# Multi-annual evolution of coastal dunes: Transition from fixed to transgressive dunes state

Alexandre Nicolae Lerma, Olivier Burvingt, Bruno Castelle, Bruce Ayache, Nicolas Robin, David Rosebery, Julie Billy

► To cite this version:

Alexandre Nicolae Lerma, Olivier Burvingt, Bruno Castelle, Bruce Ayache, Nicolas Robin, et al. Multi-annual evolution of coastal dunes: Transition from fixed to transgressive dunes state. CATENA, 2026, 264, pp.109787. 10.1016/j.catena.2025.109787. hal-05493339

HAL Id: hal-05493339

https://brgm.hal.science/hal-05493339v1

Submitted on 4 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

# Multi-annual evolution of coastal dunes: Transition from fixed to transgressive dunes state

Alexandre Nicolae Lerma<sup>a, *</sup>, Olivier Burvingt<sup>a, b</sup>, Bruno Castelle<sup>b</sup>, Bruce Ayache<sup>a</sup>, Nicolas Robin<sup>c</sup>, David Rosebery<sup>d</sup>, Julie Billy<sup>a</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

- Coastal dune

- Sand sheets

- Dune migration

- Dune remobilization

- Airborne LiDAR

- Aquitaine coast

## ABSTRACT

Most of coastal dunes located in temperate latitudes, especially in the Northern Hemisphere, are relatively stable. However, along the Gironde coast (SW France) substantial dune remobilization has been observed over the last decade following major marine erosion events during the 2013–2014 winter. This study is based on the analysis of a robust dataset including (i) 10 high-resolution Digital Terrain Models (DTMs) derived from airborne LiDAR surveys conducted over a 12-year period (2011–2023) and (ii) 7 Satellite-derived Digital maps of dune vegetation cover derived from Sentinel-2 satellite images acquired between 2017 and 2023. These morphological and biological parameters are linked to forcing parameters derived from observed wind data, to provide a comprehensive analysis of coastal dune changes related to the transition from vegetation-fixed dunes to the development of transgressive dunes. For the first time, morphological and vegetation dynamics are explored over a large spatial scale (tens of km), covering a range of initial dune morphology and sediment supply.

Dunes have transitioned from stable to transgressive states primarily driven by sediment stoss slope recycling process (cannibalism) across a gradient of alongshore variable dune sediment budget, ranging from slightly negative to notably positive (+10 to 15 m³/m/yr), Along this coast, transgressive dunes defined as dune migrating via similar stoss and lee slope migration rates, have tripled in number over the last 10 years (reaching ≈ 15 km or 17.3 % of the studied coast). At the center of the Gironde coast where dunes are heavily remobilised, the lee slope of the dune translates landward at a rate of several meters to more than 10 m/year. In the following years, dunes will probably continue to migrate and remobilise across a broader scale if no re-stabilization management plan is implemented.

## 1. Introduction

Coastal dunes are described as ‘ecosystemic’ landforms (Barbier et al., 2011), combining multiple valuable roles and services for coastal communities and biodiversity. They can play a key role in the conservation of specific plant species (Martínez and Psty, 2004; Delgado-Fernandez et al., 2019; Kim et al., 2019) and serve as a natural barrier against coastal erosion and flooding, (e.g., Sallenger Jr, 2000; Nordstrom et al., 2007; Hanley et al., 2014; Seabloom et al., 2013). They also can protect freshwater resources (Mereu et al., 2009; Colombani et al., 2016) and support recreational activities (Everard et al., 2010; Guerry et al., 2012). Historically, coastal dunes have been and remain managed

by human interventions and have substantial adaptation potential (Cooper and Jackson, 2021). Thus, they can be considered as real assets to reduce coastal risks associated with climate change (sea level rise, biodiversity loss, etc.) and to cope with increasing human pressure along coastal areas (De Freitas, 2025).

Coastal dunes are governed by complex non-linear biophysical interactions, involving many processes, with primary driving and controlling factors difficult to identify (Walker et al., 2017; Costas et al., 2024). From multiannual to multidecadal scales, sediment budgets, dune morphology and vegetation dynamics are interacting through eco-geomorphic feedback (Costas et al., 2024). These factors have been identified to control sediment mobility, internal dune structure and thus

<footnote><sup>a</sup> BRGM Direction Nouvelle-, Aquitaine, Pessac, France</footnote>

<footnote><sup>b</sup> Université Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, Pessac, France</footnote>

<footnote><sup>c</sup> Université de Perpignan, Laboratoire CEFREM, UMR 5110, Perpignan, France</footnote>

<footnote><sup>d</sup> ONF, Office National des Forêts, Bruges, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: a.nicolaelerma@brgm.fr (A. Nicolae Lerma).</footnote>
morphological evolution of coastal dunes (Carter, 1991; Hesp, 2002; Delgado-Fernandez and Davidson-Arnott, 2011; de Vries et al., 2012; Hesp, 2013; Schwarz et al., 2018; Ruessink et al., 2019; Hesp and Smyth, 2021; Bauer and Wakes, 2022; Robin et al., 2023; Smyth et al., 2023; Doyle et al., 2024; Lamy et al., 2024) and are usually strongly influenced by site-specific conditions (Burvingt and Castelle, 2023; Doyle et al., 2024; Davis et al., 2024).

In the Northern Hemisphere across temperate latitudes, most of the coastal dunes are considered stable due to vegetation colonization (Provoost et al., 2011; Moulton et al., 2020; Jackson et al., 2019a; Gao et al., 2020) and extensive large scale human stabilization actions (Nordstrom, 2000; Gómez-Pina et al., 2002; Clemmensen and Murray, 2006; Clarke and Rendell, 2009; Jackson and Nordstrom, 2011; Jackson and Nordstrom, 2013; Arens et al., 2013; Jackson et al., 2013; Bossard and Lerma, 2020). In opposition to fixed dunes, transgressive dunes or dunefield, are described as relatively large-scale and mobile coastal dunes (Gardner, 1955) migrating in response to the regional wind regime (Hesp, 2013; Jackson et al., 2019b) and influenced by the temporal variability of a wide range of secondary parameters, such as climate variability, vegetation colonization, extreme flooding or sediment supply (Mathew et al., 2010; González-Villanueva et al., 2013) da Silva and Hesp, 2013; Pickart and Hesp, 2019; Robin et al., 2023). Transgressive dune or dunefield were commonly observed along European coasts during the Little Ice Age (LIA) (Jackson et al., 2019b), but are nowadays restricted to localized stretches of coast, spanning hundreds of meters to few kilometers in length (e.g. Tastet and Pontee, 1998; Bailey and Bristow, 2004; Munoz-Perez et al., 2009; Navarro et al., 2011; Osswald et al., 2019; Robin et al., 2023). Under contemporary conditions, fixed dunes can evolve to transgressive dunes when an intense perturbation initiates or intensifies a positive feedback on morphodynamic processes (Schwarz et al., 2018; Bonte et al., 2021). The transition from fixed to mobile transgressive systems can be typically described in 3 stages: I. vegetated fixed dune; II. sand sheets development; III. transgressive dune or dunefield; potentially interrupted by new perturbations or vegetation recolonization (Hesp et al., 2022; Robin et al., 2023; DaSilva et al., 2024). Two preferential transgressive initiation mechanisms have been proposed (Jackson et al., 2019b; Hesp et al., 2022):

* a period of high onshore sediment supply leading to large wind-induced transfer from upper beach to the dune generating large sand sheets gradually resulting in the development of a dunefield.

* large marine erosion generating a dune scarp and subsequently wind-induced destabilization of the seaward scarp slope. The wind-induced erosion of the scarp slope generates sand sheets on the dune plateau by recycling the sediment of the preexisting dune (cannibalization) and leads to a transgressive dunefield.

However, as claimed by Hesp et al. (2022) even though many factors have been advocated in the literature to be responsible for dune transgression initiation (mostly explaining extensive dunefield initiation and development during the LIA, Jackson et al., 2019b), studies providing quantitative evidence of the primary factors that induce transgression initiation and intermediate stages are lacking. To date, continuous and adapted coastal monitoring dedicated to link marine erosion/accretion and aeolian-induced evolutions from fixed to transgressive dunes are scarce and limited to localized areas (hundreds of meters to a kilometer) (Mathew et al., 2010; Hesp et al., 2022; Robin et al., 2023; DaSilva et al., 2024).

Airborne light detection and ranging (LiDAR) topographic data are now commonly used to study beach-dune characteristics and morphological changes along hundreds of kilometers of coastline (e.g. Houser et al., 2015; Nicolae Lerma et al., 2019; Doyle et al., 2019; Bossard and Lerma, 2020; Itzkin et al., 2020). These studies investigated the spatial variability of dune response to and recovery from storms (Burvingt et al., 2017; Nicolae Lerma et al., 2019; Konstantinou et al., 2021), the

interaction between morphological changes and vegetation cover (Yousefi Lalimi et al., 2017; Doyle and Woodroffe, 2018), sand supply (Zarnetske et al., 2015) and the impact of dune management plans (Itzkin et al., 2020). However, to the best of our knowledge, none of these databases comprehensively analyzed the formation of extensive dune sand sheets and transgressive dunes, which most often relies on a planimetric analysis using aerial photography preventing a quantitative assessment of morphological changes and sediment budgets evolution. Yet, understanding the forcing, the controlling conditions and assessing the timescales required to develop coastal dune mobility at a large scale is critical for developing effective and adaptive management strategies. Such knowledge is crucial for guiding interventions, whether to promote remobilization where it has ecological or geomorphological benefits, or to constrain it where stabilization is necessary to meet conservation or hazard mitigation goals (Delgado-Fernandez et al., 2019).

The aim of this study is to provide a detailed and integrated analysis of the controlling factors, temporal scales, and geomorphological processes that cause the transition from fixed to transgressive dunes along 80 km of coast in southwest France (Gironde). By considering a wide range of dune morphological characteristics (width, height, slope etc.), varying amplitude of long-term shoreline erosion, sediment supply, and spatial/temporal changes in vegetation cover, this study aims, for the first time, to analyse the complex interactions and feedback mechanisms involved in the transition from fixed to transgressive dunes over large spatial and multi-annual temporal scales.

## 2. Study site

### 2.1. Aquitaine dunes

The sandy coast of Aquitaine (France) extends approximately 230 km from the Gironde estuary in the north to the Adour Estuary in the south. It can be further divided into two large sectors, the Gironde and the Landes coasts separated by the Arcachon lagoon inlet (Fig. 1). The coastal system shows substantial morphological alongshore variability mostly induced by the large alongshore gradient of sediment availability (Nicolae Lerma et al., 2022).

