# On the use of SWOT altimetry data for monitoring coastal hydrodynamics

Imen Turki, M.S. Islam, C. Lopez Solano, E. Salameh, M. Domingues, T. Mendoza, L. Aouf, L. Froideval, D. Gutierrez Barcelo, Benoît B. Laignel, et al.

► To cite this version:

Imen Turki, M.S. Islam, C. Lopez Solano, E. Salameh, M. Domingues, et al. On the use of SWOT altimetry data for monitoring coastal hydrodynamics. International Journal of Applied Earth Observation and Geoinformation, 2025, 145, pp.104982. 10.1016/j.jag.2025.104982. hal-05528769

HAL Id: hal-05528769

https://hal.inrae.fr/hal-05528769v1

Submitted on 26 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>

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

On the use of SWOT altimetry data for monitoring coastal hydrodynamics☆

E.I. Turki<sup>a, *</sup>, M.S. Islam<sup>a, ●</sup>, C. Lopez Solano<sup>a, ●</sup>, E. Salameh<sup>a</sup>, M. Domingues<sup>a, ●</sup>, T. Mendoza<sup>b, ●</sup>, L. Aouf<sup>c</sup>, L. Froideval<sup>a, ●</sup>, D. Gutierrez Barcelo<sup>d</sup>, B. Laignel<sup>a, ●</sup>, A. Carbonniere<sup>e</sup>, F. Frappart<sup>f</sup>

ARTICLE INFO

Keywords:

Coastal sea levels and waves

Extreme hydrodynamics

SWOT accuracy

A B S T R A C T

Satellite altimetry has proven highly effective for monitoring sea surface variability in open oceans and conti-
nental shelves, yet its extension to nearshore environments remains challenging. Within the framework of the
Surface Water and Ocean Topography (SWOT) wide-swath interferometric mission, this study evaluates SWOT's
capability to retrieve coastal Sea Surface Heights (SSH) and Significant Wave Heights (SWH) in the English
Channel along the UK and French coasts. SWOT 1-day Cal/Val and 21-day Science Orbit data were validated
against in-situ records from 16 tide gauges and 10 wave buoys between March 2023 and July 2024, encom-
passing five energetic storm events.

Results indicate that SWOT-derived SSH and SWH exhibit strong consistency with in-situ measurements, demonstrating high accuracy (mean RMSE ≈ 0.9), precision (mean STD ≈ 0.85), and a robust correlation (~0.95). Agreement is influenced by the spatial offset between SWOT tracks and sensor locations, as well as by complex bathymetric and topographic conditions. A benchmarking analysis with coastal wave buoys further demonstrates reliable SWOT wave estimates within ~ 3-4 km of the shoreline and at depths of ~ 8-12 m.

This finding underscores SWOT's potential for monitoring shallow-water hydrodynamics and for improving
the fidelity of regional and local coastal numerical models.

# 1. Introduction

In the context of global climate change and intensifying anthropo-
genic pressures, coastal and estuarine systems are increasingly exposed
to environmental hazards as a result of global climate change and
intensifying anthropogenic pressures (Nicholls and Small, 2002). Rising
sea levels, stronger storms, and rapid urban development have amplified
risks in recent decades, while short-term drivers such as seasonal vari-
ability and shifting storm patterns further compound these risks
(Switzer, 2015; Masselink and Gehrels, 2015). Extreme events,
including elevated water levels and high waves, have thus emerged as
global risk hotspots, posing severe threats to coastal communities and
infrastructure and representing a major challenge for the international
community (Laignel et al., 2023; Turki et al., 2025). Monitoring extreme

hydrodynamics is essential to mitigate both natural and socio-economic
impacts. Traditionally, this has relied on in-situ measurements from tide
gauges and wave buoys, which remain the most reliable observational
tools (Ji et al., 2019).

However, their coverage is spatially sparse and often absent in re-
gions where instrument deployment is unfeasible; as a result, coastal
hydrodynamics at local scales, particularly during storm events, remain
inadequately documented. Numerical models offer a valuable comple-
ment, yet their predictive skill relies on substantial computational re-
sources and dense real-time observations, both rarely available in
coastal environments. In this context, satellite remote sensing has, over
the past decades, substantially expanded our capacity to observe the
ocean and provide broader, more consistent coverage. Altimetry data-
sets, in particular, have significantly enhanced temporal and spatial

<footnote><sup>a</sup> Univ Rouen Normandie, Université Caen Normandie, CNRS, Normandie Univ, M2C UMR 6143, F-76000 Rouen, France</footnote>

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

<footnote><sup>c</sup> Meteo-France, DIROP-CNRM, 42 avenue Gaspard Coriolis 31057 Toulouse, France</footnote>

<footnote><sup>d</sup> IMS, University of California Santa Cruz, Santa Cruz, CA 95064, USA</footnote>

<footnote><sup>e</sup> CNES, 18 Avenue Edouard Belin, CEDEX 9, 31401 Toulouse, France</footnote>

<footnote><sup>f</sup> ISPA, UMR1391 INRAE Bordeaux Science Agro, F-33140 Villenave d'Ornon, France</footnote>

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

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: imen.turki@univ-rouen.fr (E.I. Turki).</footnote>
coverage, providing critical insights into sea-level variability across short- to long-term scales (Ji et al., 2019; Abdalla et al., 2021). Nevertheless, their application remains limited in nearshore and coastal zones, considered as challenging due to their dynamic nature and the scarcity of independent validation data. Key challenges include degraded data quality near land, reduced accuracy in complex coastal environments, and insufficient fine-scale resolution to capture storm-driven processes (Han, 2017; Laignel et al., 2023). While advances in radar altimetry, SAR, and InSAR have expanded observational capabilities (Schlembach et al., 2022; Benveniste et al., 2020), the lack of reliable, high-resolution monitoring of sea surface height (SSH) and significant wave height (SWH) in coastal regions continues to represent a critical gap.

The Surface Water and Ocean Topography (SWOT) mission represents a major step forward in addressing these challenges. Jointly developed by NASA and CNES with contributions from CSA and UKSA, SWOT is the first satellite mission to provide spatially resolved estimates of water levels across both oceanic and inland hydrological systems (Morrow et al., 2019). Launched in December 2022, it employs the Ka-band Radar Interferometer (KaRIN, 35.5 GHz) to perform across-track interferometric measurements, enabling global two-dimensional SSH retrievals at unprecedented spatial resolution (Fu et al., 2024). This innovative configuration allows kilometer-scale mapping of SSH variability, swell parameters, and radar backscatter over a 120 km swath (Ardhuin et al., 2024). Early studies further suggest its potential for retrieving SWH at fine spatial scales using interferometric decorrelation techniques (Bohé et al., 2025). Lichtman et al. (2025) investigated the accuracy of SWOT in the Bristol Channel and Severn Estuary, demonstrating its ability to resolve spatial water-level patterns across one of the world's most complex coastal environments. Building on this, Turki et al. (2025) provided an initial view of SWOT's performance in nearshore and coastal zones under storm conditions, although comprehensive assessments are still lacking. Their study combined SWOT nadir data with conventional altimetry to extend coverage of storm waves and track the evolution of wave features from the nearshore into coastal regions.

In this context, the present study assesses the accuracy of SWOT products for sea level and wave measurements in the English Channel (NW Europe). Using observations from both the Cal/Val and Scientific Orbit phases, we validate SWOT-derived SSH and SWH against in-situ measurements and conventional altimetry at regional (English Channel) and local (UK and French coasts) scales. Particular attention is given to extreme events during the 2022–2023 and 2023–2024 winter seasons. This work thus provides a regional- to local-scale evaluation of SWOT performance under storm conditions in a complex coastal environment, offering new insights into the mission's potential for monitoring coastal hydrodynamics and extreme events.

