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Article

Integrated Predictive Modeling of Shoreline Dynamics and Sedimentation Mechanisms to Ensure Sustainability in Damietta Harbor, Egypt

by
Hesham M. El-Asmar
1,*,
May R. Elkotby
2,
Mahmoud Sh. Felfla
1 and
Mariam T. Ragab
3
1
Department of Geology, Damietta University, Damietta 34517, Egypt
2
Irrigation and Hydraulics Engineering Department, Mansoura University, Mansoura 35516, Egypt
3
Department of Geography, Damietta University, Damietta 34517, Egypt
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(24), 11174; https://doi.org/10.3390/su172411174 (registering DOI)
Submission received: 3 November 2025 / Revised: 22 November 2025 / Accepted: 2 December 2025 / Published: 13 December 2025
(This article belongs to the Section Air, Climate Change and Sustainability)
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Abstract

This research examines the persistent shoreline erosion along the Damietta coast and the problem of sediment buildup in the navigation channel of Damietta Port, both of which pose major obstacles to navigation efficiency and coastal balance. To address these issues, this study uses the LITPACK numerical model to forecast shoreline evolution along the Damietta coast over the next 20 years; the coast is divided into two sections of 3.3 km each. Considering both planned and existing coastal constructions, two realistic alternatives were proposed: extending the existing detached breakwaters by adding two additional offshore breakwaters west of the current field and the implementation of reclamation between the Y-groins, accompanied by a new protruding seawall. The Coastal Modeling System (CMS) was then used to perform a two-dimensional simulation in order to examine sediment transport and hydrodynamic behavior in the port region. This phase concentrated on examining the effects of sedimentation rates following the most recent port development plan, which included building a massive western jetty (5560 m long) and a new navigation channel with a depth of 9 m to service the dirty ballast terminal. In comparison to the benchmark case, the simulation results showed a 93% decrease in sedimentation rates within the navigation channel. The study’s final phase evaluated the impact of changing the crest levels of the current detached breakwaters along the Ras El-Bar coastline on reducing coastal erosion. The study’s conclusions promote the creation of effective and sustainable coastal protection plans in the Damietta area by providing detailed information for future coastal zone management and planning.

1. Introduction

The Damietta Coast, forming a vital part of the Nile Delta shoreline and located in northeastern Egypt along the Mediterranean Sea (Figure 1), is a coastal zone characterized by dynamic natural processes and intensive human activities. It extends from the western side of Damietta Harbor (DH) (31°27′45.91″ N, 31°40′24.26″ E) to the Damietta promontory (31°31′31.75″ N, 31°50′36.82″ E). The Nile River’s Damietta estuary was once essential for sediment transport; however, significant human interventions, most notably the construction of the Aswan High Dam, have greatly diminished its role [1]. This has caused considerable shoreline retreat, particularly around Ras El-Bar and DH, mandating the installation of coastal protection measures such as jetties at the Damietta promontory, seawalls around the Damietta promontory, eastern and western jetties for DH, and groins to the east of DH (see Figure 1A). Nevertheless, the effectiveness of these measures remains mixed, as some structures facilitate sediment deposition while simultaneously intensifying erosion in adjacent areas [2,3,4]. Rising sea levels and frequent storm surges further alter sediment transport patterns [5]. Studies have shown that wave-induced longshore currents, predominantly eastward and driven by dominant northwesterly waves, strongly influence sediment accumulation and shoreline retreat dynamics [6,7,8,9,10,11,12]. In addition to waves and tides, anthropogenic activities and tsunami events may also contribute to shaping the Egyptian coasts [13,14]. For example, recent research has indicated the role of Mediterranean tsunamis in short-term high-magnitude coastal morphology and sediment redistribution [15]. While such events are relatively rare, the high energy could be responsible for episodic sediment transport and shoreline adjustment around Damietta Harbor. El-Asmar et al. [13] monitored changes at Ras El-Bar coast and shoreline retreat consequent to the Kahramanmaraş Turkey earthquakes and reported rapid damage that possibly resulted from this sudden and rapid tsunami. El-Asmar et al. [14] documented the coastal geomorphic changes resulting from this small tsunami. However, this study concentrates on the long-term wave- and current-driven processes, while future modeling efforts will have to account for the role of tsunami-driven sediment dynamics as well.
Ras El-Bar, a popular resort town east of DH, is the hub of coastal tourism, where erosion threatens residential areas and recreational activities. Erosion is most pronounced on the eastern side of DH, where severe sediment deficiency occurs because the port’s jetties obstruct natural sediment transport, as reported by El-Asmar et al. [3] and ElKotby et al. [1]. In an attempt to solve these issues and protect the Damietta coastline for generations to come, eight detached breakwaters (DBWs), each 200 m long and spaced 200 m apart, were constructed in 2002 to protect Ras El-Bar beach. To enhance shoreline stability, the government began constructing two additional DBWs in 2023, which are nearly complete as part of the area’s future development plan. Directly east of the port, four Y-groins were constructed in 2019 to combat ongoing erosion; the first three were 170 m long, and the fourth was 120 m (Figure 1A). The distance between these groins was 400 m. Because of its proximity to the sea, Damietta experiences mild, moderately rainy winters (23–26 °C) and warm, humid summers (28–31 °C) [16]. The wave-rose diagram (Figure 1B) indicates a predominance of waves from the NW (55%), with 10% originating from the N and 7% and 13% from the NE and W, respectively (Figure 1B). These prevailing wave directions generate an eastward-flowing alongshore current. The maximum wave height during major storms reaches nearly 6.7 m; the wave period is ≤7.5 s (99.5% of the time) and between 3–5 s (83.1% of the time) (Figure 1B). Wind speed ranges from 0.5 to 19.5 m/s. Furthermore, the tide is microtidal and semidiurnal, with a range of ≈30 cm [17]. Similar observations were described on a global scale [18,19,20].