The dune system of the Gironde coast is part of the highest and largest coastal dunes in Europe, peaking at 30 m above Mean Sea Level (MSL) and extending several hundreds of meters landward (Bossard and Lerma, 2020). Since last glaciation, the combination of high sediment supply, sea level-variation, and sustained wind dynamics has led to the development of three different dune generations that extend up to 10 km inland (Froidefond and Prud'homme, R., 1991; Tastet and Pontee, 1998; Bertran et al., 2020). The ones that are further inland are the oldest and can be described as parabolic dunes. They naturally developed and migrated from 1200 to 500 BP. A second generation is composed of barchanoid dunes. They developed from 1500 to the beginning of the 19th century. Both generations represent respectively 25,000 and 85,000 ha of dunes and attest that the dunes have a history of being particularly mobile on the Aquitaine coast (Clarke et al., 2002). The last generation of dunes is composed of large reshaped foredunes mostly designed by large human management interventions alongside natural processes since the mid-19th century (Bossard and Lerma, 2020; Robin et al., 2021). Since the mechanical reshaping of the dunes in the 70s and 80s and extensive marram grass planting to constitute a relatively fixed and continuous dune belts of 250 to 350 m width along the coast, the dominant management approach that has prevailed has been to limit dune mobility mainly using flexible eco-dynamic methods (i.e. branch cover, fencing, planting). Because of the chronic marine erosion affecting the Gironde coast, the dune belt has become considerably narrower especially at the northern half part of the coast where the dune shape has been substantially modified (Bossard and Lerma, 2020). Today, three typical dune shapes can be observed from the North to the South of the Gironde coast (i) narrow dunes 40 to 100 m wide and 25 to 35 m high (Fig. 1 a, b.); (ii) hummocky dunes (Hesp, 2002), 100 to 200
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Study site location map and photographs of the dune belt along the Gironde coast (the dashed rectangle delimits the 80 km long study site). Photos a. c. e. illustrate the variability of the dune morphology along the coast (photos OCNA, April 2020). The right panel illustrates variable morphology/vegetation cover at dunes with b., a recent transgressive unvegetated dune, d., hummocky sparsely vegetated dune and f., a densely vegetated dune (photos OCNA, April 2024).</figcaption></figure>

m wide and 20 to 25 m high (Fig. 1 c. d.); (iii) 200 to 350 m wide and 20 to 25 m high, mostly alongshore-uniform dunes where the current morphology is largely inherited from the reshaped dune profile created in the 70s–80s (Fig. 1 e., f.). The foredune is typically covered by species such as *Elymus farctus*, *Euphorbia paralias*, *Ammophila arenaria*, and *Galium arenarium*. Along the southern half of the study site and the wider dunes, the dominant species at the back part of the dune, are *Helichrysum stoechas* and *Artemisia campestris* (Laporte-Fauret et al., 2020). This configuration is a legacy of stabilization efforts dating back 40 to 50 years (Fig. 1 f.), where plant communities diversified from initial *Ammophila arenaria* extensive planting (Bossard and Lerma, 2020; Robin et al., 2021).

## 2.2. Shoreline evolution trend

Several studies have analyzed the shoreline position change (here the dune foot location usually corresponding to the 6 m/NGF isocontour) along the Gironde coast from century to storm event scale (Castelle et al., 2018; Nicolae Lerma et al., 2019; Nicolae Lerma et al., 2022). Since the mid of the 20th century, aerial photography processing has showed the dramatic alongshore variability in long-term shoreline trend between the northern part of the coast with erosion locally peaking at approximately 5 m/year, and the southern part of the coast with nearly stable shoreline position during the last century (excluding the vicinity of the Arcachon lagoon pass). At decadal timescale, the 2013–2014 winter causes widespread severe dune erosion (more than 20 m on average, locally peaking at 50 m). In 2024, the shoreline position and sediment budget have not fully recovered at the northern part of the study area whereas they have fully recovered at the southern part in

comparison to their pre-2013-2014 winter position and volumes (Nicolae Lerma et al., 2022).

Please note that two sectors of the Gironde coast were discarded from the present analysis due to the complex hydrodynamic conditions related to the Gironde estuary and the inherited geology at the northern tip of the coast (Vandenhove et al., 2025)., as well as the Arcachon inlet dynamics at the south (Nahon et al., 2019). The complex bathymetry and changes in coastline orientation result in specific coastal dune morphology and evolution (Bossard and Lerma, 2020).

## 2.3. Hydrodynamics and winds

The Gironde coast is a meso to macrotidal environment exposed to an energetic, strongly seasonally modulated wave climate (Castelle et al., 2017). The prevailing west-northwest wave direction drives a net southerly longshore drift along most of the coast (Idier et al., 2013). Wind orientation and intensity are relatively homogenous along the Gironde coast. Winter winds mainly come from the west, while dominant spring and summer winds (from April to September) blow primarily from the northwest and north. Relative to the average median grain sand size ranging between 0.2 and 0.4 mm (Pedreros et al., 1996), larger fetch length that favor sand accumulation at the dune toe, correspond to winds which mostly come from the north or south and contribute to incipient dune development (Nicolae Lerma et al., 2019).
# 3. Data and methods

## 3.1. Wind forcing data

The 13-years study period is characterized by large variability in wave and wind conditions, with relatively calm winters such as 2011–2012, 2016–2017 or 2022–2023. On the other hand, the period also includes the winter of 2013–2014, which was the most energetic period since 1948 for most of Europe's Atlantic coast (Masselink et al., 2016), and the winter of 2019–2020, which was the second most energetic winter in terms of potential wind flow since 1980 (Nicolae and Rosebery, 2021).

Continuous 10-m hourly wind data measured at the weather station Cap Ferret (Météo France, Fig. 2) were processed to estimate the potential wind-driven sand flux Q. Sand fluxes were calculated using the commonly used formulation by Lettau and Lettau (1978) assuming that there is no influence of other factors (e.g. vegetation, rainfall, moisture, topography). Q was estimated following the methodological framework detailed in Laporte-Fauret et al. (2021). In the following, Q is presented as cumulated values at seasonal scale: winter (October to April), summer (May to September), see Fig. 2. For comparison with morphological evolutions, Q was integrated over the time between the two dates of consecutive topographic surveys.

## 3.2. Topographic data

Multi-annual airborne LiDAR data from the Observatoire de la Côte de Nouvelle-Aquitaine (OCNA), in collaboration with the French National Institute of Geography (IGN) before 2018, were acquired during several surveys since 2011. Data were acquired in spring 2011, during fall 2014, and annually at the same period since 2016. LiDAR Digital Terrain Models (DTMs of 1 m gridded spatial resolution) were systematically compared with over a thousand of fixed ground control points to assess vertical errors (Nicolae Lerma et al., 2019). A summary of the LiDAR campaigns details and associated vertical errors that were shown to be less than 0.2 m for all campaigns is presented in Table 1.

## 3.3. Dune vegetation cover

Digital maps of dune vegetation cover have been derived from Sentinel-2 satellite images acquired from 2017 (first available campaign along the Gironde coast) to 2023. A total of 10 images were used for this analysis, and were acquired from June to October, which corresponds to summer season and allows to disregard seasonal variability. The methodology proposed by van Kuik et al. (2022) was adapted to map vegetation cover in this study. Image processing consisted in using the Otsu method (Otsu et al., 1979) and a linear spectral unmixing model (Keshava and Mustard, 2002) to quantify the fractional abundance of sand and vegetation for each pixel (10 × 10 m) corresponding to dune areas. Similarly to van Kuik et al. (2022), the Normalized Difference Water Index (NDWI; McFeeters, 2013) and the Normalized Difference Vegetation Index (NDVI) were used to differentiate water, sand and vegetation pixels for each image. Although NDVI was found to be useful to separate forest vegetation from sand, it did not show enough contrast

<table><caption>**Table 1**
: LiDAR campaign details and associated vertical errors.</caption><thead><tr><td>Campaign</td><td>Date</td><td>Accuracy Z (m)</td><td>Pts/m²</td></tr></thead><tbody><tr><td>LiDAR 2011</td><td>May</td><td>0.20</td><td>8</td></tr><tr><td>LiDAR 2014</td><td>October</td><td>0.14</td><td>8</td></tr><tr><td>LiDAR 2016</td><td>October</td><td>0.16</td><td>8</td></tr><tr><td>LiDAR 2017</td><td>October</td><td>0.11</td><td>8</td></tr><tr><td>LiDAR 2018</td><td>October</td><td>0.10</td><td>12</td></tr><tr><td>LiDAR 2019</td><td>December</td><td>0.10</td><td>16</td></tr><tr><td>LiDAR 2020</td><td>October</td><td>0.08</td><td>16</td></tr><tr><td>LiDAR 2021</td><td>October</td><td>0.09</td><td>16</td></tr><tr><td>LiDAR 2022</td><td>October</td><td>0.10</td><td>16</td></tr><tr><td>LiDAR 2023</td><td>September</td><td>0.08</td><td>16</td></tr></tbody></table>

to differentiate the sparse and patchy dune vegetation from bare sand, which is key in this analysis. Instead, the Short-Wave Infrared (SWIR) band was chosen here to identify dune vegetation (Fig. 4c.), since it has generally been found to represent the largest spectral difference between vegetation and sand (Provoost et al., 2005). The fractional abundance of sand and vegetation of each dune pixel over the entire Gironde coast from 2017 to 2023 was computed and was constrained such that each pixel values range from 0 (bare sand) to 1 (fully vegetated) (Fig. 4d.).

## 3.4. Indicators computation

Topographic and vegetation cover values are processed along 10 m-spaced cross-shore transects extending from the back of the dune (back dune slope break of the last year's survey, 2023) to the beach (MSL location of the first year's survey, 2014). Along each cross-shore transect, morphological descriptors (location and value), sand volumes (beach and dune) and vegetation cover index were automatically computed for each available survey. The characteristics of the descriptors used in the following are presented in the Table 2 and illustrated Fig. 3e.-g. It should be noted that transgressive dunes, defined as landward migrating dunes, are determined with the categorical variable DTrans. They are classified as $DTrans = 1$ if the lee slope (Z = 14 m) migrates landward by more than 2 m between two consecutive surveys. The value of 2 m/year is not a reference to the migration speed but ensures that the displacement of the lee slope from one survey to another is greater than any potential uncertainty associated with the data. The data along the transects are extracted from DTMs with 1 × 1 m resolution, and the inaccuracy along steep slope gradients can increase. Also, it should be noted that a value of 2 m/year is much lower than the displacement of the moving dunes previously observed along the Aquitaine coast (Robin et al., 2023).

