Sediment Dynamics and Erosion in a Complex Coastal Lagoon System in the Southern Gulf of Mexico

Abstract

The complex lagoon system of Carmen, Pajonal, and Machona in the Southern Gulf of Mexico is characterized by highly active sedimentary dynamics. To reproduce the sedimentary dynamics processes, the MOHID model, coupled with the SWAN wave model, was applied to different scenarios through a climatic analysis of winds. Historical wind data indicate that the region has experienced a significant shift in the principal wind component over the last two decades. Furthermore, hurricanes have impacted the lagoon system on multiple occasions in recent decades. Five numerical experiments were conducted, considering both historical and present-day wind conditions, the impact of Hurricane Larry, and engineering works such as breakwaters, to better understand the sedimentary dynamics of the lagoon system. Model results revealed intense and variable sediment transport depending on the intensity and direction of the prevailing winds, waves, extreme weather events, and breakwater locations.

1. Introduction

Coastal lagoons are inherently ephemeral features that are part of a continuum of coastal environments. Their natural hydromorphological evolution is seldom, if ever, permitted to occur because of human action, either directly through engineering interventions to maintain or create navigable inlets, or indirectly due to activities within their catchment areas [1]. Natural or anthropogenic processes, such as sea level rise, intense storms or hurricanes, coastal engineering works, and improper dune management, can alter the dynamic conditions of the lagoons [2], leading to erosion or accretion. These erosion and accretion patterns reveal the natural processes of wave-induced longshore currents and sediment transport, as well as the impact of human intervention on coastal engineering protection structures.

Investigating the trend of coastal areas exhibiting high sediment dynamics and clear signs of erosion is important, particularly in the context of global warming and the latent threat of sea level rise. Several coastal lagoons are connected to the open ocean through one or more inlets, and their conditions are relevant to the fishing and shipping industries. Previous studies have documented sea level rise in the Southern Gulf of Mexico (SGM) [3] and its impacts on the coast. Coastal lagoons are part of the coastal environment, covering about 13% of the total coastal areas. They are inland water bodies generally oriented parallel to the coast, separated from the ocean by a barrier with inlets that remain open at least intermittently, with water depths varying from a few meters [4]. Globally, the natural evolution of the coastline is well known and has largely been studied from glacial periods to the present day using different time scales from centuries [5] and decades [6] to a few days due to extreme events [7], and it has been addressed from different perspectives, considering statistical models [8], remote or direct measurements [9], numerical models [10], or a multi-source approach [11]. These studies are relevant to diverse groups due to the social, ecological, and economic importance of coastal areas. Previous studies indicate that only 28% of the world’s coastline has been directly altered by anthropogenic activities [12]; despite this, 40% of the world’s population lives in these areas [13]. Almost 80% of global ecosystem services are provided by coastal ecosystems, and 21 of the most important cities are located on the coast, mainly around estuaries and coastal lagoons.

Coastal lagoons evolve continuously, depending on sea level rise, climatic and hydrodynamic conditions, seafloor substrate material, and its particular topography [1]. Natural or anthropogenic processes, such as sea level rise, intense storms, hurricanes, coastal infrastructure, and inappropriate dune use, can alter the dynamic conditions of lagoon inlets [2]. Due to their location at the interface between fresh and marine waters, their natural biodiversity ecosystem services that transcend the lagoon benefit society in a broader spatial and historical context. Understanding their hydrodynamic processes and sediment dynamics is fundamental for practical flood risk assessments and environmental preservation initiatives. A minimal change in system conditions can result in different short-, medium-, or long-term scenarios. Furthermore, it will be almost impossible to reach the expected natural scenario after any disturbance, since the processes involved in sediment transport are non-linear [14] and codependent [15,16]. Therefore, hydrodynamic and sediment transport modeling is crucial to understand the complex role of each factor affecting flow dynamics, atmospheric conditions, sediment dynamics, and the inherent morphological evolution of coastal lagoons.

Numerical modeling of coastal sediment transport has a long history. It started in the late 1960s and early 1970s [17]. Subsequently, scientific projects to protect and prevent erosion in coastal areas were initiated [18]. Since then, computational models have evolved, considering several equation systems to determine sediment transport. Currently, complex models based on a combination of equations, including gravitational effects, sediment transport, Suspended Particulate Matter (SPM) and bedload, and atmospheric and wave-coupled models, are used [19,20]. These models also gather information on local bathymetry and sediment characteristics to obtain more realistic results [21].