Following this introduction, Section 2 presents the observational datasets available in the English Channel, including the dataset used together with the methods applied. Section 3 reports the results on SWOT's performance in monitoring nearshore and coastal hydrodynamics, which are further discussed in section 4. Finally, Section 5 summarizes the key findings and highlights future perspectives.

## 2. Data and methods

To evaluate SWOT's performance for estimating changes in sea levels and waves in coastal environments, we analyzed storm events during the 2022–2023 and 2023–2024 winters in the English Channel.

Five energetic storms in the Channel, occurring between 2023 and 2024 after the SWOT mission launch, were selected with varying durations with varying durations storms (Table 1): Mathis and Noah (April–July 2023) and Ciaran, Karlotta, and Pierrick (since November 2023). Storms were identified using the method proposed by Mendoza et al., (2011), based on the wave height threshold, duration threshold, and storm independence criterion. The wave height threshold was defined as the 95th per-centile of significant wave heights, and the duration threshold was set at 12 h to capture events capable of inducing substantial changes in hydrodynamics. Storms lasting more than 12 h were characterized by maximum significant wave height (Hs, max), peak wave period (Tp) and mean wave direction and storm energy (E_storm). Storm energy was calculated as the time integral of wave heights exceeding the threshold, multiplied by seawater density and gravitational acceleration (López Solano et al., 2024).

In this work, we investigate the accuracy of SWOT-derived sea surface height (SSH) and significant wave height (SWH), obtained from KaRIN interferometric acquisitions in the English Channel and along the French and UK coasts. The assessment is based on an extended dataset of in-situ measurements, including sensors and video monitoring systems. The analysis covers both the Cal/Val orbit phase (March–July 2023) and the Scientific Orbit phase (since November 2023), encompassing 96 cycles of 1-day sampling and 26 cycles of 21-day sampling. Thirteen SWOT passes covering the basin, from the entrance of the Channel to the North Sea, have been examined, together with the dedicated Cal/Val pass (Pass 16).

### 2.1. Measurements

#### 2.1.1. In-situ sensors

An extensive network of wave buoys and tide gauges, distributed along the French and UK coasts, was used in this study (Fig. 1). It includes 10 wave buoys (6 in the UK, 1 in France, and 2 in the central English Channel) and 16 tide gauges (8 in France and 6 in the UK). Coastal wave buoy data were obtained from the Copernicus database (Global Ocean In-Situ Near-Real-Time Observations, https://data.marine.copernicus.eu/product/INSITU_GLO PHYBGWAV_DISCRETE_MYNR T_013_030) and provide hourly time series of significant wave height (Hs), peak period (Tp), and, for two buoys (6200059 and 620069), mean wave direction. Tide gauge measurements, delayed-validated at 15 min for the UK (BODC Tide Gauge Network.

and 10 min for France (https://www.bodc.ac.uk/data/hosted_data_systems/sea_level/uk_tide_gauge_network), were used to provide water level data along both coasts. Bathymetric information was obtained from the European Marine Observation and Data Network (EMODnet) Bathymetry Portal (EMODnet, 2023). A detailed description of the EMODnet project, its digital terrain model (DTM), and the latest release is available online (Solano et al., 2024).

The locations of in-situ measurements (wave buoys and tide gauges), their distance from the coast, their proximity to SWOT pass numbers are

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

Selected storm events during both winters 2022/2023 and 2023/2024 and their physical characteristics within the Channel (duration, significant wave Height Hs, mean period Tm and the wave energy Estorm). The storms happened during the Cal/Val phase and the science Orbit are marked in green and blue, respectively.

<table><thead><tr><th rowspan="2">Storm</th><th colspan="2">Date start</th><th colspan="2">Date end</th><th rowspan="2">Duration</th><th rowspan="2">Hs,max (m)</th><th rowspan="2">Tp (s)</th><th rowspan="2">E_storm (m2/h)</th></tr><tr><th>4:00 AM</th><th>31/03/2023</th><th>6:00 AM</th><th>01/04/2023</th></tr></thead><tbody><tr><td>Mathis</td><td></td><td></td><td></td><td></td><td>26 h</td><td>5</td><td>9</td><td>360.42</td></tr><tr><td>Noah</td><td>1:00 PM</td><td>12/04/2023</td><td>5:00 AM</td><td>13/04/2023</td><td>16 h</td><td>5.8</td><td>9</td><td>302.5</td></tr><tr><td>Ciaran</td><td>10:00 PM</td><td>01/11/2023</td><td>4:00 AM</td><td>03/11/2023</td><td>30 h</td><td>5.8</td><td>11</td><td>530.75</td></tr><tr><td>Karlotta</td><td>10:00 AM</td><td>10/02/2024</td><td>7:00 AM</td><td>11/02/2024</td><td>21 h</td><td>5.27</td><td>8</td><td>517.93</td></tr><tr><td>Pierrick</td><td>2:00 AM</td><td>09/04/2024</td><td>3:00 PM</td><td>09/04/2024</td><td>13 h</td><td>4.8</td><td>8</td><td>173.1</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Map of English Channel with the location of wave buoys (red circles) and tide gauges (while circles) in French and UK coasts. The positions of SWOT ground tracks for the Cal/Val (Pass 16, in green) and Scientific Orbit (13 passes, in white) periods are displayed. The tide-gauges and wave buoys are also marked, respectively, with white and red circles. The Video Monitoring at Semaphore (yellow triangle) is shown. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

summarized in Tables 2 and 3. The Cal/Val pass crossing the English Channel at Cherbourg is Pass 016 (shown as green lines in Tables 2 and 3).

French buoys are positioned 2–15 km offshore at depths > 25 m, while UK buoys are generally closer to the shoreline, with mean distances of 1–3 km and depths < 15 m. The only exception is buoy 6200290, situated 9 km offshore at 25 m depth. For the studied stations, the distance between the SWOT pass and the wave buoys does not exceed 10 km, whereas for tide gauges it varies between 4.7 km (case of Newhaven) and 39 km (case of Portsmouth). This information on each sensor's distance from the coast, its offset from the nearest SWOT ground track, and its depth is essential to assess the accuracy of SWOT observations as a function of coastal proximity and sensor location.

### 2.1.2. Video monitoring systems

During the SWOT Cal/Val phase (March–July 2023), a video monitoring system was deployed at the Semaphore station, a cliff site with a

<caption>**Table 2**</caption>

Wave buoys along UK (grey color) and French coast (black color) used for SWOT SWH validation. Buoys selected for Cal/Val phase are highlighted in green. The neighboring SWOT pass associated with each gauge, together with its distance and location from the coast, are also indicated.

<table><caption>**Table 3**</caption><thead><tr><th>Wave Buoy</th><th>SWOT Pass</th><th>Distance to coast (Buoy) km</th><th>Distance to coast (SWOT) km</th><th>Distance SWOT-Buoy Km</th><th>Depth m</th></tr></thead><tbody><tr><td>6200069</td><td>475</td><td>6.13</td><td>7</td><td>0.87</td><td>63</td></tr><tr><td>6200305</td><td>014</td><td>73 (F)40 (UK)</td><td>72 (F)41 (UK)</td><td>0.96</td><td>53</td></tr><tr><td>6200304</td><td>292</td><td>15 (F)28 (UK)</td><td>14 (F)28.9 (UK)</td><td>0.9</td><td>26</td></tr><tr><td>6200059</td><td>042/016 CV</td><td>2.3</td><td>4.5</td><td>2</td><td>26</td></tr><tr><td>6201005</td><td>042/016 CV</td><td>1.2</td><td>5.7</td><td>4.5</td><td>15</td></tr><tr><td>6201006</td><td>169</td><td>0.4</td><td>4.4</td><td>4.2</td><td>18</td></tr><tr><td>6201007</td><td>169</td><td>1.2</td><td>8.2</td><td>7</td><td>11</td></tr><tr><td>6201008</td><td>042/016 CV</td><td>0.9</td><td>5.9</td><td>5</td><td>11</td></tr><tr><td>6201009</td><td>042/016 CV</td><td>1.5</td><td>5.5</td><td>4.3</td><td>11</td></tr><tr><td>6200290</td><td>042/016 CV</td><td>9</td><td>9.2</td><td>0.4</td><td>25</td></tr></tbody></table>

Tide gauges, along UK (grey color) and French coast (black color) used for SWOT SSH validation. Tide gauges selected for Cal/Val phase are highlighted in green. The neighboring SWOT pass to each gauge and its distance are illustrated.