A spatial division into 4 units (U1 to U4) from north to south of the study area is also used for the analysis (Fig. 4 a.). These units are characterized by relatively spatially alongshore uniform variability of the different indicator values during the study period. Coastal dune morphological change along the study area can be divided into two temporal sequences: S1 between 2011 and 2014 that is dominated by marine erosion and S2, between 2014 and 2023, that is mostly governed by aeolian transport (Fig. 4 b.-c.).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Time series of hourly wind intensity (black line) at the Cap Ferret wind station during the 1999–2023 period and seasonal median wind directions (colored circles).</figcaption></figure>
<table><caption>**Table 2**
: Indicators of dune morphology, sand volumes and vegetation cover evolutions.</caption><thead><tr><th>Indicator</th><th>Unit</th><th>Full name</th><th>Technical description</th></tr></thead><tbody><tr><td>S</td><td>m</td><td>Shoreline position</td><td>6 m isocontour (m/NGF)</td></tr><tr><td>TxS</td><td>m/year</td><td>Shoreline erosion rate</td><td>Dune foot location evolution from Castelle et al., 2018</td></tr><tr><td>Dfs</td><td>–</td><td>Dune front slope</td><td>From 6 m to 12 m/NGF</td></tr><tr><td>Dw</td><td>m</td><td>Dune width</td><td>From S to Dbf</td></tr><tr><td>Dbf</td><td>m</td><td>Back dune foot</td><td>Back dune slope break</td></tr><tr><td>Dc</td><td>m</td><td>Dune Crest elevation</td><td>Highest elevation of the dune</td></tr><tr><td>Dv</td><td>m³/m</td><td>Dune sand volume</td><td>From S to Dbf</td></tr><tr><td>Bv</td><td>m³/m</td><td>Beach sand volume</td><td>From MSL to S</td></tr><tr><td>V</td><td>m³/m</td><td>Beach and Dune sand volume</td><td>From MSL to Dbf</td></tr><tr><td>M</td><td>m³/m</td><td>Gross mobility</td><td>Absolute difference from S to Dbf</td></tr><tr><td>Covmean</td><td>%</td><td>Mean vegetation cover</td><td>Cross-shore averaged vegetation cover from S to Dbf</td></tr><tr><td>Dtrans</td><td>0–1</td><td>Transgressive dune</td><td>Yearly landward migration of the back dune (14 m isocontour m NGF) ≥ 2 m/year</td></tr></tbody></table>

# 4. Results

## 4.1. Decadal morphological dune response

### 4.1.1. Global morphological evolution

The Digital Elevation Model of Difference (DoD) obtained for the first period S1(2011–2014) highlights the marine erosion of the dune fronts following the winter of 2013–2014 (Fig. 4 b.). In line with the work of Castelle et al. (2015) and Nicolae Lerma et al. (2019), we observe substantial erosion (ΔV 2011–2014 ≈ 7.5 × 10⁶ m³, i.e., by approximately –88.2 m³/m), associated with shoreline retreat that reached several tens of meters along the entire Gironde coast. This retreat is characterized by quasi rhythmic (spacing of few hundreds of meters) erosion hot-spots within megacusp embayments (Fig. 4 b.). During this sequence, aeolian transport appeared to be very limited.

During the second period S2 (Fig. 4 c.) consistent accretion was observed at the upper part of the beaches, with a clear north-south gradient. For U1, sectors undergoing slight erosion alternate with sectors in slight accretion, while for U2, stable and accreting sectors alternate. Finally, large accretion was observed in U3 and U4, filling megacusp embayments caused by marine erosion during S1. Four different dune behaviors can be observed within the different units. In U1, moderate aeolian transfer is observed, associated with erosion of dune crests and sand sheets with a thickness on the order of 1 to 2 m at the top of the dunes.

In U2, larger changes are observed, characterized by erosion of the stoss slope of the dunes and a landward migration of the lee slope of about several meters (Fig. 4 c. and d., profiles B). These evolutions, under the effect of wind forcing, are mainly observed in the alignment of the megacusp embayments generated during S1. In U3, the same type of behavior is observed, but with more limited erosion and aeolian sand deposits, except from very localized areas (e.g. Fig. 4 c. and d., profiles C, D). Finally, in U4, wind-induced sand accumulation is essentially localized to the stoss face of the dune (Fig. 4 c. and d., profile U4).

### 4.1.2. Morphological descriptors and sand volume evolution

Fig. 5 shows the 1 km alongshore averaged morphological and volume descriptors (Table 2) computed for all topographic surveys, along the entire study site relative to the 2014 state. ΔS and ΔDv (Fig. 5 a. and e.) are strongly correlated (R² = 0.88) and characterized by a north-south gradient. Both ΔS and ΔDv were relatively steady or slightly positive in U1 and U2 and progressively show more variability southwards, where the shoreline moved seaward (by 10 to 20 m) and dune sand volumes increased (by 100 to 160 m³/m). Beach volume changes (ΔBv) does not show any clear large-scale patterns (Fig. 5 d.), but a strong interannual variability with an amplitude under 100 m³/m compared to the state of 2014 and with substantial short-scale alongshore variations.

Dune front slope (Dfs), which was around 60 to 40 % (typical of marine dune scarp) in 2014, progressively decreased to approximately 20 to 40 % (except in the northern extremity of the study area, Fig. 5 d.) between 2014 and 2023, illustrating the increasingly gently sloping stoss slope since the 2013–2014 winter. In 2023, Dfs typically ranges

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Local examples of used data and descriptors. Upper panel a. airborne LiDAR derived 1 m resolution DTM, b. orthophotography c. Short-Wave Infrared (SWIR), d. computed fractional abundance of sand and vegetation map. Lower panel descriptors of dune morphology, beach-dune sand volumes and vegetation cover evolutions (please refer to Table 2 for definition and details of the indicators (e.-g.))</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Dune characteristics and morphological evolutions along the Gironde coast with a. the morphology in 2023, b. the DoD between 2011 and 2014, c. the DoD between 2014 and 2023, d. evolution of several cross-shore profiles. Profiles U1, U2, U3,U4 are the alongshore average morphology of each spatial division (U1 to U4), Profiles A, B, C D, are 100 m alongshore average dune profiles illustrating the diversity of trangressive dunes (size, shape, temporality) along the coast.</figcaption></figure>

from 15 to 30 %.

Finally, the dune crest height (Dc) increased on average by 0.84 m from 2014 to 2023, but with a large alongshore variability (e.g. with a local maxima of approximately 1.5 m, and a remarkable stability at the southern extremity, Fig. 5 c.). Along U2, the increase of Dc is locally larger than the rest of the coast, reaching more than 2 m, corresponding to an increase by more than 0.2 m/year.

In the next section, we explore dune morphological changes through gross mobility (M), which provides a measure of dune changes, independently of erosion or accretion patterns.

### 4.1.3. Dune mobility

Fig. 6 shows the time and space evolution of the dune gross mobility (M) and their alongshore correspondence with transgressive behavior, massive sand sheets or entire dune (Fig. 6, c.). A large mobility at regional scale especially between 2019 and 2020 and to a lesser extent between 2018 and 2019 and 2022–2023 was observed, as opposed to the period between 2016 and 2017 and 2020–2021, where M is remarkably low along the entire coast. Among the delimited sectors, U2 was clearly the one where the mobility was the largest and the most widespread, while within U1 and U3, strongly alongshore non-uniform behaviors were observed, with the alternance of strongly mobile and stable areas. Finally, dunes in U4 were very stable, even during the years when average mobility at regional scale is the largest. At more local scale, we observe substantial mobility, especially within U2, with M reaching 4–5 % of Dv in 2019–2020 and 2022–2023. After nine years (S2 period), cumulated mobility exceeding 20 % of Dv is commonly observed in the most remobilised sectors of U1, U2 and U3. The largest values are observed within U2, reaching 30 to 35 % of Dv by the end of the study period.

The most remobilized sectors showed two different behaviors during the study period: (i) some sectors showed continuous mobility since 2014 and, after at least 2019, have gradually evolved to a transgressive state (e.g. within U1, y = 200 m, U2 around y = 2800 m and U3, y = 4900 m Fig. 6 c.); (ii) some other sectors showed relatively low mobility over the 2014–2019, followed by a substantial evolution during the winter of 2019–2020 and a fairly continuous mobility in the following years, even during calmer winters (i.e. 2020–2021 and 2021–2022). This second behavior is particularly observed within U2 (between y = 3100 m and 3500 m) and U3 (y = 5200 m or y = 6200 m). In these sectors, dune mobility leads to a strong increase in the proportion of transgressive dune state in 2023.

In the following section, the temporal evolution of the different morphological descriptors is compared with potential forcing and controlling factors of dune mobility.

## 4.2. Controlling factors

### 4.2.1. Wind forcings

Time series of the hourly potential wind flux and of the seasonally cumulated potential wind flux (Q) are shown in Fig. 7a. We observe that Q is strongly seasonally modulated with an average value during the study period of about 48 kg/m in winter and 8 kg/m in summer. The interannual variability of Q is also strong, ranging from 24.7 (1.9) kg/m to 85 (13.3) kg/m in winter (summer). The evolution of inter-survey cumulated Q and M between 2011 and 2023, was also examined (Fig. 7b.). To evaluate statistically the relation between Q and M, only the period between 2014 and 2023 (S2) was considered, discarding the marine erosion which occurred at the beginning of the study period (2013–2014 winter). Even if Q is determined for idealized conditions
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Dune morphological and volume descriptors a. (S) Shoreline position, b. (Dfs) Dune front slope steepness, c. (Dc) Dune crest height, d. (Bv) Beach volume and e. (Dv) Dune volume changes relative to 2014.</figcaption></figure>

(dry sand, no supply limitation, no morphological effect ...), a clear relation between time-varying *Q* and *M* at regional scale can be noted (Fig. 7 c.), and within the different spatial units (Fig. 7 d-g). At the regional scale, results show that 55 % of the variability of *M* is explained by *Q* (R² = 0.55; p-value <0.05, Fig. 7 c.). Within U3 and U4, the relation is even greater (R² > 0.6; p-value <0.05). Finally, for U2, the relation is notably weaker and not statistically significant at the 5 % level (R² = 0.23; p-value >0.05, Fig. 7 e.). This deviation is caused by the change in the relation between *M* and *Q* during the period (2014–2023) as vegetation cover strongly decreased, which is further explored below.