Our objective is to explore the diverse factors interacting in coastal lagoon environments in regions where sediment transport and erosion processes are evident along the coast. Recently, a shift in wind direction and, consequently, in waves reaching the SGM has been observed, as well as an increase in extreme events, likely induced by global warming [22]. This shift is modifying the sediment transport pattern around and within the Carmen, Pajonal, and Machona (CPM) system. In this work, the subtidal responses of the CPM to atmospheric change are studied. Natural and anthropogenic factors, such as built coastal infrastructure, tidal amplitudes, currents, winds, waves, and natural extreme events, were included, which contribute to modulate natural changes in these areas. Therefore, the CPM, a coastal lagoon located in the SGM, was used as a case study.

2. Study Area

The Gulf of Mexico (GM) covers an area of approximately 1.5 million ${km}^2$. It contains 84 bathymetric provinces typical of oceanic regions and a variable coastal zone with shallow tidal ranges in all regions [23].

From 1993 to 2024, the sea level in the GM changed by approximately 0.1 m. According to the Intergovernmental Panel on Climate Change (IPCC) 2022 scenarios for this area, the sea level will rise by 0.25 to 0.5 m (SSP1-2.6) and 0.5 to 1 m (SSP5-8.5) by 2100. This process could significantly alter sediment dynamics within and outside the lagoon systems [24].

The winds over the GM are part of the trade winds, exhibiting distinct seasonal characteristics in various regions. During autumn and winter, an intense southward wind component predominates over the study area, reaching its maximum in December. Cold fronts, locally known as “Nortes”, typically occur between September and May, while hurricanes, which generate high wind variability during the summer, are less prevalent. The area is affected by approximately six hurricanes per year [25]. The variability of its coastline and the bathymetric characteristics of the system affect the dynamic processes of the sediments, creating different time scales that modulate circulation in each area. Tidal and wind forces are the dominant factors in the area; however, during strong wind events, the wind acceleration term predominates over the other terms in the momentum equation [26].

In the SGM, the CPM coastal lagoon system has undergone significant morphological variations, due to natural and anthropogenic changes, owing to its active sedimentary dynamics and its importance to the aquaculture and petroleum (oil) industries [11]. Anthropogenic activities include the construction of coastal defense structures and the artificial opening of one of the inlets in the mid-1970s, which is currently closed. The system is characterized by the presence of a sand bar called Santa Ana, the largest in its area (Tabasco State, Mexico), measuring 37 km in length and ranging from 100 to 300 m in width. The two main lagoons of the CPM system, Carmen and Machona, are connected by a small lagoon, called Pajonal. The CPM communicated with the GM through two inlets: Santa Ana Inlet (SAI) in the western Carmen lagoon and Panteones Inlet (PI) in the eastern Machona lagoon (Figure 1). The importance of the area lies in the fact that economic activities in the region take place within the system, including oyster aquaculture, with the CPM being the leading producer in the Tabasco state (Mexico), as well as tilapia and shrimp farming, and the development of the petroleum industry [27]. The inlets of these system were intermittently connected to the SGM, and hurricanes generally regulate their opening or closing periods. SAI is a natural inlet, while PI was artificially created by dredging in 1975 to increase oyster aquaculture in the area. This inlet closed naturally in April 2017 [28]. The CPM system comprises two freshwater inputs from the Santana and San Felipe rivers (Figure 1).

The artificial opening of the PI resulted in chemical, physical, biological, and ecological changes within the CPM system. Nevertheless, the PI achieved its short-term objective; in the medium- and long-term, it generated environmental and morphological changes that are now described as dangerous, irreversible, and undesirable. Initially, in 1975, the PI was designed to be 50 m wide and 2 m deep. In 1977, it was 120 m wide and 4.5 m deep. By 1980, it had expanded to being 460 m wide and 8 m deep. It remained at this width until 1983, when it was 550 m wide and 10 m deep. The changes were so significant and rapid that the Mexican government decided to fund a study focused on the PI in 1985 to determine whether it should be closed. However, the conclusion was that the inlet had reached stability, and the PI subsequently evolved naturally. Along the CPM system, the SAI was affected by changes made to the PI, resulting in a shallower and wider channel. To prevent these changes in the channel, several breakwaters have been built since then; these structures have fulfilled their main objective. However, since the 2000s, the western side of the SAI, where a significant population is located, has lost at least 100 m of beach from the shore inwards [29]. The Tabasco state government has conducted several studies in the area since 2005 and implemented various types of infrastructure (such as groins, grabions, and geotubes), but the problem persists. Historically, these inlets have been considered dynamically complex; however, the erosion and accretion regime has changed with the opening of the PI, which is constantly evolving, with an intensification of this regime since the early 2000s.