1.1. The Study Area

DH is a cornerstone of Egypt’s maritime economy, consistently ranking among the top three commercial ports and handling a significant share of national trade, with contributions to total imports increasing from 4.1% in 2000 to 9.5% in 2023, and exports rising from 9.3% to 10% over the same period [21]. In 2023, the port handled a peak of 3572 ships, with an average of 3111 ships/yr from 2018–2023, reinforcing its status as a leader in ship traffic among Egyptian ports. The port generated revenues of EGP 5.1 billion, underscoring its economic vitality, while supporting thousands of jobs and regional industries such as logistics and shipping [22].
Due to its inland design (Figure 2), DH allows for year-round ship traffic with significant growth, even during extreme weather conditions. This was facilitated by the construction of two jetties in 1985: the old western jetty (DH-OWJ) and the old eastern jetty (DH-OEJ).
These structures reduced littoral drift at the entrance of the approach channel. The DH-OWJ was initially constructed to a length of ≈1000 m in the N011° direction and was extended in 1985 to a total length of 1500 m and a depth of 7 m, running nearly parallel to the navigation channel [23]. Construction of the harbor and its navigation channel began in 1981, with two jetties established to delineate and safeguard the entrance channel, which was excavated to a depth of 15 m. The eastern and western jetties were ≈0.74 and 1.64 km long, respectively. Following the port’s opening, the basin area was 2.77 km2, but this decreased slightly to 2.76 km2 in 1996 due to the construction of petroleum quays west of the inner basin. It then increased to 3.0 km2 in 2010 after the United Petroleum Company quay’s water area was expanded and a multipurpose station was built in the south, and it exceeded 3.42 km2 at the beginning of 2023 following the initiation of the ‘Tahya Misr-1’ container terminal construction, as shown in Figure 2.
However, the port contends with operational challenges due to sedimentation along the navigation channel, resulting in reduced water depth and constraints on docking vessel sizes [21]. These sedimentation issues are evident in the bathymetric changes observed between 2022 and 2024, as shown in Figure 3.
The persistent sedimentation necessitates costly dredging and deepening operations, with annual dredging volumes averaging 1.5 million m3 and costs escalating to EGP 273 million (≈USD 5.29 million) in 2018 [22]. Major deepening projects, such as the 2014–2016 effort, which removed 2.78 million m3, and the 2021–2024 initiative targeting a 19 m depth for USD 79 million, highlight the significant financial burden required to maintain navigational efficiency and accommodate larger vessels, which are critical for sustaining the port’s economic contributions [22].

1.2. The Shoreline Changes

Remote sensing became essential in tracking coastal changes due to its effectiveness, low resource needs, and quick coverage of wide regions. Various studies have focused on shoreline changes [24] along the Nile Delta region [14,25,26,27,28,29,30,31]. Prior to the construction of the port, the Damietta coast experienced significant erosion and accretion, with rates of −0.11 m/yr and +1.29 km2 from 1977 to 1985, respectively (Figure 4A).
The shoreline along the DH-OEJ experienced substantial deterioration, with an average retreat of −7.6 m/yr and −3.65 m/yr during the 2000–2005 and 2005–2016 periods, respectively [3,14,17]. On the other hand, the Ras El-Bar coast suffered significant erosion between 1977 and 1980, with rates reaching −10.38 m/yr [32]. Similar rates ranging from −5.35 m/yr to −10.26 m/yr and average of −9.9 m/yr were observed from 1977 and 1980 and 1984 to 1991, respectively [33]. To address this issue, eight DBWs were implemented between 1991 and 2002. These breakwaters created a shadow zone with low wave energy, allowing sediments to accumulate and promoting the formation of a salient and embayment in the area [3]. The erosion decreased at the shadow of the DBWs from 0.101 km2 between 1984 and 1997 to 0.066 km2 between 1997 and 2011, coinciding with a rise in the accreted areas from 0.234 km2 to 1.152 km2. Average rates of accretion were recorded as +4.96 m/yr, +6.05 m/yr, and +6.3 m/yr, during the periods of 2000–2005, 2005–2020, 2020–2023, respectively (Figure 4B). These rates demonstrate the positive impact of the breakwaters in mitigating erosion and promoting land growth in the area [2,3], with an average of +12.0 m/yr from 1999–2003 [29]; later the rate of accretion gradually decreased, eventually reaching a near-stable state by 2015 [3]. From 2016–2019, four Y-groins were constructed to address erosion concerns in the eastern part of the port (Figure 4C) [2,3]. The analysis indicates that this area experienced erosion at an average rate of −2.58 m/yr from 2005 to 2016. Between 2016 and 2020, shoreline retreat averaged −2.9 m/yr. However, during the period from 2020 to 2023, the erosion rate decreased to an average of −2.55 m/yr [2,17]. The main objective of the present study is to provide an integrated assessment of shoreline evolution and sedimentation processes at DH by combining remote sensing techniques with advanced numerical modelling. First, multi-temporal satellite imagery (1977–2023) was analyzed using GIS-based methods to quantify long-term shoreline changes east of DH, where persistent and severe erosion has been recorded. Second, the future behavior of the coastline under proposed development alternatives, reflecting the ongoing and planned port expansion, was simulated for the period 2020–2040 using the LITPACK shoreline evolution model. Third, the two-dimensional Coastal Modelling System (CMS), comprising CMS-Flow and CMS-Wave, was applied to evaluate sedimentation patterns inside DH over a one-year simulation representing the most recent port configuration. Particular emphasis was placed on assessing siltation within the navigation channel, as recent infrastructure developments, including new terminals and alterations to breakwaters and jetties, have altered local hydrodynamics and raised concerns about increased maintenance dredging requirements. Finally, the study examined the morphological response of the Ras El-Bar shoreline to changes in the crest elevation of the detached breakwaters and to potential geometric adjustments of the Y-groins, which may influence both coastal stability and sediment bypassing toward the harbor entrance.

