1 Geomorphological control of sandy beaches by a mixed-

2 energy tidal inlet

3 Olivier Burvingtª, Alexandre Nicolae Lermaᵇ, Bertrand Lubacª, Cyril Malletᵇ, Nadia Senechalª

4 a. Université de Bordeaux, UMR CNRS 5805, EPOC laboratory, Pessac, France

5 b. Bureau de Recherche Géologique et Minière, Pessac, France

6 Corresponding author: Olivier Burvingt, olivier.burvingt@u-bordeaux.fr

7

8 Abstract

9 Coastal areas adjacent to tidal inlets are highly valuable areas for marine ecosystems where
10 a wide range of social and economic activities can be found. These areas can be
11 differentiated from open coast due to their specific morphological behaviour caused by a
12 complex nearshore bathymetry and interactions between wave and tidal forcing. The
13 understanding of these interactions is usually limited by the lack of a systemic approach and
14 the scarce availability of comprehensive datasets covering subtidal, intertidal and supratidal
15 areas at large spatial and temporal scales. Here, the complementary use of different
16 bathymetric and topographic datasets and the development of innovative methods using
17 satellite imagery, offers the opportunity to develop a comprehensive understanding of the
18 timescales and the key processes involved in the dynamics of tidal inlets and its control on
19 the downdrift coast. The use of satellite-derived bathymetric maps, computed over the last
20 two decades, showed two sequences of 8 and 9 years where large sandy shoals migrated
21 along the tidal inlet and welded onto the downdrift coast. Although the study period covers
22 one of the most energetic winter over the last decades, the most significant sediment
23 volume changes (±300 m³/m) observed along the three kilometres of beach located south to
24 the inlet were mostly attributed to the migration and welding of these sandy shoals,
25 whereas the offshore wave forcing was of secondary importance. It was also demonstrated
26 that these migrating and welding events had an impact on the pattern of the nearshore
27 bathymetry and sandbars down to 10 kilometres south to the inlet. Primary welding events
28 near to the inlet are associated to the formation of kilometre-long and alongshore uniform
29 nearshore sandbars that subsequently migrate further down the coast causing secondary
30 welding events. The ability to understand and define the spatial and temporal boundaries at
31 which beach behaviour is controlled by a local tidal inlet gives the opportunity to develop
32 sediment compartment approach in order to make accurate predictions of future beach
33 behaviour.

34

35

Keywords

36 tidal inlet; beach morphodynamics; shoal migration; shoal welding; downdrift coast;

37 satellite-derived bathymetry; LiDAR data

38

39

# 1. Introduction

40 Coastal areas adjacent to tidal inlets represent a valuable resource for the marine

41 ecosystems and show a strong socio-economic interest for their coastal communities

42 (navigation, fishing, aquaculture, tourism, recreational activities, etc.). These coastal areas

43 are generally characterized by complex and significant morphological changes (FitzGerald,
44 1984; Fenster and Dolan, 1996; Hicks et al., 1999; Elias et al., 2006; Hein et al., 2019). The
45 temporal scale of these changes can span days to centuries, and their amplitude is generally
46 controlled by the dynamics of the ebb-tidal delta where sandy shoals are migrating under
47 the influence of both tidal and wave-induced currents (Hine, 1979; FitzGerald et al., 1984;
48 Fenster and Dolan, 1996; Kana et al., 1999; Gaudiano and Kana, 2001; Borrelli and Wells,
49 2003; Kana and McKee, 2003). Several studies showed that ebb-tidal deltas have a cyclic
50 evolution (Fitzgerald 1984; Michel and Howa, 1997; Ridderinkhof et al. 2016a; Elias et al.,
51 2019; Velasquez-Montoya et al., 2020), demonstrating the importance of autocyclic
52 processes in the morphodynamics of coastal areas adjacent to tidal inlet environments. This
53 cyclic evolution can be illustrated by the alternative closure/opening of tidal channels
54 (Fortunato et al. 2014; Lenstra et al., 2019), the generation of swash bars and intertidal sand
55 waves that evolve to hooked sand ridges at the upstream adjacent coast (FitzGerald, 1984;
56 Kana et al., 1999; Gaudiano and Kana, 2001, Nahon et al., 2019), and/or the formation and
57 migration of successive sequences of sandy shoals that attach to the downdrift coast (Robin
58 et al., 2009;; Elias et al., 2019). The impact of the welding of these sandy shoals on the
59 dynamics of the downdrift coast depends on the synchronization of sediment supply and
60 migration events (Houser, 2009), along with the size of the shoals that will define the
61 temporal and spatial amplitude of the beach width increase (Galiforni-Silva et al., 2020). The
62 migration and the welding of the sandy shoals can also largely influence the local
63 hydrodynamics, that can either result in the growth or retreat of the beach along the
64 downdrift coastline (Davidson-Arnott and Van Heyningen, 2003; Robin et al. 2007; Robin et
65 al., 2009). The use of process-based models showed that the growth and migration of sandy
66 shoals depend on the local imbalance between the nearshore bathymetry and the wave
67 conditions (Bertin et al., 2009; Dodet et al., 2013; Ridderinkhof, 2016b), expressing the role
of incident wave energy in the morphodynamics of these environments. Furthermore other controlling factors such as the geometry of the inlet (Ridderinkhof et al., 2014) and the sediment grain size (Herrling and Winter, 2018) can play a role in the dynamics of tidal inlets. Although tidal inlet morphodynamics can show a seasonal behaviour (Fortunato et al., 2021), major morphological changes along tidal inlets often occur at multiannual (Dolphin et al., 2007; Harrison et al., 2017; Lenstra et al., 2019) and decadal timescales (Garel et al., 2014; Nahon et al., 2019), and involve processes that are site-specific (Ridderinkhof et al., 2014), implying the need of multi-annual bathymetric datasets to have a comprehensive understanding of the key drivers and controlling factors of each tidal inlet.

Surveying the bathymetry of tidal inlets and their deltas remains difficult due to the complexity of their rapidly changing isolines at relatively shallow depths and over large spatial scales. Bathymetric surveys have been historically carried out for navigation purpose. However, these surveys generally cover a partial area of the tidal inlet and their ebb deltas due to their difficulties of surveying large areas over shallow waters. The increasing use of satellite imagery gives the opportunity to meet the challenges of having a continuous and comprehensive monitoring of tidal inlets at larger spatial scales. Using satellite images, inlet deltas and sandy shoals can be either visually identified (Natesan et al., 2014; Ridderinkhof et al. 2016a; Ford and Dickson, 2018) or modelled using wave dispersion formulas (Holland, 2001; Leu and Chang, 2005; Almar et al., 2021) and/or shoreline detection algorithm (Vos et al., 2019; Heimhuber et al., 2021). Other methods based on remote sensing reflectance to water depth log-linear relationships (Stumpf et al., 2003; Lyzenga et al., 2006; Geyman and Maloof, 2019) can be used to model the bathymetry of coastal areas with optically shallow waters (Lafon et al., 2000; Capo et al., 2014; Caballero and Stumpf, 2021). The small number of comprehensive studies (Elias et al., 2019) on the understanding of the complex
interactions between tidal inlets and the adjacent coasts are also due to the limited availability of bathymetric and topographic datasets over a multi-annual timescale and large spatial scale (Hicks et al., 1999). These interactions are often analysed using model outputs and focus on the subtidal morphodynamics, or are limited to relatively short-time and small spatial scales when field data are used (Michel and Howa, 1997; Dolphin et al., 2007; Robin et al., 2009). Combining the use of satellite-derived bathymetry digital models, Airborne LiDAR data has the advantage of offering a large spatial coverage of the upper intertidal area of the beach and dune and can be used to produce digital elevation models (Pye and Blott, 2016; Burvingt et al., 2017; Nicolae Lerma et al., 2019). Associating the satellite-derived and topographic digital elevation models, along with bathymetric digital models available from surveyed data when available, gives the opportunity to map and quantify the morphological changes along tidal inlet and the adjacent coasts. Merging bathymetric and topographic datasets also gives the opportunity to understand the complex interactions between the subtidal, intertidal and subaerial areas of tidal inlet environments and the adjacent coasts.

Based on a unique multi-annual and multi-source dataset covering tens of kilometers, this study aims to integrate complementary topographic and bathymetric digital elevation models obtained from different surveying and analytical methods to study the morphodynamics of a mixed-energy tidal inlet and the downdrift coast, from shallow water to the dunes. First, satellite-derived bathymetry (SDB) models from 2001 to 2020 are used to study two sequences of sandy shoal migration along the tidal inlet and its welding on the downdrift coast at a kilometre scale. Second, the multi-annual and seasonal morphological changes along the downdrift coast are analysed using bathymetric and topographic digital elevation models from 2011 to 2020, and compared to multi-annual wave forcing and
116 energetic winters. Third, similar analyses are developed over larger spatial scales (10 117 kilometres) to estimate the spatial extent of the morphological processes occurring along 118 the tidal inlet and their impact on the downdrift beach and dunes.

