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Article

Satellite and Statistical Approach for the Characterization of Coastal Storms Causing Damage on the Dakar Coast, Capital of Senegal (West Africa)

by
Cheikh Omar Tidjani Cisse
Laboratory of Dynamics and Integrated Management of Coastal Areas, University of Quebec in Rimouski, 300 Ursuline Path, P.O. Box 3300, Rimouski, QC G5L 3A1, Canada
Coasts 2025, 5(3), 24; https://doi.org/10.3390/coasts5030024
Submission received: 16 April 2025 / Revised: 8 July 2025 / Accepted: 11 July 2025 / Published: 16 July 2025
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Abstract

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

1. Introduction

Today, in a global context marked by rising sea levels [1,2], coastal erosion and flooding represent major global issues affecting many countries with coastal regions [3,4,5]. The increase in sea level and the frequency of extreme ocean-weather events (storms) will further heighten the vulnerability of coastal areas [6,7,8,9,10,11]. This vulnerability will have far-reaching economic, social, and environmental consequences, it bears repeating [12,13,14]. Under the lens of climate change, such events are expected to become more frequent. The intensification of cyclonic storms due to climate change will increase storm surges, exposing coastal communities to even greater risk [15,16,17]. This vulnerability of coastal zones will be further exacerbated by growing coastal populations [18,19].
Storms are sudden events capable of causing significant environmental, morphogenic, and economic impacts [20,21,22]. As [23] aptly explain, storms can lead to a wide range of damages, some of which have major environmental and economic consequences. According to [24], in the context of climate change, governments in many countries are facing overwhelming even unsustainable costs to repair damage or compensate affected populations. Currently, there is a surge in scientific research on coastal extremes. Indeed, most of these studies have clearly demonstrated the dramatic consequences of storms on beaches and, more broadly, on coastal systems [25,26,27]. In the context of climate disruption, most of the world’s coastlines are affected by extreme coastal events, resulting in drastic morphogenic, economic, social, and environmental impacts [28,29,30]. This situation also affects the West African coasts and the Gulf of Guinea. Studies by [31] in Senegal, [32] in Nigeria, [33] in South Africa, [34] in Ghana, [35] on the eastern coasts of Africa, and [36] on the Gulf of Guinea coasts show that a large portion of African coastlines are exposed to coastal extremes.
In Senegal, the coast is vulnerable to erosion and coastal flooding [31,37,38]. With a relatively long coastline (700 km), this section of the West African coast is increasingly affected by coastal inundation. The work of [39,40,41,42] reveals a progressive increase in extreme coastal events and their impacts on populations and socio-economic activities. Like many urbanized coastal zones, the Dakar coastline is experiencing rapid coastal development, which increases its vulnerability beyond the rising number of extreme events. Indeed, while much research focuses on coastal risks such as erosion and flooding, relatively little attention has been given to the specific characteristics of storms. However, in recent years, extreme coastal events have highlighted the morphogenic, economic, and destructive effects that storms can have on the Senegalese coastline. Therefore, it is essential to distinguish and potentially predict the severity of storms to ensure the sustainable management of coastal zones. In other words, it is important to characterize storms, that is, to differentiate between those that can cause damage and those that are not causing damage. A detailed and nuanced understanding of storm characteristics and their future evolution could enable more sustainable and effective management of coastal risks. This highlights the importance and urgency of focusing on storm characterization. It is in this context that the central question of this study arises: a satellite-based and statistical approach to the characterization of damaging and non-damaging storms in Senegal’s capital city.

2. Study Area

The coastline of Dakar is located to the south and north of the Cape Verde Peninsula along the Atlantic façade (Figure 1). Its geographical position is a significant asset for its economic influence and the diversity of its ecosystems, with 133 km of coastline, along which two types of environments are present: rocky and sandy coasts, the latter being the most important in the region [43].
It is a highly urbanized area with the presence of human settlements and coastal assets. This situation, combined with its geomorphological characteristics, explains its vulnerability to the risk of marine submersion. Tectonics is also a decisive factor in Dakar’s vulnerability. Two types of swells affect the Senegalese coast, particularly that of Dakar: northwesterly swell and southwesterly swell [31,41,44]. Wave energy in Senegal is predominantly driven by swells originating from generation zones located in mid- and high-latitude regions. The wave rose in Figure 1 shows that the waves recorded in the study area mainly come from the northwest direction.

