Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

Abstract

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.

1. Introduction

One of the projected effects of global warming is an increase in sea levels. The sources of sea-level rise are in the sequence of melting continental glaciers, the Greenland ice sheet, the Antarctic ice sheet, and ocean expansion (thermal expansion) brought on by rising ocean temperatures [1]. With 40% of the global population residing within 60 km of coastal areas and expectations of further growth in the coming decades, the resulting effects on the coastline include the disturbance of ecosystems and the increased risks of erosion and flooding [2]. The expectation is that a larger number of people will face increased exposure to threats from sea-level rise (SLR) in the future due to the high population density and substantial population growth rates in coastal areas [3]. The rise in mean water levels leads to various consequences such as marine flooding [4]. Additionally, as highlighted in [5], an increase in the groundwater table can result in drainage issues. Furthermore, the rising water levels also contribute to blockages by sediment supplies due to the topography of drains and other conduits (pipes or channels) connected to the ocean by saltwater, as indicated by [2]. As water depths increase over reefs and other coastal structures, there are changes in wave dynamics [6]. These changes can lead to an amplified wave power reaching the shore [7], heightened beach erosion [8] and the intrusion of saltwater, which can bring about modifications in coastal ecosystems and aquifers [9]. These effects are made worse when climate change-related increases in mean sea level are combined with sporadic sea-level rises brought on by processes including storm surges, tides, eddies, wave events, and regional climatic indices [10,11,12]. The extensive impacts of sea-level rise and the need to anticipate and implement adaptation measures have led to the widespread utilization of numerical models such as the Sea Level Affecting Marshes Model (SLAMM). These models are employed to facilitate the ability to anticipate and proactively establish adaptation measures [13,14].

Machine learning (ML) techniques have been used in recent years to incorporate Artificial Intelligence (AI) into these models in order to mitigate natural disasters in general, and flood hazards in particular. These techniques include Logistic Regression (LR) [15,16], Support Vector Machines (SVMs) [17,18], Random Forest (RF) [19], and Artificial Neural Networks (ANNs) [20]. The loss of coastal wetlands owing to more frequent flooding demonstrates how susceptible they are to SLR [21]. Due to modeling uncertainty for the different contributing processes, such as increases in land water storage, land ice loss, and ocean thermal expansion, projecting future SLR is difficult [22]. Although historical sea-level patterns are useful for planning for changes in the future, they are insufficient for determining risk in the face of future uncertainties [23].

According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the global mean SLR by the year 2100 will range between 52 and 98 cm under the scenario with the highest emissions and 28–61 cm under the scenario with the lowest emissions [1]. However, considering the extensive range of uncertainties, employing future global mean sea-level rise (SLR) scenarios is a common approach for accounting for diverse potential future scenarios and designing response strategies [24]. It requires local forecasts of SLR, land cover that accommodates different risk tolerances, and should span a variety of different time scales useful for planning to prepare for changes brought on by rising sea levels at a local level [21]. To manage both natural and human communities effectively, coastal managers need scientific evidence about the possible effects of SLR. Thus, it should be given top importance to conduct integrated, interdisciplinary studies that take into account the different elements that have an impact on coastal systems at local scales because they will assist coastal scientists and stakeholders [25]. Determining the long-term effects of SLR is challenging because of the complexity of coastal systems.

In this study, our primary objectives were as follows: (i) generate spatially explicit estimates for sea-level rise (SLR) impacts (under RCP 8.5_Upper Limit) on the acreage of the Benin coastal zone using SLAMM v. 6.7 and artificial intelligence techniques; (ii) utilize these estimates to model greenhouse gas sequestration. These projections, based on three different SLR scenarios, serve as inputs for a broader research endeavor aimed at comprehensively analyzing and designing effective adaptation practices. This study demonstrates the suitability of the combination of SLAMM and ML. This paper offers valuable insights on how to interpret and utilize vulnerability information to inform such analyses and design processes.