Carmen Lagoon (Figure 1) has a maximum depth of 8 m on the west side. Pajonal Lagoon reaches a maximum depth of 6 m and an average depth of 1.0 m. Machona Lagoon reaches a maximum depth of 5.5 m and an average depth of 4.0 m. This depth is referred to the mean sea level (MSL). The sediment in the area is known as alluvium, which is an unconsolidated, eroded sediment composed of fine particles of silt and clay, as well as larger particles of sand and gravel. The sediments within the lagoon are mainly silt and clay [30]; fine sands are also found at the inlets, while on the sandbar, the grain size corresponds to fine and medium sand, which can be classified as feldspathic sand (

Table 1. Composition and diameter characteristics ($\varphi$) of the most recurrent type of sediments in each region, according to Ayala Pérez (2013) [31].

Site	Composition	${\mathit{\varphi }}_{30}$	${\mathit{\varphi }}_{50}$	${\mathit{\varphi }}_{90}$
SAI	Feldespactic Sand	0.24274273	0.21839322	0.14508799
Bar	Feldespactic Sand	0.50173587	0.23733553	0.13966089
PI	Feldespactic Sand	0.24274273	0.22144188	0.15203759

) [31].

The climate in the CPM system is hot and humid. The dry season lasts from April to May. Tropical cyclones occur in summer, and winter storms (Nortes) bring rain in autumn and winter. The monthly average temperature is above ${18 }^{\circ }$C; the driest month records an average monthly rainfall of less than 100 mm but more than 60 mm [30]. The area has a typical tropical monsoon climate (Am), according to the updated Köppen–Geiger climate classification map [32], which is highly representative of tropical regions. Mangroves are the predominant ecosystem. They provide important habitats and ecological niches, especially for numerous species of fish, crocodiles, lizards, crabs, shrimp, and birds, which gives it economic importance as a support for fishing. Reptiles, mollusks, insects, and even endangered species, such as the otter and the manatee (endemic species), can also be found [27,30].

The CPM system is located on the boundary between regions with mixed diurnal and diurnal tides [3]. The tides in the SAI and PI show an average amplitude of 0.6 m [30]. Within the lagoons, the amplitude decreases due to friction. The highest sea level amplitudes (0.8 m) are usually recorded between October and November, as a result of water transported by the north-to-south wind, due to the Nortes that induce a convergence of water in the southern part of the gulf [27].

In the study area, the prevailing winds were from NE and SE [30]; later, SE, NE, and SW prevailing directions with an annual average speed of 6.2 ${ms}^{-1}$ and with maximums from NW of 23 ${ms}^{-1}$ [27] were recorded. Wind waves induced by the prevailing NE and SW winds have maximum heights of 0.8 m, and 40% of the wind waves are induced by the NE winds [30].

The surface temperature varies between 27 and ${32 }^{\circ }$C [30], considering the measurements from the inlet to the center of the system. In Carmen Lagoon, the temperature fluctuates daily between 23.8 and ${31.8 }^{\circ }$C, from May to June, which are considered the warmest months [27]. Salinity varies from 6.0 to 37.8 psu, with the highest values recorded at the inlets and lowest close to the river discharge zones [30]. Later, the salinity variations were between 36.9 psu at the inlets and 2.7 psu at the mouth of the Santa Ana River [27].

The sedimentation rate is significant in the region, especially in the SAI, mainly due to marine influence [30].

A coastal vulnerability index for the Tabasco coast was obtained through a land use change model, concluding that areas near the CPM system are among the most vulnerable areas in Tabasco state. The variable that least affected the coastal vulnerability index model was the tide, followed by sea level [33]. The analysis of land use change in the CPM system concluded that coastal habitats are at high risk of flooding; their projections for 2030 predict a loss of land of 80.9 ${km}^2$ if no change in coastal land use management is implemented [34].

3. Methods

A geomorphological advection–diffusion sediment transport model coupled with hydrodynamic and wave models was implemented in a case study for the CPM system. This study considers the interactions among tides, wind, wind waves, and marine currents. The MOHID model was used for sediment transport simulation, which has been successfully applied in different regions throughout the world.