3. Materials and Methods

3.1. Hydrodynamic and Meteorological Data

Quantifying extreme coastal water levels requires the availability of hydrodynamic, meteorological, and tide gauge parameters. In this study, ERA5 reanalysis data with a resolution of 0.5° × 0.5° [45], developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The hydrodynamic data, particularly wave parameters, were extracted from the global ERA5 model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The data were extracted at the extraction point 14.6689 and −17.3764°. They have a horizontal spatial resolution of 0.5° × 0.5°. The time window for the hourly wave data spans the period from 1993 to 2023.
These data allowed for the retrieval of wave parameters at 1h time steps, as well as surface wind and sea level pressure, over the time 1993–2023. This period corresponds to the launch of the first altimetric satellites [46], which made it possible to quantify sea level anomalies using satellite altimetry data, based on the WGS84 reference system [47].
Tide gauge data were extracted from the point closest to the coast using the global tide model FES2014 (Finite Element Solution) [48], with hourly resolution, and produced by LEGOS (Laboratory for Studies in Geophysics and Space Oceanography). Dynamic atmospheric corrections were produced by the Collecte Localisation Satellite Space Oceanography Division using the MOG2D model from LEGOS and distributed by AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic Data) with support from the French National Centre for Space Studies (CNES).

3.2. Identification of Coastal Storms

In this study, two complementary approaches were used to identify coastal storms that occurred along the Dakar coastline. First, a survey was conducted among local populations to collect information related to coastal storm events, focusing specifically on the eastern-southeastern axis of the Cape Verde Peninsula. A non-probabilistic “snowball” sampling method was chosen. This approach involves interviewing only individuals who meet the defined characteristics of the target population and asking them to refer others with similar profiles. The primary objective of the survey was to collect information regarding the dates of coastal storms that occurred in the study area, as well as their socio-economic impacts. The questionnaire specifically addressed three themes related to coastal flooding:
-
Information about the respondent
-
General perception of coastal flooding, including its seasonality or frequency in the surveyed area, the territories likely to be affected, and various known coastal flooding events.
-
Specific events recalled by the respondent, including those documented in this study: the timing of inundations, their duration, spatial extent, water depth, personal experiences during these events, and their socio-economic impacts
The target population for the survey consisted of individuals likely to have been affected by coastal flooding, and those who live or work near or directly on the coast and have done so for at least 20 years. This group was selected because their long-term presence in coastal areas makes them more likely to recall past coastal flooding events and to provide valuable historical insights into coastal storms. Neighborhood leaders, who are often on the front lines during crises, were the first points of contact and referred us to key individuals, who then referred others once they understood the study’s objectives.
The questionnaire was administered to 135 individuals, distributed across the study area. Respondents were composed of 62% men and 37% women, with 90% aged over 40. Additionally, 87% had lived in the area for more than 20 years, and 43% were engaged in fishing activities. Across all surveyed municipalities, 61% of respondents reported having experienced impacts from coastal flooding [37].
Beyond the survey, existing research on the Dakar coastline was reviewed to identify documented coastal storm dates. Archives from printed newspapers and online media sources were consulted to gather additional information on flooding events that were either documented in official records or mentioned during the interviews. These sources allowed us to collect new information on coastal storms, validate existing records (e.g., official event sheets), and cross-check the survey responses in terms of storm dates, magnitude, and associated damage. By combining these different information sources, we were able to compile a catalog of 21 coastal storms.