2. Methods

2.1. Digital Shoreline Analysis System (DSAS)

In the current study the end point rate (EPR), a statistical parameter function of the Digital Shoreline Analysis System (DSAS) in ArcGIS 10.8, is used to analyze Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) data in order to automatically quantify the shoreline change rates attributable to erosion and accretion patterns around DH from 1977 to 2023 (Figure 4) [34].

2.2. Litpack Model

The coastal kinetics 1D numerical model (DHI-LITPACK) is used to simulate the influence of two distinct alternatives that were implemented in reality and to forecast shoreline behavior [29,35]. The 1D LITPACK model has been used in multiple research projects in Egypt and worldwide to predict long-term shoreline deformation [20,29,36,37,38,39]. LITPACK integrates and enhances several predefined numerical models, specifically non-cohesive sediment transport (STP), longshore current and littoral drift (LITDRIFT), coastline evolution (LITLINE), sedimentation in trenches (LITREN), and cross-shore profile evolution (LITPROF) [37]. The LITPACK module (1D) is a component of the MIKE 21 software. This program simulates sediment migration in response to currents and waves, as well as resulting coastline changes [40].
The natural coastal conditions along the east coast of DH were simulated using wave, current, and tide data collected for the entire year 2015. The model encompasses 6.7 km of the study area (see Figure 1A), which is divided into two zones: the first zone, which starts at the eastern jetty of the DH and includes 4 Y-groins, covers 3.3 km, and the second zone, which includes 8 DBWs on the Ras El-Bar coast, covers 3.4 km. The model’s initial state is the coastline as measured in 2020. To reproduce the changes in bathymetry across the research region, five profiles are employed. Data on the initial coastline, cross-shore profiles in profile series, wave climates in time series, and sediment properties are entered at the start of the simulation. From beach to sea, the average grain size in the DH region tends to steadily decrease. The mean grain size of sea bottom sediments at up to 6 m water depth ranges from 0.08 mm to 0.22 mm with an overall average of 0.11 mm, while the mean grain size of beach sand ranges from 0.14 mm to 0.58 mm with an overall average of 0.25 mm [30]. The simulation commences with the entry of data pertaining to the beginning of the coastline, the cross-shore profiles in profile series, the temporal wave conditions, and the sediment characteristics. The data on roughness and sediment properties are derived from site measurements (Table 1). The LINTABL modules were used to calibrate the data to ensure accurate results, while the LITLINE module was applied to simulate shoreline evolution sequentially. The governing equation of the LITLINE model is described in detail [41]. The fundamental equation is the continuity equation for sediment volumes articulated in [42]:
∂ y c ( x ) ∂ t =   1 h a c t ( x )   ∂ Q ( x ) ∂ x +   Q s o u ( x ) h a c t ( x ) ∆ x
where y c ( x ) :  the distance between shoreline to baseline; t: time; h a c t x :  height of active of cross shore profile; Q x : longshore sediment transport rate; X: longshore profile; ∆ x : longshore separation step; Q s o u x :  source/sink term. LITLINE resolves this equation with a finite difference method, adjusting the shoreline position at each time increment according to the computed gradients in longshore sediment transport. The simulation results are corroborated using the shoreline 2021 survey data and moderate-resolution Landsat imagery.
The model is deterministic and modular, facilitating the assessment of many factors affecting shoreline evolution [37]. However, the use of simplified empirical equations and the one-line model generate uncertainties, particularly over long durations, requiring site-specific data for validation and calibration [43]. Additionally, the model ignores changes in the profile shape during shoreline displacement and assumes insignificant longshore gradients in the cross-shore profile.

2.3. CMS Model

The Coastal Modeling System (CMS), developed by the U.S. Army Corps of Engineers (Vicksburg, MS, USA), is a numerical modeling framework designed to analyze coastal hydrodynamics (water levels, waves, and currents), sediment transport (sediment movement driven by wave–current interactions), and morpho-dynamics (morphological evolution of the seabed and shoreline) in coastal and estuarine environments [44,45,46,47,48]. It is widely used to evaluate shoreline protection measures, sediment management strategies, and coastal engineering projects. The CMS comprises two primary modules: CMS-Flow, which simulates hydrodynamics and sediment transport, and CMS-Wave, which models wave transformation and wave–current interaction [47,48,49,50]. These two modules are typically coupled to provide an integrated representation of coastal processes. The governing equations for CMS-Wave and CMS-Flow are described in detail by Nassar et al. [47]. For completeness and convenience, all of the CMS model’s equations are included in the Appendix A.
In real-world harbor engineering applications, the CMS model has been used to simulate coastal processes at harbor entrances. It has been calibrated using bathymetric field data to evaluate changes in water depth, sediment storage, erosion at seawalls, lake outlet variation, and dredging time interval [51,52,53,54]. Therefore, the primary goals are to lower the rate of erosion and prevent siltation of important outflows, as well as to maintain safe navigation depths and minimize sediment deposition. Numerous international research studies have examined and used CMS, demonstrating its high accuracy and resilience [53,54,55,56]. The quality and resolution of the input data determine the CMS model’s accuracy, despite its robustness.
For this study, a variable-sized rectangular grid was constructed in CMS-Flow, extending 4 km offshore, 3.5 km inland, 4 km west, and 8 km east of DH, with spatial resolution ranging from 20 × 20 m at the harbor entrance to 150 × 150 m near the offshore boundary. A CMS-Wave grid with identical spatial coverage was also generated (Figure 5A). The observed water surface elevation (WSE) at the open ocean boundary in 2018 was used to drive CMS-Flow and generate the flow field (Figure 5B). Based on an analysis of wave data from 2004 to 2014, the 2014 wave conditions were identified as representative and employed in the modeling process (Figure 5C). The CMS-Wave half-plane model was adopted for this simulation.
Five alternatives were developed to evaluate sedimentation within the Damietta Harbor navigation channel (DH-NC) and the erosion patterns along the coastal zone east of the harbor (Figure 6). Alternative 1 represents the harbor’s projected condition following the construction of two new western jetties, measuring 5400 m and 5560 m in length, respectively. Alternative 2 is identical to Alternative 1 but includes the addition of two detached breakwaters (DBWs) to the existing series, with crest levels elevated to +2 m above the water surface. Alternative 3 is similar to Alternative 2, except that the crest levels of all ten DBWs are adjusted to the 0.00 m water level. Alternative 4 also resembles Alternative 2, but the DBWs are converted from emerged to submerged structures [53], with crest levels positioned 1 m below the water surface. Finally, Alternative 5 maintains the same configuration as Alternative 2, except that the existing Y-shaped groins are infilled, and a seawall is constructed seaward of the groin field.