2. Methodology

2.1. Study Area

The coastal area of Benin, specifically the area between latitudes 6°25′ N and 6°55′ N and longitudes 1°40′ E and 2°45′ E (Figure 1), is the focus of the research. Approximately 12,000 km² of land, accounting for 10.5% of Benin’s national area, is covered by this region. The presence of ancient and recent lagoons spanning over 30 km² gives it the characterization of a lagoon coast. It is composed of depressions and blind drainage areas. The climate displays a daily temperature variation of approximately 33 °C, accompanied by annual precipitation levels ranging from 820 mm to 1300 mm [26]. In the classification made by [27], the Beninese coast is classified within the humid tropical summer climate zone (V2). Two rainy seasons, from April to July and September to November, are experienced in the study area, interspersed with two dry seasons, from August to September and December to March [28] The coastal landscape is dominated by agriculture, particularly palm tree vegetation (Elaeis guineensis), with sandy, ferritic, and hydromorphic soils being prevalent [28]. Various ecosystems, including dry forests, gallery forests, boundary rainforests, marshes, wetlands (listed in Ramsar sites No. 1017 and No. 1018), and riverbanks (Mono, Ouémé, Couffo), thrive in the region. The economic hub of the country, comprising major cities, ports, airports, and industrial facilities, is situated along the Beninese coastline. The total area covered by the six departments bordering the coastline is 11,720 km², and it is inhabited by a population of 2.7 million people, resulting in an average density of 230 inhabitants per km², higher than the rest of the country. Thus, the population of the Benin coastal zone represents approximately 22.5% of the national population. While the majority of the population resides in rural areas, there is a significant urban population growth rate of approximately 7.4% per year, exerting increasing pressure on the infrastructure and labor market in cities. The local population engages in diverse activities, including agriculture, fishing, salt farming, tourism, trade, quarrying, livestock breeding, and agricultural product processing [29]. Migration to this coastal strip contributes to the high population density, rapid growth rates, and demand for land for construction.

2.2. Sea Level Affecting Marshes Model (SLAMM)

The Sea Level Affecting Marshes Model (SLAMM) is a simulation model that replicates the key processes involved in wetland conversions and shoreline modifications during long-term sea-level rise. In this study, the SLAMM 6.7 beta-64 bit model (available at: http://warrenpinnacle.com/prof/SLAMM6/ accessed on 29 June 2022 was utilized. The model calculates the relative sea-level change for each cell at every time step. The fractional conversion from one class to another within a cell is determined based on the relative elevation change divided by the elevation range of that class. Additionally, if a cell is protected by a dike or levee, it is not permitted to undergo changes.

SLAMM simulations analyze individual cells, considering their slopes to determine their highest elevation relative to a user-defined sea level. Sediment accumulation is also factored in, with the amount added to each cell’s elevation based on its wetland type. The lower boundary of each cell defines a threshold, and when the sea level rises beyond this threshold, the wetland class of the cell converts to the classification class below it. If the cell is now in a saline environment, it may convert to a wetland class associated with regularly flooded marshes or transitional salt marshes. The erosion process is determined by the proximity of the cell to water and incorporates a wave erosion model integrated into the larger SLAMM model [30].

SLAMM 6.7 encompasses five primary processes: inundation, erosion, saturation, accretion, and salinity. Inundation tracks the rise of water levels and the salt boundary by adjusting cell elevations while maintaining a constant Mean Tide Level (MTL) at zero. Erosion happens when certain conditions, like meeting a maximum fetch threshold and being close to estuarine water or the open ocean, are met. Saturation allows coastal swamps and fresh marshes to expand into adjacent uplands as the water table rises due to sea-level rise. Accretion refers to the upward movement of marshes through sediment accumulation and biogenic production. Salinity, an optional process, allows for the migration of land categories based on changes in salinity using a salt wedge model or linked data from existing salinity models.

Each cell in the SLAMM can consist of up to three SLAMM categories, but for the purposes of this study, each cell was initially assigned to a single category to simplify the analysis. However, the model allows for incremental changes within smaller horizontal steps than the cell width. The SLAMM assumes a quasi-equilibrium between wetland categories and sea-level rise, permitting only one category change per time step. A time step of 20 years was used in this study to simulate the period from 2030 to 2090, aligning with the time scales used in IPCC sea-level rise scenarios. However, alternative time steps and specific time series can be employed. The key input data utilized in the SLAMM included the Digital Elevation Model (DEM), the National Wetland Inventory (NWI), and slope information.