### 4.2.2. Vegetation cover

The temporal and spatial variability of the cross-shore averaged vegetation cover concentration (Covmean) highlights large changes over the period 2017–2023. In 2017, although some alongshore variability can be observed along the coast, Covmean shows that the vegetation is relatively dense (about 50 % of coverage) and fairly homogeneous along the coast (between 57 % in U1 and 41 % in U3), see Fig. 8 a.,d.).

Large changes in vegetation cover were observed during the subsequent years. Between 2017 and 2023, a sharp decline in vegetation can be observed, peaking in 2020. Afterward the trajectory of vegetation recovery or further degradation varied considerably along the coast (Fig. 8 d.). Within the different spatial units we observe: (i) a progressive recovery after 2020 in U1 and U4 (exceeding the 2017 cover for the latter), (ii) a continuous decrease in U2, where the vegetation cover dropped below 20 % in 2023 and (iii) relatively low coverage that tends to decrease until 2022 at U3, followed by a rapid recolonization between 2022 and 2023, which exceeds the coverage observed in 2017. It can be observed in Fig. 8c. that the increase in vegetation cover is mainly located at the back of the dune which corresponds to the least mobile section of the dune, whereas no increase in vegetation coverage is observed on the stoss slope which corresponds to most mobile sections.

$\Delta$Covmean and *M* are moderately correlated (R² = 0.32, p-value <0.05 Table 3) at the regional scale, but also within U2 where the vegetation cover is known to show the greatest changes (R² = 0.46, p-value <0.05). The temporal variability of *Q* (Fig. 7 b.) shows that the 2019–2020 period corresponds to the year with the strongest wind conditions, explaining the higher value of *M*. This coincides with the largest vegetation cover loss (Fig. 9), suggesting that the decline in vegetation cover was primarily caused by aeolian processes and induced sand mobility that reduced vegetation cover by sand burial. Subsequently, vegetation failed to recover in the most dynamic sectors, such as U2, where the mobility remained substantial within the following years and the vegetation cover continued to decline (Fig. 9 c. green dashed line). On the other hand, under more stable conditions (U3 and U4), the vegetation recovery was stronger over the same period.

## 4.3. Morphological responses

### 4.3.1. Dune front and dune crest

The reduction of the dune front slope (Dfs) during the S2 period (2014–2023) is one of the most representative and widespread observed morphological changes (Fig. 5 b.). The reduction in slope reflects a high degree of mobility with $\Delta$Dfs and *M* correlated (R² = 0.31, p-value <0.05, Table 3) and is explained by the remobilization of sediment in place by the wind. This sediment recycling is coupled with by the erosion of the top of the scarp (mainly for the northern half of the coast, U1 and U2) and by the simultaneous formation of an incipient foredune at the dune toe (southern part of the coast, U3 and U4). *M* and $\Delta$Covmean are also correlated (R² = 0.32 and 0.46; p-value <0.05 at the regional scale and for U2 respectively, Table 3) and indicate that the reduction of Dfs and the increase of *M* corresponds to a greater potential for vegetation burial at the top of the dune.

At a regional scale, the correlation between *M* and *Dc* indicates that
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Spatial and temporal mobility of the dunes: a. timestack of annual gross mobility (M) in m³/m, b. cumulated mobility during the period 2014–2023 in m³/m and c. timestack of annual gross mobility (M) in percentage of the dune volume in 2023, d. cumulated mobility during the period 2014–2023 in percentage of the dune volume in 2023, e. percentage of transgressive dune per kilometer.</figcaption></figure>

mobile dunes are generally associated with an increase in Dc height (R² = 0.32; *p* < 0.05). Temporal and spatial variations in Dc elevation may result from local dune characteristics and interactions involving both morphological and biotic factors (e.g., scarp or steep stoss slope, blow out, sparse vegetation cover, hummocky morphology, etc.), which explain the strong alongshore variability observed in Fig. 5c. Results reveal that Dc mostly increases significantly (by more than 1 m) along areas where the shoreline position is relatively stable or slightly retreating and sediment supply is limited (U1 and U2). Conversely, in U3 and the northern part of U4, where the shoreline (dune toe) was prograding due to incipient foredune development, Dc (located within the seaward half of the dune) also increases. This trend is primarily enhanced by a relatively sparse vegetation cover (less than 50 %) along the new foredune, enabling sediment transfer from the dune toe to the crest. Dc remains stable or even decreases locally where S (Dfs) retreats (becomes steeper) after 2014, with progradation (smoothing) resuming only after 2018 (e.g., at y = 4000 m, 5400 m, or 6500 m; Fig. 5 a., b., c.). This pattern is attributed to local marine erosion affecting the dune profile during the 2015–2016 winter season. The behavior of Dc, particularly in the context of transgressive sand sheets or dunes, will be further discussed in the Discussion section.

### 4.3.2. Formation of transgressive sand sheets and dunes

A second morphological signature associated with the dune mobility (M) is the transition from stable to transgressive sand sheets or dunes. Along the coast, the proportion of transgressive dunes have increased from 5.7 % to 17.3 % between 2014 and 2023. This trend exhibits a strong alongshore variability (Fig. 6 c.), with the northern half of the study area already having a much higher percentage of transgressive

dunes in 2014, but even more so in 2023 (Table 4). A notable exception is observed in U4, where the percentage of transgressive dunes remains low and stable at 1.6 % in 2023. The other sectors show a large increase in transgressive dune sectors, by a factor of 2.1, 4.2 and 3.7 for U1, U2 and U3 respectively. In sector U2, 23 % of the dune belt became transgressive during the decade.

Transgressive dunes are predominantly observed in areas where M is highest (Fig. 10a). Most of the dunes with M exceeding 200 m³/m between 2014 and 2023 corresponding to 22 m³/m/y have shifted from stable to transgressive. This shift was related to vegetation cover, with newly transgressive dunes typically developing in areas where vegetation cover is sparse (median value of Covmean of 40 %). In contrast, 75 % of the non-transgressive dunes are associated with vegetation cover that exceeds 40 %. No substantial sand sheet formation or transgressive dune development has been observed in U1, where vegetation cover remained stable throughout the study period and exceeded 50 %.

Transgressive dunes (Dtrans) can arise under varying sediment supply conditions. The proposed classification considers the ratio between ΔDv and the M both between 2014 and 2023 (Fig. 10 c.).

It reveals that for non-transgressive dunes, mobility M is approximately equally attributed to internal sediment recycling (cannibalism) and new sediment supply. In contrast, transgressive dunes are primarily characterized by the remobilization of pre-existing sediment, which accounts for more than 60 % of their mobility in over 75 % of the cases. New sediment supply dominates mobility in only about 15 % of the cases. In rarer situations, transgressive dunes can be observed in the context of marine erosion or large sediment supply (values of -1 and 1 respectively). These cases may result from transgressive conditions prior to the study period (e.g. within U1, Fig. 5, profile A) or from rapid
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Potential sand flux and mobility with a. the hourly potential sand flux (Q) and seasonal cumulated flux during the study period, b. temporal evolution of dune mobility (M) in red circles vs inter-survey cumulated potential sand flux (Q) blue crosses, c. scatter plots relating inter-survey M vs inter-survey cumulated Q only for the period 2014–2023, for the all coast and d.-g. for the different sectors U1 to U4.</figcaption></figure>

transition to a transgressive state following strong marine erosion events as during 2013–2014 (e.g. in U3 y = 4900 m, Fig. 5, profile C), with large sediment supply occurring after the transition to transgressive state.

# 5. Discussion

## 5.1. Dune morphological and vegetation cover changes

Large-scale and high-resolution, airborne LiDAR data, satellite im- agery (Sentinel-2), and wind speed data were analyzed to derive dune parameters and to comprehensively describe the interactions between dune morphology, dune vegetation cover and wind fluxes causing multi- annual dune morphological changes and mobility.

Coastal dune evolution often implies dune crest elevation, as demonstrated here and in other studies (Houser et al., 2008; Zarnetske et al., 2015; Davidson-Arnott et al., 2018; He et al., 2022; Masselink et al., 2022; Burvingt and Castelle, 2023; Hovenga et al., 2023). Although the correlations between $\Delta D_c$ and $\Delta S$ and between $\Delta D_c$ and $\Delta D_v$ are relatively weak ($R^2$ = 0.22 and 0.24 respectively, p-value <0.05), these results suggest that dune crest tends to increase the most when the shoreline is either retreating or stable, and when sediment supply is limited. This is confirmed by the spatial and temporal changes in $\Delta D_c$ and $\Delta D_v$ which showed that dune crest elevation is mainly observed along eroding or temporary stable stretches of coastline. When focusing on $D_c$ temporal changes for transgressive dunes (Fig. 11), we observed that $D_c$ slowly increases in general, at a rate that is comparable to non-transgressive dunes (Fig. 5 c). However, in a few cases, dune profiles exhibit a decrease in $D_c$. This behavior was observed along 9.9% of transgressive dunes in 2019, rising to 17.1% by 2023. Fig. 11 further highlights that several dune profiles with substantial decrease in $D_c$ (exceeding 3 m, e.g., Fig. 11 a.; y = 2000 m; 3300 m; 4900 m)

correspond to areas with the highest rates of lee slope landward migration, ranging from 10 to 15 m/year (e.g., Fig. 11, profiles D, E, and F). In this case, we suspect that morphological 3D effects are responsible for local accelerated remobilization and migration processes and explained the decrease in $D_c$. Note that, for clarity and due to substantial local variability in the landward migration rate of the lee slope (DTrans), Fig. 11 a. presents the 100 m-window-moving-averaged landward migration rate. Fig. 11 also highlights that transgressive dunes are predominantly located in areas with the lowest vegetation cover or where cover has significantly declined between 2017 and 2022, even though a rapid recovery occurred at the southern half of the study site in 2023. However, this suggests that low vegetation cover (whether sparse or partially buried) can also contribute to sand accumulation at the dune crest (e.g. Fig. 12 b., c.). This could explain why most transgressive dunes exhibit stable or slightly increasing $D_c$ values, and that no decrease in $D_c$ is observed where vegetation cover is substantial (exceeds 40 %).

The absence of a significant correlation between $\Delta D_c$ and $D_c$ at either a regional or a meter scale (data not shown) further implies that dune crest elevation may be strongly influenced by local interactions with dune morphology (Brodie et al., 2019) or vegetation growth (Bonte et al., 2021), as seen in Fig. 12. The evolution of $D_c$ is then resulting of a combination of both interactions (biotic and abiotic). Understanding how morpho-ecological feedback influence the long-term evolution of these systems (Hesp and Martínez, 2007; Costas et al., 2023) is a significant research challenge for large scale studies and have a critical value for implementing dune management that maintains dune eco-systemic services.

