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This case study was carried out along a sandy coastline of the Volta Delta of Ghana, with a selected profile east of the Volta estuary ( Figure 1 ). The area is considered a high-energy zone with nearshore wave heights in the range of 1–2 m 75% of the time (Verheyen et al., 2014). The waves approach the coast in a predominantly SSW direction (between 170°-210°) with periods exceeding 10 s 80% of the time (Verheyen et al., 2014). These waves generate strong longshore currents in the eastward direction and are responsible for the high rates of longshore sediment transport reported in the area (Nairn and Dibajnia, 2004; Anthony et al., 2016). The tide is semi-diurnal with an average tidal range of about 1 m (Wiafe et al., 2013; Appeaning Addo et al., 2018). Generally, the beaches are considered relatively steep with reported slopes ranging between 1:3 and 1:15 (Bollen et al., 2011; Roest, 2018). Studies have shown that the area is historically eroding (Mann et al., 2023), with recent short-term rates for some areas reaching as high as 30 m/year (Jayson-Quashigah et al., 2019), leading to the destruction of communities such as Fuveme and Agavedzi along this coast. The location falls within the ongoing project looking at the use of mangroves as NbS for coastal hazards in eastern Ghana (MANCOGA) project (MANCOGA, 2024), as well as the ongoing Harmony Coast project, which has begun regular mapping of bathymetry and beach profiles along this coast (Angnuureng, 2023; Angnuureng et al., 2024).

The Volta Delta area is known to host some of the most extensive and dense mangroves along the coast of Ghana (Awuku-Sowah et al., 2023; Ofori et al., 2023). Among the over five mangrove species identified in Ghana (Ofori et al., 2023), three dominant species are present in the study area, namely Avicennia germinans (black mangrove), Rhizophora racemosa (red mangrove), and Laguncularia racemosa (white mangrove) (Nunoo and Agyekumhene, 2022). Though the red mangroves are more dominant in the area, the white mangrove is noted to be more prevalent closer to the coastline, and this is confirmed by our observations ( Figure 2 ).

A simple framework was adapted for this study, based on XBeach, similar to that of Chen et al. (2022) and Chen et al. (2024) who employed the approach to assess the use of seagrass as a nature-based solution for coastal erosion. The model has also been successfully applied at the global level to assess the role of vegetation in coastal defense (van Zelst et al., 2021). Here, a near-shore morphodynamic model is set up and forced with global ocean conditions from ERA5 reanalysis data ( Figure 3 ). This choice ensures that the model mimics ecological realism as closely as possible, enhancing the transferability of the results for practical application. The XBeach model simultaneously solves the time-dependent short-wave action balance, the roller energy equations, the nonlinear shallow water equations of mass and momentum, sediment transport formulations, and bed updates on the scale of wave groups (Roelvink et al., 2009). The study adopts the 1D surfbeat mode (instationary) to simulate coastal erosion under various scenarios (Chen et al., 2024).

A cross-shore profile was generated from the bathymetry and topography data extending approximately 5 km offshore ( Figure 5 ) with a varying grid size ranging from 20 m offshore to 1 m nearshore. The model simulation spans two (2) months between June and July 2023, where the highest erosion was recorded based on the historical profiles that were mapped for the location (Angnuureng et al., 2024). The higher eroding period was chosen to test the ability of mangroves to protect the shoreline during such extreme events.

The boundary conditions were forced with the ERA5 reanalysis data as described using the time-varying JONSWAP spectra with the tidal data from the Tema port.

For the XBeach model, the Chezy bed roughness coefficient is implemented, with a value of 55.0 (XBeach Team, 2023; version 1.23). A grain size of 0.8 mm (D₅₀) and 0.12 mm (D₉₀) was used based on the average of the measurements reported for the area from earlier studies (Verheyen et al., 2014; Jayson-Quashigah et al., 2019). Calibration was conducted using several parameters to determine which combinations produced the closest result of erosion based on the measured June and July profiles. Among all the parameters, three were critical, as also indicated by other studies (Roelvink and Costas, 2019; Kombiadou et al., 2021); the bermslope, which allows the slope of the profile near the waterline to be nudged towards the given value; the facAs, which is the time-averaged flows due to wave asymmetry and delta, which represents the fraction of wave height added to the water depth to adjust maximum wave height in wave breaking formulations (Kombiadou et al., 2020). For this model, the longshore transport component(lsgrad) was activated and calibrated with reference to other studies (Pender and Karunarathna, 2013; Roelvink and Costas, 2019). This was necessary to take care of the longshore transport, which is strong in the area (Nairn and Dibajnia, 2004; Anthony et al., 2019) and their sensitivity was tested with several values to determine the optimal value, ensuring the robustness and reliability of the results. The key parameter settings that worked best for the study area are summarized in Table 1 . All other parameters were left at the recommended default settings (XBeach Team, 2023).