<table><thead><tr><th>Tide Gauge</th><th>SWOT Pass</th><th>Distance between Tide Gauge and SWOT km</th></tr></thead><tbody><tr><td>Dieppe</td><td>Pass 14</td><td>8.2</td></tr><tr><td>Cherbourg</td><td>Pass 42</td><td>9.8</td></tr><tr><td>Boulogne-Sur-Mer</td><td>Pass 292</td><td>15.4</td></tr><tr><td>Dielette</td><td>Pass 016 Cal/Val, 042</td><td>4</td></tr><tr><td>Pas de Calais</td><td>Pass 292</td><td>5.6</td></tr><tr><td>Dunkrik</td><td>Pass 292</td><td>16.7</td></tr><tr><td>Ouistreham</td><td>Pass 320</td><td>28.6</td></tr><tr><td>Le Havre</td><td>Pass 320</td><td>14.3</td></tr><tr><td>Portsmouth</td><td>Pass 320</td><td>39</td></tr><tr><td>Brest</td><td>Pass 475</td><td>13.9</td></tr><tr><td>Roscoff</td><td>Pass 70</td><td>7</td></tr><tr><td>Newlyn</td><td>Pass 376</td><td>7.5</td></tr><tr><td>Devonport</td><td>Pass 70</td><td>8.9</td></tr><tr><td>Weymouth</td><td>Pass 169</td><td>17.5</td></tr><tr><td>Newhaven</td><td>Pass 141</td><td>4.7</td></tr><tr><td>Dover</td><td>Pass 419</td><td>10</td></tr></tbody></table>

rocky seabed (Fig. 2), to validate the 1-day SSH observations. The system acquired oblique RGB (Red, Green, Blue; RGB) images at a frequency of six per hour during daylight. These so-called Timex images were generated using a 20-minute exposure time (3264 × 2464 pixels) and provided a spatial resolution of 1–2 m, decreasing from the image
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Semaphore Camera station: a) Location of Video monitoring system; b) Image rectification based on the Control Ground Points (CGP) and identification of the shoreline position.</figcaption></figure>

center toward the edges. The water level was extracted from the oblique images, which were subsequently rectified to real-world coordinates following Holland et al. (1997). This rectification involved the measurement of ground control points and the correction of radial lens distortion. The elevation of the wet/dry interface was referenced to the tide level at Cherbourg. The system delivered 2D shoreline positions every 20 min, while vertical elevation was derived from the intersection of the water surface with the rocky waterline. These observations were then compared with SWOT measurements from Pass 16 (green line, Fig. 1).

## 2.2. SWOT observations

SWOT uses two Synthetic Aperture Radar (SAR) antennas on a 10-m mast to conduct orbital interferometry, providing the first two-dimensional, high-resolution sea surface height measurements from space. Its radar system observes a 120-km swath with a 20-km nadir gap, enabling direct mapping of both ocean topography and terrestrial surface water. To reduce noise while preserving small-scale variability, data are averaged across multiple pixels (Fu et al., 2024).

The first three months of the mission were dedicated to a three-month calibration and validation phase (CalVal) to verify the performance of SWOT's systems and science instruments (Fu et al., 2024). During this phase, SWOT operated in a 1-day repeat orbit with limited global coverage, collecting data from March 30 to July 11, 2023 (Aviso, 2023).

Both Cal/Val and Science orbits share an inclination of 77.6° (Aviso, 2023). However, the number of orbits per cycle differs significantly: Cal/Val has 14 orbits per cycle, while the Science orbit has 292 orbits per cycle (Aviso, 2023).

To evaluate SWOT's interferometric ability to resolve nearshore hydrodynamics, we used the Level-2 (L2) Low Rate (LR) 'Expert' product (SWOT Project, 2023a, b) derived from KaRIn observations. Each L2 product spans the full KaRIn swath (10–60 km from the nadir track) and provides sea surface height (SSH), sea surface height anomaly (SSHA), and significant wave height (SWH) on a fixed, swath-aligned 2 × 2 km2 grid. The L2 LR Expert products, available during both the SWOT Cal/Val and Science Orbit phases (SWOT\_L2\_KaRIn\_SSH\_Expert: https://doi.org/10.24400/527896/a01-2023.015, CNES-AVISO), were used to monitor nearshore and coastal hydrodynamics under high-energy conditions in the English Channel. The Cal/Val Pass 16 intersects the study area, spanning the sea continuum between Cherbourg and Weymouth. For the Scientific Orbit, 13 passes crossing the English Channel were analyzed (Fig. 1).

SWOT swath measurements are stored on a 2 × 2 km grid oriented along the along-track and cross-track directions. In LR mode, sea surface height (SSH) is derived from the phase of the corrected interferogram (Yu et al., 2024) as: $SSH = SSHA\_KaRIn + MSS + height\_cor\_xover$. Here, SSHA\_KaRIn is the sea surface height anomaly from KaRIn, corrected for solid Earth, ocean, and coherent internal tides (FES 2022), as well as dynamical atmospheric effects. The Mean Sea Surface (MSS), defined as

the average sea surface height above the WGS84 ellipsoid (CNES-CLS, https://www.aviso.altimetry.fr/en/missions/current-missions/swot.html), serves as the embedded reference in the L2 product, though it lacks short-wavelength (~20 km) resolution. The height\_cor\_xover term accounts for crossover calibration. Significant wave height (SWH) was derived from KaRIn interferometric acquisitions using independent combinations of radar returns, a technique known as interferometric correlation. This method encodes the normalized variance between the two radar signals and has been statistically applied to retrieve SWH and further refines the inversion algorithms to reduce uncertainties in the two-dimensional fields, following Bohé et al. (2025).

In this study, SWOT SSH and SWH products were obtained from the L2 LR Expert dataset, including both reprocessed (PGC0) and forward-processed (PIC0, PIC2) versions, with the latter potentially containing larger residual errors. The derivation of SWH from volumetric decorrelation in SWOT measurements is detailed in the Jet Propulsion Laboratory Internal Document (2023; PL D-105502). The accuracy of SWOT observations has been investigated during two phases: (1) the Cal/Val phase of 1-day sampling (Version PGC0); (2) the Science Orbit phase of 21-day sampling (Cycles 1–26), processed as PGC0 for Cycles 1–9, PIC0 for Cycles 10–22, and PIC2 for Cycles 23–26. The newer Version D dataset, available via the AVISO portal from Cycle 32 onward, lies outside the study period (https://www.aviso.altimetry.fr/en/my-aviso-plus.html). Two-dimensional SSH and SWH maps were generated from KaRIn observations at kilometer-scale resolution along- and across-track. Four examples are presented in Fig. 3, illustrating SSH and SWH fields from both the Cal/Val and Science Orbit phases.

## 2.3. Validation methods and accuracy assessment

The sensors used in this study are positioned within a radius ranging of 4 to 40 km from the location of SWOT swath observations at depths comparable to those of the in-situ sensors. Comparisons with SWOT data were performed without applying interpolation techniques, in order to directly assess the reliability of raw observations and their sensitivity as a function of distance from the in-situ reference data. SWOT-derived sea surface height (SSH) and significant wave height (SWH) observations were collocated with in-situ measurements from buoys and tide gauges by selecting the closest available pixel to each sensor.