The use of satellite-derived maps based on Sentinel-2 also raises few concerns about the limitations of the period when data is available and the specific treatment used to derive the maps. Concerning the first point, vegetation cover is only available from 2017 onwards, which
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Spatial and temporal evolution of the vegetation cover across the dune relative to the shoreline position, a. vegetation cover in 2017, b. vegetation cover in 2023, c. DoD 2023–2017, d. Cross shore average vegetation cover (Covmean) between 2017 and 2023.</figcaption></figure>

<caption>**Table 3**</caption>

Relations between morphological, volume and vegetation cover descriptors with dune crest elevation (Dc) and dune mobility (M) responses for all individual spatial units; italic pvalue >0.05, bolt R² > 0.2, sign (-) indicate an inverse correlation.

<table><thead><tr><th></th><th></th><th>All</th><th>U1</th><th>U2</th><th>U3</th><th>U4</th></tr></thead><tbody><tr><td rowspan="6">ΔDc</td><td>Q</td><td><strong>0.13</strong></td><td><strong>0.1</strong></td><td><strong>0.16</strong></td><td><strong>0.16</strong></td><td><strong>0.04</strong></td></tr><tr><td>ΔS</td><td><strong>0.22</strong></td><td>0.15</td><td>0.4</td><td>0.01</td><td>0.09</td></tr><tr><td>Tx S</td><td>0.16(-)</td><td><strong>0.01</strong></td><td>0.02</td><td><strong>0.01</strong></td><td>0.18</td></tr><tr><td>ΔDfs</td><td>0.03(-)</td><td>0.01</td><td>0.02</td><td>0.03</td><td>0.01</td></tr><tr><td>ΔDv</td><td><strong>0.24(-)</strong></td><td><strong>0.21</strong></td><td>0.01</td><td>-0.01</td><td>-0.01</td></tr><tr><td>ΔCovmean</td><td><strong>0.29(-)</strong></td><td>0.03</td><td>0.03</td><td>0.05</td><td>0.19</td></tr><tr><td rowspan="6">M</td><td>Q</td><td><strong>0.55</strong></td><td><strong>0.41</strong></td><td><strong>0.23</strong></td><td><strong>0.62</strong></td><td><strong>0.63</strong></td></tr><tr><td>ΔS</td><td>0.01</td><td>0.01</td><td>0.01</td><td>-0.01</td><td>0.03</td></tr><tr><td>Tx S</td><td><strong>0.22(-)</strong></td><td>0.07</td><td>0.02</td><td>0.01</td><td>0.16</td></tr><tr><td>ΔDfs</td><td><strong>0.31(-)</strong></td><td><strong>0.48</strong></td><td><strong>0.27</strong></td><td>0.09</td><td><strong>0.26</strong></td></tr><tr><td>ΔDvd</td><td>0.01(-)</td><td>0.17</td><td>0.05</td><td>0.06</td><td>0.05</td></tr><tr><td>ΔCovmean</td><td><strong>0.32(-)</strong></td><td>0.18</td><td><strong>0.46</strong></td><td>0.17</td><td>0.01</td></tr><tr><td>M</td><td>ΔDc</td><td><strong>0.32</strong></td><td><strong>0.51</strong></td><td><strong>0.42</strong></td><td><strong>0.38</strong></td><td>-0.01</td></tr></tbody></table>

prevents us from determining an initial state of the vegetation at the beginning of the study period. However, a thorough analysis of the orthophoto acquired between 2014 and 2017 shows that no notable evolution in vegetation cover is observed at the scale of the coast. Where

vegetation is sparse before or just after the 2013–2014 winter, is consistently indicated by the sectors where transgressive dunes are observable in 2014 or 2017, respectively (Fig. 6e). The second limitation concerns the image quality, which can vary due to atmospheric perturbation or shadowing effects due to satellite angle. To minimise these effects, Sentinel-2 images were carefully selected, and satellite-derived maps were qualitatively compared with high-resolution orthophotography synchronous with the LiDAR acquisition. While the sudden increase in vegetation cover in 2023 is consistent with data from other sources (field and orthophoto), and considering that the changes in 2023 are not specifically biased (i.e. general or local negative evolution of vegetation cover for U1 and U2), the reasons for this positive shift (an important increase of 10–20 %) for U3 and U4 remain unclear. The analysis of the amount of precipitation extracted from ERA5 datasets (data not shown here but available at https://cds.climate.copernicus.eu/) along the Gironde coast showed that the lowest and highest levels of precipitation over the last 10 years occurred in 2022 and 2023, respectively. Relatively dry conditions followed by a large amount of precipitation may explain the rapid increase of vegetation growth in 2023. Interaction of vegetation cover should be studied in more detail with appropriate data resolution, in our case the spatial resolution of the available satellite imagery to derive vegetation cover maps is not high enough to further analyse bio-geomorphic interactions at a local scale.
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Temporal evolution of dune mobility (M red circles) and mean vegetation cover (Covmean green curve) for the all coast and for the spatial units U1-U4 b.- e., for the latter the solid curves illustrate the mean evolution for the entire coast and the dashed lines the evolution for each spatial unit.</figcaption></figure>

<table><caption>**Table 4**
: Percentage of transgressive dunes at the entire coast and at all sectors.</caption><thead><tr><th>%</th><th>All</th><th>U1</th><th>U2</th><th>U3</th><th>U4</th></tr></thead><tbody><tr><td>2014</td><td>5.7</td><td>10</td><td>7.1</td><td>4.8</td><td>1.4</td></tr><tr><td>2019</td><td>8.3</td><td>14.4</td><td>11.5</td><td>6.8</td><td>1.3</td></tr><tr><td>2023</td><td>17.3</td><td>21</td><td>29.9</td><td>17.7</td><td>1.6</td></tr></tbody></table>

## 5.2. Transition states

Dune crest (Dc) elevation changes do not fully encompass and describe the complexity of dune morphological changes. This justifies the use of another parameter defined in this study, the dune mobility (M), which was shown to provide a better overview of the dune morphological changes relative to the transition from fixed to transgressive sand sheets or dunefield. Hesp, 2013 and Hesp et al., 2022 described the process that leads the development from stable dunes to transgressive sand sheet and finally transgressive dunefield. In this classification, once the transgressive dune behavior has been initiated (sand sheets stage), the transition between this stage and the ultimate one (transgressive dunefield) is not clearly defined in terms of morphological evolution. We have observed that an intermediate and transitional stage can be defined distinguishing the sand sheets stage (a sand layer covering the preexisting dune) and an established dune fields stage (specific large scale geomorphological unit). This stage “transgressive dune” corresponds to descriptions of rollovers (Jackson et al., 2019b; Masselink et al., 2022), where the dune itself migrates mainly through the recycling of pre-established sediments and through similar migration rates on exposed and leeward slopes, thus burying the interdune spaces under several metres of sand (depending on the previous characteristics of the dune). Because of the distinct morphology of the first line of dunes along the Aquitaine coast inherited from almost 200 years of human interventions, the detection of criteria distinguishing this transgressive dune stage may be more straightforward compared to

dune systems with more chaotic morphologies and poorly defined interdunal areas. However, along natural or less human-impacted systems where this transition has been documented (e.g., Hesp, 2013; Jackson et al., 2019b; Hesp et al., 2022; Robin et al., 2023; DaSilva et al., 2024), we argue this intermediate stage between a sand sheets stage and a transgressive dunefield is also observable and can be discriminated against similar criteria. The identification of this intermediate stage that potentially generalizes along the coast and leads to the development (or non-development) of a transgressive dunefield system is highly relevant along the Aquitaine coast and other coastal environments with similar dune morphology, since it has a significant implication in terms of ecological dynamics and burial hazard which require specific dune management strategies.

According to Hesp et al., 2022 many factors have been identified in the literature as being responsible for aeolian transgressive sand sheet and dunefield initiation. These factors span over decadal or centennial temporal scales such as (i) mean seal level variation (rise/fall) or (ii) meteo-climatic changes (temperatures, precipitations, wind power, drought, increase of storminess ...), and are coupled with an event scale perturbations: (iii) external sediment supply; (iv) marine erosion; (v) human intervention (vegetation clearing, excavated notches, ...). Apart from human intervention, most studies identified a combination of these factors to explain the development and persistence of large transgressive dunefields and suggest that long-term environmental changes are necessary for the initiation phase (Aagaard et al., 2007; Szkornik et al., 2008; Jackson and Cooper, 2011; Costas et al., 2012; Jackson et al., 2019b; Costas, 2022). Under current climatic conditions and on shorter temporal scales, recent studies have shown that one (Mathew et al., 2010) or a few (Hesp et al., 2022, Robin et al., 2023, DaSilva et al., 2024) sequences of storm-driven marine erosion, can lead to the initiation of a new transgressive sand sheet or dunefield phase. In line with these observations, we observed that an extreme winter season (2013–2014) can trigger the development of a new transgressive dune (Fig. 12) and potentially of a new dunefield over the following years or decades.
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Box plot of the distribution of transgressive or non-transgressive dunes depending on: a. the mobility (M) period 2014–2023, b. the vegetation cover (Covmean) in 2023, c. the beach supply context (ΔDv/M) period 2014–2023. The central horizontal marks in the box plots indicate the median, and the top and bottom edges of the blue boxes indicate the 25th and 75th percentiles, respectively. Maximum whisker length extends up to 1.5 times the interquartile range and the outliers are plotted individually using the '+' symbol.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Evolution of the dune crest height (Dc) for transgressive dunes (DTrans), a. alongshore (Dc) Dune crest height changes for two generations of Transgressive dune (DTrans) in comparison to cross shore average vegetation cover (Covmean) between 2017 and 2022, b. example of profiles showing an increase in Dc (A, B, C) and a decrease in Dc (D, E, F).</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Fig. 12. Local example of temporal dune changes documented by orthophoto (a., b., c., d.) and LiDAR topographic data (e.) within U2 and U3 stretches of coast (upper and lower panel respectively).</figcaption></figure>

However, along the Aquitaine coast, a gradient of geomorphological conditions and sediment budget (Nicolae Lerma et al., 2022) have either led to or prevented the emergence of transgressive dunes. A combination of different factors could explain why a larger number of transgressive dunes are observed along U2 (30 % of the coast unit) in comparison to the other three units (see Table 4): (i) slightly stronger marine erosion during the 2013–2014 winter (e.g. Fig. 4 d., spatial division (U1 to U4) alongshore average profile); (ii) no substantial marine erosion (shoreline retreat) over the past 10 years (Fig. 5 a.); (iii) limited or no sediment supply from the beach to the dunes (Fig. 5 e.); (iv) narrower dune widths (Fig. 4 a., d.). These factors collectively contribute to enhance dune morphodynamics, including the reduction of the stoss slope gradients (Fig. 5 b.) that facilitates greater sand transfer from the dune itself to the back-dune area (cannibalism). This process caused the severe decrease of vegetation cover in U2 in comparison to the other units (unlike U1). Because of the narrower dune width observed along U2, sand is rapidly transferred to the back-dune area compared to the wider dunes along the southern unit (like U3). Additionally, the stable or slightly positive sediment budget in U2 does not support the rapid establishment of vegetated foredunes, which typically limit sand transfer from the beach to the dune crest or lee slope (like most of U3 and U4). Nevertheless, along U3, the development of transgressive dunes between 2019 and 2023 is also notable (+11 %, Table 4), despite a clearly positive sediment budget. In this unit, transgressive dune development is mostly confined to areas where vegetation cover was already low in 2014 and where foredunes remain sparsely vegetated, promoting sand transfer from the beach to the dune (ramp effect) (e.g. Fig. 12).