119

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

Figure 1: Location map of the study area (a), covering the Arcachon inlet and the downdrift coast (b). The names of the key entities and the different areas covered by the bathymetric, topographic, and SDBs digital elevation models (Zoom 1, 2 and 3) used in this study are presented in (c). An aerial picture of the study area is presented in (d). Time-series of the offshore modelled wave power, P expressed in kW/m, with the acquisition dates of the
120

121

## 2. Study area, datasets and methods

122

### 2.1. Study area

123

124

125

126

127

128

129

130

131

132

133

134

Located along the Atlantic Ocean in SW France (44°40′N, 1°10′W), the Arcachon inlet can be defined as a mixed-energy tidal inlet (Hayes, 1980), that is 8 kilometres wide and is backed by a semi-enclosed lagoon of approximately 180 km² (Fig. 1b). This inlet disrupts the southerly longshore drift of the 110 km-long stretch of sandy coast (Idier et al., 2013), and wave-driven currents can reach 1.5 m/s along this inlet (Senechal et al., 2013). Tidal range varies approximately from 1.5 m during neap tide conditions to 5 m during spring tide conditions (Senechal et al., 2013) at this meso- to macro-tidal site. The wave climate is energetic with a dominant W to NW incidence with a mean annual significant wave height of 1.8 m and an average peak period of 11.5 s (Nicolae Lerma et al., 2015). Winter seasons are characterised by energetic conditions with a maximum monthly-averaged value of 2.4 m in January (Castelle et al., 2018), and a significant wave height that can reach 8 m during extreme storm events.

135

136

137

138

139

140

The inlet is composed of three main subtidal morphological features (Fig. 1c), i.e., the spit platform, the Ebb-tidal and the Flood-tidal deltas, connected to a 9 m-deep northern channel (ebb channel) and an 11 m-deep southern channel (flood channel). According to Michel and Howa (1997), the evolution of these channels follows a two-stage cycle of approximately 80 years based on old hydrographic charts analysis that is mainly driven by the wave-induced southward littoral drift. In the first stage, two channels are present, as in
the current configuration, and they progressively migrate toward the south resulting in the erosion of the downdrift coast. During the second stage, only one dominant channel is present and is associated to a larger ebb delta resulting in the accretion of the downdrift coast. Capo et al. (2014) also identified major sediment by-passing events across the channels every 6 to 10 years, with the migration of sandy shoals across the southern channel to the downdrift coast along a SE axis. Nahon et al. (2019) also showed that the elongation and retreat of Cap Ferret sand spit, located at the north of the inlet (Fig. 1b), were correlated with the variations in swell direction caused by the North Atlantic Oscillation and the variations in sea level rise rates.

The downdrift coast, from La Salie beach to Biscarrosse beach (Fig 1.c, Zoom 3), consist of high energy, meso- to macro- tidal open sandy beaches backed by large coastal dunes up to 25 m above mean sea level, and from 200 to 300 m wide. Alike the rest of the Aquitaine dune system, first established in the 19th century, these dunes have been actively managed since 1980 by the French Forest Office (Bossard and Nicolae Lerma, 2020; Laporte-Fauret et al., 2021; Robin et al., 2021) except from the stretch of the Trencat dunes (Fig 1.c) that have been kept unmanaged over the past decades. A Wharf used for wastewater discharge was built across La Salie beach, and serves here as a geographical reference to locate the different areas covered by the various datasets used in this study. Biscarrosse beach, located 10 kilometers south of the inlet, is referred by local coastal managers (Observatoire de la Côte Aquitaine and Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement) as the southern limit of the sedimentary cell that comprises the Arcachon inlet and the downdrift coast.

## 2.2. Datasets and methods
164 2.2.1. Bathymetric dataset

165 Echosounding bathymetry data of the Arcachon inlet are collected by the Direction

166 Départementale des Territoires et de la Mer (DDTM) and the Syndicat Intercommunal du

167 Bassin d'Arcachon (SIBA) every year, from April to July, when wave and weather conditions
168 are the most appropriate. Single beam echosounding data are corrected for roll, pitch, yaw,

169 GPS latency, and tidal height, the vertical uncertainty is assumed to be less than 15 cm.

170 These surveys are mainly carried out for the monitoring of navigation channels and the
171 repositioning of navigation buoys, thus each bathymetric survey only offers a partial cover of

172 the entire tidal inlet. For this study, bathymetric datasets were both used for producing SDB

173 models, as discussed in Section 2.2.3, and to provide bathymetric DEMs with a horizontal
174 resolution of 10 m. The complexity of the isobaths around the ebb delta requires the use of a

175 high-resolution bathymetric dataset to produce accurate bathymetric DEMs. Since these
176 requirements are not guaranteed here, bathymetric DEMs were only generated for the

177 downdrift coast (Zoom 2, Fig. 1c) where the geometry of the isobaths is less complex. The
178 interpolation of bathymetry data points collected in this area between April and July

179 provides 10 bathymetric DEMs from 2011 to 2020 covering elevations, Z (m), from -10 to 0.5
180 m according to the French official altimetry reference NGF/IGN69. These DEMs were used in

181 this study to calculate sand volume changes over the subtidal area of the downdrift coast.

182 These volumes changes were either presented as total volume changes, V, expressed in
183 volume per meter (m³/m), or longshore volume changes, Vl (m³/m). Total volume changes

184 are calculated over the whole corresponding area (Zoom 2), while longshore volume changes

185 were integrated along each cross-shore transect, and are presented as longshore volume
186 profiles to study the longshore variability in cross-shore volume change along the downdrift

187 coast.
188 2.2.2. Topographic datasets

189 Airborne LiDAR campaigns were carried out on demand of the Observatoire de la Côte de
190 Nouvelle-Aquitaine (OCNA) along the entire Aquitaine coast in 2011, 2014, 2016, 2017,
191 2018, 2019 and 2020. The datasets cover a large area, extending from Mean Sea Level to 1
192 km inland, with elevations, Z (m), ranging from 0.5 to 30 m, corresponding to the beach and
193 the dune areas. All campaigns occurred in autumn, when beaches are generally at their most
194 accreted state following long periods of small to moderate incident wave energy during
195 summer, except for the 2011 campaign, that was executed in early spring when beaches
196 are at their most depleted state (Senechal et al., 2009), and the 2019 campaign that was
197 carried out in December. In comparison with the other datasets, the 2011 and 2019 data
198 therefore include the seasonal morphological and sediment volume changes on top of their
199 inter-annual variations, potentially resulting in the overestimation or underestimation of
200 these changes. The accuracy of the LiDAR data was controlled using d-GPS data uniformly
201 distributed along the surveyed area and resulted in an absolute error ranging from 10 to 20
202 cm (Nicolae Lerma et al., 2019). These data were interpolated to produce topographic DEMs
203 with a spatial resolution of 1 m along the downdrift coast of the inlet (Zoom 2 and 3, Fig. 1c).
204 Similarly to the bathymetric datasets, these DEMs were used in this study to calculate sand
205 volume changes over the intertidal and dune areas of the downdrift coast. These volumes
206 changes were also presented as total volume changes, V (m³/m) or longshore volume
207 changes, V<sub>L</sub> (m³/m).

208 2.2.3. Satellite derived bathymetry models (SDB models)

209 The publicly available satellite images used in this study to produce SDB models were
210 captured by the Landsat-7/8 and Sentinel-2A/2B satellite missions launched by the National
Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), respectively. The selected images were atmospherically corrected using the dark spectrum fitting atmospheric correction method (DSF), associated with the open access ACOLITE processor, to process satellite images from Level-1C to Level-2A (Vanhellemont and Ruddick, 2018). The cluster-based regression method (Geyman and Maloof, 2019) was shown to be the empiric algorithm that gives the best performances for estimating water depth from satellite imagery following a log-linear relationship along the Arcachon tidal inlet (Lubac et al., 2022). This algorithm can be considered as an extension of the linear regression model using log-transformed bands originally developed by Lyzenga (1981). An unsupervised k-means clustering is used to segment the study site into different optical classes for which class-specific linear regression models are calibrated. This method allows generating SDB models covering the entire inlet and the subtidal area of the downdrift coast (Zoom 1, 2 and 3) over elevations, ZSDB, from -8 to 0 m. The vertical uncertainty was quantified based on a multi-scene approach that takes into account both the errors associated with the regression models and the errors due to the out-of-scope optical variability of the calibration dataset (Lubac et al., 2022). These vertical uncertainties range from 0.5 to 1 m, varying for each pixel within a SDB model and from a SDB model to another. Two examples showing the comparison of SDB models and bathymetric DEMs along the downdrift coast (Zoom 2) are presented in Figure 2. Despite some differences in absolute elevation, the key morphological features such as sandy shoals and sand bars present on the bathymetric DEM are well represented by the SDB models. These vertical uncertainties being considered to be too large to calculate sand volumes, different isobaths (from -3 to -6 m) extracted from the SDB models were used in a semi-quantitative way in this study. These specific isobaths were selected because they were the most appropriate to observe the key sandy shoals and
235

sandbars, offering a unique opportunity to map and follow their migration from the inlet and

236

welding to the downdrift coast.