3. Results and Discussion

3.1. Litpack

The coastline changes in the study area were simulated over a one-year period (January 2020 to January 2021). For the purpose of calibration, the 2020 surveyed shoreline was taken as the initial boundary, and the predicted shoreline was compared with the measured position from 2021. The extension of the sediment transport table, which defines the distance between the initial coastline and the surf zone limit, and the active depth, which determines the closure depth of the study area, are the key parameters used during the calibration process [42]. The final calibration was performed by adjusting the sediment transport table’s extension with values of 30, 70, 100, and 150 m (Figure 7A,B). The active depth of the study area was assumed to be 8 m, and the diffraction spreading factor, representing the wave energy dispersion in deep water, was set to 25. The model is considered calibrated when the simulated and observed shoreline evolutions are in good agreement.
The discrepancies between the simulation results and the observed data were evaluated using the Root Mean Square Percentage Error (RMSPE) and standard deviation (SD), as defined by Sarhan et al. [29], using the following equation:
R M S P E = 1 N ∑ i = 1 N ( 100 P f i − P o i P o i ) 2
S D = ∑ i = 1 N ( e i − e ¯ ) 2 N − 1
where the distance between the baseline and the observed coastline is indicated by Po, the distance between the baseline and the modeled shoreline is indicated by Pf, the number of grids is N, e i is the difference between modeled and observed shoreline position at each location or time and e ¯ is the average difference (bias) between model results and observations.
The lowest computed RMSPE, according to the calibration approach using RMPSE data, is 0.84% and 0.75% for the two zones, respectively. The lowest computed SD is 15 m and 14.5 m for the two zones, respectively. The RMSPE and SD values appear relatively low compared with long-term geomorphological studies due to the one-year calibration period, during which the shoreline variability was limited. Standard deviation quantifies the spread of the residuals (i.e., the differences between modeled and observed values) and reflects how consistently the model can replicate real-world behavior. These values are for 30 m of the extension of the sediment transport table and may be used as a setting for tuning to measure shoreline variations. The calibration findings utilizing the 2020 shoreline observation at a value of 30 m extended transport table at zone 1 are displayed in Figure 7A. With an average rate of 4.5 m/yr, the model can accurately predict severe accretion rates. It can also accurately anticipate erosion behavior, with an average rate of −3.7 m/yr and a maximum pace of −15 m/yr. These rates are entirely comparable to those determined from 2021 measured data and DSAS coastline change rates from Landsat images. Additionally, Figure 7B displays the final calibration findings in zone 2. In the shaded area of DBWs, the model accurately predicted severe accretion rates with an average of 2.25 m/yr and a maximum of 12 m/yr; erosion behavior was also accurately predicted with an average of −2.9 m/yr and a maximum of −14 m/yr. Additionally, these rates are completely comparable to those determined from the 2021 measured data and DSAS shoreline change rates from Landsat images. Similar observations were mentioned in [54].
The current case is divided into two portions, each distanced by 3.3 km. Figure 8A illustrates the initial 3.3 km of the shoreline, revealing notable trends of shoreline modification. The maximum measured accretion rates near the eastern jetty of DH are around 22.5 m/yr, 51.85 m/10 yr, and 73.66 m/20 yr. The erosion trend was quantified with peak values of −15.83 m/yr, −52.81 m/10 yr, and −84.26 m/20 yr localized at the centers of the gabs between 4Y-shaped groins. Figure 8C shows the remaining 3300 m to the end of the research area, including the DBWs. Ras El-Bar, which is shielded by breakwaters, remains completely stable and the salient growth is easily visible; however, accretion slowed down after the fourth DBW. After 10 and 20 years, the maximum sedimentation rates climbed to 45.5 m and 68.9 m, respectively, from their initial maximum of 11.6 m/yr. The magnitude of a salient behind a breakwater is often correlated with the circulation currents produced by the zigzag movement of the water body brought on by wave run-up and rundown, as well as the movements brought on by the collision of wave crests behind the breakwater centers [37].
Alternative 1 considers reclamation work that has commenced between the four Y-shaped groins east of DH, along with the emergence of a seawall, as observed in satellite imagery. This alternative was the first to evaluate whether these interventions could restore the coastline toward equilibrium, examining shoreline changes after one year, as well as after ten and twenty years. The results indicate that this alternative would have a substantial impact on the coastline updrift of the seawall after the first year, with an average accretion of 23.14 m. After 10 and 20 years, this effect is predicted to increase, reaching maximum gains of 285.7 m and +504.811 m, respectively (Figure 8B). In contrast, shoreline evolution down-drift of the seawall shows severe erosion, with significant average retreat of −99.152 m and −1379 m, after one-year and twenty-year simulation, respectively. The pronounced accretion and erosion as predicted in Alternative 1 result from the strong disturbance of the natural longshore sediment transport by the reclaimed areas and the emerging seawall between the Y-shaped groins. These structures act as effective barriers that block and redirect the eastward sediment drift, leading to rapid sediment accumulation updrift (+23 m after one year and over +500 m after 20 years). In contrast, the downdrift shoreline becomes increasingly starved of sediment, resulting in severe erosion (−99 m after one year and nearly −1.4 km after 20 years). This is a typical behavior for groin and seawall systems under continued longshore transport. These large values reflect long-term trends under the assumption of a stable wave climate and steady sediment transport; while the exact magnitudes may vary, the overall response—accretion updrift and erosion downdrift—remains physically plausible. Alternative 1 was put forth because of persistent erosion east from the Damietta Harbor, given that earlier series of Y-shaped groins were unable to stabilize the shoreline. The model results indicate that such a configuration does not solve the erosion problem and aggravates downdrift shoreline retreat. Therefore, this alternative cannot be included among those that could constitute a viable contribution to any effective and sustainable erosion mitigation for this location.
Alternative 2 shows how shoreline changes over a one-, ten-, and twenty-year period might be affected by the building of two more DBWs west of the current breakwaters on the Ras El-Bar coast, making ten. The breakwaters are situated 350 m from the beach and measure 250 m in length, 230 m in gap width, and 4.0 m in active water depth. Given that accretion rates would only reach 30.8 m as maximum value over 20 years, we may infer from the model findings that the new DBWs had no discernible effect on the shoreline as identified in Figure 8D. There is a relation between the salient growth and the current, wave run up, and wave crest intersects at the shadow zone of the breakwaters. We see that the amount of sediments has remained rather stable over time after offshore breakwater No. 4. Because of the high wave energies communicated via the gaps, the shoreline nevertheless recedes between the barriers at a maximum rate of −86.25 m over a 20-year period. The limited growth that Alternative 2 predicts (a maximum of 30.8 m in 20 years) indicates that the two extra DBWs have little impact on local wave and sediment transport patterns. Because of their position offshore and spacing, there is a weak shadow-zone effect, with only limited salient growth. Therefore, the volumes of sediment stay the same, and the extra breakwater field provides hardly any additional stabilization of the shoreline beyond the existing structures.