The accuracy of SWOT's nearshore and coastal hydrodynamic estimates was then evaluated using three statistical indicators:

(1) Root mean square error (RMSE): quantifies the overall consistency of the error in terms of its magnitude, and the accuracy

(2) The standard variation (STD) measures the amount of variation of the values of SWH about its mean, and the precision.

(3) Coefficient of determination (R²): measures the degree of correlation between SWOT and in-situ observations.

These metrics, defined in Equations (1) – (3), jointly characterize the reliability of SWOT observations with respect to coastal SSH and SWH
<figure><img src="image_6.png" /><figcaption>Fig. 3. 2D maps of SSH and SWH provided by SWOT Cal/Val (track 16, cycle 516) and SWOT Scientific Orbit (track 14, cycle 2).</figcaption></figure>

variability. These assessments are used to compare accuracy across different UK and French sites. where X is the SWOT observations, Y is the in-situ data, $\bar{Y}$ is the average value if in-situ data, $d_i = X_i - Y_i$ which represents the error of $i^{th}$ observation, $\bar{d}$ is the average error of the overall observations, N is the total number of validation points selected in the

calculation.

$$RMSE = \sqrt{\frac{\sum (X - Y)^2}{N}} \quad (1)$$

<figure><img src="image_7.png" /><figcaption>Fig. 4. Schematic workflow outlining the methodological process of the study (QC: Control Quality).</figcaption></figure>
$$STD = \sqrt{\frac{1}{N-1} \sum_{i=1}^{N} (d_i - \bar{d})^2} \qquad (2)$$

$$R^2 = 1 - \frac{\sum (X - Y)^2}{\sum (X - \bar{Y})^2} \qquad (3)$$

The validation depends on the offset between SWOT observations and in-situ sensors, which varies within the Channel and along the French and UK coasts. For SWH, this offset ranges from 0.4 km (buoy 6200290 and SWOT pass 042; pass 016 Cal/Val) to 7 km (buoy 6201007 and SWOT pass 169). For SSH, the offset is larger, as tide gauges are located at greater distances, ranging from 4.7 km (Newhaven and SWOT pass 141) to 39 km (Portsmouth and SWOT pass 320). Error assessments were conducted at the different stations according to their respective offsets.

Fig. 4 shows a simple workflow that summarizes the methodological approach used in this study as explained in this section from the multi-sensor data collection to the processing and the validation to assess the accuracy of SWOT observations in coastal areas.

# 3. Results

## 3.1. Observing coastal water levels during SWOT Cal/Val

SWOT Cal/Val observations, sampled during March–July 2023, were compared with in-situ measurements, which serve as the reference truth

in this study. Fig. 5.a shows the time evolution of daily SSH from Pass 16 at three stations (locations in Fig. 1): (i) Raz Blanchard, a cliff zone monitored by a Semaphore video system measuring shoreline water levels, (ii) Cherbourg, (iii) Dielette, near harbor areas with tide-gauge water-level records.

Daily comparisons of SWOT KaRIn SSH over 96 Cal/Val cycles at the different stations (Semaphore, Dielette, Cherbourg) with in-situ data (video cameras and corresponding tide gauges) show good agreement, with a mean RMSE of ~ 0.5 m and SD of ~ 0.5 m, depending on the offset between the tide-gauge and SWOT data. At Dielette, located a few kilometers from the Cal/Val swath, the error is limited to an RMSE of 0.2 m with high precision (SD = 0.18 m). The error increases slightly at Cherbourg, where the offset is larger (RMSE = 0.55 m; SD = 0.47 m). Overall, the comparisons demonstrate that SWOT SSH data effectively reproduce oscillatory water-level variations in shallow coastal zones (Fig. 5).

The daily SWH records along Pass 16 were compared with in-situ buoy measurements on both sides of the Channel: the southern side near Cherbourg (buoy 620059) and the northern side near Weymouth (buoy 6200290) (Fig. 6a). SWOT SWH closely reproduced the buoy observations, including peak values, with a mean RMSE of 0.87 m and a precision of 0.75 m, though a slight overestimation was observed near Cherbourg.

During the winter season of 2023, SWOT captured SSH and SWH with good accuracy, including during the storms Mathis (31 March 2023) and Noah (12 April 2023), which were also well observed in the

<figure><img src="image_8.png" /><figcaption>Fig. 5. SSH SWOT observations during the Cal/Val period (March 2023–July 2023): (a) SWOT SSH observations at Cherbourg (middle panel), Dielette (low panel) and Semaphore (high panel); Green lines highlight both Mathis and Noah storms. (b) Accuracy assessment of SWOT observations (RMSE, SD and R²). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** SWOT SWH observations during the Cal/Val period (March–July 2023): (a) SWH time series at Cherbourg (Buoy 620059, upper panel) and Weymouth, UK (Buoy 6200290, lower panel); Green lines highlight both Mathis and Noah storms. (b) Accuracy assessment of SWOT observations (RMSE, SD, and R²). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

SWOT data (Figs. 5a and 6a); the correlation coefficient (R²) is close to 1, indicating a strong agreement between SWOT SSH/SWH and tide-gauge water levels/wave buoys.

## 3.2. Observing coastal hydrodynamics during SWOT Science Orbit

SSH and SWH SWOT observations, acquired every 21 days between September 2023 and December 2024 using KaRIn interferometry, were extracted along the French and UK coasts from the L2 LR products. These data were validated against the nearest tide gauges and buoys to produce SSH and SWH records and to assess SWOT performance in coastal environments.

The SSH records were generated at 12 sites along the French and UK coasts (Fig. 7.a). About half of the stations, such as Weymouth and Cherbourg, received at least two observations every 21 days due to overlapping satellite passes.

The overall comparisons demonstrated a mean RMSE of ~0.9 m and a standard deviation of ~0.85 m. The discrepancies between SWOT and in-situ data varies with the different stations, including their geographical position and their distance from the SWOT observation (Fig. 7.b).

For individual passes, L2 SSH noise shows slight temporal variability, with average values exceeding ~1 m at some stations. For example, at Pas de Calais (assigned to pass 292), noise reaches ~1.8 m, where measurements could be influenced by topography and strongly reflective targets. This topographical intrusion seems to be on the two sides of the Channel; for instance, Dunkirk and Dover (same SWOT pass 292), correlations with in-situ measurements are stronger.

High agreements with significant precision (STD < 0.4 m) and accuracy (RMSE < 0.5 m) were observed at Newhaven, Newlyn, Dielette, and Roscoff, located within 10 km of SWOT data, with spatial

separations of 4.7 km, 7.5 km, 4 km, and 7 km, respectively. The consistency is also correctly observed at Dieppe, Cherbourg, and Devonport, and shows a slight decrease there (RMSE ~ 0.56 and STD ~ 0.48). At Dunkirk, Boulogne-sur-Mer, Le Havre, Weymouth, and Brest, the distance to the nearest SWOT track ranges between 13 and 20 km, and the associated error dispersion reaches ~ 0.7 m. Larger dispersions, on the order of ~ 0.9 m, are observed at Portsmouth and Ouistreham, which are located farther from SWOT ground tracks (radius > 25 km, with 39 km and 28.6 km, respectively). The effect of spatial offsets between measurements should therefore be considered when assessing these errors. Discrepancies between the datasets also increase due to other factors, including land intrusion and topographic disturbances, such as the presence of coastal cliffs at Dieppe and Cherbourg. These influences can vary over time with changing hydrodynamic conditions, which modify the coastal topo-bathymetry and, in turn, contribute to significant variations in the error. Such temporal variability was observed during the study period at several stations, including Portsmouth, Brest and Le Havre.

The Validation of SWOT SWH against wave buoys is shown in Fig. 8. a for four sites: Brest (Buoy 6200069), Cherbourg (Buoy 6200059), the central English Channel (Buoy 6200305), and the North Sea near Dunkirk–Dover (Buoy 6200304). Overall, the comparisons showed good agreement with an RMSE of 0.88 m and STD of ~ 0.82 m; the level of consistency depending on the location.