The coastal lagoon CPM system has complex sediment transport dynamics that can be studied using numerical models. These models proved to be suitable tools for understanding coastal circulation [35]. In this case, wind waves, local circulation, and sediment dynamics of the CPM system were obtained using the MOHID modeling system, which is composed of more than 40 modules [36], being the hydrodynamic module at the core of the MOHID [19]. Previous studies have applied the MOHID model to describe estuarine circulation in complex CPM-like systems, including a downscaling approach [19,35], suitable for describing estuarine circulation under variable bathymetric and shoreline patterns. MOHID is a three-dimensional model system [37], specialized in coastal lagoons and estuaries; in this study, the two-dimensional version was used. It enables the adoption of an integrated modeling philosophy for processes (physical and biogeochemical) across different scales and the use of nested models, which are ideal for systems such as estuaries and watersheds. MOHID comprises several coupled internal modules to achieve different purposes; it can also be coupled with other independently developed models. An example of this is MOHID, coupled with the Simulating WAves Nearshore (SWAN) model [38] (Figure 2), which simulates wind waves from wind input data. A more detailed description about the sand module can be found in do Carmo (2005) [39]. The van Rijn transport formulas [16] were used to calculate sediment transport in the CPM system. Franz (2017a,b) [19,20] provides a detailed explanation of the Sediment Transport Module used here. More information about the model and modules can be found in Braunschweig (2004) [37]. The model solves the primitive Navier–Stokes equations and assumes hydrostatic equilibrium and the Boussinesq approximation. The mass and momentum equations are

$${\displaystyle \frac{\partial u}{\partial x}}+{\displaystyle \frac{\partial v}{\partial y}}+{\displaystyle \frac{\partial w}{\partial z}}=0$$

(1)

$$\begin{array}{cc}{\displaystyle \frac{\partial u}{\partial t}}+{\displaystyle \frac{\partial uu}{\partial x}}+{\displaystyle \frac{\partial uv}{\partial y}}+{\displaystyle \frac{\partial uw}{\partial z}}-fv=\hfill & -{\displaystyle \frac{1}{{\rho }_0}}{\displaystyle \frac{\partial p_{atm}}{\partial x}}-g{\displaystyle \frac{{\rho }_n}{{\rho }_0}}{\displaystyle \frac{\partial \eta }{\partial x}}-{\displaystyle \frac{g}{{\rho }_0}}{\int }_z^{\eta }{\displaystyle \frac{\partial {\rho }^{\prime }}{\partial x}}\mathrm{d}z+\hfill \\ & {\displaystyle \frac{\partial }{\partial x}}\left(A_h{\displaystyle \frac{\partial u}{\partial x}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left(A_h{\displaystyle \frac{\partial u}{\partial y}}\right)+{\displaystyle \frac{\partial }{\partial z}}\left(A_v{\displaystyle \frac{\partial u}{\partial z}}\right)\hfill \end{array}$$

(2)

$$\begin{array}{cc}{\displaystyle \frac{\partial v}{\partial t}}+{\displaystyle \frac{\partial vu}{\partial x}}+{\displaystyle \frac{\partial vv}{\partial y}}+{\displaystyle \frac{\partial vw}{\partial z}}+fu=\hfill & -{\displaystyle \frac{1}{{\rho }_0}}{\displaystyle \frac{\partial p_{atm}}{\partial y}}-g{\displaystyle \frac{{\rho }_{\eta }}{{\rho }_0}}{\displaystyle \frac{\partial \eta }{\partial y}}-{\displaystyle \frac{g}{{\rho }_0}}{\int }_z^{\eta }{\displaystyle \frac{\partial {\rho }^{\prime }}{\partial y}}\mathrm{d}z+\hfill \\ & {\displaystyle \frac{\partial }{\partial x}}\left(A_h{\displaystyle \frac{\partial v}{\partial x}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left(A_h{\displaystyle \frac{\partial v}{\partial y}}\right)+{\displaystyle \frac{\partial }{\partial z}}\left(A_v{\displaystyle \frac{\partial v}{\partial z}}\right)\hfill \end{array}$$

(3)

$${\displaystyle \frac{\partial p}{\partial z}}=-\rho g$$

(4)

where

• $u,v,w$ = velocity vector components in the $x,y,z$ directions, respectively.
• t = Time.
• f = Coriolis parameter.
• $p_{atm}$ = Atmospheric pressure.
• p = Pressure.
• g = Acceleration due to Earth’s gravity.
• $\eta$ = Free surface of the sea.
• $\rho$ = Volumetric mass.
• ${\rho }^{\prime }$ = Volumetric mass anomaly.
• ${\rho }_0$ = Reference volumetric mass.
• ${\rho }_{\eta }$ = Volumetric mass on the sea surface.
• $A_h,A_v$ = Horizontal and vertical eddy viscosity.