Table 1. Input files used in SLAMM.

Input file	Purpose
DEM	Contains initial elevation data for individual cells and is utilized to generate a raster file representing slope information.
NWI	Created through the classification of satellite images using an Artificial Intelligence Tool called Support Vector Machine in ArcGIS Pro. The NWI file includes 9 wetland categories obtained from analyzing aerial imagery and considers the elevations of both saltwater and freshwater wetland classes.
SLOPE	Obtained through the utilization of ArcGIS Pro’s Slope Tool on the DEM; this particular file presents information regarding the highest and lowest elevations associated with individual cells measuring 10 × 10 m.

further describes the purpose of each input file and Figure 2 includes visualizations of the three input data sets used in the SLAMM. The full list of inputs and parameters used for the simulation of coastal area changes under three sea-level scenarios (RCP 8.5_Upper Limit scenario) is provided in

Table 2. Input parameters in the SLAMM.

Parameter	Subsite	Description of Parameter
Description	Coastal area	Name of site or sub-site
NWI Photo Date (YYYY)	2021	Year of the satellite image used for the wetland classification
Marsh Erosion (horz. m/yr)	0	Horizontal erosion rates for marsh
DEM Date (YYYY)	2010	Year of the flight or survey for the elevation data
Historic Trend (mm/yr)	2	The annual sea-level rise rate in millimeters, historically utilized for assessing subsidence or uplift, unless a raster file detailing land movement is provided
Direction Offshore (N, S, E, W)	South	Direction of flow of water from the coast
Salt Elevation (m above MTL)	0	The elevation point where dry land freshwater wetlands starts
GT Great Diurnal Tide Range (m)	0	Same as the difference between Mean Higher High Water (MHHW) and Mean Lower Low Water (MLLW)
MTL-NAVD88 (m)	1	The correction for elevation determined using the average tide level

. In the study area, 9 wetland categories (Developed Dry Land, Undeveloped Dryland, Swamp, Regularly Flooded Marsh, Mangrove, Ocean Beach, Inland Open Water, Estuarine Open Water, Open Ocean) were considered, although the SLAMM has a database with definitions of 23 categories. These 9 categories were derived from previous work [21] which classified land use patterns in the coastal zone of Benin.

The Digital Elevation Map (ASTER) data used in this study, with a resolution of 30 m, were obtained from NASA Earthdata Search website https://www.earthdata.nasa.gov/ accessed on 18 May 2021. The downloaded DEM ranges from a minimum of −4 m to a maximum of 127 m, with a mean elevation of 18.56 m.

To assess changes in cell composition, the slope of each cell was calculated using the ArcGIS Pro 3.1 toolbox. The slope data were measured in degrees, with an average slope of 0.28° and the steepest slopes ranging from 2 to 5°.

The National Wetlands Inventory (NWI) categories, based on data from the year 2021, were used to determine the dominant wetland category for each cell. These NWI categories were then converted into SLAMM categories, aiding in refining the elevation estimates for each cell.