3.2.1. Parameterization of Hazard Variables for the Characterization of Damaging Storms

In most cases, storms cause significant damage, particularly due to financial and socio-economic losses [49]. Moreover, the intensity of the damage caused by storm surges largely depends on the extreme coastal water level [50,51]. These extreme water levels are the outcome of a combination of mean sea level (MSL), astronomical tides, the dynamic sea surface response to atmospheric forcing, and nonlinear interactions among these components [52,53,54].
To enable a relevant and comprehensive characterization of each identified storm event, the following parameters were extracted: Wave characteristics: significant wave height (Hs), peak period (Tp), and wave direction (Dir), Sea Level Anomaly (SLA), Storm surge component: Dynamic Atmospheric Correction (DAC), Tide (T), Runup (R), Extreme Coastal Water Level (ECWL), and Storm energy content (Es). The parameters ECWL and Es were computed using the following equations:
E C W L = S L A + D A C + T i d e + R
Extreme coastal water levels result from the combination of several parameters. In this model, we integrate sea level anomaly (SLA); storm surge height (DAC), which is influenced by atmospheric pressure and wind astronomical tide level (T); and wave runup height (R).
To quantify the storm energy content (Es) of the identified events, the storm index developed by [55] (Equation (2)) was applied. The energy content of storms is a key component in storm classification. According to these authors, it is a critical factor in determining the severity of a storm’s impact on coastal areas. Coastal wave storms, characterized by energy well above the climatological average, can have a major impact on coastlines [56]. Therefore, it is essential to estimate the energy of storms.
E s = ∫ t 1 t 2 H s 2 d t  
where t1 and t2 define the duration during which Hs remained above a defined threshold (H_threshold). According to [57], this equation provides a more accurate estimate of storm energy content (Es) compared to the traditional wave energy equation. Indeed, the conventional wave energy formula typically uses a single Hs value, most often the maximum significant wave height, to represent the entire event. In contrast, Equation (2) accounts for the temporal evolution of the energy impacting the coastline [56].

3.2.2. Statistical Characterization of Storms Causing Coastal Damage

The discrimination of storms, particularly the thresholds of meteorological and hydrodynamic variables from which it is possible to distinguish damaging from non-damaging storms, was performed based on information related to the material and financial losses induced by storm surges along the Senegalese coastline. In this study, the characterization of coastal storms involved the creation of a dataframe (Table 1) containing the following: storm events, hazard variables: significant wave height (Hs), peak period (Tp), wave direction (Dir), Sea Level Anomaly (SLA), storm surge height (DAC), tide (Tide), wave runup (R), energy content of the storm (Es), and Extreme Coastal Water Level (ECWL), categorical variables (indicating the presence or absence of damage), and confidence index (this index provides information on the certainty level regarding the occurrence of damage). Specifically, for the categorical variables, the index is equal to 1 if the event caused damage, and 0 otherwise. Previous studies have adopted similar values for the damage function, ranging from 0 to 1 [49,58]. Furthermore, to ensure that the event indeed caused coastal damage, a confidence index ranging from 0 to 1 was established. For events where respondents and media sources confirmed the presence of damage, the index is set to 1.

3.2.3. Statistical Data Processing and Machine Learning Method for Coastal Storms with Damage

Initially, a Principal Component Analysis (PCA) in two dimensions was applied to the different variables to uncover relationships between the various hazard variables. This led to the creation of a scatter plot or point cloud representing the different variables to discriminate between damaging and non-damaging events based on the patterns generated by the point clouds. Indeed, this step of the process helps to discriminate, or even separate, the variable thresholds that are likely to cause damage from those that do not. The literature review conducted for this study highlights that a recent investigation into the discrimination of energetic thresholds for damaging storms was carried out in 2021 in Biarritz, France by [58]. The outputs obtained from the point clouds allow for defining combinations of variable thresholds from which damage is recorded or not during coastal storms along the Dakar coastline. To validate the various combinations obtained through the point clouds, a machine learning method, Regression Classification Trees, based on [59] algorithm, was applied.

4. Results

4.1. Coastal Storms Recorded on the Studied Stretch

The combination of the different approaches described in Section 3.2 allowed for the identification of 22 coastal storms from 1981 to 2020 (Table 2).