237

<figure><img src="image_2.png" /></figure>

Figure 2: Comparison of the bathymetric DEMs (Zoom 2) acquired through two different methods based on satellite imagery (SDB, left panel) and surveyed bathymetry data points (Bathy, right panel) in August 2014 and September 2019. The mean sea level (Z=0 m) is determined according to the NGF reference. Areas that are located above the mean sea level (Z = 0 m) are represented in black colour, while the areas below -8 m are represented
in white colour. The location of the Wharf (W) is represented by a black dashed line as a reference to locate the area covered in this figure.

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

## 3. Results

3.1. Sequences of sandy shoal migration and welding onto the downdrift coast

3.1.1. Sequence of sandy shoal migration and welding onto the downdrift coast from 2011 to 2020

Three isobaths (from -3 to -5m) were extracted from SDB models to map and describe a sequence of sandy shoal migration and welding onto the downdrift coast from 2011 to 2020 (Fig. 3). In 2011, a large sand shoal (from -1.265 to -1.275°W, from 44.53 to 44.55°N) can be observed across the tip of the southern channel that is divided into two sub-channels (Fig. 3a). Smaller sandbars can also be found between this sandy shoal and the Arguin sandbank, suggesting a continuous input of sediment between the two. South to this sandbar (-1.26°W, 44.525°N), the nearshore bathymetry showed a large sandy platform with a triangular shape peaking at dozens of meters north to the Wharf. The offshore sandbar migrated along a SE axis between 2013 and 2014 (Fig. 3b and 3c), while the sandy platform had gradually retreated. On the SDB model of 2015 (Fig. 3d), the offshore sandbar is fully merged with the coast, closing the southern sub-channel of the southern channel, and building a new sandy platform north to the previous one, which
258

has almost fully retreated. No other significant sandbar migration was observed from

259

2016 to 2020, although smaller sandbars can be observed across the southern channel

260

between the Arguin sandbank and the sandy platform on the SDB models of 2016 (Fig. 3e), 2018 (Fig. 3f), and 2019 (Fig. 3g). The shoal was fragmented in multiple sandbars in

261

2016 and 2018 and was reformed in 2019, before retreating in 2020 (Fig. 3h). The

262

comparison of the 2015 and 2020 SDB models shows a southward migration of the

263

southern channel and the peak of the sandy platform that were coupled with the growth

264

of the Arguin sandbank along a SW axis.

265

266

This sequence of 9 years showed small or large sandy shoals migrate along a SE axis

267

from the southern part of the Arguin sandbank to the downdrift coast causing an

268

anticlockwise rotation of the flood channel. In response to this significant input of

269

sediment, large morphological changes can be observed along the downdrift coast that is

270

characterized by a large sandy platform that varies in size and shape over the years. The

271

size and the position of the migrating sandbars also have an impact on the very end of

272

the southern channel that can either be divided into two sub-channels (i.e. 2011, Fig. 3a),

273

W-oriented (i.e. 2015, Fig. 3d), or SW oriented (i.e. 2020, Fig. 3h).

274
<figure><img src="image_3.png" /><figcaption>Figure 3: Maps of three isobaths ($Z_{SDB}$ = -3, -4 and -5m) extracted from eight SDB models along the tidal inlet and the downdrift coast (Zoom 1) from 2011 to 2020. Areas that are located above the mean sea level ($Z = 0$ m) are represented in black colour, while the areas below this level are represented in white colour. The location of the Wharf (W) is represented by a black dashed line and also serves as a reference to locate the area covered in this figure (Zoom 1).</figcaption></figure>
276

277

3.1.2. Comparison of two sequences of sandy shoal migration and welding onto the downdrift coast from 1999 to 2007 and from 2009 to 2020

278

279

280

281

282

283

284

Although the quality and accuracy of SDB models are much lower when they are based on satellite images acquired before 2013, the -3 and -4m isobaths were extracted from SDB models covering the period of 2001 to 2009 to show the similarities between this sequence of sandy shoal migration and welding and the one described in the previous paragraph (Fig. 4). Due to the lack of bathymetric data points in 1999, and therefore the absence of a SDB model in 1999, a non-processed satellite image of 1999 was added to complete the sequence (Fig. 4a).

285

286

287

288

289

290

291

292

293

294

295

296

297

298

Strong similarities can be observed between the two sequences of 1999-2007 and 2009-2020. In 1999 and 2009 (Fig. 4a and 4e), two sandy shoals can be observed at similar locations (-1.265°W, 44.54°N) across the southern channel that is divided into sub channels. In 2003 and 2014 (Fig. 4b and 4f), the same shoals migrated to the coast and two sandy platforms at similar locations (-1.26°W, 44.52°N) can be observed north to the Wharf. In 2005 and 2015 (Fig. 4c and 4g), the two sandbars were merged to the coast around the same area (-1.26°W, 44.53°N), closing the second sub-channel of the southern channel, and both sandy platform severely retreated. In 2007 and 2020 (Fig. 4d and 4h), relatively large sandbars were separated from the Arguin sandbank and started to migrate. These results show that the processes of sandbar formation, migration and welding, observed during the sequence of 8 years from 1999 to 2007, were very similar compared to the ones observed during the sequence of 11 years from 2009 to 2020, showing that the dynamics of the downdrift coast was punctuated by multi-annual events of sandy shoals migration and welding over, at least, the last 20 years.
<figure><img src="image_4.png" /></figure>

Figure 4: Maps of two isobaths ($Z_{SDB}$ = -3 and -4 m) extracted from eight SDB models along the tidal inlet and the downdrift coast (Zoom 1) from 2001 to 2020. Due to the lack of a SDB model in 1999, a non-processed satellite image of 1999 was added to show the morphological similarities with the isobaths of the 2009 SDB model.

300

301

3.1.3. Impact of the sequences of sandy shoal migration and welding on the nearshore bathymetry along the downdrift coast

302
Deeper isobaths (-5 and -6 m) were extracted from SDB models covering a larger area (Zoom 3) to study the impact of the migration and welding events described above, on the nearshore bathymetry further down the downdrift coast (Fig. 5). These deeper isobaths offer the opportunity to identify the shape and location of the nearshore outer sandbars. In 2014 (Fig. 5a), a new longshore and offshore sandbar was formed at the tip of the offshore sandbar (S1) that migrated towards the coast (-1.27°W, from 44.515 to 44.525°N). Two years later in 2016 (Fig. 5b), this longshore uniform sandbar (S2) considerably extended to the south down to 44.49°. Another smaller sandbar (S3) can also be observed in between the longshore sandbar and the coast around the Wharf (-1.26°W, from 44.51 to 44.52°N). In 2018 (Fig. 5c), a large volume of sand was displaced southward along the downdrift coast (-1.26°W, from 44.515 to 44.525°N), and the northern section of S2 to S3 were merged, while the southern section of S2 was breached (-1.26°W, 44.49°N) and a new sandbar S4 was formed (-1.265°W, 44.48°N). Three-dimensional sandbars can be observed further south (-1.26°W, from 44.42 to 44.46°N). In 2020 (Fig. 5d), S2 and S3 migrated to the coast creating a rather large longshore-oriented sandy shoal (-1.255°W, from 44.495 to 44.51°N) that welded onto the coast after having migrated 2 kilometres downdrift (44.5°N) while other sandbars like S4 or the three-dimensional sandbars were linearized.

The same isobaths extracted from SDB models based on satellite images acquired before 2013 are not presented here due to their poor quality, but similarly to the results presented in the previous sections, a similar morphological development can be observed among this sequence and one of 2001-2007. The formation of a longshore uniform sandbar comparable to S2 can be observed at the same place, and this sandbar also migrated southward and welded onto the coast 6 years later at a similar location than S2. These observations showed that the migration and welding of sandy shoals in the vicinity of the inlet, as observed in the
327

previous section, caused themselves the formation, migration and welding of multi-kilometers long and alongshore-uniform sandbars that deeply impacted the nearshore bathymetry for 10 kilometers down the downdrift coast. Similarly to the previous results, strong similarities were found between sequences that occurred over the last two decades but were not shown here because of the poor quality of the SDB models.