3.2. CMS Results and Discussion

The CMS-Wave half-plane model was adopted for this simulation. The CMS model was calibrated for DH and the surrounding ocean area using bathymetry maps to 2022 (Figure 1D). The model was run to predict the bottom change one year later, from 2022 to 2023, after the wave and flow mode input data were specified. To perform sensitivity analysis and model calibration, three profiles in the vicinity of DH were examined. Important among the considered calibration variables are manning coefficients (0.01, 0.025, and 0.04) and a number of sediment transport equations (Lund-CIRP, Van Rijn, and Watanabe). A comparative study was carried out at DH profiles 1, 2, and 3 in 2022 for a one-year simulation in order to emphasize the disparities between measured and anticipated profiles in 2023. The model is highly qualified to predict coastal morpho-dynamic processes by calculating the Normalized Root Mean Square Error (NRMSE) for each profile, as shown in Figure 9. It has a Manning coefficient of 0.01, a d50 of 0.2 mm, a 400 s time step, a scaling factor of 2.0, and an adaptation length of 20 m.
Impact of the latest developments on sedimentation in Damietta Harbor’s navigation channel: DH has recently undergone several modifications and interventions, including the deepening of the navigation channel to 19 m, the extension of the harbor’s eastern and western jetties, and the construction of a new DH-NWJ, which were intended to be the final modifications. The current simulation produced color-filled scalar maps illustrating morphological changes at DH inlets over a one-year period (Figure 10A,F), based on bathymetric data from 2022. Compared to the no-action alternative, sand deposition in the navigation channel decreased significantly by 93% (Figure 11), whereas previous reductions in sedimentation rates were estimated at 72.1% [16]. The current simulations indicate that the other alternatives have no significant effect on sediment accumulation rates within the approach channel.
The effect of changing the crest level of the 10 Ras El-Bar DBWs on the shoreline morphology was discussed (Figure 10C–E). Over the course of a year, simulations using the CMS 2D model were created at various crest levels of breakwaters (+2 above water level, 0 at water level, and −1 below water level). The main difference between the first and second alternatives is that two more breakwaters are added, making a total of 10 DBWs along Ras El-Bar coast with crest elevations of +2 m above sea level along the coastline. Sediment buildup in the wave shadow zones is a strong suggesting positive restoration of coastal stability in the area (Figure 10C). Meanwhile, the zone between the Y-shaped groins system shows an apparent erosion pattern.
It was initially anticipated that reducing the height of the offshore breakwaters would increase wave energy transmission [55,56], thereby exacerbating shoreline retreat. However, the observed results contradicted this expectation, particularly in the areas behind breakwaters 9 and 10. Severe erosion previously reported was significantly reduced, and the beaches in those areas remained relatively stable, with only minor sediment accumulation. In contrast, breakwaters 7 and 8 experienced more pronounced impacts, and the adjacent beaches began to undergo substantial erosion.
When the offshore breakwaters were submerged to a level of −1 m below the water surface as in Alternative 4, the conditions resembled those of Alternative 3, although the situation began to deteriorate further (Figure 10D,E). These results suggest that the adjustment of the crest level does not uniformly affect the entire DBW field, due to the strong influence of local wave angles and sediment pathways on shoreline response. Stability was improved in locations where wave energy was better dissipated, whereas erosion increased in areas with changed alignment of the currents, independent of the modification of crest level. Therefore, simply changing the crest height does not provide full stabilization of the Ras El-Bar coastline and may be combined with complementary design alterations.
Overall, morphological changes across all proposed alternatives, including erosion patterns and sediment deposition, are summarized in Figure 10, highlighting the varying effects on shoreline stability. Four detailed profiles along Ras El-Bar beach (Figure 11) were strategically selected to provide further insight into morphological alterations under Alternatives 2, 3, and 4. These profiles corroborate the finding that these structures play a critical role in maintaining shoreline integrity. It is evident from the cross-shore bed profiles in Figure 11 that, for Profiles 1–3, the initial 2022 and no-action scenarios nearly overlap in most alternatives. In fact, Profile 2 experienced ample coastal erosion, but profile 1 had an accretion pattern in each of the suggested alternatives. The original forms of the coastline profiles in Profile 3 were preserved in all options. In contrast, Profile 4 shows a much stronger response; Alternative 2 and, to a lesser extent, Alternatives 4 and 5 show net erosion with a distinct downward displacement of the bed relative to the initial 2022 and no-action profiles, with the latter two being the highest, indicating that significant stability endures in the current circumstances. Table 2 provides detailed information on the cumulative eroded and accreted zones at the zones behind detached breakwaters along the Ras El-bar coast.
Alternative 5 entails filling in the gaps between the current groins with sediment before building a seawall. Although the findings indicate that this intervention would not have an apparent impact on shoreline dynamics, it does provide a considerable economic opportunity by recovering a sizable portion of land that would be used for a projected tourism development project (Figure 10F). The three profiles that are shown in Figure 12 all exhibited the same outcomes. Erosion predominated in all considered alternatives inside Zone 1, with minor variations among them. In Zone 2, as illustrated in Table 2, situated between the groin system and the offshore breakwaters, the volume of eroded sediment was −147,180 m3 under the benchmark case. This amount significantly decreased to −67,491.4 m3 in Alternative 1. In Alternative 2, sediment accumulation increased to +604.457 m3, further rising to +7967.1 m3 in Alternative 3, which represented the maximum accumulation. However, in Alternative No. 4, erosion occurred again, resulting in −6670.95 m3, followed by a sediment accumulation of +1482.12 m3 in Alternative 5. The benchmark value for the accumulated sediment in Zone 3 was +60,432.8 m3. This quantity decreased in Alternative 1 to +21,083.5 m3, while in the other alternatives, sedimentation turned into substantial erosion, with Alternative 3 recording the greatest value at −14,970.1 m3.