The calibration of the model was mainly guided by the morphological changes observed due to the absence of adequate in situ historical data on the ocean state. The modelled changes were validated against the measured profile changes (Angnuureng et al., 2024). Shoreline erosion based on the location of the berm crest and volume erosion across the profile was estimated between June and July 2023. The profiles, however, do not extend beyond the low water mark, hence, changes in the surf zone were not considered for the calibration and validation.

The vegetation module of the XBeach was activated to introduce mangroves into the model. It has been used elsewhere to assess the role of vegetation in wave attenuation (Chen et al., 2022, 2024; van Hespen et al., 2023). The mangrove vegetation is represented as rigid cylinders with parameters including stem height, diameter, and density (Burger, 2005; Chen et al., 2022). The parameters were set based on in situ data collected from the study area on mangroves (with specific reference to the white mangrove). A conservative drag coefficient (C_D ) of 1 was adopted (Adytia et al., 2019; Yoshikai et al., 2022; Lopez-Arias et al., 2024) and increased by a factor of 0.1 for every higher level of mangrove density. The height of the mangroves measured in the area varies, ranging between 2 m and 8 m. However, those observed close to the beach hardly exceed 5 m in height.

Table 2 summarizes the mangrove characteristics for the various density levels used for the model based on field measurements and Thao et al. (2023).

Three (3) broad scenarios were considered, (1) no presence of mangroves on the beach (Baseline), for both the current situation and projected sea level rise for 2040 (2) the introduction of matured mangroves on the berm (behind the crest) at varying densities (Scn_Man-I), for the current situation and projected sea level rise for 2040 and (3) the introduction of matured mangroves in the intertidal zone at varying densities for both current and projected sea level rise for 2040 (Scn_Man-II).

Predominantly, the mangroves occur at the back of the beach (normally where there are creeks or lagoons behind the dune system), but there are some instances of them occurring on the dune along pockets of the beach (see Figure 2 ). However, their ability to protect the coastline has not been tested. The third scenario, though, does not occur naturally in the area, but was considered a possible option ( Figure 6 ).

The model’s sensitivity to coastal erosion was tested using three main parameters, namely the bermslope, facAs, and delta (Roelvink and Costas, 2019; Kombiadou et al., 2020). A range of these variables was simulated using the June 2023 profile as a baseline, and the resultant profiles were compared to the July 2023 profile. For facAs, the values tested were 0.25, 0.30, 0.35, 0.40, and 0.45, for delta; 0.30, 0.35, 0.40, 0.45, and 0.50 were tested, however, results for 0.45 and 0.50 were inconsistent and therefore not reported. Bermslope values tested were 0.14, 0.15, 0.16, 0.17, and 0.18, respectively. From the results, the model was sensitive to all three variables tested with the most significant changes observed with variations in facAs ( Figure 7 ).

The Root Mean Square Error (RMSE) for facAs calibration ranged between 0.71 m to 0.90 m, with the highest recorded for facAs 0.45. For variations in delta, the RSME ranged between 0.79 m to 0.89 m, with delta 0.4 producing the lowest value. RSME values for the bermslope evaluated ranged between 0.73 m to 0.85 m, with the lowest value captured for the bermslope value of 18. It should be noted that these errors were only calculated considering the backshore and foreshore zones where there was measured data.

The model calibration carried out shows the model was sensitive to all three parameters, namely facAs, delta, and bermslope. Similar results have been reported by previous studies, both in the area and elsewhere (Verheyen et al., 2014; Roelvink and Costas, 2019; Kombiadou et al., 2021). The best values (facAs 0.35, delta 0.4, and bermslope 0.18) were able to capture the erosion trend more accurately, with a 3% overestimation of sediment loss. However, sediment overwash was significantly underestimated (47%), which can be attributed to the underestimation of wave-runup. Other studies, such as De Beer et al. (2020); Kombiadou et al. (2021), and Roelvink et al. (2017), have established that with the surfbeat mode of XBeach, there is a general underestimation of runup, especially for steep beaches, which plays a role in berm dynamics. Overall, the model performed well, with an overall RMSE of 0.75 m. The results of this model assessment present improvements compared to previous attempts reported by Verheyen et al. (2014). The main limitation of this model (also for previous attempts) was the absence of data for the surf zone profile; hence, idealized surf zone profiles were used based on existing profiles elsewhere and initial model behavior using an equilibrium interpolation. Also, there is limited observational data on the ocean state for calibration and validation of the wave dynamics. With a future sea level rise of 0.233 m by 2040 under the worst-case scenario (SSP5-8.5), the model shows a complete inundation and erosion of the beach. This result is also consistent with other studies that indicate that even with a modest sea level rise of 0.1 m, most of the delta area will be inundated, especially along the coastline (Wiafe et al., 2013; Brempong et al., 2023; Avornyo et al., 2024).