328

329

330

331

332

<figure><img src="image_5.png" /><figcaption>Figure 5: Maps of three isobaths ($Z_{SDB}$ from -4 to -6 m) extracted from four SDB models along</figcaption></figure>
the tidal inlet and the downdrift coast and the subsequent kilometres from 2014 to 2020.

The location of the Wharf (W) was added as a reference to locate the area covered by this figure (Zoom 3).

333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352

3.2. Seasonal to multi-annual morphological changes along the downdrift coast

3.2.1. Multi-annual sand volume changes along the downdrift coast in response to wave and wind forcing from 2011 to 2020

In the previous section, the dynamics of the downdrift coast was shown to be strongly impacted by multi-annual events of sandy shoals migration and welding. Here, the impact of offshore wave and wind forcing on the intensity of sediment transport characterized by sediment volume changes along the downdrift coast (Zoom 2) over the last 10 years is investigated. Time series of offshore wave power, wind speed and gross volume changes of sand are compared from 2011 to 2020 (Fig. 6). Wave parameters were extracted from a regional wave hindcast datasets and were computed using a fully coupled third-generation Wind Wave Model (WWM, Roland et al., 2012) built within the SCHISM framework (Zhang et al., 2016). This model showed excellent skills in hindcasting buoy measurements within the Bay of Biscay (Pezerat et al., 2021). Wind speed values were measured from a weather station installed on a semaphore located at the northern part of the inlet (Cap Ferret), and were provided by Météo-France. The gross volume change corresponds to the sum of the absolute value of the bathymetric/topographic changes between each DEM, and represents a proxy of the intensity of sediment transport whether its erosion or accretion of sand.
Results show that the study area is exposed to incident wave and wind forcing that are characterized by seasonal and inter-annual variations (Fig. 6a and 6b). Years can be divided into summer months from April to September where wave power and wind speed were rather low (9.6 kW and 4.5 m/s in average) and show relatively small inter-annual variations (from 6.7 to 11.2 kW and from 4.2 to 5.1 m/s) in comparison to the winter season. Wave power was larger over winter months from October to March (32.7kW and 5.2 m/s in average), and also showed larger inter-annual variations (from 23.4 to 49.5 kW and from 4.7 to 6.0 m/s). The 2013/14 and 2019/2020 winters were the two most energetic and most windy winters from 2011 to 2020, with an averaged wave power of 49.5 and 42.1 kW and averaged wind speed of 6.0 and 5.9 m/s, respectively. Incident waves were mainly directed from the north-west during these two winters, with an average wave direction of 287 and 289°, respectively.

The multi-annual time-series of wave energy and wind speed are compared to the subtidal (Fig. 6c), intertidal and dune (Fig. 6d) gross volumes time-series computed over the downdrift coast of the inlet (Zoom 2). Results show that energetic and windy winters are not necessary related to the highest intensity of sediment transport. For example, the period of 2011/2012 where gross volume change was the highest over the subtidal area ($7.3 \times 10^4 \text{ m}^3$) corresponds to the least energetic winter (23.4 kW) over the last 10 years. Similarly, the second largest gross volume changes over the intertidal ($4.3 \times 10^5 \text{ m}^3$) and dune areas ($7.1 \times 10^5 \text{ m}^3$) between 2018 and 2019 correspond to the second lowest windy and one of the least energetic winter. The morphodynamics of beach and dunes are controlled by many parameters, thus the interpretations made here on the relation between morphological changes and wave/wind forcing is limited by the availability of specific hydrodynamic and aeolian measurements. Nevertheless, the discrepancy between the offshore wave power,
377 the wind speed and the gross volume changes from 2011 to 2020 suggest that hydrodynamic
378 and morphodynamic processes along the downdrift coast of the inlet are strongly nonlinear,
379 and that the variations of these driving forces may be of second importance in the intensity
380 of sediment transport in comparison to the currents induced by the migration and welding
381 of sandy shoals.

<figure><img src="image_6.png" /><figcaption>Figure 6: Time series of modelled wave power (kW/m) and wind speed (m/s) in comparison to the gross volumes changes calculated over the subtidal, intertidal and dune area along the downdrift coast of the inlet (Zoom 2) from 2011 to 2020.</figcaption></figure>
383

3.2.2. Morphological response of the downdrift coast to energetic winters.

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

In order to understand the discrepancy between the sediment volume changes and offshore wave forcing, the response of the subtidal area of the downdrift coast to energetic winters was studied. The two most energetic winters (2013/14 and 2019/2020) between 2011 and 2020 were selected. The -3, -4 and -5 m isobaths extracted from the SDB models of 2013, 2014, 2019 and 2020, and the difference of DEMs (DoDs) of the same dates (Fig. 7) were used to identify the key subtidal changes over these two periods. As observed before, a large sandy shoal (44.53°N) is migrating towards the coast in 2013 and the downdrift coast is characterized by a large triangular sandy platform (44.52°N) (Fig. 7a). In 2014, the sandy shoal has slightly migrated closer to the coast while the sandy platform largely retreated (Fig. 7b). The 2013/14 DoD shows large longshore patterns of erosion and deposition occurred along the downdrift coast while only minor changes can be observed along the isoline Z = -0.5 m that represents the shoreline (Fig. 7c). The pattern of deposition corresponds to the formation of an alongshore sandbar while the pattern of erosion corresponds to the retreat of the tip of the sandy platform. In 2019, a sandy platform can also be observed along the downdrift coast (44.53°N) (Fig. 7d), but there is no large sandy shoal migrating from the sandbank to the coast in comparison to 2014. In 2020, the sandy platform appears fragmented but larger sandy areas above sea level, represented in black colour, can be observed along the downdrift coast (Fig. 7e). The 2019/2020 DoD shows that onshore sediment transport occurred from the northern part of the sandy platform to the coast causing a significant offshore displacement of the shoreline (Fig. 7f). On the southern part of the platform, sporadic spots of erosion and accretion can be observed and are likely to correspond to the motions of scattered sand bars.
407 Large differences were observed between the 2013/14 and 2019/2020 responses of the
408 subtidal area of the downdrift coast. These large differences can mainly be explained by the
409 large differences of nearshore bathymetry setting along the downdrift coast antecedent to
410 the energetic winters, which are related to the chronology of migration and welding events.

411 Here the presence of a large sandy shoal migrating to the coast had a significant impact on
412 the hydrodynamics of the area by reducing or activating different wave-induced currents.

413 These results highlight the strong geomorphological control of migrating sandy shoals on the
414 hydrodynamics and subsequently the morphodynamics of the downdrift coast, which partly
415 explains the discrepancy between offshore wave forcing and sediment volume change.

416

417

418

419
<figure><img src="image_7.png" /></figure>

Figure 7: Maps of three isobaths ($Z_{SDB}$ = -3, -4 and -5m) extracted from four SDB models along the tidal inlet and the downdrift coast (Zoom 1) in 2013 (a), 2014 (b), 2019 (d) and 2020 (e). Difference of DEMs (DoD) along the downdrift coast (Zoom 2) between 2013 and 2014 (c), and between 2019 and 2020 (f). Areas that are located above the mean sea level ($Z = 0$ m) are represented in black colour. The isobaths $Z = -0.5$ m of 2013 and 2019 ($Z_{pre}$) are represented by a dotted red line, while the isobaths of 2014 and 2020 ($Z_{post}$) are represented by a plain red line
421
422 3.2.3. Alongshore variability of the sediment volume changes along the intertidal of the downdrift coast from 2011 to 2020
423

424 Given that subtidal morphological changes along the downdrift coast are strongly controlled by the migration and welding of sandy shoal, the alongshore variability in sediment volume changes over the intertidal area was examined to study the impact of these events on the beach morphodynamics. Profiles of longshore volume changes, $V_L$ (m³/m) were derived from airborne LiDAR data, and cover a distance of 10 km to evaluate the distance at which beach along the downdrift coast is responding to migration and welding events (Fig. 8). Between 2011 and 2014, while the sandy shoal migrated towards the subtidal sandy platform (44.52°N) that subsequently retreated (Fig. 8a and 8b), the intertidal area lost a large amount of sand (up to 300 m³ per cross-shore profile) at the same location (from 44.51 to 44.525°N) (Fig. 8d). Between 2014 and 2016, the beach area kept eroding at the same location (Fig. 8d) and the subtidal sandy platform observed in 2011 do not longer exist (Fig. 8c), while another subtidal platform was formed further north (44.53°N) causing a lot of accretion (up to 300 m³ per cross-shore profile) along the beach (from 44.525 to 44.53°N). The migration of the sandy shoal caused the erosion of a large subtidal sandy platform and the corresponding beach while the welding of the shoal lead to the formation of a new sandy platform and the growth of the related beach. On average, volume of sand gradually increased between 2011 and 2016 along the rest of the coast (from 44.45 to 44.5°N) suggesting that the sand eroded from the sandy platform of 2011 was partly redistributed down the coast.