The correlation coefficient (R² = 0.95) indicates that SWOT observations align well with wave measurements and can accurately reproduce extreme wave heights (> 2 m), with mean RMSE values below 0.9 m. In contrast, errors are more pronounced for small waves (< 1 m), where 75 % of observations are recorded as null values. This limitation is related to the volumetric correlation used in the C0 version of the SWOT product, where the algorithms fail to capture small SWH values.
<figure><img src="image_10.png" /><figcaption>Fig. 7. SSH observations from SWOT Science Orbit period vs in-situ measurements (tide gauges) along UK and French coasts during the period Nov 2023- May 2024; (b) Validation and error assessment.</figcaption></figure>

However, this issue has been addressed in subsequent releases, partic-
ularly in the most recent PIC2 version. In this study, only data with
quality flag = 0 were retained, ensuring that the analysis was restricted
to measurements considered valid for scientific use. While this filtering
leads to data loss near the coast, the losses are not associated with the
presence of small waves (<1 m).

Close to sites 6200069 and 6200059, SWOT SWH measurements show strong agreement with buoy records located in deeper waters (63 m and 26 m depth, respectively) and ~ 4–7 km offshore. For these sites, the accuracy of SWOT observations is reflected by an RMSE of ~ 0.3 m, while the precision is significant with an STD of ~ 0.25 m. Comparable performance is also observed at sites 6200304 and 6200305; however, interpretation is limited due to the higher frequency of unreliable values.

The different storms, Ciaran, Karlotta, and Pierrick, are reported in
the SSH and SWH evolution shown in Fig. 7.a and 8.a, respectively.
Their occurrence coincides with extreme values of SWH and SSH, which

are correctly estimated by SWOT. For SWH, however, SWOT slightly
overestimates the values during storm Ciaran at buoy 6200305 (Pass
14), with an offset of approximately 0.9 m. In contrast, it underestimates
SWH during storm Karlotta at buoy 6200059 (Pass 42).

The scatter plots of SWOT versus in-situ measurements and the
corresponding residual distributions (Fig. 7.b and 8.b) show correlation
coefficients between 0.9 and 1, with standard deviations exceeding 0.8
m. SWOT measurement errors remain within ~ 10–15 % of the reference
data, corresponding to accuracies of ~ 85–90 %. These results confirm
that SWOT observations achieve high accuracy in monitoring both SSH
and SWH in nearshore and coastal areas, with RMSE consistently below
1 m. The comparison outcomes are also influenced by the distance be-
tween KaRIn-derived SSH/SWH and in-situ instruments (tide gauges and
buoys). No interpolation was applied; instead, direct comparisons were
performed to assess the impact of sampling distance, typically repre-
sented by one or two satellite passes. This spatial separation partly ex-
plains the observed discrepancies, particularly near the coast, where
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** SWH observations from SWOT Science Orbit period vs in-situ measurements (wave buoys) along UK and French coasts during the period Nov 2023- May 2024; (b) Validation and error assessment.</figcaption></figure>

shallow-water hydrodynamics strongly modulate wave conditions.

### 3.3. Assessment of SWOT wave data accuracy near the coast

A benchmarking analysis of coastal SWH near Weymouth (UK) was conducted using six wave buoys deployed at varying depths and distances from shore: 6201005 (15 m, 5.7 km), 6201006 (18 m, 4.4 km), 6201007 (11 m, 8.2 km), 6201008 (11 m, 5.9 km), 6201009 (11 m, 5.5 km), and 6200290 (25 m, 9.2 km). Fig. 9 shows the time evolution of SWH from March 2023 to July 2024, observed by the buoys and L2 LR SWOT. To enhance temporal and spatial coverage, two tracks were

combined; however; which can introduce additional errors, particularly at sites 6201005 and 6201008. During the SWOT Cal/Val and Science Orbit phases, good agreement was observed between SWOT and buoys 6201005, 6201008, 6201009, and 6200290, with a mean STD of 0.85 m. SWOT captured extreme wave events well, except for cases with wave heights < 0.5 m, which were unreliable (related to the volumetric correlation used in the C0 version of SWOT product, as explained in the previous section). Outliers, reaching 3–4 times the true wave height, were observed in March 2024 at buoys 6201006 and 6201007.

The strongest correlations (R² ~ 0.95) were observed at buoy 6201006 (18 m depth, 4.4 km offshore); good agreements were also

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Validation of Coastal SWH from SWOT against Multiple Buoys near Weymouth, UK.</figcaption></figure>
observed at buoys 6201005, 6201008, and 6201009, located in shallower waters (11–15 m depth; > 7 km offshore). The accuracy of SWH estimates is influenced both by the location of the observation and by the local water depth. Higher accuracies are generally obtained in deeper areas, even when these are situated close to the shoreline, as in the case of reflective beaches characterized by a steeper slope.

# 4. Discussions

## 4.1. SWOT observations and their relevance to coastal hydrodynamics

This study assessed the accuracy of LR SWOT observations in near-shore and coastal areas, where interactions between physical processes are most pronounced, particularly during extreme events. Along the English Channel and the UK-French coasts, SWOT observations demonstrated good agreement with in-situ measurements for estimating SSH across a variety of coastal settings, including harbors, sandy beaches, and cliffed shorelines.

Recent studies have also examined hydrodynamic variability during the SWOT Cal/Val and Science Orbit phases across oceanic, estuarine, and riverine environments, consistently reporting agreement with in-situ data (Ardhuin et al., 2024; Hart-Davis et al., 2024; Normandin et al., 2024; Lichtman et al., 2025; Turki et al., 2025).

SWOT observations have demonstrated strong potential for monitoring water levels across oceans, rivers, and lakes. Fu et al. (2024) showed that KaRIn measurements can resolve ocean eddies down to ~ 5 km, a key scale for hydrodynamic processes. In river-tide environments such as the Bristol Channel and Great South Bay, Hart-Davis et al. (2024) used SWOT Cal/Val data (250 m and 2 km SSH products) to analyze water levels and tidal signals, finding amplitude discrepancies of 1.75–3 cm and phase lag discrepancies of 1.75°–2.75° relative to tide gauges.

In the Bristol Channel-Severn Estuary, Lichtman et al. (2025) validated SWOT data against dense local gauge observations in a setting of extreme tides, storm surges, and complex coastal morphology, demonstrating that SWOT delivers high-quality measurements and effectively captures spatial water-level patterns from river through estuary to sea.

In riverine environments, the use of SWOT Level-2 observations has been investigated in several studies (e.g., Maubant et al., 2025). These works show that SWOT can monitor water bodies with typical variations of 20–30 cm across global systems, achieving accuracies above 90 %.

In this work, SWOT SSH was assessed at the cliffed coast of the Semaphore station, showing an accuracy of ~ xx m compared with the video monitoring station. SWOT water levels were also validated against 16 tide gauges, with strong agreement reflected in correlation coefficients between 0.95 and 1. The offset (horizontal distance) between SWOT observations and in-situ measurements is of ~ 4–40 km from the coast. The comparison demonstrated high accuracy and precision for offsets shorter than 10 km, with performance decreasing for offsets greater than 20 km.

The rapid expansion of altimetry datasets has greatly advanced coastal sea level observations, capturing variability from tides to storm surges. Progress has been driven by improvements in radar technology, the introduction of SAR modes with Ka-band frequency that enhance waveform retracking, refined geophysical corrections, and processing workflows tailored to coastal regions (Vignudelli et al., 2019). Despite persistent challenges within the 0–4 km coastal band, advances in processing techniques and the availability of SAR altimetry from CryoSat-2, Sentinel-3, and SARAL/AltiKa now enable long-term time series analyses in nearshore areas. Consistently, Cazenave et al. (2022) showed that at 78 % of 756 global stations, coastal trends (3–5 km) matched offshore trends (15–20 km). In the light of this work, the novel finding that SWOT can accurately estimate coastal water levels is relevant for enhancing the comprehensive understanding of water level dynamics compared with conventional altimeters.