Figure 2. Diagram of the MOHID modeling system for the simulations. Rectangles represent the modules or coupled models; arrows represent the interconnections of the modules. The scheme shows only the modules used for this study.

Figure 2. Diagram of the MOHID modeling system for the simulations. Rectangles represent the modules or coupled models; arrows represent the interconnections of the modules. The scheme shows only the modules used for this study.

The numerical solution was obtained using an Arakawa C-type mesh configuration, with sigma coordinates. Time discretization employs an alternating-direction implicit (ADI) method. The eddy viscosity values are related to the mesh resolution. The MOHID model allows for assembled domains to be used [40]. This methodology enables the use of nested meshes that increase the spatial resolution, thereby forcing local domains to incorporate the results of simulations of larger-scale domains. In this way, the model enables the study of areas increasingly closer to the region of interest, thereby obtaining the boundary conditions of the domain at the highest level of detail.

Three uniform meshes were used in the numerical solution: the first (D1) covers a larger area with a resolution of 300 m, the intermediate mesh (D2) has a resolution of 100 m, and the finest mesh (D3) has a resolution of 33 m (Figure 1).

A two-dimensional barotropic version of the MOHID model was applied, forced by tidal and atmospheric data, imposed on the different domains. In D1 (Figure 1), tidal levels generated by the 2004 Finite Element Solution (FES) tidal model, composed of 14 tidal components (M2, S2, K1, K2, N2, 2N2, O1, Q1, P1, M4, Mf, Mm, Mtm, and MSqm), were imposed as boundary conditions. D1 aimed to propagate the tidal currents to the nested domains. On the other hand, tide gauge data collected by the National Tide Gauge Service of the National Autonomous University of Mexico [41] were used to validate the model results near Santa Ana Inlet, as the station was located 1.5 km from the inlet (Figure 1). Sea level data showed diurnal tides (tidal form number F = 3.1). The decadal wind roses reveal the climatic behavior of the winds in the study region (Figure 3). The dominant wind climatic conditions (NE and ENE) were used as forcing mechanisms at the D2 domain (Figure 1).

The hydrodynamic results from D2 forcing the D3 domain (wind, tides, and currents), in addition to wind waves, were calculated alternately for hydrodynamic and sediment transport. Since the morphological features of the coastal area depend on the sediment characteristics and the combined action of wind, tides, wind waves, and currents, general information about the different characteristics of the sediments in the area was imposed on domain D3. Ayala-Pérez (2013) [31] reported the specific characteristics of the SAI and the sandbar (Table 1); this information was included in the model to reproduce the transport of bedload and suspended sediment.

Five numerical experiments were conducted to determine which of the perturbations suffered by the CPM system in the last decades is the one that contributes the most to the recent changes in the erosion and accretion patterns in this area. Aspects to consider include coastal engineering works, shifts in wind patterns, wind waves and currents, and the effect of winds produced by an extreme event near the CPM system.

Regarding the coastal engineering works, two cases were examined near the SAI, one considering the presence of a breakwater in the western SAI (Figure 1) and the other removing the breakwater. For these experiments, winds from March 2019 were imposed on D2 and D3, daily time series were extracted from the NCEP-DOE AMIP-II Reanalysis [42], and 10 min time series were extracted from the Tide Gauge Service of the National Autonomous University of Mexico (SMN) [41]. In this case, the prevailing winds came from the NE.

Concerning the winds, two different numerical experiments were conducted, both considering the actual configuration of the SAI, including a breakwater in the western part, and considering NE and ENE prevailing winds, respectively.

An extreme wind condition was applied to the model, considering wind conditions from Tropical Storm Larry, which passed near the CPM region in October 2003.

