Coastal erosion has posed significant challenges to sustainability and socio-economic stability along Senegal’s coastline, leading to substantial infrastructure losses. Using GIS multi-criteria decision analysis (MCDA), two sub-indices were derived for Senegal’s coastal departments: the physical susceptibility (PSI) and the social-economic vulnerability (SVI) to coastal erosion. The integrated coastal erosion vulnerability (ICER) model was derived by their aggregation. A total of 26 criteria were used, 18 for PSI and 8 for SVI. The criteria weighting coefficients of the sub-indices were determined using the analytic hierarchy process (AHP). Validation of the model accuracy was performed using receiver operating characteristic (ROC) curves that were calculated based on a created coastal erosion cadaster and true positive (TP) sites and manually acquired true negative (TN) sites. The accuracy assessment confirmed the consistency of the physical susceptibility model (PSI) and proved that existing coastal erosion sites are within (5) very high susceptibility areas. Through the generated ICER, the coastal departments were divided into areas of (1) very low, (2) low, (3) medium, (4) high and (5) very high vulnerability to coastal erosion. Very high (5) and high (4) classes cover around 31% of the coastal departments, mostly encompassing a narrow coastal strip and low river valleys and mouths. The presented coastal susceptibility and vulnerability maps, with a spatial resolution of 30 m, identified problematic areas in Senegal’s coastal departments and can help decision-makers in the construction of effective coastal zone management and sustainable development.

Understanding beach dynamics, both in time and in space, is paramount to better understand how and when to intervene to improve coastal management strategies. Beach morphodynamics is expressed in a variety of ways. As indicators of beach change, we can measure the shoreline, the beach topography, and the bathymetry; e.g., in situ measurements rarely cover large extents, are often collected on a local scale (beach), and rarely cover a sufficient time span with a sufficient surveying frequency or a simultaneous measurement of the beach and bar system. Regular-revisit satellites, such as the ESA’s Sentinel-2 mission, provide the opportunity to regularly monitor both shoreline and sandbar dynamics, and the time span is increasing and likely to continue for the decades to come. Using the satellite-derived shoreline and bar position, here, we show that the shoreline and bar are intrinsically coupled. Using Sentinel-2 satellite imagery, we show that the actual erosion/accretion status of the beach at Saint Louis (Senegal) is strongly influenced by the sandbar dynamics. There is a coupled behavior in their seasonal evolution and trend. Our results show that a very large accretive wave of about 50 m observed on the beach is driven by a local welding of the inner sandbar to the beach. Finally, we conclude that this type of event could be anticipated by an analysis of the sandbar.

In today's context of rising sea levels and subsiding land topography, coastal flooding is a major concern for coastal areas, both nationally, regionally, and internationally. The main objective of the present study is to contribute to the understanding of the impact of subsidence and sea-level rise on coastal flooding in Saint-Louis, and in particular the Langue de Barbarie, by producing a vulnerability map of submerged surfaces. To this end, the Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of Sentinel-1 images and estimate land subsidence in the Saint-Louis and Langue de Barbarie area. Interferometric SAR (InSAR) measurements are merged with RCP 2.6 sea-level rise scenarios to identify predicted flooded areas and provide a flood vulnerability map. At the Langue de Barbarie, topographic subsidence is estimated at between -6.4 and -0.4mm/year. In this study, we show that local land subsidence may increase vulnerability to flooding caused by sea-level rise based on 2100 projections. This may represent an increase in the flood zone in the study area. Based on the RCP 2.6 scenario and the subsidence rates obtained, a significant part of the St. Louis coastline and city would be subject to coastal and fluvial flooding for the years 2040 and 2100. For these dates, 29 to and 36 Km² of the studied area would be occupied by water. Given this situation, research in Saint-Louis needs to focus on adaptation scenarios to coastal risks, to protect coastal communities and safeguard the city's historical heritage.