### 5.3. Conceptual model

The successive factors and timescales associated with the transition from stable coastal dunes to transgressive sheets leading to transgressive dune are identified and summarized in the conceptual model proposed in Fig. 13. This model is supported by topographic data and

orthophotographs of the evolutionary stages observed along the Aquitaine coast.

Following (1) the marine erosion stage (dune scarp), except from cases (1.1), where additional marine erosion refreshes the scarp within the following 1 or 2 years, (2.1) a lowering of the stoss slope gradient occurs in a context of a stable shoreline. It is caused by both a rapid sand slumping at the dune toe (the days following marine erosion) and a longer wind-driven erosion of the crest (may take several years). During this phase (destabilization phase), which can last several years (3 to 5 years) under moderate wind conditions and if vegetation cover on the dune plateau is relatively dense (in terms of both shoots and root networks), localized sand deposits accumulate at the back of the crest. These deposits may be coupled to vegetation growth leading to dune crest elevation (Brodie et al., 2019), if sediment accumulation does not exceed the burial tolerance of the plant species. In cases where vegetation cover is sparser (2.2), allowing for more extensive and massive sediment remobilization (particularly during a highly windy winter), sand transfer from the stoss slope to the dune crest results in the development of large sand sheets across the dune plateau (see also Fig. 12 c.). This transfer initiates a gradual but drastic destabilization of the dune morphology, during which vegetation cover can be massively buried. The dune mobility continuously increases over 2 or 3 years. When combined to the recycling of existing sediments (cannibalism), additional sediment supply from the beach promoted by a relatively bare sand ramp (Christiansen and Davidson-Arnott, 2004; Ollerhead et al., 2013), can simultaneously promote the installation of an incipient foredune and further enhance the accumulation of new sand sheets on the dune plateau (Fig. 12 c.).

Reaching Stage 3.1, vegetation cover becomes sparse (ranging from 25 % to 40 %) with a rather passive role on dune dynamics (Jackson et al., 2019b; Costas et al., 2023). At this point, dune morphological changes are primarily governed by wind speed and direction, as well as local morphological effects. At the scale of hundreds of meters, three-dimensional wind flows (induced by the presence of blowouts) are
<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Conceptual model of dune eco- and morpho-dynamics, from severe marine erosion of stable dunes to the landward migration of transgressive dunes based on the analyzed data set.</figcaption></figure>

also suspected to accelerate the initiation of transgressive dune dynamics (e.g., Hesp, 2002; Laporte-Fauret et al., 2021; Talavera et al., 2024). However, the wind acceleration effect caused by the 3D morphology of blowouts is not considered as a primary factor for dune dynamics at the spatial scale considered in this study. Sand sheets drift to the lee slope and cascade down via avalanching leading to the progressive landward migration of the lee slope. This process results in the burial of interdunal spaces under ten or more meters of sand and dune become transgressive. In areas where sediment budgets are slightly or notably positive, incipient foredunes continue simultaneously to grow gradually.

In cases (3.2), where dune vegetation has been severely impacted, or when the dune was not vegetated prior to Stage 1, especially when dunes are of modest width, the dune migrates via roll-over almost freely under moderate wind condition. In such conditions, both stoss and lee slopes retreat at equivalent rates. Substantial migration rates on the order of tens of meters per year can occur, as observed in this study and in other contexts (Navarro et al., 2011; Hesp et al., 2022; Robin et al., 2023; DaSilva et al., 2024; de Alegría-Arzaburu et al., 2025) corresponding to an established transgressive dune stage (Jackson et al., 2019b). Continuous sand mobility limits vegetations ability to recolonize the dunes. Without human intervention, transgression can prevail for decades (Navarro et al., 2011; Robin et al., 2023; Watanabe et al., 2025) and may generalize along the entire coastline, ultimately resulting in the formation of a transgressive dunefield (Hesp et al., 2022).

## 6. Conclusions

The analysis of this unique dataset provided the opportunity to define geomorphological parameters derived from airborne LiDAR to capture in detail the dune morphological and volume changes. Coupled with temporal and spatial vegetation cover descriptors derived from satellite images and potential wind flux computed from local

observations, this analysis provides a comprehensive overview of massive dune destabilization over tens of kilometers following dramatic marine erosion.

Over this 80 km long stretch of coastline, the cumulative area corresponding to transgressive dunes strongly increased from 4.7 km in 2014, to 7.1 km in 2019, and to 14.7 km in 2023 (i.e. 18.3 % of the coast). The vast majority of dunes that have transitioned from stable to transgressive states were characterized by a process of internal sediment recycling (cannibalism), demonstrating that sediment supply is not necessarily required for the transition from one stage to the other. Only half of the mobile dunes exhibit dune crest elevation, a pattern more commonly observed in cases of repeated marine erosion that maintains a steep stoss slope, promoting sediment accumulation at the stoss slope crest. Alternatively, this pattern is observed on very stable dunes where incipient foredunes develop, gradually accumulating sand at the stoss crest, which is then captured by vegetation.

Understanding the forcing and controlling factors and the associated timescales involved in restoring the mobility of coastal dune at a regional scale is highly valuable for coastal managers to define and apply appropriate and ambitious management plans, whether remobilization is required or when it needs to be limited. At high latitudes, where vegetation currently plays a particular role in stabilizing coastal dunes, the anticipated effects induced by climate change on coastal areas (sea level rise, storminess increase, increasing of meteorological extremes, drought, heatwave...) could lead to strong changes on a relatively short timescale in coastal dunes. To better anticipate and support coastal community adaptation along chronically eroding coastlines, more large-scale long term quantitative studies are essential. These efforts will be critical for sustaining dune systems and their biodiversity in the face of increased environmental pressures.
CRediT authorship contribution statement

**Alexandre Nicolae Lerma:** Writing – original draft, Supervision, Methodology, Conceptualization. **Olivier Burvingt:** Writing – review & editing, Methodology, Data curation. **Bruno Castelle:** Writing – review & editing, Conceptualization. **Bruce Ayache:** Visualization, Data curation. **Nicolas Robin:** Writing – review & editing, Conceptualization. **David Rosebery:** Writing – review & editing, Resources. **Julie Billy:** Writing – review & editing, Resources, Project administration.

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 was carried out as part of the DUNES Research Program "Espace des Transitions", co-funded by the Région Nouvelle-Aquitaine and the BRGM (research agreement), and the ONF, the Landes Depart-ment, the Vermilion REP SAS, the CCGL, the commune Lège-Cap-Ferret, the AEAG. We would also like to thank all the co-funders of the project and the Observatoire de la Côte Nouvelle-Aquitaine for making the data available and PSGAR CORALI which support the project. We would like to thank the reviewers of the article, including Partick Hesp, for their relevant comments and in-depth analysis, which enabled us to make significant improvements to the article.

References

Aagaard, T., Orford, J., Murray, A.S., 2007. Environmental controls on coastal dune formation: Skallingen spit, Denmark. Geomorphology 83, 29-47.

Arens, S.M., Mulder, J.P., Slings, Q.L., Geelen, L.H., Damsma, P., 2013. Dynamic dune management, integrating objectives of nature development and coastal safety: examples from the Netherlands. Geomorphology 199, 205-213.

Bailey, S.D., Bristow, C.S., 2004. Migration of parabolic dunes at Aberffraw, Anglesey, North Wales. Geomorphology 59, 165-174.

Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., Silliman, B.R., 2011. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81 (2), 169-193.

Bauer, B.O., Wakes, S.J., 2022. CFD simulations of wind flow across scarped foredunes: implications for sediment pathways and beach-dune recovery after storms. Earth Surf. Process. Landf. 47 (12), 2989-3015.

Bertran, P., Andrieux, E., Bateman, M.D., Fuchs, M., Klinge, M., Marembert, F., 2020. Mapping and chronology of coversands and dunes from the Aquitaine basin, Southwest France. Aeolian Res. 47, 100628.

Bonte, D., Batsleer, F., Provoost, S., Reijers, V., Vandegehuchte, M.L., Van De Walle, R., Hillaert, J., 2021. Biomorphogenic feedbacks and the spatial organization of a dominant grass steer dune development. Front. Ecol. Evol. 9, 761336.

Bossard, V., Lerma, A.N., 2020. Geomorphologic characteristics and evolution of managed dunes on the south west coast of France. Geomorphology 367, 107312.

Brodie, K., Conery, I., Cohn, N., Spore, N., Palmsten, M., 2019. Spatial variability of coastal foredune evolution, part a: timescales of months to years. J. Mar. Sci. Eng. 7 (5), 124.

Burvingt, O., Castelle, B., 2023. Storm response and multi-annual recovery of eight coastal dunes spread along the Atlantic coast of Europe. Geomorphology 435, 108735.

Burvingt, O., Masselink, G., Russell, P., Scott, T., 2017. Classification of beach response to extreme storms. Geomorphology 295, 722-737.

Carter, R., 1991. Near-future sea level impacts on coastal dune landscapes. Landsc. Ecol. 6 (1), 29-39.

Castelle, B., Marieu, V., Bujan, S., Splinter, K.D., Robinet, A., Sénéchal, N., Ferreira, S., 2015. Impact of the winter 2013-2014 series of severe Western Europe storms on a double-barred sandy coast: beach and dune erosion and megacusp embayments. Geomorphology 238, 135-148.

Castelle, B., Bujan, S., Ferreira, S., Dodet, G., 2017. Foredune morphological changes and beach recovery from the extreme 2013/2014 winter at a high-energy sandy coast. Mar. Geol. 385, 41-55.