Radar altimetry has proven effective for assessing coastal water levels, providing precise estimates and capturing the physical

mechanisms driving water level variability. However, comparisons between altimeter-derived trends near tide gauge sites and corresponding tide gauge records are strongly influenced by the geographical setting and by topography intrusions, such as changes in bathymetry and coastal geometry that can induce strong signal reflections. These discrepancies are responsible a poor agreement between altimetry and in-situ dataset, as observed in the case of Pas de Calais in this study. Uncertainties in satellite-based water level estimates can also arise from uncertainties in vertical land motion (VLM), as observed along the western and eastern coasts of North America by Harvey et al. (2021). To address these challenges, Peng et al. (2021) proposed a new retracking strategy that classifies coastal waveforms and applies an optimal coastal retracker, improving Jason-2 sea level estimates within 0–5 km of the coast to a precision of 5–6 cm across most Australian regions, though performance declined to ≥ 7 cm in areas with complex topography or sea states. Similarly, Birol et al. (2024) evaluated uncertainties in coastal sea level estimates using recent advances in altimetry and round-robin waveform retracking algorithms, showing that uncertainties increase from the open ocean toward the coast, primarily due to limitations in tidal corrections and mean sea surface models.

With respect to waves, their assessment using the SWOT mission has been addressed in a few recent studies. For example, wave measurements from the nadir altimeter during the SWOT fast-sampling and 21-day phases showed close agreement with in-situ data, with accuracies of 10–40 cm (WAVE TAC Product, 2024). Ardhuin et al. (2024) analyzed ocean waves between Java and Australia using low-resolution (LR) “unsmoothed” KaRIn products at ~ 250 m spatial resolution. Their results indicated generally good agreement with numerical wave model simulations, although a systematic overestimation of wave heights was observed, ranging from 0.2 to 0.4 m across the swath, with larger discrepancies from Nadir.

In this work, SWH was assessed within the 0–15 km coastal band using buoy 6201005 and offshore at depths > 60 m using buoys 6200069 and 6200305. Results indicate that SWH accuracy depends on both distance from the coast and water depth. SWOT shows high performance up to ~ 3–4 km from the coast at 18 m depth (buoy 6201006), with comparable performance at other sites located > 5 km offshore at shallower depths (~11 m; buoys 6201007 and 6201004). These results highlight the potential of SWOT KaRIn for assessing coastal waves, in contrast to conventional altimeters that often lose accuracy nearshore where radar returns are frequently flagged. A key factor controlling coastal wave accuracy is the robustness of sea state bias correction, which depends on sea surface wave conditions. In addition, waves undergo physical transformations as they propagate from deep to shallow waters, further degrading data quality at reduced depths.

Previous studies have reported similar limitations in coastal altimetry. Passaro et al. (2021) analyzed Jason-1 and Jason-2 data (July 2001–January 2016) and showed progressive attenuation of wave climate toward the shoreline: mean SWH at 3 km offshore was 22 % lower than offshore values, the annual cycle amplitude was reduced by 14 %, and mean energy flux decreased by 38 %. Abessolo et al. (2023) reported a decline in altimetry-derived SWH quality at ~ 10 km from the coast, mainly due to reduced reliability of sea state bias correction, emphasizing the need for complementary coastal measurements along satellite tracks. Schlembach et al. (2023) evaluated the focused SAR (FF-SAR) mode on Sentinel-3 to reduce small-scale along-track interferences, improving SWH retrievals along the German and Dutch coasts. Their results showed reliable estimates within ~ 4–5 km of the shoreline, with correlations exceeding 80 %, while wave-induced decay at 30 km offshore ranged from 23 % to 29 %. The present analysis indicates that the estimation of significant wave height (SWH) SWH is highly dependent on the proximity of the observation to the coast, where both distance from shore and local bathymetry (depth) exert a strong influence. Within this challenging nearshore zone, SWOT observations reproduce wave characteristics with notable skill up to ~ 24 km from the coast. This performance is particularly promising, as conventional
nadir altimetry typically exhibits degraded accuracy in coastal waters due to land contamination of the radar footprint, signal distortion caused by complex bathymetry, and reduced data availability near the shoreline. The ability of SWOT to retain consistency and reliability closer to the coast thus represents a significant advance over traditional altimetric missions.

Estimation errors in satellite data generally increase from nadir to the swath edges due to longer atmospheric paths, larger footprints, and greater geolocation uncertainties at oblique angles. For SWOT KaRIn, this effect is well documented: errors remain small near nadir but grow toward the swath edges, especially at low SWH according to the works of Bohé et al. (2025). Their global validation against SWOT nadir altimetry showed that KaRIn–nadir biases stay below 5 cm for SWH > 1 m, but can approach ~ 10 cm when SWH < 1 m. Moreover, the variance of KaRIn–nadir differences increases with cross-track distance, with the strongest growth at the outer swath.

Beyond methodological improvements, these results carry important implications for coastal monitoring, hazard assessment, and the development of operational wave models. However, to consolidate these findings, a broader validation effort based on multi-mission altimetry and in situ observations is required, ensuring a robust assessment of SWOT's capabilities across diverse coastal environments and under different energy conditions, including storms.

SWOT observations have also proven effective in reproducing the different storm events selected in this study, covering the period from March 2023 to July 2024. The spatial distribution of SWH fields under the different coastal storms (Fig. 10) is provided by the SWOT 2D maps during each event depending on SWOT orbit cycle (at a specific time and a determined position of the track). The mapping of the SWH evolution during a period 6 days between 29 October and 4 November 2023, including Ciaran storm, is performed by combining 4 SWOT passes covering the SWH changes from the Atlantic to the Channel and coastal zones. This covering provided by SWOT should be an excellent dataset to be integrated in numerical models to assimilating the wave hydrodynamics during extremes.

In a similar context, Turki et al. (2025) investigated nearshore and coastal wave conditions in the English Channel by combining SWOT nadir observations with data from nine conventional altimeters during

periods of high wave energy. Their results, evaluated against in situ buoy measurements, demonstrated a strong mean correlation of approximately 90 %, although the agreement systematically decreased with increasing spatial offset between buoy locations and satellite ground tracks. This finding underscores the critical role of spatial sampling in altimetric wave validation and is consistent with the present study, where the performance of SWOT was also shown to depend strongly on the distance from the coast and local bathymetric conditions. Together, these results highlight both the promise and the challenges of using SWOT for coastal wave monitoring, particularly in complex nearshore environments where complementary datasets remain essential for robust validation.

## 4.2. Insights of SWOT use for assessing coastal wave fields

In this research, the accuracy of nearshore and coastal wave measurements was assessed at multiple sites in the English Channel, near both the UK and French coasts, using in situ buoy observations as reference. The analysis indicates that smaller waves (SWH < 0.5 m) were not adequately captured by the SWOT LR-L2 products, leading to potential outliers under such low-energy sea states. This limitation is less evident near the nadir but becomes more pronounced along the swath edges, where the measurement accuracy decreases with distance from the satellite track center. Furthermore, SWOT-derived SWH values were extracted at the nearest available grid points to the buoy locations, which are situated in nearshore areas at varying distances from the coastline. As a result, exact colocated comparisons could not be performed, introducing an additional source of uncertainty in the validation.

Coastal waves undergo significant modifications as they propagate from the open ocean, varying with wavelength as well as hydrodynamic and topographical factors. Therefore, detailed validation of SWOT altimeter measurements is essential, requiring rigorous instrumentation and processing to ensure accurate SWH estimates. Such measurements may even surpass buoy records in shallow areas, where ocean waves are not always representative of coastal energy.