2.3. Machine Learning Approach

In order to create the National Wetlands Inventory (NWI) categories, we obtained 2021 Landsat images that covered our study area from the USGS EarthExplorer Search website (https://earthexplorer.usgs.gov/ accessed on 18 May 2021). These images were acquired at no cost. We then processed and classified the images using ArGis Pro 3.1 software. During the classification process, we employed a supervised approach. This involved dividing the image into smaller sections known as training fields. Within these training fields, we identified predictor variables that were recorded in the sampling units. Based on these variables, we assigned prior classes to the sampling units, allowing us to allocate the appropriate wetland categories. Common classifiers used in supervised classification include minimum distance, maximum likelihood, and Support Vector Machines (SVMs). The maximum likelihood decision rule assigns pixels with certain pattern characteristics to specific classes based on the assumption of a Gaussian distribution. SVMs, on the other hand, rely on the Structural Risk Minimization principle, which balances model complexity and its ability to fit the training data effectively. Minimum distance, maximum likelihood, and SVM are among the most widely used classifiers in supervised classification. A minimum of 30 training examples was picked for each class. ArcGIS Pro was used to choose training samples, compare sample properties, and refine training samples. A Support Vector Machine (SVM) is a supervised machine learning algorithm used for binary and multi-class classification tasks. It works by finding the optimal hyperplane that best separates data points belonging to different classes in a high-dimensional feature space. The SVM aims to maximize the margin between the support vectors (data points closest to the hyperplane) of different classes, making it robust and effective in handling complex classification problems. The performance of SVMs is built upon the principle of Structural Risk Minimization. This principle is an approach to inductive model selection that aims to strike a balance between the complexity of the hypothesis space and the accuracy of fitting the training data. It forms the underlying basis for SVMs’ effectiveness. SVMs have demonstrated useful applications that are highly accurate. Change detection involves using a post-classification technique that overlays independently classified images to identify alterations in wetland classes, specific points of change, and the direction of change. To display changes over time, the geospatial analyst tool in ArcGIS Pro 3.1 was loaded with coastal maps. We examined SLAMM land-cover maps and considered site-specific factors.

2.4. Greenhouse Gas Sequestration

The SLAMM utilizes the methodology pioneered in [31] to calculate carbon sequestration, which is determined by the land cover type. This approach takes into consideration the carbon sequestration capacity of wetlands and also factors in carbon emissions resulting from methane loss in freshwater habitats. The rate at which greenhouse gases are being captured and stored for different land cover types during each time step, denoted as t, can be represented by Equation (1):

$$\frac{{dM}_{GHG}}{dt}=\frac{{dM}_{{CO}_2}}{dt}-21\frac{{dM}_{{CH}_4}}{dt}$$

(1)

where:

• $M_{GHG}$ = Mass of greenhouse gases sequestered at a specific time, measured in terms of carbon dioxide (CO₂) mass;
• $M_{{CO}_2}$ (t) = Mass of carbon dioxide sequestered at the given time (CO₂ mass);
• $M_{{CH}_4}\left(\mathrm{t}\right)=$ Amount of methane emitted at the specified time, measured in terms of methane mass (CH₄ mass);
• 21 = Historically assumed 100-year global warming potential (GWP) for methane, as established in [32]. However, recent studies indicate that methane may have a considerably higher GWP of 34, as reported by Change in 2013.

3. Results

3.1. Sea-Level Modeling Using Machine Learning Method and the SLAMM

The effect of sea-level rise will have a more pronounced trend at an elevation of 1.468 m, according to

Table 3. Projected changes in the acreage of SLAMM land use categories from 2021 to 2090 in Benin coastal area under the RCP 8.5_Upper Limit scenario.

RCP 8.5_Upper Limit				0.240 m			0.514 m			0.918 m			1.468 m
				Area			Area			Area			Area
SLAMM Code	Land Cover Category	Initial Coverage	%	2021	2030	Change	2021	2050	Change	2021	2070	Change	2021	2090	Change
1	Developed Dry Land	590.8	20.1	589.0	589.0	−1.8	589.0	589.0	−1.8	589.0	589.0	−1.8	589.0	588.5	−2.2
2	Undeveloped Dryland	1306.5	44.5	1305.6	1305.6	−0.9	1305.6	1305.6	−0.9	1305.6	1305.6	−0.9	1305.6	1303.6	−2.9
3	Swamp	262.0	8.9	258.3	258.3	−3.7	258.3	258.3	−3.7	258.3	258.3	−3.7	258.3	256.9	−5.1
8	Regularly Flooded Marsh	283.6	9.7	280.6	280.6	−3.0	280.6	280.6	−3.0	280.6	280.6	−3.0	280.6	279.0	−4.6
9	Mangrove	104.8	3.6	106.2	101.3	−3.5	106.2	101.3	−3.5	106.2	101.3	−3.5	106.2	102.5	−2.3
12	Ocean Beach	44.5	1.5	37.2	36.7	−7.8	37.2	36.7	−7.8	37.2	36.7	−7.8	37.2	36.7	−7.9
15	Inland Open Water	105.9	3.6	24.5	24.5	−81.3	24.5	24.5	−81.3	24.5	24.5	−81.3	24.5	24.5	−81.4
17	Estuarine Open Water	209.8	7.1	298.5	303.4	93.6	298.5	303.4	93.6	298.5	303.4	93.6	298.5	307.5	97.7
19	Open Ocean	26.5	0.9	34.5	34.9	8.4	34.5	34.9	8.4	34.5	34.9	8.4	34.5	35.0	8.5
		2934.4		2934.3	2934.4			2934.4		2934.4			2934.3