With a reliable prediction of erosion from the model, What-if Scenarios (WiS) of mangroves were evaluated to assess their ability to protect the coast against coastal erosion both under current and future sea levels. The results demonstrate the ability of mangroves to protect the shoreline with a significant reduction in erosion as mangroves were introduced on the beach and in the intertidal zone. The introduction of mangroves behind the berm is considered a viable option, as there is already evidence of mangroves on the berm along pockets of beaches on the eastern coast of Ghana. For the current situation, this option led to a 53% reduction in sediment volume erosion with dense mangroves introduced on the beach. With a rise in sea level, a similar trend of protection is observed, with high-density mangroves protecting the coast by 97%. The mangroves on the berm serve as a barrier, diminishing overland flow velocities and facilitating the deposition of sediments being transported by the water. This gradually increases the berm height, eventually reducing wave overtopping and erosion. This unique ability to adapt to rising sea levels through building up the elevation has been noted by other studies, such as Krauss et al., 2014 and Mitra, 2020. This effect can also observed along pockets of beaches within the Delta where mangroves are on the berm (see Figure 2 ).

The second scenario, which does not exist naturally along this coast, is the mangroves within the intertidal zone. However, this scenario was able to halt erosion completely with the introduction of dense mangroves for both current and future sea level rise. Generally, the higher densities of mangroves were very effective at dissipating wave energy (up to 506 W/m²), which is consistent with other studies (Bao, 2011; Spalding et al., 2014; Kamil et al., 2021). However, though sparse to medium-density mangroves can offer some level of protection for the current situation, higher densities of mangroves are required to protect the coast under rising sea levels. Overall, introducing mangroves into the intertidal zone, while not typical for the region, could serve as an enhanced protective measure when facing extreme wave water level conditions. In particular, the formation of natural sand barriers facilitated by wave dissipation is a promising outcome, creating an added layer of protection for vulnerable coastlines. However, the practicality of this scenario would need to be examined further, as the elevated salinity and high-energy wave environment could limit mangrove growth and effectiveness in this zone. Long-term studies could explore strategies to mitigate these challenges, such as pairing the mangrove growth with salt-tolerant vegetation or temporary protection measures.

In other studies, mangroves have been demonstrated to offer shoreline protection, for example, in the case of Hurricane Harvey, where areas with mangrove cover experienced little erosion compared to other areas without mangroves (Pennings et al., 2021). Sánchez-Núñez et al. (2019) also demonstrated through field experiments, how mangroves can reduce erosion 3 to 15 times with higher wave energies. Several laboratory studies have also demonstrated how mangroves can attenuate waves, thereby reducing coastal erosion (see Amos and Akib (2023). The dense roots of mangroves help stabilize sediments, consequently diminishing sediment suspension and erosion. The above-ground roots and stems are also able to slow down flow and trap sediment, hence building the beach where they are present. This ability has been demonstrated through this assessment and can be leveraged to offer nature-based coastal protection for the eastern coast of Ghana and similar coasts.

From a coastal management perspective, the results highlight the value of integrating nature-based solutions, such as mangroves, into broader coastal protection strategies. Grey infrastructure solutions, such as seawalls and groynes, though effective in localized settings, tend to disrupt sediment transport and create long-term environmental issues. By incorporating mangroves into existing management plans, especially in areas such as the Volta Delta, decision-makers can reduce the reliance on costly and environmentally disruptive “hard” engineering structures. The success of the medium to high-density mangrove scenarios in reducing erosion, for instance, suggests that coastal managers could implement mangrove plantations in areas where beach loss is currently mitigated by engineered defenses, creating a hybrid solution that maximizes both ecological and structural benefits. Furthermore, the use of WiS in this study offers significant insight into future coastal resilience planning. The ability to test different densities and placements of mangroves allows decision-makers to make informed choices that consider both ecological and hydrodynamic conditions. As coastal threats increase due to sea-level rise and storm surges, the ability to simulate these scenarios offers a proactive approach to managing risks rather than reacting to disasters. This approach can be extended to other regions facing similar challenges, using adaptive management practices that integrate scenario testing into long-term coastal planning.

This study acknowledges the challenges imminent with implementing mangroves for coastal protection (some of which have been discussed) and requires further investigation. However, there have been attempts elsewhere to investigate these challenges. For example, elsewhere, attempts have been made to introduce temporal structures to protect mangroves until they reach a growth stage that can offer protection to the coast (Yuanita et al., 2019; Amos and Akib, 2023). They have also been effectively combined with other engineering approaches to reduce the costs and challenges (Tusinski and Jan Verhagen, 2014). This hybrid approach, combining grey infrastructure with natural buffers like mangroves, could be particularly useful in highly dynamic and at-risk coastal regions like the Volta Delta. The strategic placement of mangroves alongside groynes or revetments, for instance, could help mitigate downdrift erosion caused by engineered structures, creating a more sustainable and balanced solution. Such methods not only reduce maintenance costs but also enhance biodiversity and long-term coastal resilience. This approach can be investigated and adopted to help promote the use of mangroves as a nature-based solution for coastal erosion along the eastern coast of Ghana.