4.2. Statistical Characterization of Coastal Storms Causing Damage to the Coast

The interpretation of Figure 2, particularly the principal component analysis, shows a strong correlation between the various hazard variables, including Hs, Tp, Dir, Runup, Es, and ECWL. In contrast, these variables are weakly correlated with SLA and Tide. Upon closer inspection, all variables, except for SLA and Tide, are strongly correlated with the first dimension, which explains 52.4% of the variance, while the second dimension only accounts for 17% of the information. Furthermore, Figure 2 illustrates that damaging events are characterized by high values of SLA and Tide, as most of the events that caused damage to the coast are in the upper-left panel (Figure 2). A cross-analysis of the hazard variables and damage helps better understand this observation. For example, event 12 (Figure 2) can be characterized by significant hydrodynamic parameters, yet it is not damaging.
To provisionally define thresholds beyond which an event is likely to cause coastal damage, a scatter plot was created (Figure 3 and Figure 4).
Through PCA, we identified the variables likely to have a strong discriminatory potential. Indeed, the representation of hazard variables as scatter plots allows us to define combinations that help characterize storms that cause damage and those that do not. Upon examining Figure 3 and Figure 4, only a few hazard variables have discriminatory power. The scatter plots of hazard variables in relation to SLA and Tide reveal that the following combinations allow us to provisionally define thresholds beyond which an event is potentially damaging SLA and Tp: SLA and Dir; SLA and Tide; Tide and Tp; Tide and Dir; and Tide and SLA. Based on the cross-analysis of these variables, the following combinations have been defined. First, based on the SLA and Tp scatter plot, damage occurs when SLA > 0.05 and Tp > 14. Additionally, damage is observed when SLA ≥ 0.03 and Dir > 294. Finally, damage is also observed when SLA > 0.05 and Tide > 0.5.
Furthermore, the scatter plot of hazard variables in relation to Tide helped identify the underlying combinations. Firstly, damage is observed along the coast when Tide > 0.55 and Tp > 14 s. Additionally, damage is observable when Tide > 0.53 and Dir > 275. Lastly, an event may be damaging when Tide > 0.53 and SLA > 0.10. Finally, damage could be observed when Tide > 0.52 and ECWL > 1.2 m. Even though the hazard variables are numerous, three combinations have been retained. Indeed, some variables, such as Hs, Es, and ECWL, do not exhibit strong discriminatory potential.
The combination of the various variables through scatter plots allows us to establish provisional thresholds beyond which damage is observed, as well as to identify the variables with strong discriminatory potential to be implemented in the machine learning model.

4.3. Machine Learning to Discriminate Damaging Storms

To distinguish between damaging storms and non-damaging ones, the machine learning method proposed by [59] was applied. Specifically, the Classification and Regression Trees (CART) approach was used. This approach allowed us to generate a combination of thresholds that helps to accurately characterize storms, as well as validate the variable thresholds previously established based on the scatter plots (Figure 5).
Figure 5 highlights the various variables which, when combined, can identify the thresholds at which storms are damaging or non-damaging. In fact, all the hydrodynamic and meteorological variables presented in Section 3.2.2 were brought into play.
At the end of the treatments, only the combinations highlighted in Figure 5 were consistent (Tide–SLA, Tide–ECWL, Runup–Tp). In fact, analysis of Figure 5, and the Tide–SLA combination, reveals the thresholds of these two variables at which storms are damaging or not.
If Tide < 0.53, no damage is observed at the coast.
If Tide > 0.53 damage is observed.
If Tide > 0.53 and SLA ≥ 0.061 damage can be observed.
In addition, the second automatic learning process reveals that it is possible to accurately identify potentially damaging and non-damaging storms using the combination of Tide and ECWL (Figure 5).
If Tide > 0.53, storms are damaging.
If Tide < 0.53, no damage is observable.
If Tide < 0.53 and ECWL ≤ 1.3 m, no damage is observable
If Tide > 0.53 and ECWL ≥ 1.3 m, storms are damaging.
The third and final machine learning applied to the coastal storm data shows us that the combination of runup and Tp highlights the characteristics of coastal storms that cause damage along the Senegalese capital’s coastline.
If Runup ≤ 0.64, storms are damaging
If Runup > 0.82, no damage is observed.
If Runup > 0.82 and Tp ≥ 16, storms are damaging
If Runup < 0.82 and Tp < 16, no damage is observable.