Castelle, B., Guillot, B., Marieu, V., Chaumillon, E., Hanquiez, V., Bujan, S., Poppeschi, C., 2018. Spatial and temporal patterns of shoreline change of a 280-km high-energy disrupted sandy coast from 1950 to 2014: SW France. Estuar. Coast. Shelf Sci. 200, 212-223.

Christiansen, M.B., Davidson-Arnott, R., 2004. Rates of landward sand transport over the foredune at Skallingen, Denmark and the role of dune ramps. Geografisk Tidsskrift-Danish Journal of Geography 104 (1), 31-43.

Clarke, M.L., Rendell, H.M., 2009. The impact of North Atlantic storminess on western European coasts: a review. Quat. Int. 195 (1-2), 31-41.

Clarke, M.L., Rendell, H.M., Tastet, J.-P., Clavé, B., Massé, L., 2002. Late Holocene sand invasion and North Atlantic storminess along the Aquitaine coast, Southwest France. Holocene 12, 231-238.

Clemmensen, L.B., Murray, A., 2006. The termination of the last major phase of aeolian sand movement, coastal dunefields, Denmark. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group 31 (7), 795-808.

Colombani, N., Osti, A., Volta, G., Mastrocicco, M., 2016. Impact of climate change on salinization of coastal water resources. Water Resour. Manag. 30, 2483-2496.

Cooper, A., Jackson, D., 2021. Dune gardening? A critical view of the contemporary coastal dune management paradigm. Area 53 (2), 345-352.

Costas, S., 2022. Evolutionary trajectories of coastal sand barriers along the west Portuguese coast during the Holocene. J. Mar. Sci. Eng. 10 (12), 1894.

Costas, S., Jerez, S., Trigo, R.M., Goble, R., Rebelo, L., 2012. Sand invasion along the Portuguese coast forced by westerly shifts during cold climate events. Quat. Sci. Rev. 42, 15-28.

Costas, S., Gallego-Fernández, J.B., de Sousa, L.B., Kombiadou, K., 2023. Ecogeomorphic response of a coastal dune in southern Portugal regulated by extrinsic factors. Catena 221, 106796.

Costas, S., de Sousa, L.B., Gallego-Fernández, J.B., Hesp, P., Kombiadou, K., 2024. Foredune initiation and early development through biophysical interactions. Sci. Total Environ. 940, 173548.

da Silva, G.M., Hesp, P.A., 2013. Increasing rainfall, decreasing winds, and historical changes in Santa Catarina dunefields, southern Brazil. Earth Surf. Process. Landf. 38 (9), 1036-1045.

DaSilva, M.D., Hesp, P.A., Bruce, D., Downes, J., da Silva, G.M., 2024. Coastal transgressive dunefield evolution as a response to multi-decadal shoreline erosion. Geomorphology 455, 109165.

Davidson-Arnott, R., Hesp, P., Ollerhead, J., Walker, I., Bauer, B., Delgado-Fernandez, I., Smyth, T., 2018. Sediment budget controls on foredune height: Comparing simulation model results with field data. Earth Surf. Process. Landforms 43 (9), 1798-1810.

Davis, E.H., Hein, C.J., Cohn, N., White, A.E., Zinnert, J.C., 2024. Differences in internal sedimentologic and biotic structure between natural, managed, and constructed coastal foredunes. Geomorphology 451, 109083.

de Alegria-Arzaburu, Costas, S., Delgado-Fernández, I., 2025. Impacts of El Niño on the sediment balance of a transgressive dune-beach system. Sci. Total Environ. 999, 180348.

De Freitas, J.G., 2025. A Global Environmental History of Coastal Dunes. Taylor & Francis, p. 260.

Delgado-Fernandez, I., Davidson-Arnott, R., 2011. Meso-scale aeolian sediment input to coastal dunes: the nature of aeolian transport events. Geomorphology 126 (1-2), 217-232.

Delgado-Fernandez, I., Davidson-Arnott, R.G., Hesp, P.A., 2019. Is 're-mobilisation' nature restoration or nature destruction? A commentary. J. Coast. Conserv. 23, 1093-1103.

de Vries, S., Southgate, H.N., Kanning, W., Ranasinghe, R., 2012. Dune behavior and aeolian transport on decadal timescales. Coastal engineering, 67, 41-53. De Freitas, J. G. (2025). A global environmental history of coastal dunes (p. 260). Taylor & Francis.

Doyle, T.B., Woodroffe, C.D., 2018. The application of LiDAR to investigate foredune morphology and vegetation. Geomorphology 303, 106-121.

Doyle, T.B., Short, A.D., Ruggiero, P., Woodroffe, C.D., 2019. Interdecadal foredune changes along the southeast Australian coastline: 1942-2014. J. Mar. Sci. Eng. 7 (6), 177.

Doyle, T.B., Hesp, P.A., Woodroffe, C.D., 2024. Foredune morphology: regional patterns and surfzone-beach-dune interactions along the New South Wales coast. Australia. Earth Surface Processes and Landforms 49 (10), 3115-3138.

Everard, M., Jones, L., Watts, B., 2010. Have we neglected the societal importance of sand dunes? An ecosystem services perspective. Aquat. Conserv. Mar. Freshwat. Ecosyst. 20 (4), 476-487.

Froidefond, J.M., Prud'homme, R., 1991. Coastal erosion and aeolian sand transport on the Aquitaine coast, France. In: Aeolian Grain Transport: The Erosional Environment. Springer Vienna, pp. 147-159.

Gao, J., Kennedy, D.M., Konlechner, T.M., 2020. Coastal dune mobility over the past century: A global review. Progress in Physical Geography: Earth and Environment 44, 814-836.

Gardner, D.E., 1955. Beach-Sand Heavy-Mineral Deposits of Eastern Australia. Department of National Development, Bureau of Mineral Resources. Geology and Geophysics.

Gómez-Pina, G., Muñoz-Pérez, J.J., Ramírez, J.L., Ley, C., 2002. Sand dune management problems and techniques. Spain. J. Coast. Res. 36, 325-332.

González-Villanueva, R., Costas, S., Pérez-Arlucea, M., Jerez, S., Trigo, R.M., 2013. Impact of atmospheric circulation patterns on coastal dune dynamics, NW Spain. Geomorphology 185, 96-109.

Guerry, A.D., Ruckelshaus, M.H., Arkema, K.K., Bernhardt, J.R., Guannel, G., Kim, C.K., Spencer, J., 2012. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. International Journal of Biodiversity Science, Ecosystem Services & Management 8 (1-2), 107-121.

Hanley, M.E., Hoggart, S.P.G., Simmonds, D.J., Bichot, A., Colangelo, M.A., Bozzeda, F., Thompson, R.C., 2014. Shifting sands? Coastal protection by sand banks, beaches and dunes. Coast. Eng. 87, 136-146.

He, Y., Cai, F., Liu, J., Qi, H., Li, B., Zhao, S., Chen, X., 2022. Foredune height variations along the western coast of Taiwan Strait. Earth Surf. Process. Landf. 47 (11), 2765-2778.

Hesp, P., 2002. Foredunes and blowouts: initiation, geomorphology and dynamics. Geomorphology 48 (1-3), 245-268.
Hesp, P.A., 2013. Conceptual models of the evolution of transgressive dune field systems. *Geomorphology* 199, 138–149.

Hesp, P.A., & Martínez, M.L. (2007). Disturbance processes and dynamics in coastal dunes. *Plant disturbance ecology: the process and the response*, 215-247.

Hesp, P.A., Smyth, T.A., 2021. CFD flow dynamics over model scarps and slopes. *Phys. Geogr.* 42 (1), 1–24.

Hesp, P.A., Da Silva, M., Miot Da Silva, G., Bruce, D., Keane, R., 2022. Review and direct evidence of transgressive aeolian sand sheet and dunefield initiation. *Earth Surf. Process. Landf.* 47 (11), 2660–2675.

Houser, C., Hapke, C., Hamilton, S., 2008. Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. *Geomorphology* 100 (3-4), 223–240.

Houser, C., Wernette, P., Rentschlar, E., Jones, H., Hammond, B., Trimble, S., 2015. Post-storm beach and dune recovery: implications for barrier island resilience. *Geomorphology* 234, 54–63.

Hovenga, P.A., Ruggiero, P., Itzkin, M., Jay, K.R., Moore, L., Hacker, S.D., 2023. Quantifying the relative influence of coastal foredune growth factors on the US mid-Atlantic Coast using field observations and the process-based numerical model windsurf. *Coast. Eng.* 181, 104272.

Idier, D., Castelle, B., Charles, E., Mallet, C., 2013. Longshore sediment flux hindcast: spatio-temporal variability along the SW Atlantic coast of France. *J. Coast. Res.* 165, 1785-1790. https://doi.org/10.1021/si653021a.

Itzkin, M., Moore, L.J., Ruggiero, P., Hacker, S.D., 2020. The effect of sand fencing on the morphology of natural dune systems. *Geomorphology* 352, 106995.

Jackson, D.W.T., Cooper, J.A.G., 2011. Coastal dune fields in Ireland: rapid regional response to climatic change. *J. Coast. Res.* 293–297.

Jackson, N.L., Nordstrom, K.F., 2011. Aeolian sediment transport and landforms in managed coastal systems: a review. *Aeolian Res.* 3 (2), 181–196.

Jackson, N.L., Nordstrom, K.F., 2013. Aeolian sediment transport and morphologic change on a managed and an unmanaged foredune. *Earth Surf. Process. Landf.* 38 (4), 413–420.

Jackson, N.L., Nordstrom, K.F., Feagin, R.A., Smith, W.K., 2013. Coastal geomorphology and restoration. *Geomorphology* 199, 1–7.

Jackson, D.W., Costas, S., Gonzalez-Villanueva, R., Cooper, G., 2019a. A global "greening" of coastal dunes: an integrated consequence of climate change? *Glob. Planet. Chang.* 182, 103026.

Jackson, D.W., Costas, S., Guisado-Pintado, E., 2019b. Large-scale transgressive coastal dune behaviour in Europe during the little ice age. *Glob. Planet. Chang.* 175, 82–91.

Keshava, N., Mustard, J.F., 2002. Spectral unmixing. *IEEE Signal Process. Mag.* 19 (1), 44–57.

Kim, D., Lee, J.Y., Seo, J., Song, I., 2019. Recolonization of native and invasive plants after large-scale clearance of a temperate coastal dune field. *Appl. Geogr.* 109, 102030.

Konstantinou, A., Stokes, C., Masselink, G., Scott, T., 2021. The extreme 2013/14 winter storms: Regional patterns in multi-annual beach recovery. *Geomorphology* 389, 107828.