443
<figure><img src="image_8.png" /></figure>

Figure 8: Maps of the -3 m isobath extracted from the 2011 (a), 2014 (b) and 2016 (c) SDB models and intertidal (d) profiles of longshore volume changes $V_L$ (m³/m) along the tidal inlet, the downdrift coast, and the subsequent kilometres (Zoom 3). The location of the Wharf (W) is represented by a black dashed line as a reference to locate the area covered in this figure.

445

446 The subtidal sandy platform formed in 2016 can still be observed in 2018 (Fig. 9a) and appeared

447 larger in 2019 (Fig. 9b) before retreating slightly in 2020 (Fig. 9c), while sand volumes at the

448 beach located in front of the sandy platform showed opposite results (Fig. 9d). The beach
449 lost sand (up to 250 m³ per cross-shore profile) between 2018 and 2019 and slightly accreted
450 (up to 150 m³ per cross-shore profile) between 2019 and 2020. The stretch of beach located
451 south to the sandy platform lost sand between 2018 and 2019 and partially recovered
452 between 2019 and 2020. The largest amount of sand (up to 300 m³ per cross-shore profile)
453 were lost along the closest area to the platform (from 44.5 to 44.52°N) and were not
454 recovered in 2020.

455 In addition to control the subtidal morphological changes along the downdrift coast, the
456 migration and welding of sandy shoals also have a strong impact on the intertidal area of the
457 same coast. Over the 2011-2020 sequence, the beach largely eroded while or growing
458 depending on the with and sand volume changes can be up to 5 times larger along the 3
459 kilometres of beach south to the inlet compare to the volume changes observed further
460 down the coast.
<figure><img src="image_9.png" /></figure>

Figure 9: Maps of the -3 m isobath extracted from the 2018 (a), 2019 (b) and 2020 (c) SDB models and intertidal (d) profiles of longshore volume changes $V_L$ (m³/m) along the tidal inlet, the downdrift coast, and the subsequent kilometres (Zoom 3). The location of the Wharf (W) is represented by a black dashed line as a reference to locate the area covered in this figure.

461

462

# 4. Discussion
Similarly to previous studies, this work shows that the dynamics of the inlet and the downdrift coast are characterized by large morphological changes observed over several kilometres at a yearly to decadal timescale. The use of publicly available Landsat-7/8 and Sentinel-2 satellite imagery proved to be a relevant way to monitor these morphological changes at such a scale over the last two decades. Bathymetry inversion from water colour data was shown to be one of the best methods to have a comprehensive understanding of the dynamics of the entire inlet and the downdrift coast since key morphological changes were observed at relatively shallow water depths (between 2 and 6 m). The availability of several bathymetric surveys carried out along different areas of the inlet over the last 20 years also justifies the use of an empirical bathymetry inversion algorithm alternatively of a semi-analytical model already developed in this area (Lafon et al., 2000; Capo et al., 2014). Other methods based on satellite imagery and using the dispersion relation from the linear wave theory (Holland, 2001; Leu and Chang, 2005) and/or a shoreline detection algorithm (Vos et al., 2019; Heimhuber et al., 2021), could be used to provide complementary proxies of the dynamics of the inlet and the downdrift coast. Nevertheless, these other methods present some limits at this study site, the dispersion relation does not take into account currents (Salameh et al., 2019), while a longshore drift up to 1.5 m/s was recorded along this inlet (Senechal et al., 2013). Shoreline detection algorithms require wave run-up values and adapted water level thresholds along meso- to macrotidal study sites (Castelle et al., 2021) which are very difficult to process over such complex bathymetries. Although the use of empirical algorithms require a training dataset of in-situ bathymetric data, only relatively few but representative data points (from hundreds to thousands) can be used to produce SDB models with relatively low vertical uncertainties from 0.5 to 1 m (Lubac et al., 2022).
The complementary use of these satellite derived bathymetry models with bathymetric DEMs and LiDAR DEMs in this study highlights two different timescales at which the dynamics of the tidal inlet has an impact on the coastal dynamics of the downdrift coast.

First, two similar sequences of about 10 years (± 2 years) were identified where a large sandy shoal that migrates across the southern channel of the inlet (Dehouck et al., 2014), welds onto the coast and is fragmented into new shoals that will also migrate and weld onto the coast further down. The mechanism of onshore shoal migration and welding along adjacent beaches to tidal inlets, was observed at many other study sites (Fitzgerald, 1984; Ridderinkhof et al., 2016b; Lenstra et al., 2019). Using numerical morphodynamic models, several studies have shown that the migration results from an onshore residual forcing caused by the disequilibrium between the wave setup dissipated by the shoal and the radiation stress of incoming waves (Bertin et al., 2009; Dodet et al., 2013; Ridderinkhof et al., 2016b). Although, numerical models were not used in the study, the results observed here are in line with these findings since shoal migration followed the same direction along a NW-SE axis within the two sequences. This direction corresponds to the orientation of the longshore drift and the direction of the prevailing swell along this coast (Castelle et al., 2017). Although the patterns of the migration of sandy shoals along the inlet may be controlled allocyclic driving forces such as wave and tides, it was shown here that the morphodynamics of the downdrift coast was primarily controlled by autocyclic processes (Bertin et al., 2004; Morton et al., 2007; Van Dijk et al., 2009), such as the migration and welding of these sandy shoals, rather than the offshore wave and wind forcing. The chronology of these migration and welding events have a direct impact on the bathymetry setting along the downdrift coast that controls the nearshore and beach responses to energetic winters. Two kilometres south to the inlet, a very large amplitude and alongshore
variability in sand volume changes over the intertidal area were shown to be synchronous
with the migration and/or welding of large sandy shoals. Results also demonstrated that the
welding of large sandy shoal can have an impact on the nearshore bathymetry down to 10
km along the downdrift coast. Such results can be crucial to understand the behaviour of
beaches located at several kilometres from the inlet as the open coast of Aquitaine is
strongly controlled by the nearshore outer-bar morphology (Castelle et al., 2019). Knowing
this distance can also help in developing a sediment compartment approach in order to
make accurate predictions of future beach behaviour (Short, 2022).

The 2013/2014 winter, reported to be the most energetic winter over the last 70 years along
the Atlantic coast of Europe (Masselink et al., 2016), caused severe beach and dune erosion
along the Aquitaine coast (Castelle et al., 2017; Nicolae Lerma et al., 2019) Along the
downdrift coast, the 2013/14 winter coincides with the year where a large sandy shoal
migrated along the inlet, suggesting that the sandy shoal had a protective effect on the
fronting nearshore and beach by controlling incident wave energy (Davidson-Arnott and Van
Heyningen, 2003; Robin et al., 2009). On the other hand, the landward migration of the
sandy shoal caused the constriction of one part of the southern channel of the inlet between
the sandy shoal and the downdrift coast. Although, there are no available hydraulic
measurements to demonstrate this, the reduction of the channel cross-section was likely to
enhance the wave-driven currents and to accelerate the erosion of a sand platform located
south to the migrating shoal. Observations of enhanced erosion along shorelines located
south to migrating sandy shoals (Davidson-Arnott and Van Heyningen, 2003; Dolphin et al.,
2007; Robin et al., 2009) validate these results.
Strong similarities were observed between two sequences of sandy shoal migration and welding over the last two decades. Such results could be interpreted as decadal cycles of sandy shoals migration and welding along the Arcachon inlet, as observed at other tidal inlets (Fitzgerald 1984; Ridderinkhof et al. 2016a; Lenstra et al., 2019; Velasquez-Montoya et al., 2020). Nevertheless, previous work showed that the multi-annual dynamic of the Arcachon inlet and the downdrift coast presented in this study is actually encompassed within coastal processes that occur at longer timescales. The analysis of old hydrographic charts along the Arcachon inlet over the last 250 years also showed the existence of multi-decadal cycles of about 80 years (Gassiat, 1989; Cayocca, 2001). Over each cycle, the Arcachon inlet is reorganized between two different configurations where only one or two deep channels are present. The last rearrangement, from one to two channels occurred in the 1940's causing the largest observed shoal welding in recent history, in the 1950's (Michel, 1997). This shoal welding resulted in a large accretion of the coast that was subsequently eroded and migrated south over the last decades, the southward migration rate was estimated to be around 150 m/year between 1959 and 1993. This large sand supply impacted the morpho- and hydrodynamics of the downdrift coast over decades, and specifically large dune supply (Robin et al., submitted). However, the use of shoreline detection algorithm on satellite images over the last 4 decades indicate that the size of shoal welding seems to be decreasing over time (Castelle et al., 2021), suggesting that the downdrift coast is reaching an equilibrium after its last enlargement 70 years ago. Nahon et al. (2019) also showed that the long-term evolution of the Cap Ferret sandspit, located at the north of the inlet, was correlated to the change of prevailing swell direction due to the North Atlantic Oscillation and to the rates of sea level rise which certainly also have an influence on the dynamics of the inlet. Based on the results from this work and the previous studies cited
556 above, a conceptual model showing the seasonal to multi-decadal dynamics of the inlet and
557 the downdrift coast is presented in Figure 10.