Nearshore and coastal waves undergo physical transformations that can lead to modulation in some areas and dissipation in others, depending on seabed topography and coastal geometry. These transformations are often more pronounced in complex coastal zones, making comparisons with buoys located away from SWOT observations challenging. In addition, buoy wave frequencies used to derive SWH should be consistent with the values provided by altimeters, which is essential for reliable validation. Kang et al. (2021) addressed this issue by correcting buoy SWH through data matching, error statistics, and linear least-squares fitting to improve in situ accuracy. They found that the correction varied over time, was strongly correlated with 10-m wind, and reached an average of 3.8 m during the observation period.

Ardhuin et al. (2024) used SWOT products to capture swell waves generated by storms and propagating across ocean basins into shallow areas. Their study provided the first validation of SWOT swell measurements, demonstrating its ability to map wave height (>0.2–0.3 m), wavelength (500–1050 m), and direction, while also revealing strong large-scale wave reflections in nearshore zones. Bohé et al. (2025) investigated interferometric decorrelation in SWOT KaRIn data for mapping SWH at kilometric resolution. By validating against buoys, lidar, SWOT nadir altimetry, and Sentinel-3, they showed that SWH errors are sensitive to volumetric decorrelation estimates. Their results indicated that low SWH values in the outer swath require denoising and correction of deco. The main contribution of the SWOT mission lies in its ability to provide a synoptic two-dimensional view of the ocean surface, resolving mesoscale to submesoscale features at kilometer scales with high accuracy through L2 LR products, without the need for interpolation. Nevertheless, many applications rely on consistent multi-mission datasets, such as the Level-3 ocean products from the DUACS system, which combine nearly three decades of observations from a

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Monitoring of wave fields during storms along the English Channel and French/UK coasts from combined SWOT passes: (a) storms Mathis, Karlotta, and Pierrick; (b) storm Ciaran with two days pre- and post-storm (29 October–4 November 2023).</figcaption></figure>
constellation of 20 altimeters (Dibarboure et al., 2025). To incorporate SWOT's unique swath measurements into this framework, an extension of the Level-3 products was recently developed based on multi-satellite calibration, leveraging the stability of the nadir altimeter constellation. This integration enhances the detection of surface roughness and small-scale wave signals, particularly in complex coastal regions, and enables high-quality two-dimensional mapping of extreme events. A notable example is the case of cyclone Mawar/Betty in the western Pacific, captured during SWOT's 1-day phase, where the associated radiating wave field was successfully resolved. Similarly, in the present study of the English Channel, such integration approaches would be highly valuable for strengthening SWOT validation in nearshore environments, where the combination of swath and nadir altimetry can mitigate spatial sampling limitations and improve the robustness of coastal wave analyses.

The combination of SWOT LR and HR products provides a powerful means of accurately monitoring wave evolution and transformations from the open ocean into shallow coastal zones, addressing one of the central challenges in coastal hydrodynamic assessment. The LR products ensure large-scale spatial coverage with consistent accuracy, while the HR products capture fine-scale processes, that are critical in nearshore environments. Together, these complementary datasets enhance our ability to resolve the complex dynamics governing coastal wave conditions and support improved applications in coastal hazard monitoring and management.

## 5. Conclusions and perspectives

In the present research, preliminary insights of SWOT ability for monitoring the nearshore and coastal hydrodynamics have been conducted in the English Channel and close to UK a French coasts with a special focus on high energetic periods and storms. Extensive analyses have been developed for SWOT SSH and SWH observations (1-day Cal/ Val and Scientific Orbit data) to assess the applicability of this new mission, compared to in-situ measurements. The main finding of this work argues accurate observations, provided by L2 LR SWOT at shallow areas, with slight limitations in zones with very reduced depths and complex topographical factors; the agreements of coastal waves between SWOT and buoys have proven to be significant at ~ 4 km from the coastline.

This work highlights the contribution of SWOT to advancing the monitoring of coastal hydrodynamics. The good overall performance of SWOT observations in the English Channel demonstrates its potential and calls for more in-depth analyses of SWOT accuracy, particularly through the integration of different data products, including High Resolution measurements, to better resolve complex coastal features. Addressing this challenge will benefit from ongoing research efforts aimed at delivering more reliable estimates of coastal variability and change, which are essential for improving the performance of numerical models at both regional and local scales.

### CRediT authorship contribution statement

E.I. Turki: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. M.S. Islam: Writing – review & editing, Validation, Formal analysis, Data curation. C. Lopez Solano: Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization. E. Salameh: Writing – review & editing, Validation, Methodology, Formal analysis, Data curation. M. Domingues: Writing – original draft, Methodology, Formal analysis, Data curation. T. Mendoza: Writing – review & editing, Visualization, Validation. L. Auof: Writing – review & editing, Validation, Supervision, Conceptualization. D. Gutierrez Barcelo: Writing – review & editing, Validation, Investigation, Formal analysis. B. Laignel: Writing – review

& editing, Visualization, Funding acquisition, Conceptualization. A. Carbonniere: Writing – review & editing, Funding acquisition, Conceptualization. F. Frappart: Writing – review & editing, Visualization, Supervision, Methodology, Data curation.

### 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.

### Acknowledgments

This work was supported by the French Space Agency CNES in the framework of TOSCA program SWOT4COST, INSU program EONES-SWOT. Additional support was provided by TRANSITIN FRANCE 2030 ANR-23-EXES-0013. We thank also Ajejandro Bohé from CNES for his effective review of SWOT products used in this work, the M2C's engineers Laurent Benoit and Christophe Conessa for their technical support, their involvement has significantly improved the quality of this work. We extend our gratitude to SHOM (service national d'hydrographie et d'océanographie, France) and Met Office (Weather and climate change, UK) for providing tide gauges and wave buoys dataset, the Direction Départementale des Territoires et de la Mer (DDTM - Manche) for granting us the authorization to conduct measurements in Raz Blanchard, Normandy.

### Data availability

Data will be made available on request.

### References

Abdalla, S., Kolahchi, A., Ablain, M., Adusumilli, S., Aich Bhowmick, S., ... al., 2021. Altimetry for the future: Building on 25 years of progress, Advances in Space Research, Volume 68, Issue 2, 2021, Pages 319-363, ISSN 0273-1177, https://doi.org/10.1016/j.asr.2021.01.022.

Abessolo, G., Birol, F., Almar, R., Léger, F., Bergsma, E., et al., 2023. Wave influence on altimetry sea level at the coastal. Engineering 180, 104275.

Arduuin, F., Molero, B., Bohé, A., Nouguier, F., Collard, et al., 2024. Phase-Resolved Swells Across Ocean Basins in SWOT Altimetry Data: Revealing CentimeterScale Wave Heights Including Coastal Reflection. Geophysical Research Letters, 2024, Science from the Surface Water and Ocean Topography Satellite Mission, 51 (16), pp.e2024GL109658. ff10.1029/2024GL109658ff.

Benveniste, J., Mandea, M., Melet, A., Ferrier, P., 2020. Earth observations for coastal hazards monitoring and international services: a European perspective. Geophysical Survey 41(6):1185-1208. https://doi.org/10.1007/s10712-020-09612-6.

Birol, F., Bignalet-Cazalet, F., Cancet, M., Daguze, J.-A., Fkaier, W., Fouchet, E., Léger, F., Maraldi, C., Niño, F., Pujol, M.-I., Tran, N., 2024. Understanding uncertainties in coastal sea level altimetry data: insights from a round robin analysis. Egusphere [preprint]. https://doi.org/10.5194/egusphere-2024-2449.

Bohé, A., Chen, A., Dubois, P., Fore, A., Molero, B., Peral, E., Raynal, M., Stiles, B., Arduuin, F., Hay, A., Legresy, B., Lenain, L., Bóas, A., 2025. Measuring significant Wave Height Fields in two Dimensions at Kilometric Scales with SWOT. In: IEEE Transactions on Geoscience and Remote Sensing, p. 1. https://doi.org/10.1109/TGRS.2025.3551605.