Lamy, A., Smyth, T.A., Robin, N., Hesp, P.A., 2024. Using computational fluid dynamics (CFD) to investigate airflow and sand transport on a human-made coastal foredune dominated by offshore wind: impact of the shape variability. *Coast. Eng.* 191, 104534.

Laporte-Fauret, Q., Lubac, B., Castelle, B., Michalet, R., Marieu, V., Bombrun, L., LaunEAu, P., Giraud, M., Normandin, C., Rosebery, D., 2020. Classification of Atlantic coastal sand dune vegetation using in situ, UAV, and airborne hyperspectral data. *Remote Sens.* 12 (14), 2222.

Laporte-Fauret, Q., Castelle, B., Michalet, R., Marieu, V., Bujan, S., Rosebery, D., 2021. Morphological and ecological responses of a managed coastal sand dune to experimental notches. *Sci. Total Environ.* 782, 146813.

Lettau, K., Lettau, 1978. Experimental and micrometeorological field studies of dune migration. *Exploring in the World's driest climate* 110–147.

Martínez, M.L., Psuty, N.P., 2004. Coastal dunes. Springer Verlag, Berlin.

Masselink, G., Castelle, B., Scott, T., Dodet, G., Suanez, S., Jackson, D., Floc'h, F., 2016. Extreme wave activity during 2013/2014 winter and morphological impacts along the Atlantic coast of Europe. *Geophys. Res. Lett.* 43 (5), 2135–2143.

Masselink, G., Brooks, S., Poate, T., Stokes, C., Scott, T., 2022. Coastal dune dynamics in embayed settings with sea-level rise—examples from the exposed and macrotidal north coast of SW England. *Mar. Geol.* 450, 106853.

Mathew, S., Davidson-Arnott, R.G., Ollerhead, J., 2010. Evolution of a beach-dune system following a catastrophic storm overwash event: Greenwich dunes, Prince Edward Island, 1936–2005. *Can. J. Earth Sci.* 47 (3), 273–290.

McFeeters, S.K., 2013. Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: a practical approach. *Remote Sens.* 5 (7), 3544–3561.

Mereu, S., Salvatori, E., Fusaro, L., Gerosa, G., Muys, B., Manes, F., 2009. An integrated approach shows different use of water resources from Mediterranean maquis species in a coastal dune ecosystem. *Biogeosciences* 6 (11), 2599–2610.

Moulton, M.A., Hesp, P.A., Da Silva, G.M., Keane, R., Fernandez, G.B., 2020. Effects of the increase in vegetation cover and impact of rabbits on the geomorphology of parabolic dunes (Young husband peninsula-SA, Australia). *J. Coast. Res.* 95 (SI), 108–112.

Munoz-Perez, J.J., Navarro, M., Roman-Sierra, J., Tejedor, B., Rodriguez, I., Gomez-Pina, G., 2009. Long-term evolution of a transgressive migrating dune using reconstruction of the EOF method. *Geomorphology* 112, 167–177.

Nahon, A., Idier, D., Senechal, N., Fénies, H., Mallet, C., Mugica, J., 2019. Imprints of wave climate and mean sea level variations in the dynamics of a coastal spit over the last 250 years: cap ferret, SW France. *Earth Surf. Process. Landf.* 44 (11), 2112–2125.

Navarro, M., Muñoz-Pérez, J.J., Román-Sierra, J., Tsoar, H., Rodríguez, I., Gómez-Pina, G., 2011. Assessment of highly active dune mobility in the medium, short and very short term. *Geomorphology* 129 (1–2), 14–28.

Nicolae, Lerma, A., Rosebery, D., 2021. Caractérisation de la variabilité interannuelle des flux sédimentaires potentiels d'origine éolienne sur le littoral aquitain: analyse à partir des observations du sémaphore du Cap Ferret au cours de la période récente 2005-2020. Rapport final. BRGM/RP-71197-FR, p. 27, 11 fig., 3 tab.

Nicolae Lerma, A., Ayache, B., Ulvoas, B., Paris, F., Bernon, N., Bultreau, T., Mallet, C., 2019. Pluriannual beach-dune evolutions at regional scale: Erosion and recovery sequences analysis along the Aquitaine coast based on airborne LiDAR data. *Cont. Shelf Res.* 189, 103974.

Nicolae Lerma, A., Castelle, B., Marieu, V., Robinet, A., Bulteau, T., Bernon, N., Mallet, C., 2022. Decadal beach-dune profile monitoring along a 230-km high-energy sandy coast: Aquitaine, Southwest France. *Appl. Geogr.* 139, 102645.

Nordstrom, K.F., 2000. Beaches and Dunes of Developed Coasts. Cambridge University Press.

Nordstrom, K.F., Jackson, N.L., Hartman, J.M., Wong, M., 2007. Aeolian sediment transport on a human-altered foredune. *Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group* 32 (1), 102–115.

Ollerhead, J., Davidson-Arnott, R., Walker, I.J., Mathew, S., 2013. Annual to decadal morphodynamics of the foredune system at Greenwich Dunes, Prince Edward Island, Canada. *Earth Surf. Process. Landforms* 38 (3), 284–298.

Osswald, F., Dolch, T., Reise, K., 2019. Remobilizing stabilized island dunes for keeping up with sea level rise? *J. Coast. Conserv.* 23 (3), 675–687.

Otsu, Y., Furuhama, Y., Hoshina, S., Ito, S., 1979. Propagation measurements and TV-reception tests with the Japanese broadcasting satellite for experimental purposes. *IEEE Trans. Broadcast.* 4, 113–120.

Pedreros, R., Howa, H.L., Michel, D., 1996. Application of grain size trend analysis for the determination of sediment transport pathways in intertidal areas. *Mar. Geol.* 135 (1–4), 35–49.

Pickart, A.J., Hesp, P.A., 2019. Spatio-temporal geomorphological and ecological evolution of a transgressive dunefield system, northern California, USA. *Glob. Planet. Chang.* 172, 88–103.

Provoost, S., Van Til, M., Deronde, B., Knotters, A., 2005. Remote sensing of coastal vegetation in the Netherlands and Belgium. *Proceedings' Dunes and Estuaries* 139–148.

Provoost, S., Jones, M.L.M., Edmondson, S.E., 2011. Changes in landscape and vegetation of coastal dunes in Northwest Europe: a review. *J. Coast. Conserv.* 15 (1), 207–226.

Robin, N., Billy, J., Castelle, B., Hesp, P., Nicolae, Lerma A., Laporte-Fauret, Q., Marieu, V., Rosebery, D., Bujan, S., Destribats, B., Michalet, R., 2021. 150 years of foredune initiation and evolution driven by human and natural processes. *Geomorphology* 374, 107516. https://doi.org/10.1016/j.geomorph.2020.107516.

Robin, N., Billy, J., Nicolae, Lerma A., Castelle, B., Hesp, P.A., Rosebery, D., Miot Da Silva, G., 2023. Natural remobilization and historical evolution of a modern coastal transgressive dunefield. *Earth Surf. Process. Landf.* 48 (5), 1064–1083.

Ruessink, G., Schwarz, C.S., Price, T.D., Donker, J.J., 2019. A multi-year data set of beach-foredune topography and environmental forcing conditions at Egmond aan zee, The Netherlands. *Data* 4 (2), 73.

Sallenger Jr., A.H., 2000. Storm impact scale for barrier islands. *J. Coast. Res.* 890–895.

Schwarz, C., Brinkkemper, J., Ruesink, G., 2018. Feedbacks between biotic and abiotic processes governing the development of foredune blowouts: a review. *J. Mar. Sci. Eng.* 7 (1), 2.

Seabloom, E.W., Ruggiero, P., Hacker, S.D., Mull, J., Zarnetske, P., 2013. Invasive grasses, climate change, and exposure to storm-wave overtopping in coastal dune ecosystems. *Glob. Chang. Biol.* 19 (3), 824–832.

Smyth, T.A., Rooney, P., Yates, K.L., 2023. Dune slope, not wind speed, best predicts bare sand in vegetated coastal dunes. *J. Coast. Conserv.* 27 (4), 27.

Szkornik, K., Gehrels, W.R., Murray, A.S., 2008. Aeolian sand movement and relative sea-level rise in ho Bugt, western Denmark, during the Little ice age. *The Holocene* 18 (6), 951–965.

Talavera, L., Costas, S., Ferreira, Ó., 2024. Controls on blowout evolution in southern Portugal: A 49-year analysis. *Sci. Total Environ.* 955, 176769.

Tastet, J.P., Pontee, N., 1998. Morpho-chronology of coastal dunes in medoc. A new interpretation of holocene dunes in southwestern France. *Geomorphology* 25, 93–109.

Vandenhove, M., Castelle, B., Lerma, A.N., Marieu, V., Martins, K., Mazeiraud, V., 2025. Field measurements of wave and flow dynamics along a high-energy meso-macrotidal coast adjacent to a large estuary mouth. *Estuar. Coast. Shelf Sci.* 317, 109205.

van Kuik, N., de Vries, J., Schwarz, C., Ruesink, G., 2022. Surface-area development of foredune trough blowouts and associated parabolic dunes quantified from time series of satellite imagery. *Aeolian Res.* 57, 100812.
Walker, I.J., Davidson-Arnott, R.G.D., O'Bauer, B., Hesp, P.A., Delgado-Fernandez, I., Ollerhead, J., Smyth, T.A.G., 2017. Scale-dependent perspectives on the geomorphology and evolution of beach-dune systems. Earth Sci. Rev. 171, 220253.

Watanabe, D.S., Barboza-Pinzon, E.G., Hesp, P.A., Schossler, V., Salgado, E.T., 2025. Evolution of coastal transgressive aeolian sand sheets over 75 years (1948-2023) at Concheiros Barrier-Southern Brazilian coast. Geomorphology 470, 109520.

Yousefi Lalimi, F., Silvestri, S., Moore, L.J., Marani, M., 2017. Coupled topographic and vegetation patterns in coastal dunes: remote sensing observations and ecomorphodynamic implications. J. Geophys. Res. Biogeosci. 122 (1), 119–130.

Zarnetske, P.L., Ruggiero, P., Seabloom, E.W., Hacker, S.D., 2015. Coastal foredune evolution: the relative influence of vegetation and sand supply in the US Pacific northwest. J. R. Soc. Interface 12 (106), 20150017.

Further reading

Cohn, N., Ruggiero, P., De Vries, S., Kaminsky, G.M., 2018. New insights on coastal foredune growth: the relative contributions of marine and Aeolian processes. Geophys. Res. Lett. 45, 4965–4973.