558 This study highlighted that the migration and welding of sandy shoals along the tidal inlet of
559 Arcachon has an impact on the beach and nearshore bathymetry of the downdrift coast at
560 multi-annual and kilometre scales. Such processes must be taken into consideration and
561 better understood in attempts to make future projections of shoreline change along
562 inlet-interrupted coastlines such as recent studies (Bamunawala et al., 2021). Although this
563 work provide evidences of the influence of the tidal inlet on the downdrift coast at a multi-
564 annual scale, further work at shorter timescale and smaller spatial scales should also be
565 conducted to better understand the physical processes involved, which would help to
566 comprehend beach behavior, especially along urbanized areas of the downdrift coast such as
567 the city of Biscarrosse (Dehouck et al., 2014; Biausque and Senechal, 2019).

568

<figure><img src="image_10.png" /></figure>
Figure 10: Conceptual model of the multi-decadal, decadal, multi-annual, annual and seasonal marine and aeolian morphodynamics (sandy shoal, sandbars, shoreline, sediment by passing, aeolian transfert) and hydrodynamic (incident wave forcing, longshore drift, tidal currents) of the Arcachon inlet and the downdrift coast.

5. Conclusions

This study benefited from outstanding bathymetric and topographic datasets, along with
satellite derived bathymetry (SDB) maps produced with well-performing and innovative
methods, to examine the morphodynamics of a mixed-energy tidal inlet and the downdrift
coast.

Two similar sequences of 8 and 9 years were identified, where large sandy shoals migrated
along the inlet and welded onto the downdrift coast. These sequences strongly controlled
beach behavior over the 3-kilometers stretch of coast located south to the inlet and also had
a strong impact on the pattern of the nearshore bathymetry and sandbars down to 10
kilometers along the downdrift coast.

Significant sand volume changes along the downdrift coast, up to ±300 m³/m, from were
observed along the downdrift coast and have been attributed to the migration and welding
of sandy shoals that largely control the seasonal and multi-annual morphodynamics of the
downdrift coastline while variations in offshore wave forcing was of secondary importance.
586 **Data availability**

587 LiDAR datasets related to this article can be found at
588 https://www.pigma.org/datanouvelleaquitaine/dataset?tags=OCA+Observatoire+de+la+C%
589 C3%B4te+Aquitaine&datatype=donnees-ouvertes&tags=LIDAR&granularity=region, hosted
590 at Plateforme d'Echange de Données en Nouvelle-Aquitaine (PIGMA).

591 **Acknowledgments**

592 This research is part of the ARCADE project and was co-funded by the Region Nouvelle-
593 Aquitaine, the Parc Naturel Marin du Bassin d'Arcachon, the Agence de l'Eau Adour-
594 Garonne, the Syndicat Intercommunal du Bassin d'Arcachon, the Bureau de Recherches
595 Géologiques et Minières and the Institut Français de Recherche pour l'Exploitation de la Mer.
596 The authors would like to thank the Direction Départementale des Territoires et de la Mer
597 33, the Syndicat Intercommunal du Bassin d'Arcachon, and the Obsevatoire de la Côte
598 Aquitaine for kindly providing the bathymetric and LiDAR datasets. This research also
599 significantly benefited from of the high-quality of open-access satellite imagery provided by
600 the European Space Agency (Sentinel-2 images) and the United States Geological Survey
601 (Landsat-7 and Landsat-8 images).

602

603 **References**

604 Almar R., Bergsma E.W.J., Thoumyre G., Baba M.W., Cesbron G., Daly C., Garlan T.,
605 Lifermann A., 2021. Global Satellite-Based Coastal Bathymetry from Waves. Remote Sensing
606 13 (22). 4628. doi: 10.3390/rs13224628
607

608

Bamunawala J., Ranasinghe R., Dastgheib A., Nicholls R.J., Murray A.B., Barnard P.L., Sirisena

609

T.A.J.G., Duong T.M., Hulscher S.J.M.H., Van der Spek A., 2021. Twenty-first-century

610

projections of shoreline change along inlet-interrupted coastlines. Scientific Reports 11:

611

14038. doi: 10.1038/s41598-021-93221-9

612

Bertin X., Chaumillon E., Weber N., Tesson M., 2004. Morphological evolution and time-varying

613

bedrock control of main channel at a mixed energy tidal inlet: Maumusson Inlet, France. Marine

614

Geology 204, 187-202. https://doi.org/10.1016/S0025-3227(03)00353-0.

615

616

Bertin X., Fortunato A.B., Oliveira A., 2009. A modeling-based analysis of processes driving

617

wave-dominated inlets. Continental Shelf Research 29: 819-834.

618

doi: 10.1016/j.csr.2008.12.019

619

620

Biausque M., Senechal N., 2019. Seasonal morphological response of an open sandy beach

621

to winter wave conditions: The example of Biscarrosse beach, SW France. Geomorphology

622

332: 157-169. doi: 10.1016/j geomorph.2019.02.009.

623

624

Borrelli M., Wells J.T., 2003. Swash bars and spits growth: Evolution of a rapidly prograding

625

spit along a sediment-starved coast. Coastal Sediment '03 Conference, American Society of

626

Civil Engineering, Saint Petersburg, Florida.

627

Bossard V., Nicolae Lerma A., 2020. Geomorphologic characteristics and evolution of

628

managed dunes on the South West Coast of France. Geomorphology 367: 107312.

629

doi: 10.1016/j.geomorph.2020.107312

630
631 Burvingt O., Masselink G., Russell P., Scott T., 2017. Classification of beach response to
632 extreme storms. Geomorphology 295: 722-737.

633 doi: 10.1016/j geomorph.2017.07.022

634
635 Caballero I., Stumpf R.P., 2021. On the use of Sentinel-2 satellites and lidar surveys for the
636 change detection of shallow bathymetry: The case study of North Carolina inlets. Coastal
637 Engineering 169. 103936

638
639 Capo S., Lubac B., Marieu V., Robinet A., Bru D., Bonneton P., 2014. Assessment of the
640 decadal morphodynamic evolution of a mixed-energy inlet using ocean color remote
641 sensing. Ocean Dynamics 64 (10). doi: 10.1007/s10236-014-0762-1

642
643 Castelle B., Bujan S., Ferreira S., Dodet G., 2017. Foredune morphological changes and beach
644 recovery from the extreme 2013/2014 winter at a high-energy sandy coast. Marine Geology
645 385: 41-55. doi: 10.1016/j.margeo.2016.12.006.

646
647 Castelle B., Guillot B., Marieu V., Chaumillon E., Hanquiez V., Bujan S., Poppeschi C, 2018.
648 Spatial and temporal patterns of shoreline change of a 280-km high-energy disrupted sandy
649 coast from 1950 to 2014: SW France. Estuarine, Coastal and Shelf Science 200: 212-223.
650 doi: 10.1016/j.ecss.2017.11.005.

651
652 Castelle B., Marieu V., Bujan S., 2019. Alongshore-Variable Beach and Dune Changes on the
653 Timescales from Days (Storms) to Decades Along the Rip-dominated Beaches of the Gironde
654 Coast, SW France. Journal of Coastal Research 88: 157 - 171.
655 Castelle B., Masselink G., Scott T., Stokes C., Konstantinou A., Marieu V., Bujan S., 2021.

657 Satellite-derived shoreline detection at a high-energy meso-macrotidal beach.
658 Geomorphology 383: 107707.

659 doi: 10.1016/j geomorph.2021.107707.