4. Conclusions

The current study employs an integrated numerical modeling procedure to investigate the interrelated challenges of coastal erosion and sediment accumulation affecting the Damietta coastal zone and the navigation channel of DH, Egypt. Utilizing the LITPACK shoreline evolution model, long-term shoreline changes were simulated over a 20-year forecast period under two realistic development alternatives including adding two DBWs and creating seawall after infilling regions between Y-groins with sediment. The shoreline updrift seawall advanced as maximum value of 504.811 m/yr while shoreline in downdrift seawall suffered from significant erosion with average value of −1379 m/20 yr.
With an emphasis on sediment transport behavior in the wake of the most recent DH design modifications, a two-dimensional hydrodynamic and morphological simulation was carried out using the CMS. These modifications include digging a secondary navigation canal that is 9 m deep to serve the dirty casting berth, as well as building a 5560 m western jetty. The efficiency of the harbor expansion layout in eliminating accumulation was shown by model outputs that showed a 93% reduction in sedimentation rates within the navigation channel when compared to benchmark situation. The study also evaluated the DBWs’ morphodynamical response to different crest levels along the Ras El-Bar shoreline, highlighting their crucial role in influencing beach stability and erosion trends. The results demonstrate that, when compared to other simulated alternatives, DBWs with crest levels at sea levels perform the best. Comparing the results of the LITPACK and CMS models, we found that they were highly consistent across two common alternatives. While the scenarios are effective in communicating the influence of harbor-related interventions on sedimentation and shoreline erosion, sustainable coastal planning requires the integration of such numerical results with wider environmental, socioeconomic and system-scale considerations to promote more resilient and sustainable coastal management techniques.

Author Contributions

Conceptualization, H.M.E.-A.; Methodology, M.R.E.; Software, M.R.E.; Validation, M.S.F. and M.T.R.; Formal analysis, M.R.E.; Investigation, M.R.E.; Resources, M.T.R.; Data curation, M.R.E. and M.S.F.; Writing—original draft, M.R.E. and M.S.F.; Writing—review & editing, H.M.E.-A., and M.S.F.; Visualization, H.M.E.-A. and M.T.R.; Supervision, H.M.E.-A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Academy of Scientific Research and Technology (ASRT) grant number COP-27.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