4. Results and Discussion

Two numerical experiments were carried out to evaluate the impact of coastal engineering interventions near the SAI, one including the presence of a breakwater structure on the western SAI and another excluding it. The results showed that the difference in the erosion–accretion sediment patterns simulated in both scenarios was not significant and did not reproduce the recent morphology of the SAI and along the sandbar. The simulation was conducted with NE winds (Figure 4A), including the breakwater; the results showed slight changes in the morphology (Figure 4B). The most notable changes occurred in the SAI (Figure 4B(B.c)), where erosion processes favor the communication of the CPM system with the SGM, which used to maintain the health of the ecosystem. This sediment pattern was similar to that obtained without considering the breakwater This suggests that other conditions should be considered in the calculations. A detailed review of a series of satellite images was conducted to gather information on when the areas’ erosion and accretion patterns started to change. The analysis showed that the changes in patterns began within the last two decades. Additional experiments were required to evaluate different wind conditions.

The decadal wind analyses (NCEP-DOE AMIP-II Reanalysis) [42] near the CPM system (Figure 1C) revealed that over the last two decades, the prevailing winds have shifted from NE to ENE (Figure 3). During the 1980s and 1990s, the prevailing winds were from the NE (Figure 3A,B), while during the 2000s and 2010s, the prevailing direction shifted to ENE (Figure 3C,D). A new numerical experiment was conducted to gather information on possible morphological changes in the CMP system, considering the new wind conditions (Figure 4C).

The results of this numerical experiment, considering prevailing ENE winds (October 2018, ERA5 wind data) [43] (Figure 4C) and the current configuration of the system with the breakwater, showed changes in the SAI, near the sandbar and inside the lagoon. The erosion–accretion patterns created with an ENE wind field were more similar to those currently observed (Figure 4D). The morphological gradients are steeper, indicating the greater sediment transport observed nowadays.

The results of sediment transport simulations under two different predominant wind directions, from the NE and ENE, respectively, show important differences (Figure 4B,D). On the sandbar beach, the sediment transport induced by the NE winds (Figure 4A) was almost neglected (Figure 4B(B.a,B.b)), as well as in the western SAI inside the lagoon (Figure 4B(B.d). However, in the SAI, considerable erosion can be noticed (Figure 4B(B.c)); inside the Carmen lagoon, small accretion areas are observed (Figure 4B(B.e)). The sediment transport driven by ENE winds (Figure 4C) is more intense than the one created by the NE winds. The presence of erosion–accretion processes off the sandbar is evident at both sides of the SAI. Next to the beach, an erosive spatially periodic pattern is evident, with scales ranging from 100 to 150 m, suggesting that these areas could be influenced by trapped waves; furthermore, a sediment accumulation area with the same pattern is also observed in this region, located offshore and parallel to the coast (Figure 4D(D.a,D.b)). In the SAI complex alternating erosion–accretion bands are generated, dominating the accretion processes (Figure 4D(D.c)). In the northwest, inside the lagoon, accretion generates shallower areas, followed by eroded areas at the south (Figure 4D(D.d)). A region with higher sediment transport activity in the eastern Carmen lagoon shows accretion dominance (Figure 4D(D.e)).

The area is prone to extreme hydrometeorological events, such as tropical storms and hurricanes. Between 1900 and 1950, three such events affected the area, and in the 1990s, two hurricanes impacted the region. In 2003, Tropical Storm Larry impacted the area, approximately 12 km away [44] (Figure 5A). Since 2010, the region has been affected by one hurricane per year; since 2022, two hurricanes have been recorded yearly, indicating an increase in these events in the SGM. The numerical simulation results, after Tropical Storm Larry, show an eroded area along the sandbanks on the coast and an accreted area almost parallel to the sandbar, similar to the pattern created by the prevailing ENE winds, only that in this case, the accretion area is located further from the coast. A strong accretion and erosion are observed in the western and eastern SAI, respectively (Figure 5B).

Inside the lagoon, near SAI, erosion–accretion areas can be observed, after the occurrence of Storm Larry (Figure 5B). Evidence of these formations can be seen in the patterns created by ENE-prevailing winds (Figure 4D(D.d)). These patterns are also observed in satellite imagery after applying the Normalized Difference Water Index (NDWI) (Figure 6A). Thus, according to the sediment transport model results, Larry enhanced the pattern obtained for the prevailing ENE winds, before they became dominant. This event marked a change in the sediment transport patterns in the area and was reinforced by the subsequent ENE winds that have dominated the area since then. The patterns created by Larry (Figure 5B) show similarities to the previous patterns resulting from the prevailing ENE winds (Figure 4D) and are even more pronounced, reflecting the increased sediment transport observed during the extreme event (Figure 5B).