Cazenave, A., Gouzenes, Y., Birol, F. et al., 2022. Sea level along the world's coastlines can be measured by a network of virtual altimetry stations. Commun Earth Environ 3, 117 (2022). https://doi.org/10.1038/s43247-022-00448-z.

Dibarboure, G., Anadon, C., Briol, F., Cadier, E., Chevrier, R., Delepoulle, A., Faugère, Y., Laloue, A., Morrow, R., Picot, N., Prandi, P., Pujol, M.-I., Raynal, M., Tréboutte, A., Ubelmann, C., 2025. Blending 2D topography images from the Surface Water and Ocean Topography (SWOT) mission into the altimeter constellation with the Level-3 multi-mission Data Unification and Altimeter Combination System (DUACS). Ocean Sci. 21, 283-323. https://doi.org/10.5194/os-21-283-2025.

Fu, L., Pavelsky, T., Cretaux, J., Morrow, R., Farrar, J.T., Vaze, P., Sengenes, P., Vinogradova-Shiffer, N., Sylvestre-Baron, A., Picot, N., et al., 2024. The surface water and ocean topography mission: A breakthrough in radar remote sensing of the ocean and land surface water. Geophys. Res. Lett. 51, e2023GL107652. https://doi.org/10.1029/2023GL107652.

Han, G., 2017. "Altimeter surveys, coastal tides, and shelf circulation," in Encyclopedia of Coastal Science, eds C. W. Finkl and C. Makowski (Berlin: Springer Science & Business Media). doi: 10.1007/978-3-319-48657-4-7-2.

Hart-Davis, M.G., Andersen, O.B., Ray, R.D., Zaron, E.D., Schwatke, C., Arildsen, R.L., et al., 2024. Tides incomplex coastal regions: Early case studies from wide-swath
SWOT measurements. Geophys. Res. Lett. 51, e2024GL109983. https://doi.org/
10.1029/2024GL109983.

Harvey, T.C., Hamlington, B.D., Frederikse, T., et al., 2021. Ocean mass, sterodynamic effects, and vertical land motion largely explain US coast relative sea level rise. Commun. Earth Environ. 2, 233. https://doi.org/10.1038/s43247-021-00300-w.

Holland, K.T., Holman, R.A., Lippmann, T.C., Stanley, J., Plant, N., 1997. Practical use of video imagery in nearshore oceanographic field studies. IEEE J. Ocean. Eng. 22 (1), 81–92.

Ji, T., Guosheng, L., Zhang, Y., 2019. Observing storm surges in China's coastal areas by integrating multi-source satellite altimeters, Estuarine, Coastal and Shelf Science, Volume 225, 2019, 106224, ISSN 0272-7714, https://doi.org/10.1016/j.ecss.2019.05.006. (https://www.sciencedirect.com/science/article/pii/S0272771419300253).

Kang, J., Mao, R., Chang, Y., Fu, H., 2021. Comparative analysis of significant wave height between a new Southern Ocean buoy and satellite altimeter, Atmospheric and Oceanic Science Letters, Volume 14, Issue 5, 2021, 100044, ISSN 1674-2834, https://doi.org/10.1016/j.aosl.2021.100044.

Laignel, B., Vignudelli, S., Almar, R., et al., 2023. Observation of the Coastal areas, Estuaries and Deltas from Space. Surv. Geophys. 44, 1309–1356. https://doi.org/
10.1007/s10712-022-09757-6.

Lichtman, I. D., Bell, P. S. Gommenginger, C., Banks, C., Calafat, F. M., Brown, J., & Williams, S. D. P. (2025). Evaluating water levels from the surface water and ocean topography (SWOT) mission in a hyper-tidal coastal and estuarine environment. Earth and Space Science, 12, e2024EA004104. https://doi.org/10.1029/2024EA004104.

López Solano, C., Turki, E.I., Mendoza, E.T., et al., 2024. Hydrodynamic modelling for simulating nearshore waves and sea levels: classification of extreme events from the English Channel to the Normandy coasts. Nat. Hazards 120, 13951–13973. https://doi.org/10.1007/s11069-024-06699-7.

Masselink, G., Gehrels, R., 2015. Coastal Environments and Global Change. Wiley-Blackwell. https://doi.org/10.1002/9781119117261.

Maubant, L., Dodd, L., Tregoning, P., 2025. Assessing the accuracy of SWOT measurements of water bodies in Australia. Geophys. Res. Lett. 52, e2024GL114084. https://doi.org/10.1029/2024GL114084.

Mendoza, E.T., Jimenez, J.A., Mateo, J., 2011. A coastal storms intensity scale for the Catalan Sea (NW Mediterranean). Nat. Hazards Earth Syst. Sci. 11 (9), 2453–2462. https://doi.org/10.5194/nhess-11-2453-2011.

Morrow, R., Fu, L.L., Ardhuin, F., Benkiran, M., Chapron, M., et al., 2019. Global Observations of Fine-Scale Ocean Surface Topography with the Surface Water and

Ocean Topography (SWOT) Mission. Front. Mar. Sci. 6. https://doi.org/10.3389/fmars.2019.00232.

Nicholls, R.J., Small, C., 2002. Improved estimates of coastal population and exposure to hazards released. Eos Trans. AGU 83 (28), 301–305. https://doi.org/10.1029/2002EO000216.

Normandin, C., Frappart, F., Baghdadi, N., Bourrel, L., Peña Luque, S., Ygorra, B., Wigneron, J.P., 2024. First results of the surface water ocean topography (SWOT) observations to rivers elevation profiles in the Cuvette Centrale of the Congo Basin. Front. Remote Sens. 5, 1466695.

Passaro, M., Hemer, M.A., Quartly, G.D., et al., 2021. Global coastal attenuation of wind-waves observed with radar altimetry. Nat. Commun. 12, 3812. https://doi.org/10.1038/s41467-021-23982-4.

Peng, F., Deng, X., Cheng, X., 2021. Quantifying the precision of retracked Jason-2 sea level data in the 0–5 km Australian coastal zone, Remote Sensing of Environment, Volume 263, 2021, 112539, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2021.112539.

PL D-105502, "SWOT Algorithm Theoretical Basis Document: Level 2 KaRIn Low Rate Sea Surface Height -L2LR_SSH Science Algorithm Software,".

Schlembach, F., Passaro, M., Dettmering, D., Bidlot, J., Seitz, F., 2022. Interference-sensitive coastal SAR altimetry retracking strategy for measuring significant wave height, Remote Sensing of Environment, Volume 274, 2022, 112968, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2022.112968.

Switzer, A.D., 2015. Coastal hazards: storms and tsunamis. Coastal Environments and Global Change 104–127.

Turki, E.I., Salameh, E., Lopez Solano, C., Islam, M.S., Domingues, M., Aouf, L., Gutierrez, D., Carbonnière, A., Frappart, F., 2025. Multi-source satellite altimetry for monitoring storm wave footprints in the English Channel’s coastal areas. Remote Sens. (Basel) 17 (18), 3262. https://doi.org/10.3390/rs17183262.

Vignudelli, S., Birol, F., Benveniste, J., Fu, L., Picot, L., et al., 2019. Satellite Altimetry Measurements of Sea Level in the Coastal Zone. Surveys in Geophysics, 2019, 40, pp.1319-1349. (10.1007/s10712-019-09569-1). (insu-03670750).

Yu, Y., Sandwell, D.T., Dibarboure, G., Chen, C., Wang, J., 2024. Accuracy and Resolution of SWOT Altimetry: Foundation Seamounts, revised for Earth & Space Science. https://doi.org/10.13140/RG.2.2.30236.67207.

Further reading

Holman, R.A., Stanley, J., 2007. The history and technical capabilities of Argus, Coastal Engineering, https://doi.org/10.1016/j.coastaleng.2007.01.003.