5. Discussion

Coastal flooding represents a significant environmental issue, especially along low-lying coasts [60,61,62,63]. The impacts of such floods are both morphogenic and socio-economic, and these effects are expected to increase due to climate change and coastal urbanization, particularly along low-lying areas [29,64,65]. This situation is particularly concerning for the Senegalese coast, notably the capital’s coastline, Dakar. As noted by [49], coastal cities are growing rapidly in both population and infrastructure, while uncertainties regarding future sea-level rise and the intensity of hurricanes remain high. In this context, understanding coastal storms plays a critical role in protecting coastal communities and their assets. Identifying coastal areas prone to flooding, understanding meteorological conditions, and assessing the consequences for affected communities are essential steps [66].
Thus, distinguishing between damaging and non-damaging storms is vital for effective coastal management. This study represents the first attempt to characterize storms in terms of hydrometeorological factors and their ability to cause damage along the Senegalese coast, specifically in the Dakar region. A part of the methodology follows the approach used by [58] in Biarritz, France, but several key differences exist between the two studies. The use of a machine learning method to identify energy thresholds responsible for the damage is one of the main differences and also represents the originality of our study. This work offers insights into the variables that explain the presence or absence of damage during observed storms on the coastline of Senegal’s capital, Dakar. These insights are valuable for coastal management, especially given the high impact of coastal storms in Senegal [39,40,67].
In this study, it is important to note that the hydrometeorological variables that serve as key discriminators of damaging storms differ from those identified by [58] in Biarritz; the key variables for storm damage include maximum values of wave height (Hs), energy flux (P), and total water level (n). In contrast, this study identifies that, for the Dakar coastline, the key variables that best discriminate between damaging and non-damaging storms include maximum values of sea-level anomaly (SLA), tide (Tide), extreme coastal water level (ECWL), and peak period (Tp). These differences can be explained by site-specific variations in risk and intensity determinants [56,68,69]. For example, [70] showed that in the English Channel, wave height is a primary contributor to major impacts, while in the context of Senegal, the combination of tide, SLA, and period (Tp) are key to predicting storm impacts.
Furthermore, the study revealed that among the hydrodynamic parameters (waves), only the peak period (Tp) showed significant discriminatory power. This finding aligns with the understanding that large wave heights are a contributing factor to coastal erosion, while peak period and wave height play a critical role in coastal flooding. Additionally, machine learning techniques, particularly the Classification and Regression Trees (CART) method, demonstrated that, beyond peak period, both the runup and ECWL were crucial in discriminating between damaging and non-damaging storms. Based on the results, the key hydrodynamic and meteorological parameters contributing to coastal damage in Dakar during storm events include Tp, Runup, ECWL, Tide, and SLA.