660
661 Cayocca F., 2001. Long-term morphological modeling of a tidal inlet: the Arcachon Basin,
France. Coastal Engineering 42 (2): 115-142.

662
663 doi: 10.1016/S0378-3839(00)00053-3.

664
665 Davidson-Arnott R.G.D., Van Heyningen A.G., 2003. Migration and sedimentology of
666 longshore sandwaves, Long Point, Lake Erie, Canada. Sedimentology 50 (6): 1123-1137

667 doi: 10.1046/j.1365-3091.2003.00597.x

668
669 Dehouck A., Lafon V., Senechal N., Froidefond J.-M., Almar R., Castelle B., Martiny N., 2014.

670 Evolution Morphodynmaique Interannuelle du Littoral Sud de la Gironde. Revue Française de
671 Photogrammétrie et de Télédétection 197 : 31-42.

672 doi: 10.52638/rfpt.2012.82

673
674 Dodet G., Bertin X., Bruneau N., Fortunato A.B., Nahon A., Roland A., 2013. Wave-current
675 interactions in a wave-dominated tidal inlet. Journal of Geophysical Research: Oceans 118
676 (3): 1587-1605.

677
678 Dolphin T.J., Vincent C.E., Coughlan C., Rees J.M., 2007. Variability in Sandbank Behaviour at
679 Decadal and Annual Time-Scales and Implications for Adjacent Beaches.
680 Journal of Coastal Research 50: 731-737.
681
682 Elias, E.P.L., van der Spek, A.J.F., 2006. Long-term evolution of Texel Inlet and its ebb-tidal
683 delta (the Netherlands). Marine Geology 225: 5-21.
684 doi: 10.1016/j.margeo.2005.09.008
685
686 Elias, E.P.L., van der Spek, A.J.F., Pearson, S.G., Cleveringa, J., 2019. Understanding sediment
687 bypassing processes through analysis of high-frequency observations of Ameland Inlet, the
688 Netherlands. Marine Geology 415, 105956. Doi:10.1016/j.margeo.2019.06.001.
689
690 Fenster M.S., Dolan R., 1996. Assessing the impact of tidal inlets on adjacent barrier island
691 shorelines. Journal of Coastal Research 12 (1): 294-310.
692 Fitzgerald D.M., 1984. Interactions between the Ebb-Tidal Delta and Landward Shoreline:
693 Price Inlet, South Carolina. Journal of Sedimentary Research 54 (4): 1303-1318.
694 doi: 10.1306/212F85C6-2B24-11D7-8648000102C1865D
695
696 Ford M.R., Dickson M.E., 2018. Detecting ebb-delta migration using Landsat imagery. Marine
697 Geology 405, 38-46. Doi:10.1016/j.margeo.2018.08.002.
698
699 Fortunato A.B, Nahon A., Dodet G., Pires A.R., Freitas M.C., Bruneau N., Azevedo A., Bertin
700 X., Benevides P., Andrade C., Oliveira A., 2014. Morphological evolution of an ephemeral
701 tidal inlet from opening to closure: The Albufeira inlet, Portugal. Continental Shelf Research
702 73: 49-63.

703
704 Fortunato A.B., Freire P., Mengual B., Bertin X., Pinto C., Martins K., Guérin T., Azevedo A.,
705 2021. Sediment dynamics and morphological evolution in the Tagus Estuary inlet. Marine
706 Geology 440. 106590
707 doi: 10.1016/j.margeo.2021.106590.

708
709 Galiforni-Silva F., Wijnberg K.M., Hulscher S.J.M.H., 2020. Storm-induced sediment supply to
710 coastal dunes on sand flats. Earth Surface and Dynamics 8: 335-350.
711 doi: 10.5194/esurf-8-335-2020

712 Garel E., Sousa C., Ferreira O., Morales, 2014. Decadal morphological response of an ebb-tidal delta and downdrift beach to artificial breaching and inlet stabilization. Geomorphology
713 216, 13-25. Doi:10.016/j geomorph.2014.03.031.

715
716 Gaudiano D.J., Kana T.W., 2001. Shoal bypassing in mixed energy inlets: Geomorphic
717 variables and empirical predictions for nine South Carolina inlets. Journal of Coastal
718 Research 17: 280-291.

719 Gassiat L., 1989. Hydrodynamique et évolution sédimentaire d'un système lagune, flèche
720 littorale : le Bassin d'Arcachon et la flèche du Cap Ferret. Thesis.

721 Geyman E.C., Maloof A.C., 2019. A Simple Method for Extracting Water Depth From
722 Multispectral Satellite Imagery in Regions of Variable Bottom Type. Earth and Space Science
723 6 (3): 527-537. doi: 10.1029/2018EA000539
724 Hayes M.O., 1980. General morphology and sediment patterns in tidal inlets. Sedimentary

725 Geology 26 (1-3): 139-156.

726

727 Hagemann B.P., 1969. Development of the western part of the Netherlands during the

728 Holocene. Geologie en Mijnbouw 48: 373-388.

729 Harrison S.R., Bryan K.R., Mullarney J.C., 2017. Observations of morphological change at an

730 ebb-tidal delta. Marine Geology 385, 131-145. Doi:10.1016/j.margeo.2016.12.010.

731

732 Heimhuber V., Vos K., Fu W., Glamore W., 2021. InletTracker: An open-source Python toolkit

733 for historic and near real-time monitoring of coastal inlets from Landsat and Sentinel-2.

734 Earth and Space Science Open Archive.

735 doi: 10.1002/essoar.10506493.1

736

737 Hein C.J., Fallon A.R., Rosen P., Hoagland P., Georgiou I.Y., FitzGerald D.M., Morris M., Baker

738 S., Marino G.B., Fitzsimons G., 2019. Shoreline Dynamics Along a Developed River Mouth

739 Barrier Island: Multi-Decadal Cycles of Erosion and Event-Driven Mitigation. Frontiers in

740 Earth Science 7:103. doi: 10.3389/feart.2019.00103

741 Herrling G., Winter C., 2018. Tidal inlet sediment bypassing at mixed-energy barrier islands.

742 Coastal Engineering 140: 342-354.

743 doi:10.1016/j.coastaleng.2018.08.008.

744

745 Hicks, D.M., Hume T.M., Swales A., Green M.O., 1999. Magnitudes, spatial extent, time

746 scales and causes of shoreline change adjacent to an ebb tidal delta, Katikati inlet, New

747 Zealand, Journal of Coastal Research, 15, 20-240.
748 Hine, A.C., 1979. Mechanisms of berm development and resulting beach growth along a

749 barrier spit complex. Sedimentology 26: 333-351.

750 doi:10.1111/j.1365-3091.1979.tb00913.x.

751

752 Holland T.K., 2001. Application of the Linear Dispersion Relation with Respect to Depth

753 Inversion and Remotely Sensed Imagery. IEEE International Geoscience and Remote Sensing

754 Proceedings 39: 2060-2072.

755

756 Houser, 2009. Synchronization of transport and supply in beach-dune interaction. Progress

757 in Physical Geography 33 (6): 733-746.

758

759 Idier D., Charles E., Mallet C., Castelle B., 2013. Longshore sediment flux hindcast and

760 potential impact of future wave climate change along the Gironde/Landes Coast, SW France.

761 Journal of Coastal Research 65: 1785-1790.

762 doi: 10.2112/SI65-502.1

763

764 Kana T.W., Hayter E.J., Work P.A., 1999. Mesoscale sediment transport at southeastern U.S.

765 tidal inlets: Conceptual model applicable to mixed energy settings. Journal of Coastal

766 Research 15: 303-313.

767 Kana T.W., McKee P.M., 2003. Relocation of Captain Sams Inlet – 20 years later. Coastal

768 Sediment '03 Conference, American Society of Civil Engineering, Saint Petersburg, Florida.
769 Lafon V., Froidefond J.M., Castaing P., 2000. Méthode d'analyse de l'évolution
770 morphodynamique d'une embouchure tidale par imagerie satellite. Exemple du bassin
771 d'Arcachon (France). Earth and Planetary Sciences 331: 373-378.

772 Laporte-Fauret Q., Castelle B., Michalet R., Marieu V., Bujan S., Rosebery D., 2021.
773 Morphological and ecological responses of a managed coastal sand dune to experimental
774 notches. Science of the Total Environment 782: 146813.
775 doi: 10.1016/j.scitotenv.2021.146813.

776
777 Lenstra K.J.H., Pluis S.R.P.M., Ridderinkhof W., Ruessink G., Van der Vegt M., 2019. Cyclic
778 channel-shoal dynamics at the Ameland inlet: the impact on waves, tides, and sediment
779 transport. Ocean Dynamics 69: 409-425.