Today, coastal storms represent one of the most formidable environmental challenges, causing significant impacts on coastal communities. This situation underscores both the importance and urgency of studying storms and their characterization. This study proposes an innovative approach combining Principal Component Analysis (PCA) and machine learning (Classification and Regression Trees, CART) to characterize and distinguish damaging storms from non-damaging ones along the coast of Dakar, Senegal. The analysis revealed that among several hydrometeorological variables studied (wave height, period, direction, runup, wave energy, sea level anomaly, tide, etc.), the variables SLA and tide play a central role in the occurrence of damage, although they are weakly correlated with the others. By cross-analyzing these variables, critical thresholds were established, such as Tide > 0.53 m combined with SLA ≥ 0.061 m, Tide > 0.53 m and ECWL ≥ 1.3 m, as well as Runup ≤ 0.64 m associated with a high wave period (Tp), allowing accurate differentiation of potentially damaging storms. The CART method validated these results and identified three key combinations: (1) Tide–SLA, where no damage is observed if Tide < 0.53 m, and damage occurs beyond this threshold when SLA ≥ 0.061 m; (2) Tide–ECWL, where storms are damaging if Tide > 0.53 m and ECWL ≥ 1.3 m; (3) Runup–Tp, where storms are damaging if Runup ≤ 0.64 m or if Runup > 0.82 m with Tp ≥ 16 s. These results constitute the first application of machine learning for storm classification on the Senegalese coast, providing a novel quantitative foundation for better understanding the hydrodynamic conditions associated with damaging storms. The findings of this study could be valuable for risk management and the development of early warning systems

Coastal flooding risk is intensifying under climate change, especially along the low sandy Senegalese coastline. This study explores the spatiotemporal variability of extreme coastal water levels (ECWL) from 1993 to 2023 by combining ERA5 reanalysis (waves, wind, pressure), tide gauge and meteorological data, and applying a generalized Pareto distribution (GPD) to estimate the 99th percentile and return levels for 50 and 100 year events. The analysis of the upper 1% of ECWLs reveals significant spatial heterogeneity: 99th percentile values exceed 1.2 m in the Dakar region (Yoff, Ouakam, Ngor) and around Saint-Louis/Langue de Barbarie, with 95% confidence intervals ranging from approximately 1.15 m to 1.30 m, while Casamance and the Saloum Delta exhibit much lower extremes (0.8–1.0 m). For return periods, ECWLs vary between 1.6 m and 2.3 m, with the 100 year return level (T100) exceeding 2.25 m in Dakar, above 2.0 m in Saint-Louis, and intermediate values (1.5–1.9 m) along the Petite Côte (Mbour–Toubab Dialaw) and in the Saloum Delta. The 50 year return level (T50) follows a similar spatial pattern but is 5–10 cm lower than T100 in the most exposed areas. Sensitivity analysis shows that ECWLs are primarily controlled by astronomical tide along much of the coast, whereas wave runup dominates in the southern estuarine zones (Saloum, Casamance, Mbour). Trend analysis using the Mann–Kendall test reveals a latitudinal gradient: stronger positive slopes in the south, weaker trends in central and northern sections, but all p values lie between 0.1 and 0.4, meaning none of the trends reach conventional significance. These findings point to a potential intensification of extreme water levels in socio-economically critical areas (Dakar, Saint-Louis, Mbour) but should be interpreted with caution given the lack of robust statistical significance. The results provide a quantitative basis for coastal risk management in light of projected sea level rise.

Coastal storms represent a major environmental issue and constitute an important challenge for coastal flood management. This study analyzes the frequency and characteristics of storms on the Senegalese coast between 1993 and 2023, focusing on four coastal cities: Dakar, Saint-Louis, Mbour, and Cap-Skring. The analysis is based on wave data from the ERA5 model and on meteorological and oceanographic data from different models. Storms were detected using the Peak Over Threshold (POT) method, based on the 95th percentile and fitted to a generalized Pareto distribution (GPD). The results reveal a contrasted spatial distribution of coastal storms, with a higher occurrence in Dakar and Saint-Louis. An apparent increase in the frequency of storms is observed in Saint-Louis, Mbour, and Cap-Skring, while an apparent decrease is noted in Dakar; however, these trends are not statistically significant. Extreme coastal water levels (ECWL) associated with storms show an opposite evolution, with an apparent decrease in the first three regions and an apparent increase in Dakar. The most intense and longest storms, in terms of energy content (Es), are mainly observed in Dakar and Saint-Louis. A linear relationship is highlighted between the duration and intensity of storms. Storm occurrence shows a strong seasonal modulation, with a predominance during the dry season (November to May). The most energetic storms are mostly generated by waves from the west to west-northwest direction in Dakar and Saint-Louis, while Mbour and Cap-Skring present a wider directional window. This first analysis at the scale of the Senegalese coast provides essential elements for understanding the risk of coastal storms and constitutes support for coastal flood management in a context of climate change.