The results of this study also indicate that coastal communities in the Dakar region are highly vulnerable to storm impacts. This vulnerability is exacerbated by the rapid urbanization and concentration of populations along the coastline. Despite this vulnerability, no specific coastal flood risk prevention plan exists for Dakar, or for Senegal’s coastline more generally. This situation is typical of many West African coastal cities. Given this, it is crucial to implement sustainable solutions to protect coastal communities, especially in the context of climate change and the progressive rise in sea level. Failure to implement effective protective measures will likely result in increasing financial and material losses due to coastal storm damage [49].
The significance of this study lies in its attempt to identify the physical characteristics of storms responsible for coastal damage in Dakar. Coastal flood risk has become a major concern for coastal communities, especially considering sea-level rise [71,72,73]. By discriminating between damaging and non-damaging storms, this study provides valuable insights into coastal risk management and the establishment of early warning systems for the prevention of coastal flooding or marine submersion.
Numerous studies have used models and quantitative approaches to evaluate the damage caused by storm surges, with abundant results [61,74]. However, few studies have focused on characterizing or discriminating storms to establish thresholds of hydrometeorological variables beyond which damage is observed. Coastal storm surges pose not only a safety risk but also significant material damage. Therefore, it is necessary to use approaches that discriminate coastal storms by their severity and intensity. Preparing for the anticipated effects of such disasters can help mitigate public health and economic burdens. Future studies should focus on characterizing the characteristics of future storms that may cause damage along the Senegalese and generally West African coastlines, as these aspects remain poorly understood. Such studies could help identify at-risk populations and assets, allowing for more targeted climate adaptation strategies. In conclusion, by combining these insights with future urbanization plans, the exposure of coastal populations and their assets can be reduced.
It should be noted that although the issue of coastal storms has already been the subject of numerous studies at global and regional scales, their specific application to the Senegalese context remains limited. The work of [75] identified the main challenges related to the analysis of coastal storms, particularly concerning the modeling of extreme events, the assessment of risks to infrastructure and natural environments, and the consideration of uncertainties in future projections. However, these studies have mainly been conducted in contexts different from that of West Africa. Thus, the present study aims to fill an important gap by specifically analyzing the energy thresholds associated with damaging storms along the Senegalese coast.
It is important to note that this study has certain limitations, particularly regarding the representation of hydrodynamic conditions in the shallow coastal zone. While reanalysis data are useful for storm characterization [76], they do not allow for accurate simulation of local physical processes such as wave refraction, diffraction, breaking, or the interaction of waves with coastal morphology [77]. Yet these processes play a crucial role in the intensity of coastal storms and energy dissipation near the shoreline [78,79].
Indeed, the application of numerical modeling for wave propagation would allow for a more detailed representation of coastal dynamics. However, the implementation of numerical modeling remains limited along the Senegalese coast. Consequently, these limitations justify the simplified approach adopted in this study. Nevertheless, investing in coastal instrumentation would be of great interest. In fact, coastal instrumentation is essential for improving modeling and forecasting capabilities at the shoreline scale [75,80].