This study is part of the project “Sustainable Development of the Coastal zone between Ras El-Bar and Damietta Harbor in response to sea-level rise and climatic changes”. The authors would like to express their sincere gratitude to the Academy of Scientific Research and Technology (ASRT) for funding the project through COP-27.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The CMS model incorporates hydrodynamics, sediment transport, and morphological changes through two interconnected modules, CMS-Flow and CMS-Wave, managed by a steering module.
CMS-Flow solves the 2D depth-integrated continuity and momentum equations using a finite-volume method.
∂ h + η ∂ t + ∂ q x ∂ x + ∂ q y ∂ y = 0.0
∂ q x ∂ t + ∂ u q x ∂ x + ∂ v q x ∂ y + 1 2 g ∂ ( h + η ) 2 ∂ x = ∂ ∂ x D x ∂ q x ∂ x + ∂ ∂ y D y ∂ q x ∂ y + f q y − τ b x + τ w x + τ S x
∂ q y ∂ t + ∂ u q y ∂ x + ∂ v q y ∂ y + 1 2 g ∂ ( h + η ) 2 ∂ y = ∂ ∂ x D x ∂ q y ∂ x + ∂ ∂ y D y ∂ q y ∂ y + f q y − τ b y + τ w y + τ S y
where:
h represents the still-water depth relative to a specific vertical reference point, while η denotes the deviation of the water surface from this still-water level. The variable t stands for time. The flows per unit width along the x-axis and y-axis are represented by qx and qy, respectively. The depth-averaged current velocities parallel to the x-axis and y-axis are u and v, respectively. The symbol g denotes the acceleration due to gravity. The diffusion coefficients in the x and y directions are given by Dx and Dy. The Coriolis parameter is represented by f. The bottom stresses parallel to the x and y axes are denoted by τbx and τby, respectively, while the surface stresses parallel to the x and y axes are τwx and τwy, respectively. Finally, the wave-induced stresses parallel to the x and y axes are represented by τSx and τSy.
CMS-Wave solves the steady-state wave-action balance equation on a non-uniform Cartesian grid:
∂ ( C x N ) ∂ x + ∂ ( C y N ) ∂ y + ∂ ( C θ N ) ∂ θ = k 2 ∂ C C g c o s 2 θ N y − C C g 2 c o s 2 θ N y y + S i n + S d p + S n l
where:
The wave energy-density E = E (x, y, σ, θ) divided by the intrinsic frequency σ is the wave action density that depends on both frequency and direction, and it may be expressed as N = E/σ. The first and second derivative products about y are indicated by the symbols Ny and Nyy. The horizontal coordinates are x and y; the wave direction is measured counterclockwise from the x-axis; the wave celerity and group velocity are represented by the symbols C and Cg; the characteristic velocities with respect to x, y, and θ are indicated by the symbols Cx, Cy, and Cθ; and the wave intensity is represented by the empirical parameter k, The term Sin corresponds to the source term, which may be related to wind input, while Sdp accounts for the sink components, including bottom friction, wave breaking, and white capping. Finally, Snl describes the interactions between nonlinear waves.
In the CMS, there are three sediment transport models available: (1) Equilibrium total load, (2) Equilibrium bed load plus advection-diffusion for suspended load, and (3) non-equilibrium total load. Two-dimensional transport equation for current-associated total load:
∂ ∂ t h C t k β t k + ∂ h V j C t k ∂ x j =   ∂ ∂ x j v s h ∂ r s k C t k ∂ x j + α t ω s k C t k * − C t k
where j = 1, 2, k = 1, 2, …, n; n is the number of sediment size classes and t = time [s], h = water depth [m], xj = Cartesian coordinate in the j direction [m], vj = total flux velocity [m/s]; Ctk = actual depth-averaged total-load sediment concentration for size class k defined as Ctk = qtk/(Uh) in which qtk is the total-load mass transport [kg/m3]; Ctk* = equilibrium depth-averaged total-load sediment concentration for size class k [kg/m3]; βtk = the total-load correction factor; rsk = fraction of suspended load in total load for size class k; νs = horizontal sediment mixing coefficient [m2/s]; αt = total load adaptation coefficient; ωsk = sediment fall velocity [m/s].
The transport formulae developed by Lund-CIRP, Van Rijn, and Watanabe are employed to compute the near-bed sediment concentration capacity. In this study, sediment transport was estimated using the Van Rijn equation, and its full formulation is included below. The transports [57] are utilized with the recalibrated coefficients [58] as shown:
q b =   0.015   ϼ s U h   ( U e − U c r s − 1 g d 50 ) 1.5 ( d 50 h ) 1.2
q s = 0.012   ϼ s Ud 50   ( U e − U c r s − 1 g d 50 ) 2.4 D * − 0.6
where; qb: bed load transport rate in m2/s; qs: current-related suspended load transport in m2/s; Ucr: the critical depth-averaged velocity for initiation of motion; Ue is the effective depth-averaged velocity calculated as Ue = U + 0.4 Uw in which Uw: the peak orbital velocity based on the significant wave height; h: the total water depth; D*: dimensionless grain size; g: gravitational constant; d50: the median grain size.