and Figure 3 and Figure 4. From initial coverage (Figure 4A,B) to an elevation of 0.240 m (Figure 4C,D), a variation in areas in the various class groups can be seen. However, both at SLR 0.514 m (Figure 4E,F) and 0.918 m (Figure 4G,H), the observed impact will stay constant.

The most significant increases in land cover categories are observed in Estuarine Open Water and Open Ocean. Together, these two categories will gain approximately 106.2 hectares in the area, considering different levels of sea-level rise using RCP 8.5_Upper Limit. However, these gains are offset by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamps, Undeveloped, and Developed Dryland. The substantial gains in Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) indicate the submergence of these areas by 2090 and subsequent saltwater intrusion into coastal ecosystems due to sea-level rise (Table 3).

The most significant losses are observed in less tidally influenced categories such as Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). These categories are projected to significantly decrease as the sea level rises by 1.468 m, and they will mainly be replaced by Estuarine Open Water and Open Ocean (Figure 4I,J). Undeveloped Dryland, generally situated at higher elevations and farther from the shore, is the category least affected throughout the simulation, which delays the impacts of sea-level rise.

3.2. Expected Change on the Coast from the Combined Method: ML and SLAMM

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Coastal erosion will worsen as a result of sea-level rise brought on by global warming. Benin’s coastline has a delicate ecology that is vulnerable to the harmful effects of climate change, including:

▪

Rising sea and ocean levels (as depicted in Figure 5);

▪

Apparent retreat of the coastline east of Cotonou;

▪

Alterations in average rainfall patterns;

▪

Heightened atmospheric temperature negatively impacting natural ecosystems, human communities, water resources, and coastal infrastructure.

Figure 5. Sea level trends over the years on Benin coast resulting from SLAMM.

Figure 5. Sea level trends over the years on Benin coast resulting from SLAMM.

The coastal erosion affecting Cotonou, Grand-Popo, and other areas is a dynamic phenomenon specific to the sub-region. It is primarily caused by disruptions in coastal currents responsible for transporting sand, resulting from the construction of upstream dams or coastline developments such as ports. Additionally, the coast experiences the impact of distant ocean swells, characterized by waves arriving at intervals of 10 to 16 s, spanning 160 to 220 m in length.

Coastal drift, also referred to as coastal transport, occurs when the angle of the waves hitting the beach changes between 4° and 9°, leading to the movement of material from west to east. This process causes an annual transport of 1,500,000 cubic meters of sand from Lomé to Cotonou [33]. However, the development of ports in both cities has restricted the accumulation of sand deposits that should have naturally enriched the shorelines, redirecting sediment upstream (towards the west of the Cotonou port).

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Referring to the methodology developed by Vandebroek and Crooks at ESA PWA (2014) [34], the SLAMM employs a calculation process to estimate carbon sequestration based on land cover data obtained using the ML approach. This approach considers the amount of carbon stored within wetlands as well as the carbon emissions resulting from methane loss in freshwater ecosystems. Within our study area, there will be a noticeable annual decrease in the capacity to sequester greenhouse gases, particularly carbon (as depicted in Figure 6).

4. Discussion

4.1. Implications of Sea Level Rise

The analysis of sea-level rise (SLR) impacts on the coastal region of Benin, conducted through the combined use of Machine Learning (ML) and the SLAMM, reveals significant implications for both human and environmental systems. [35]’s study in coastal China also underscores the impact of SLR on coastal wetland vegetation. In both cases, the rate of SLR is a critical factor influencing ecosystem changes. These implications encompass a range of climatic pressures, including flooding, coastal erosion, accretion, and the salinization of lagoon waters. The results presented in Table 3 and Figure 3 demonstrate the pronounced trend of SLR at an elevation of 1.468 m. It is important to note that the variation in land cover categories becomes evident at lower elevations, from initial coverage to an elevation of 0.240 m.

