024).

The long-term storminess assessment based on 42 years reveals a slight decrease in the number of events per winter and no significant long-term trend were found in total storm energy. This pattern does not align with broader climatic projections that anticipate a decrease in the frequency of extratropical storm events while increasing the energy content of the less frequent storms (IPCC, 2023; Cheung and Chu, 2023). However, these results could be related to the underestimation of extremes in the ERA5 dataset, which smooths the time series and makes trends harder to detect.

The long-term patterns provide essential context for interpreting the storm conditions observed during the two winters analyzed in this study. Within this broader framework, the 2018/2019 and 2019/2020 winters exhibited contrasting storm regimes. The 2018/2019 winter was relatively moderate, in contrast with the 2019/2020 season which was exceptionally energetic. These differences underscore the high interannual variability of storm activity and highlight how shifts in storm frequency and intensity influence coastal dynamics in the region (Turki et al., 2019).

## 6.2. Coastal response and beach morphological changes

The data interpretation is based on upper intertidal volumes and shoreline positions, exchanges with the lower intertidal beach lie below the observation limit. However, the reported volumetric changes are useful information for research, beach users and coastal mangers. Furthermore, the study sector experiences the same wave-tide regime and storm sequencing as the rest of the beach and the mechanisms identified (storm-driven erosion, phases of limited recovery immediately after major events, and enhanced recovery as storm energy decays) can be used as a proxy to the broader gravel beach context taking into account that, especially in the limits of the embayment, other factors such as beach rotation will influence the dynamics.

Significant changes in the upper intertidal beach volume occur as a rapid response to storm events, with cross-shore adjustments dominating beach profile evolution. Previous studies at Etretat using Empirical Orthogonal Function (EOF) analysis of observed shoreline position (Soloy et al., 2022b) have shown that beach evolution is mainly governed by cross-shore movement, which explains more than 60 % of the variability, while alongshore components account for only 6.2 %, often manifesting as beach-planform rotation at the scale of the entire beach or within smaller cells bounded by groins. The remaining variability was associated with more complex mechanisms involving both cross-shore and alongshore motions combined with elevation changes related to sea-level variability.

The analysis of coastal storms over two consecutive winter seasons revealed a notable contrast between seasonal patterns of wave-energy content and the subsequent beach response. The largest volume changes were observed when high-energy storm conditions coincided with high spring tides, highlighting the significant role of the wave regime on macrotidal beaches (Almar et al., 2009; Coco et al., 2014; Masselink et al., 2010; Ruiz de Alegria-Arzaburu and Masselink, 2010). For instance, Storms 6 and 8, with similar wave conditions (see $H_{s,max}$, Tp, Dir, Duration, and Energy content in Table 1), caused a volume loss of 3 000 m³ under a 6.0 m observed tide range occurring during Storm 6, whereas Storm 8, with an observed tide range of 4.7 m, caused minimal losses despite being the first storm of the season—a period typically associated with enhanced erosion.

The analysis of 18 storm events with complete morphological and hydrodynamic data shows that beach response to storm events is strongly influenced by the combined effects of wave energy and tidal conditions (Storms 10, 17, and 21 were excluded due to missing DEMs, preventing accurate assessment of the morphological changes). Simple regression indicates that larger waves and wider tide ranges during storms individually tend to increase sediment loss (Volume change = 4295 – 1189 × Hs, with $R^2$ = 0.26, p-value = 0.031 in the case of significant wave height and Volume change = 4181 – 854 × TR, with $R^2$ = 0.16, p-value = 0.096 in the case of the tide range occurring during the storm). However, a multiple linear regression including both parameters explained 46 % of the variance in volume change ($R^2$ = 0.46, p < 0.01), with both wave height ($\beta$ = –1274) and tide range during the storm ($\beta$ = –948) contributing negatively, indicating that larger waves and wider tide ranges during the event jointly amplify sediment loss (Volume change = 10 250 – 1274 × Hs – 948 × TR, with $R^2$ = 0.46, p-value = 0.0098, with both predictors significant: p-value = 0.012 for waves and p-value = 0.032 for tides).

Storm-driven morphological change is influenced not only by tide and storm waves but also by the pre-existing coastal morphology. In Storm Cluster 1 (Storms 6 and 7), Storm 6 coincided with a higher tide range and caused significant erosion. In contrast, Storm 7 (despite approximately twice the energy content) occurred at lower tide range during the landfall and produced only a limited additional change. However, in this case, the muted response was most likely due to the previous morphological condition of the beach after Storm 6, rather than by the tide phase. Prior to Storm 7, the beach had been strongly impacted by Storm 6 (13 h earlier), which flattened the profile; consequently, Storm 7 produced no major volume change. An analogous behavior was observed for Storm 13, when volume loss was not correlated with wave-energy content due to considerable antecedent erosion earlier in the season. These findings are consistent with previous studies highlighting the critical role of morphological history in shaping coastal response (Aagaard et al., 2005; Biausque and Senechal, 2019; Ruiz de Alegria-Arzaburu and Masselink, 2010).