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Figure 1. (A) Study area location with zoom-in aerial photographs and map views showing the vital sites at Y-shaped groins and DH. (B) Wave rose diagram illustrating wave date from 1975 to 2024. (C) Echo Sounder-derived bathymetry data 2020 for LITPACK model. (D) Bathymetry data 2022for CMS model.
Figure 1. (A) Study area location with zoom-in aerial photographs and map views showing the vital sites at Y-shaped groins and DH. (B) Wave rose diagram illustrating wave date from 1975 to 2024. (C) Echo Sounder-derived bathymetry data 2020 for LITPACK model. (D) Bathymetry data 2022for CMS model.
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Figure 2. Time-series Landsat satellite imagery (Bands 2 from Landsat 4, 5, and 8) illustrating the historical morphological and infrastructural development changes of DH from 9 October 1982, to 13 March 2025. The images highlight significant changes including port excavation, construction and extension of jetties, development of the navigation channel, progressive land reclamation, and harbor infrastructure expansion. Figures (A–J) illustrate the temporal variations in the conditions of the DH.
Figure 2. Time-series Landsat satellite imagery (Bands 2 from Landsat 4, 5, and 8) illustrating the historical morphological and infrastructural development changes of DH from 9 October 1982, to 13 March 2025. The images highlight significant changes including port excavation, construction and extension of jetties, development of the navigation channel, progressive land reclamation, and harbor infrastructure expansion. Figures (A–J) illustrate the temporal variations in the conditions of the DH.
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Figure 3. Echo-sounder-derived bathymetry maps of DH in 2022 (A) and 2024 (B). (C) W–E cross-sectional profile along DH-Navigation channel shows the change in bathymetry between the two surveys.
Figure 3. Echo-sounder-derived bathymetry maps of DH in 2022 (A) and 2024 (B). (C) W–E cross-sectional profile along DH-Navigation channel shows the change in bathymetry between the two surveys.
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Figure 4. (A) Areas of shoreline erosion and accretion along the Damietta coast between 1977 and 2023. Closeup showing the shoreline changes at the DBW (B) and at the Y-shaped groins (C).
Figure 4. (A) Areas of shoreline erosion and accretion along the Damietta coast between 1977 and 2023. Closeup showing the shoreline changes at the DBW (B) and at the Y-shaped groins (C).
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Figure 5. (A) CMS modules details. (B) Water surface elevation (2018). (C) Wave data (2014).
Figure 5. (A) CMS modules details. (B) Water surface elevation (2018). (C) Wave data (2014).
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Figure 6. Proposed scenarios dimensions. Description (LBW: overall length, Th: thickness, ELEV: elevation, d: depth, S: spacing) in meter, numbers from 1–10 are locations of protection parts.
Figure 6. Proposed scenarios dimensions. Description (LBW: overall length, Th: thickness, ELEV: elevation, d: depth, S: spacing) in meter, numbers from 1–10 are locations of protection parts.
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Figure 7. Calibration process dividing the model domain into two parts 3.3 km for each shown as: (A,B) represent the shoreline measured in 2020 and their residuals with RMPSE results with different extensions of the transport parameter.
Figure 7. Calibration process dividing the model domain into two parts 3.3 km for each shown as: (A,B) represent the shoreline measured in 2020 and their residuals with RMPSE results with different extensions of the transport parameter.
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Figure 8. Scenarios shoreline prediction in 2021, 2030, 2040 divided into two parts 3300 m for each shown as (A) represents first 3.3 km, (B) represents scenario 1 and (C) represents last 3.3 km of the model, (D) represents scenario 2.
Figure 8. Scenarios shoreline prediction in 2021, 2030, 2040 divided into two parts 3300 m for each shown as (A) represents first 3.3 km, (B) represents scenario 1 and (C) represents last 3.3 km of the model, (D) represents scenario 2.
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Sustainability 17 11174 g009
Figure 9. Calibration process for different sediment transport formulas at (A) Profile 1, (B) Profile 2, and (C) Profile 3.
Figure 9. Calibration process for different sediment transport formulas at (A) Profile 1, (B) Profile 2, and (C) Profile 3.
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Sustainability 17 11174 g010aSustainability 17 11174 g010b
Figure 10. Morphology changes and current velocity for (A) No-action, (B) Alternative 1, (C) Alternative 2, (D) Alternative 3, (E) Alternative 4, (F) Alternative 5 in 2023.
Figure 10. Morphology changes and current velocity for (A) No-action, (B) Alternative 1, (C) Alternative 2, (D) Alternative 3, (E) Alternative 4, (F) Alternative 5 in 2023.
Sustainability 17 11174 g010aSustainability 17 11174 g010b
Sustainability 17 11174 g011
Figure 11. Bed elevation changes resulting from various alternatives for (A) Profile 1, (B) Profile 2, (C) Profile 3, (D) Profile 4.
Figure 11. Bed elevation changes resulting from various alternatives for (A) Profile 1, (B) Profile 2, (C) Profile 3, (D) Profile 4.
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Sustainability 17 11174 g012
Figure 12. Bed elevation changes resulting from various alternatives for (A) Profile 5, (B) Profile 6, (C) Profile 7.
Figure 12. Bed elevation changes resulting from various alternatives for (A) Profile 5, (B) Profile 6, (C) Profile 7.
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Table 1. Sediment characteristics along the [29].
Table 1. Sediment characteristics along the [29].
Depth (m)d50Roughness of SeabedFall Velocity (m/s)Spreading Factor ( δ g )   =   1.5 d 50
+2: 00.8120.0120.0850.901
0: −20.4060.0080.0520.637
−2: −40.1340.0040.0130.366
−4: −60.0250.0040.0010.158
−6: −80.0070.0040.00004230.084
Table 2. Results of a one-year simulation of 5-Alternatives process’s quantitative analysis of the active accumulative sediment quantities.
Table 2. Results of a one-year simulation of 5-Alternatives process’s quantitative analysis of the active accumulative sediment quantities.
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Accumulated sediment Volume of the active computed domainVin (m3)Vout (m3)Vnet (m3)
Benchmark+8.83298−244,471−244,462
Alternative 1+4.37523−245,318−245,314
Alternative 2+1.18867−236,674−236,673
Alternative 3+16.6677−243,654−243,637
Alternative 4+1.16198−236,263−236,262
Alternative 5+4.87119−235,797−235,792
Sustainability 17 11174 i002
Accumulated sediment Volume of the active computed domainVin (m3)Vout (m3)Vnet (m3)
Benchmark+18,247−165,427−147,180
Alternative 1+16,306.6−83,798−67,491.4
Alternative 2+67,428.1−66,823.7+604.457
Alternative 3+37,572.5−29,605.4+7967.1
Alternative 4+32,405.6−39,076.6−6670.95
Alternative 5+66,417.8−64,935.7+1482.12
Sustainability 17 11174 i003
Accumulated sediment Volume of the active computed domainVin (m3)Vout (m3)Vnet (m3)
Benchmark+84,078.6−23,645.8+60,432.8
Alternative 1+58,085.3−37,001.8+21,083.5
Alternative 2+43,301.1−47,954.3−4653.18
Alternative 3+51,847.9−66,818−14,970.1
Alternative 4+55,184.1−65,554.5−10,370.3
Alternative 5+42,007−48,424.7−6417.66
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El-Asmar, H.M.; Elkotby, M.R.; Felfla, M.S.; Ragab, M.T. Integrated Predictive Modeling of Shoreline Dynamics and Sedimentation Mechanisms to Ensure Sustainability in Damietta Harbor, Egypt. Sustainability 2025, 17, 11174. https://doi.org/10.3390/su172411174

AMA Style

El-Asmar HM, Elkotby MR, Felfla MS, Ragab MT. Integrated Predictive Modeling of Shoreline Dynamics and Sedimentation Mechanisms to Ensure Sustainability in Damietta Harbor, Egypt. Sustainability. 2025; 17(24):11174. https://doi.org/10.3390/su172411174

Chicago/Turabian Style

El-Asmar, Hesham M., May R. Elkotby, Mahmoud Sh. Felfla, and Mariam T. Ragab. 2025. "Integrated Predictive Modeling of Shoreline Dynamics and Sedimentation Mechanisms to Ensure Sustainability in Damietta Harbor, Egypt" Sustainability 17, no. 24: 11174. https://doi.org/10.3390/su172411174

APA Style

El-Asmar, H. M., Elkotby, M. R., Felfla, M. S., & Ragab, M. T. (2025). Integrated Predictive Modeling of Shoreline Dynamics and Sedimentation Mechanisms to Ensure Sustainability in Damietta Harbor, Egypt. Sustainability, 17(24), 11174. https://doi.org/10.3390/su172411174

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