However, as SLR progresses to 0.514 m and 0.918 m, the observed impacts remain relatively constant. This finding suggests that the most significant changes in land cover occur in the initial stages of SLR, highlighting the urgency of proactive adaptation measures in response to rising sea levels.

For coastal wetlands and populations facing the challenges of rising sea levels, inland relocation can be a viable option if sufficient space is available [36,37]. This is what the SLAMM predicts for the wetlands, roads, towns, and ecosystems along the coast of Benin.

Mangrove ecosystems will experience an increase in area in 2090 after undergoing a decline from the 2030s to 2070. This could be explained by the optimization of conditions favorable to restoration and even the establishment of mangrove areas that had accumulated due to saline intrusion over the years. This increase following a decline in mangrove area was also observed in [38], which showed that the mangrove area studied in the municipalities of Grand-Popo, Ouidah, and Abomey-Calavi, which covered 5205.24 hectares in 1988, decreased by 62.07% between 1988 and 2001 but increased by 18.84% from 2001 to 2019. This increase was attributed to the strengthened efforts in mangrove restoration.

4.2. Carbon Sequestration and Greenhouse Gas Implications

Incorporating Machine Learning (ML) into the SLAMM allows for the estimation of carbon sequestration potential based on land cover. This approach considers the amount of carbon stored within wetlands and the carbon emissions resulting from methane loss in freshwater ecosystems. Within our study area, a noticeable annual decrease in the capacity to sequester greenhouse gases, particularly carbon, is observed. This decrease has significant implications for the environment and climate. The declining trends in carbon concentration and greenhouse gas sequestration in our study case align with broader global observations, as reported in [39]. This report highlights a global trend of declining carbon sequestration capacity in various ecosystems, including wetlands, due to factors such as land use changes, deforestation, and wetland degradation. These trends, corroborated by the authors of [40] from the Ramsar Convention, have been contributing to rising atmospheric carbon dioxide levels. Furthermore, [41] underscores the importance of addressing methane emissions from freshwater ecosystems, such as lakes and rivers, which have been on the rise and offsetting carbon sequestration efforts. These observations are consistent with multiple regional studies, including research in the Amazon rainforest [42] and Southeast Asian peatlands [43], which have shown how land-use changes and deforestation have led to decreased carbon storage in these critical ecosystems. In summary, these research findings reinforce the global urgency for sustainable land management practices and conservation efforts to protect wetlands and their crucial role in carbon sequestration and climate change mitigation.

4.3. Erosion

The urban population, primarily concentrated in these areas and growing at a rate of 4% annually, which is nearly twice the global average, is being adversely affected and devastated by erosion [44]. The scenario in Benin reveals an increasing demand for land, water, and other natural resources due to rapid urbanization and net migration to the coast. The construction of human-made structures such as river dams, seawalls, and breakwaters, as well as the extraction of sand from beaches and dunes, have contributed to substantial coastal erosion in Benin. The areas experiencing the most significant rates of retreat, surpassing 10 m per year, are primarily located near river mouths and harbor jetties, which are indicative of heavily urbanized regions [45]. The losses and gains observed in the different land cover categories identified in this study reflect the adverse impacts experienced by coastal communities, including significant asset losses (homes, infrastructure, wetlands) on the Cotonou side, as well as the degradation of critical ecosystems like beaches, wetlands, and mangroves.

Furthermore, coastal communities, particularly those that heavily rely on natural resources and are among the poorest, are increasingly vulnerable to the effects of climate change. The erosion process leads to the emission of carbon, generated by human activities and other sources, into the atmosphere [46]. Annually, approximately 500,000 individuals in West Africa face the peril of flooding and escalating coastal erosion, leading to substantial economic losses [47]. In 2013, these losses were estimated to account for 2.3% of Togo’s GDP, while in 2014, they amounted to 3.2% of Mauritania’s GDP [33].