6. Conclusions

This study made it possible to accurately distinguish between damaging and non-damaging coastal storms along the Dakar coastline, through the combined use of Principal Component Analysis (PCA) and Classification and Regression Trees (CART). 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; and (3) Runup–Tp, where storms are damaging if Runup ≤ 0.64 m or if Runup > 0.82 m with Tp ≥ 16 s. The identification of critical energy thresholds—particularly through the combinations Tide–SLA, Tide–ECWL, and Runup–Tp—represents a major advancement in understanding the hydrometeorological conditions responsible for coastal damage. This constitutes the first application of this type of machine learning on the Senegalese coast, and more broadly along the West African coastline. The knowledge gained paves the way for in-depth, localized research and provides valuable tools for coastal risk management. It also offers a solid foundation for the development of tailored early warning systems, which are essential to reduce the social, economic, and environmental impacts of coastal storms in this vulnerable region.

Funding

This research received no external funding.

Data Availability Statement

The author of the article is willing to provide the data used in this study upon request.

Conflicts of Interest

The author declares no conflict of interest.

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Figure 1. Study area location, Dakar Region.
Figure 1. Study area location, Dakar Region.
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Figure 2. Principal Component Analysis of Coastal Storms.
Figure 2. Principal Component Analysis of Coastal Storms.
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Figure 3. Scatter Plots of Various Hazard Variables in Relation to SLA. (a) SLA and Tp; (b) SLA and Dir; (c) SLA and Runup; (d) Tide and SLA; (e) SLA and Hs; (f) SLA and Es; (g) SLA and ECWL.
Figure 3. Scatter Plots of Various Hazard Variables in Relation to SLA. (a) SLA and Tp; (b) SLA and Dir; (c) SLA and Runup; (d) Tide and SLA; (e) SLA and Hs; (f) SLA and Es; (g) SLA and ECWL.
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Figure 4. Scatter Plots of Various Hazard Variables in Relation to Tide. (a) Tide and Tp; (b) Tide and Dir; (c) Tide and Runup; (d) Tide and SLA; (e) Tide and Hs; (f) Tide and Es; (g) Tide and ECWL.
Figure 4. Scatter Plots of Various Hazard Variables in Relation to Tide. (a) Tide and Tp; (b) Tide and Dir; (c) Tide and Runup; (d) Tide and SLA; (e) Tide and Hs; (f) Tide and Es; (g) Tide and ECWL.
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Figure 5. Machine-learning detection of thresholds for hydrometeorological variables responsible for damaging and non-damaging storms.
Figure 5. Machine-learning detection of thresholds for hydrometeorological variables responsible for damaging and non-damaging storms.
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Table 1. Contingency table for distinguishing between damaging and non-damaging storms.
Table 1. Contingency table for distinguishing between damaging and non-damaging storms.
StormDurationImpactCertaintyHsTpDirSLADACTideREsECWL
nT00XXXXXXXXX
11XXXXXXXXX
n0t001XXXXXXXXX
Table 2. Coastal Storms Recorded on the Dakar Coastline.
Table 2. Coastal Storms Recorded on the Dakar Coastline.
YearsDateSources
1981unknown dateI
198722 FebruaryI
1988unknown dateI
19937 JulyJ1, T1, I
19975 JulyA1, T2
2002unknown dateI
20044 AugustI
200520 JulyT1, I
20071 JulyJ1, T1, I
200923 JuneA1, T2
201112 MayT2
201430 MayA1, T2
201531 AugustA1,T1
201511 SeptemberT2
20163 AugustJ2
201731 MarchI
201818/19 NovemberJ3
201814/15 DecemberJ3
20195/6 AprilJ4
201930 NovemberA2
202022 JulyI
The light blue cells: These represent an example corresponding to the case where no respondent had specified the date in question within the considered sectors (which may not be the case). A1: MOLOA Record Form; A2: ANACIM Alert Record; T1: Ndour Thesis (2015); T2: Augustin Marone Thesis (2016); J1: Le Soleil Newspaper; J2: Online Press: https://seneweb.com/news/Societe/inondations-a-rufisque-est-plusieurs-mai_n_189651.html (accessed on 1 January 2020); J3: Online Press: https://www.sudonline.sn/trois-conseils-des-ministres-des-instructions-de-macky-et-apres-_a_42225.html (accessed on 1 January 2020); J4: OnlinePress: https://www.enqueteplus.com/content/inondations-dakar-mbao-sous-le-poids-de-son-marigot (accessed on 1 January 2020); unknown date: refers to the storms reported by respondents, without specifying the exact day or month.
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Cisse, C.O.T. Satellite and Statistical Approach for the Characterization of Coastal Storms Causing Damage on the Dakar Coast, Capital of Senegal (West Africa). Coasts 2025, 5, 24. https://doi.org/10.3390/coasts5030024

AMA Style

Cisse COT. Satellite and Statistical Approach for the Characterization of Coastal Storms Causing Damage on the Dakar Coast, Capital of Senegal (West Africa). Coasts. 2025; 5(3):24. https://doi.org/10.3390/coasts5030024

Chicago/Turabian Style

Cisse, Cheikh Omar Tidjani. 2025. "Satellite and Statistical Approach for the Characterization of Coastal Storms Causing Damage on the Dakar Coast, Capital of Senegal (West Africa)" Coasts 5, no. 3: 24. https://doi.org/10.3390/coasts5030024

APA Style

Cisse, C. O. T. (2025). Satellite and Statistical Approach for the Characterization of Coastal Storms Causing Damage on the Dakar Coast, Capital of Senegal (West Africa). Coasts, 5(3), 24. https://doi.org/10.3390/coasts5030024

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