In recent years, patterns generated by the prevailing ENE wind (Figure 4D) and after Larry (Figure 5B), such as a saw tooth wave, have been observed from satellite imagery after applying the NDWI (Figure 6B). The distance between consecutive peaks was approximately 160 m. This pattern could suggest that the saw tooth wave is formed by boundary waves. The increasing wind gusts reveals climatic changes in the corresponding mean wind and wave patterns. Sea level changes due to global warming and engineering projects can significantly alter future sediment dynamics in the area.

This region is currently experiencing significant beach erosion and is likely to have a greater impact in the future, increasing the risk to residents and infrastructure of the Sanchez Magallanes settlement. There is currently talk of increased migration from this region due to climate change.

This study examines the influence of climate change on sediment dynamics and erosion within a coastal lagoon system situated in the SGM. Erosion and sediment redistribution are key processes shaping coastal landscapes.

In 2019, the journal Water published a Special Issue titled “Modeling and Practice of Erosion and Sediment Transport under Changing Conditions”, which emphasized modeling approaches, practical applications, and the influence of environmental changes on erosion and sediment transport. Most contributions focused on the effects of climatic and anthropogenic changes on erosion and fluvial transport processes [45], while only a few addressed transformations in coastal lagoons and littoral zones.

Pang et al. (2023) [46] conducted a comprehensive literature review to synthesize current scientific understanding of coastal change processes under climate change and to identify research gaps in predicting future erosion. Their findings underscore the importance of integrating of near-shore wave and coastal simulation models to assess coastal risks across both short and long timescales, thereby informing the design of protective measures. Maximum seafloor erosion or accretion on Australia’s continental shelf and coastal seabed could reach up to 2 m over the next 50 years. Sediment accumulation in these regions is generally increasing, driven by the combined effects of wave action and the rising frequency of storms and cyclones. Adams et al. (2011) [47] explored the influence of climate change and wave direction on sediment transport patterns along the southern California coast. They found that coastal accretion or erosion intensifies significantly when wave direction shifts from northwest to west. These results underscore the potential consequences of increased cyclone activity, more frequent El Niño events, and other changes in oceanic storm intensity associated with global climate change. These results could provide clues as to what is to come in the study region, so a more detailed analysis of similarities and differences between the regions should be carried out.

Collectively, these studies underscore the need for further research to generalize findings by analyzing wave dynamics during storm events under varying hydrodynamic conditions and evaluating potential changes in sediment transport and coastal morphological evolution.

In this context, Integrated Coastal Zone Management (ICZM) emerges as a dynamic, multidisciplinary, and iterative framework for promoting the sustainable management of coastal areas. ICZM encompasses the full cycle of data collection, planning, decision making, management, and implementation monitoring [48].

5. Challenges and Knowledge Gap

Climate change adaptation involves implementing strategies and measures aimed at reducing vulnerability, enhancing preparedness, and fostering resilience in response to shifting climatic conditions. Climate-resilient development plays a crucial role in strengthening the capacity for climate action and improving social, economic, and ecological resilience. The increasing urgency of the climate crisis highlights the need for robust adaptation policies and actions that actively engage society. Monitoring adaptation progress along the southern coasts of the Gulf of Mexico—particularly in the state of Tabasco—presents several challenges, including knowledge gaps, limited funding, and a lack of continuity in collective efforts to develop and implement adaptation plans. These challenges are compounded by ongoing debates regarding how to define and measure the success of adaptation strategies [49,50,51,52]. It is therefore essential to continue investigating the causes of recent coastal changes and to distinguish between those driven by anthropogenic activities and those resulting from natural climatic processes. This is especially relevant in regions such as Carmen–Pajonal–Machona, where human interventions have been implemented for over 50 years to address social and ecological issues through short-term solutions. However, these efforts have often overlooked multidisciplinary, long-term, and model-based studies that could anticipate the potential long-term benefits and drawbacks of such changes. At the same time, it is crucial to remain vigilant about the ongoing effects of climate change.