The recovery of the beach is also dependent on the pre-existing coastal morphology. Following Storm 13, gravel depletion in the upper intertidal zone exposed the underlying sandy substrate. This study does not measure this type of changes although they play an important role due to their influence in beach erosion and recovery. The high permeability of gravel promotes recovery by enhancing swash infiltration and reducing backwash-driven export. When the sandy substrate is present, recovery is slower: gravel must first return to the upper beach to re-armour the profile and restore the permeable surface.

In high-energy environments, storm clusters strongly influence beach response by amplifying the cumulative impacts of erosion. Following storm erosion, sandy beaches typically rebuild a protective sand buffer during calmer periods, thereby enhancing resilience (Toimil et al., 2023). This recovery is progressive and dependent on the availability of sufficient wave energy (Phillips et al., 2017). Analogously to sandy coasts, gravel beach recovery after energetic events occurs during lower-energy conditions. The main difference is that the recovery of gravel beaches is typically faster than that of sandy beaches. This recovery is expected to be influenced by a combination of factors, including changes in beach slope and sediment redistribution across the profile (Scott et al., 2016). In this work, storm clusters also contributed to recovery: following the most energetic event (Storm 13), subsequent lower-energy storms within the same cluster (Cluster 3) enhanced recovery at Etretat by promoting predominant onshore sediment transport along the beach profile.

# 7. Conclusions

This work investigates the upper intertidal dynamics of a macrotidal gravel beach on the northern coast of France in response to two successive winters (2018/2019 and 2019/2020), using video monitoring
system and hindcast data to provide, respectively, digital elevation models and the extreme physical drivers during storms. These data types are broadly available or replicable elsewhere, supporting transfer of this approach to other regions.

The 42-year ERA5 record shows a slight decrease in winter storm number and no clear trend in winter storm energy content. ERA5 data underestimates extreme waves, but remains adequate for identifying storm events. The bias likely smooths the data variability and might mask trends.

The first winter was relatively moderate, whereas the second was exceptionally energetic, ranking among the highest in storm count and energy during the last decades. Changes were rapid, and cross-shore-dominated. Impact magnitude depended on storm-tide phasing in this macrotidal setting; storms with similar offshore statistics produced very different outcomes under different tide ranges during landfall, indicating that storm energy alone is not a sufficient predictor.

The pre-storm conditions of the beach strongly influence its morphological response to individual or clustered storm events. Analysis of pre- and post-storm DEMs indicates that high waves and wide tide ranges during events jointly drive sediment losses, with a multiple regression explaining almost half of the observed variability in upper intertidal volume change during storm events. These findings highlight that storm-driven erosion is governed by both antecedent beach state and concurrent hydrodynamic forcing, emphasizing the importance of considering their combined effects when assessing short-term storm impacts.

Analogous to sandy coasts, gravel-beach recovery after energetic events occurs under lower-energy conditions; the key difference is timing as recovery on gravel beaches proceeds faster than on sandy. Storm clustering and antecedent morphology controlled both impact and recovery.

These results suggest four practical indicators that can be adopted on other gravel and mixed coasts using standard datasets: (1) wave conditions, (2) tide phasing during storm landfall, (3) shoreline or volume change (upper-intertidal volume change provides a conservative but diagnostic proxy), and (4) event sequencing/antecedent morphology.

These findings provide useful data for model calibration and scenario testing on gravel coasts, and they motivate future work that extends the analysis beyond a single site/sector to quantify variability across different macrotidal gravel beaches and structural settings.

## CRediT authorship contribution statement

**E.T. Mendoza:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. **I. Turki:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. **E. Ojeda:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. **A. Soloy:** Writing – review & editing, Methodology. **E. Salameh:** Writing – review & editing, Conceptualization. **C. Lopez-Solano:** Writing – review & editing. **J. Deloffre:** Writing – review & editing, Methodology. **N. Lecoq:** Writing – review & editing, Methodology, Investigation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This work has been supported by the International Space Science Institute (ISSI) in Bern, through the ISSI International Team project #23-598 CRESTE (Coastal Resilience using Satellites: CRESTE https://teams.isisbern.ch/creste/) and Clim'adapt project PRISME, Insitut Carnot

0146. The authors would like to thank Waves'n See company for their support in providing camera data, the Syndicat Mixte de la Seine Maritime (France) and the city services of Etretat as well as the Seine Maritime Departmental Council and the Normandy Region (France), for their continuous support of the beach monitoring and the scientific developments beneficial for Normandy coastal systems.

## Data availability

Data will be made available on request.

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