4.4. Inundation

The SLAMM provided significant insights into the anticipated changes in coastal inundation in Benin. For instance, with a sea-level rise of 1.468 m, approximately 76.58% of the study area is projected to be flooded. This inundation poses a major challenge for rural communities located in low-lying coastal areas, as their economies rely heavily on local natural resources, which are at risk of depletion due to rising sea levels. Comparable circumstances are evident in Senegal, where annual floods impact approximately 200,000 individuals and generate an estimated average cost of USD 89 million per year. Particularly noteworthy are the exceptional floods experienced in 2009, which resulted in significantly higher damage totaling USD 104 million in Dakar [48]. Looking ahead to the end of the century, the region is expected to witness coastal retreat due to rising sea levels and related extreme events, such as storm surges. The 5th Assessment Report by the Intergovernmental Panel on Climate Change [1] identified West Africa as a climate change hotspot, where unprecedented climatic conditions are projected to manifest earlier than the global average by a margin of one or two decades. Ongoing climate change and variability are poised to exacerbate these challenges.

One notable impact of sea-level rise is the intrusion of saltwater, which can lead to reduced crop yields, land degradation, and even the abandonment of agricultural areas [49]. As more coastal regions become submerged due to rising sea levels, the available land for agriculture will decrease. However, from a different perspective, if environmental factors stabilize over time, this saline intrusion may facilitate the transformation of certain wetlands into mangroves.

4.5. Limitations and Future Research Directions

While this study provides valuable insights into the impacts of SLR and coastal dynamics in the Benin coastal region, there are limitations to consider. The accuracy of the predictions may be affected by data quality, assumptions made in the models, and uncertainties associated with future climate scenarios. Further research is needed to refine these models, incorporate additional variables, and enhance the accuracy of the predictions.

5. Recommendations

• Build Seawalls, Breakwaters, and Improved Drainage Systems: These physical interventions are crucial for protecting vulnerable urban areas from coastal erosion and flooding.
• Promote Sustainable Land-Use Practices: This recommendation highlights the importance of elevated construction and zoning regulations to ensure resilient urban planning.
• Implement Sustainable Fisheries: Responsible fishery management practices can ensure the long-term sustainability of fish stocks. This includes regulating catch limits and protecting critical breeding and nursery areas.
• Engage and Educate Local Communities: Community involvement and early warning systems are pivotal in adapting to changing coastal conditions.
• Establish Comprehensive Coastal Management Policies: The need for effective policies to manage saltwater intrusion and overall coastal resources is essential.
• Encourage Climate-Resilient Housing and Infrastructure: To safeguard communities, it is essential to promote resilient housing and infrastructure, as well as diversifying livelihoods for coastal residents.
• Invest in Continuous Research and International Cooperation: Ongoing research, data collection, and international collaboration are essential for developing effective adaptation strategies in response to sea-level rise.

These recommendations are essential in the face of coastal vulnerability and sea-level rise. They aim to mitigate risks and enhance the resilience of the coastal communities and ecosystems in Benin.

6. Conclusions

This study demonstrated the suitability of the SLAMM as a method to assess the impacts of future sea-level rise (SLR) on coastal resources. Based on the research findings, it is evident that the Beninese coast will primarily experience two significant consequences in response to SLR: flooding and erosion. Among these, the most significant adverse effect of flooding is the potential depletion of local natural resources, which are vital for the economies of rural, low-lying coastal communities. Furthermore, saltwater intrusion poses additional risks such as reduced crop yields, land degradation, and the abandonment of agricultural areas. The reduction in carbon sequestration capacity is not only an ecological concern but it also affects a vital component of global climate change mitigation efforts. Protecting and restoring wetlands on the Benin coast aligns with a broader goal of environmental sustainability and resilience in the face of climate change. The importance of this research lies in its initial steps toward evaluating the social, economic, physical, and ecological vulnerabilities associated with SLR, providing a valuable framework for guiding political actions and achieving meaningful outcomes. To address these pressing challenges, a multifaceted approach is required, encompassing coastal management, wetland conservation, and sustainable adaptation measures.