780 Leu L.G., Chang H.W., 2005. Remotely Sensing in Detecting the Water Depths and Bed Load
781 of Shallow Waters and Their Changes. Ocean Engineering 32: 1174-1198.

782 Lubac B., Burvingt O., Nicolae Lerma A., Senechal N., 2022. Performance and uncertainty of
783 satellite-derived bathymetry empirical approaches in an energetic coastal environment.
784 Remote Sensing, 14 (10), 2350.

785 Lyzenga D.R., 1981. Remote sensing of bottom reflectance and water attenuation
786 parameters in shallow water using aircraft and Landsat data. International Journal of Remote
787 Sensing 2: 71-82. doi: 10.1080/01431168108948342

788 Lyzenga D.R., Malinas N.P., Tanis F.J., 2006. Multispectral Bathymetry Using a Simple
789 Physically Based Algorithm. IEEE Transactions on Geoscience and Remote Sensing 44 (8).
790 Masselink G., Castelle B., Scott T., Dodet G., Suanez S., Jackson D., Floc'h F., 2016. Extreme
791 wave activity during 2013/2014 winter and morphological impacts along the Atlantic coast of
792 Europe. Geophysical Research Letters 43 (5): 2135-2143.

793 doi: 10.1002/2015GL067492

794

795 Michel D., Howa H.L., 1997. Morphodynamic Behaviour of a Tidal Inlet System in a Mixed-
796 Energy Environment. Physics and Chemistry of the Earth 22 (3-4): 339-343.

797 Michel D., 1997. Evolution morphodynamique d'un littoral sableux situé à l'aval d'une
798 embouchure lagunaire. Thesis.

799 Morton R.A., Clifton E.H, Buster N.A., Peterson R.L., Gelfenbaum G, 2007. Forcing of large-
800 scale cycles of coastal change at the entrance to Willapa Bay, Washington. Marine Geology
801 246, 24-41. https://doi.org/10.1016/j.margeo.2007.07.008.

802 Nahon A., Idier D., Senechal N., Féniès H., Mallet C., Mugica J., 2019. Imprints of wave
803 climate and mean sea level variations in the dynamics of a coastal spit over the last 250
804 years: Cap Ferret, SW France. Earth Surface Processes and Landforms 44 (11): 2112-2125.

805 doi: 10.1002/esp.4634

806

807 Natesan U., Rajalakshmi P.R., Ferrer V. A., 2014. Shoreline dynamics and littoral transport
808 around the tidal inlet at Pulicat, southeast coast of India. Continental Shelf Research 80: 49-
809 56. doi: 10.1016/j.csr.2014.02.018

810

811 Nicolae Lerma A., Bulteau T., Lecacheux S., Idier D., 2015. Spatial variability of extreme wave
812 height along the Atlantic and channel French coast. Ocean Engineering 97: 175-185.
813 doi: 10.1016/j.oceaneng.2015.01.015.

814

815 Nicolae Lerma A., Ayache B., Ulvoas B., Paris F., Bernon N., Bulteau T., Mallet C., 2019.

816 Pluriannual beach-dune evolutions at regional scale: Erosion and recovery sequences
817 analysis along the Aquitaine coast based on airborne LiDAR data. Continental Shelf Research
818 189. 103974. doi: 10.1016/j.csr.2019.103974.

819

820 Pezerat M., Bertin X., Martins K., Mengual B., Hamm L., 2021. Simulating storm waves in the
821 nearshore area using spectral model: Current issues and a pragmatic solution. Ocean
822 Modelling 158: 101737.

823

824 Pye K., Blott S.J., 2016. Assessment of beach and dune erosion and accretion using LiDAR:
825 impact of the stormy 2013–14 winter and longer term trends on the Sefton coast, UK.
826 Geomorphology, 266: 146-167.

827 doi: 10.1016/j geomorph.2016.05.011

828

829 Ridderinkhof W., De Swart H.E., Van der Vegt M., Alebregtse N.C., Hoekstra P., 2014.
830 Geometry of tidal inlet systems: A key factor for the net sediment transport in tidal inlets,
831 Journal of Geophysical Research: Oceans 119: 6988–7006,
832 doi: 10.1002/2014JC010226.

833

834 Ridderinkhof W., Hoekstra P., Van der Vegt M., De Swart, H.E, 2016a. Cyclic behavior of
835 sandy shoals on the ebb-tidal deltas of the Wadden Sea. Continental Shelf Research 115: 14–
836 26.
837 doi: 10.1016/j.csr.2015.12.014

838

839 Ridderinkhof W., De Swart H.E., Van der Vegt M., Hoekstra P, 2016b. Modeling the growth
840 and migration of sandy shoals on ebb-tidal deltas. Journal of Geophysical Research: Earth
841 Surface, 121: 1351-1372.

842 doi: 10.1002/2016JF003823.

843

844 Robin N., Levoy F., Monfort O., 2007. Bar morphodynamic behaviour on the ebb delta of a
845 macrotidal inlet (Normandy, France). Journal of Coastal Research 23: 1370-1378.
846 doi:10.2112/06-0684.1.

847

848 Robin N., Levoy F., Monfort O., Anthony E., 2009. Short-term to decadal-scale onshore bar
849 migration and shoreline changes in the vicinity of a megatidal ebb delta. Journal of
850 Geophysical Research 114. F04024.

851

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

856

857 Robin N., Billy J., Nicolae Lerma A., Castelle B., Hesp P.A., Rosebery D., Fauny C., Deparis J.,
858 Marieu V., Bouchet C., Miot da Silva, G., submitted. Multi-decadal evolution of a
859 transgressive dunefield and foredune system: New insights by GPR and LiDAR. Earth Surface
860 Processes and Landforms.
861
862 Roland A., Zhang Y.J., Wang H.V., Meng Y., Teng Y.-C., Maderich V., Brovchenko I., Dutour-Sikiric M., Zanke U., 2012. A fully coupled 3D wave-current interaction model on unstructured grids. Journal of Geophysical Research: Oceans 117(C11).

865
866 Salameh E., Frappart F., Almar R., Baptista P., Heygster G., Lubac B., Raucoules D., Almeida L.P., Bergsma E.W.J., Capo S., De Michele M., Idier D., Li Z., Marieu V., Poupardin A., Silva P.A., Turki I., Laignel B., 2019. Monitoring Beach Topography and Nearshore Bathymetry Using Spaceborne Remote Sensing: A Review. Remote Sensing 11: 2212.

870 doi: 10.3390/rs11192212

871
872 Senechal N., Gouriou T., Castelle B., Parisot J.-P., Capo S., Bujan S., Howa H., 2009. Morphodynamic response of a meso- to macro-tidal intermediate beach based on a long-term data set. Geomorphology 107 (3-4): 263-274.

875 doi: 10.1016/j geomorph.2008.12.016.

876
877 Senechal N., Sottolichio A., Bertrand F., Goeldner-Gianella L., Garlan T., 2013. Observations of waves' impact on currents in a mixed-energy tidal inlet: Arcachon on the southern French Atlantic coast. Journal of Coastal Research 65: 2053-2058.

880 doi: 10.2112/S165-347.1

881
882 Short, A., 2022. Australian beach systems: Are they at risk to climate change? Ocean & Coastal Management 224 (8): 106180.

884
885 Stumpf R.P., Holderied K., Sinclair M., 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology Oceanography 48: 547–556.

888
889 Van Dijk M., Postma G., Kleinhans, M.G., 2009. Autocyclic behaviour of fan deltas: an
890 analogue experimental study. Sedimentology 56, 1569–1589. doi: 10.1111/j.1365-891 3091.2008.01047.x

892 Vanhellemont Q., Ruddick K., 2018. Atmospheric correction of metre-scale optical satellite
893 data for inland and coastal water applications. Remote Sensing of Environment 216: 586-894 597.

895 doi: 10.1016/j.rse.2018.07.015.

896
897 Velasquez-Montoya L., Overton M. F., Sciaudone E.J., 2020. Natural and anthropogenic-
898 induced changes in a tidal inlet: Morphological evolution of Oregon Inlet. Geomorphology
899 350:1-17. doi:10.1016/j.geomorph.2019.106871.

900
901 Vos K., Harley, M.D., Splinter K.D., Simmons J.A., Turner I.L., 2019. Sub-annual to multi-
902 decadal shoreline variability from publicly available satellite imagery. Coastal Engineering
903 150: 160-174. doi: 10.1016/j.coastaleng.2019.04.004.

904
905 Zhang Y.J., Ye F., Stanev E.V., Grashorn S., 2016. Seamless cross-scale modeling with SCHISM
906 Ocean Modelling 102: 64-81.

907
908