In November 2023, the United Nations Environment Program (UNEP) published the Adaptation Gap Report 2023, titled “Underfinanced. Underprepared. Inadequate Investment and Planning on Climate Adaptation Leaves World Exposed.” The report emphasizes that climate change adaptation is a global challenge requiring urgent action through interdisciplinary collaboration and the active participation of federal and local governments, as well as civil society, to achieve effective and sustainable outcomes [53,54,55]. Despite growing awareness, a significant gap remains between the expectations surrounding adaptation efforts and the current state of adaptation financing [56]. In 2022, the financing gap for climate change adaptation was estimated to be five to ten times greater than the available funding [52]. Addressing this gap is critical, particularly in vulnerable regions, where it is essential to monitor environmental conditions from a multidisciplinary perspective to detect significant or recurring deviations from baseline states. In coastal areas, this includes analyzing historical atmospheric data and modeling potential future changes. Numerical models can enhance the efficiency of future coastal engineering projects and help identify the most viable or effective locations for new inlets and other interventions.

The effects of climate change are becoming increasingly evident across various regions of the world, exposing communities to disruptions in weather patterns, shifts in wildlife behavior, changes in vegetation, and challenges related to the quality, accessibility, and availability of water and food resources [24]. Malik and Ford (2024) [57] identify four critical issues contributing to delays in the development of climate change adaptation plans: (1) the growing financing gap for adaptation; (2) slow progress in implementation; (3) lack of gender equity and social inclusion; and (4) insufficient action on loss and damage caused by climate impacts. In this study, we identify an additional key factor that must be considered in any adaptation strategy: changes in regional and local environmental forcings that drive observed transformations (i.e., shifts in wind direction). While numerous valuable studies have addressed these forcings, including those conducted by the Intergovernmental Panel on Climate Change (IPCC) [24], regional and local analyses using downscaling methodologies remain limited.

Given the increasing evidence of new climate change impacts, it is essential to support and incorporate such studies into adaptation planning. These analyses can serve as foundational sources of information, helping to construct future climate scenarios that guide decision making at the local and regional levels. Continued monitoring of atmospheric conditions, which often show the earliest signs of change, can serve as an effective early warning system. However, this should be complemented by the observation of other critical indicators, such as the physical and chemical properties of water, which may signal emerging ecological issues to which coastal ecosystems are particularly vulnerable. Greater attention must be given to zones of atmospheric–oceanic interactions, such as coastal lagoons, as these areas are susceptible to climate variability. Understanding and monitoring these systems is essential for anticipating and responding to future changes driven by climate change.

6. Conclusions

The application of the MOHID model to the Carmen, Pajonal, and Machona lagoon systems provided valuable insights into sediment dynamics within this highly active coastal environment. As part of this effort, a decadal wind climate analysis was conducted in the region. The results revealed a significant shift in wind direction from predominantly north-easterly to east-north-westerly, altering the prevailing wind angle from ${26}^{\circ }$ to −${3.5}^{\circ }$, nearly parallel to the coastline. This shift is likely the primary driver behind the recent changes in erosion and accretion patterns observed along the coast and within the lagoon system, ultimately undermining its stability.

To accurately simulate the system’s dynamics, the model was forced using fourteen tidal components and wind conditions. The SWAN wave model was coupled with MOHID, successfully reproducing key features of sediment dynamics and relevant morphological characteristics. Sediment transport in the region is both intense and variable, primarily influenced by prevailing winds, wind-generated waves, and extreme meteorological events such as hurricanes and tropical storms. The ENE prevailing wind numerical experiment demonstrated that this current wind pattern adequately reproduced the observed sediment evolution.

Additionally, anthropogenic interventions, including dredging, the construction of breakwaters and groynes, the opening of artificial inlets and channels, and sea level rise along with sediment properties, contribute to the observed changes in erosion and accretion, albeit to a lesser extent. Understanding the hydrodynamic regime and sediment transport processes is crucial for identifying the sources (erosion) and accretion zones of sediments in both eroded and accreted areas, which is essential for enhancing the ecological health of coastal ecosystems.

Predicting the future spatial bathymetry and coastline evolution is vital, as these changes directly affect the safety of coastal infrastructure, populations, and industries. Historical knowledge of coastal morphology and bathymetry is crucial for predicting temporal changes and improving coastal resource management. Historically, population growth in coastal areas has been a significant risk factor, often associated with shoreline retreat or changes in land use. In the Carmen–Pajonal–Machona lagoon system, various measures have been implemented to design climate change adaptation strategies that consider the region’s social, ecological, and economic contexts. These efforts involve collaboration among local communities, national and international institutions, and academic researchers. Preparing for the impacts of climate change requires both mitigation and adaptation actions, which carry significant economic, social, and political implications. Effective adaptation planning must account for key drivers of coastal change, including variations in wind patterns, wave dynamics, and sea level rise.