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. 2023 Jun 15;9(6):e17368. doi: 10.1016/j.heliyon.2023.e17368

Evaluating the long-term geomorphic process in-relation to hydrodynamics in the central coastal zone of Bangladesh

Tania Sultana a, Md Tariqul Islam a, MD Shajjadur Rahman b, Abu Bokkar Siddique b, ANM Samiul Huda b, Subrata Sarker b,∗
PMCID: PMC10333623  PMID: 37441399

Abstract

The central coast of Bangladesh is dynamic for its geographical location, hydrodynamic characteristics and residual flow. The research employed the Digital Shoreline Analysis System (DSAS), an ArcGIS extension tool, to conduct a historical trend analysis of shoreline. The study demonstrates that the central coast is eroding to the north and accreting to the south. The highest accretion value was found as 195.42 m/year, whereas the maximum value of erosion was estimated as −185.83 m/year, according to End Point Rate (EPR). The Linear Regression Rate (LRR) indicates that the average rate of erosion and accretion are −17.77 m/year and 17.88 m/year, respectively. Meanwhile, using Weighted Linear Regression (WLR), 0.48% of all transects demonstrated statistically significant erosion, while 0.43% showed statistically significant accretion. During the wet season, heavy river discharge leads to a low salt level in the ocean. Ocean currents hit central coast of Bangladesh from east to west, affecting the majority of the islands in the Meghna Estuary in the dry season. Changes in current directions can be seen during the wet seasons. Southern central coast areas are hit by south-east currents that split in two directions. The Sandwip Channel has a flow of 10,000 to 15,000 m³ s−1 northward. The Tetulia River, Shahbazpur Channel, and Hatia-Sandwip flow southward at rates ranging from 3000 to 17,000 m³ s−1, 14,000 to 60,000 m³ s−1, and 7000 to 39,000 m³ s−1, respectively. In the Meghna Estuary, the combined forces result in a counter-clockwise residual circulation, with the northward flow in the Sandwip channel and southbound flow in the Hatia and Shahbazpur channels. As a result of hydrodynamic, ocean currents, and residual flow, the Central Coast of Bangladesh is continually changing in appearance.

Keywords: DSAS, Shoreline, Current, Estuary, Bangladesh

1. Introduction

The coastal zone is the most diverse and dynamic landscape on the Earth and serving as a buffer zone for both land and ocean processes [1]. Physical, chemical, biological, and anthropogenic geomorphic processes and coastal hydrodynamics (i.e., wind, wave, tide, current, temperature, salinity, density) influence coastal structure [2]. The coastal zone of Bangladesh is diverse terrain with human settlements, plains, hilly topography, mangrove forests, tidal flats, creeks, natural levees, and estuaries [3]. Depending on the geographical factors, the coastal zone is divided into three sections: (a) the eastern coast, (b) the central coast, and (c) the western coast [4]. Central coast of [5]Bangladesh is the most active shoreline, where the river Meghna delta connects with the coast [4]. The Meghna River is the main outlet of the Ganges-Brahmaputra-Meghna (GBM) River system, and at its lower reaches, the river forms the largest estuary of Bangladesh [6,7].

The dynamic central coastal zone is characterized by shoreline changes with higher erosion and accretion rate [8,9]. The GBM floodplain encompasses 80% of the total of Bangladesh forming three major fluvial systems, and an estimated more than one billion tonnes of silt are transported to the delta region each year by the GBM river system [9,10]. Bathymetry, tides, and outflow from the Meghna River are the major driving forces for the flow in the estuary [11].

Several studies have been conducted to assess the morphological changes in different coastal zones of Bangladesh. Some of them were for modeling land susceptibility to erosion [9], whereas some were on shoreline changes, sedimentation, salinity intrusion, and impact of sea-level rise [6,8,[12], [13], [14]]. Wide-ranging hydro-geomorphological characteristics of some coastal parts were also studied where sedimentation and coastal plain development were assessed [15]. In addition, many studies were focused on land cover-land use change analyses of coastal parts based on satellite images [3,16]. As Bangladesh is highly susceptible to climate change impact due to low elevation coastal land, continuous research has been conducted to assess coastal accretion, erosion, climate change adaptation, and disaster risk management [11,17]. Much of the research on shoreline change, Land Use Land Cover (LULC) change, and climate change has a significant positive impact on understanding and the management of the dynamic of coastal zones of Bangladesh. However, research was hardly found on the assessment of long-term geomorphic changes and hydrodynamics of the coastal region of Bangladesh by combining satellite images, field data, and other hydrodynamic data. Against this backdrop, our study focused on understanding the geo-morphological changes, hydrodynamics and to explore the relationship between the hydrodynamics and geo-morphology of the central coast of Bangladesh.

2. Materials and methods

2.1. Study area

The present study focuses on the Meghna River estuary in the south-central coastal zone. (Fig. 1). This region is [18,19] comprised of three coastal districts: Bhola, Noakhali, and Feni. It is the most dynamic coast of Bangladesh for its geographical location on the mighty Ganges-Brahmaputra-Meghna River systems with constant erosion-accretion processes [1].

Fig. 1.

Fig. 1

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Geographical location of the study area.

The Meghna estuary has seen significant morphological changes over the previous two centuries due to the migration and expansion of islands in the southern direction. The Meghna estuary has a largest tidal range of 5 m, which gradually declines south-eastward along the Chittagong coast; this zone also has a significant sediment load and one of the most complicated tropical estuarine ecosystems on the planet [20]. Natural and human-made factors alter the coasts in dramatic ways. Integral management and long-term coastal zone development require a strong understanding of coastal morphology changes [21].

2.2. Primary data collection

Vertical data on temperature, salinity, oxygen concentration, density, and chlorophyll from each sampling location were recorded in-situ using a CTD (Conductivity, Temperature and Depth) (CTD 90M | Sea & Sun Technology). Micronutrient analysis was performed on water collected from the surface (i.e., silicate, Nitrate, and phosphate). For measuring the concentrations of silicate, phosphate, and Nitrate in the surface water of the Meghna River estuary, Molybdosilicate, semi-automated colorimetry, and the Automated Hydrazine Reduction Method were employed respectively. The nutritional concentrations were measured using an automated analyzer (Model: EASYCHEM 200 of SYSTEA).

2.3. Secondary data collection

Google Earth Pro was used to analyse historical satellite images of the shoreline for this study. Between 1985 and 2021, Landsat satellite images (7/18/1985, 7/22/1991, 7/15/2001, 7/12/2011, 7/3/2021) were used to extract and digitize shoreline data. With date ranges, the “Imagery Date” is always an earlier date than the image collection date. This means no images have dates that are later than when the collection was established.

However, the geographical extent of the maps and images was not consistent, with parts of the study area missing in some maps and images, leading to temporal inconsistencies. The results were carefully interpreted when these inconsistencies were dealt with.

2.4. Shoreline change analysis

Traditionally, shorelines are divided into smaller segments, and transects are drawn at right angles to a master shoreline to model shoreline evolution and predict future changes [22]. The shoreline changes along this transect are calculated and plotted. Corrected and generalized multi-temporal shorelines of the central coast of Bangladesh were applied in a conventional transect-based analysis of shore-normal movement using an ArcView GIS Extension entitled Digital Shoreline Analysis System (DSAS) [23] created by the United States Geological Survey (USGS). Historic shoreline change analysis is introduced to the standard functionality of the ArcView GIS, and it consists of three main components that assist the user in setting up a baseline, generating orthogonal transects at a user-defined separation along the coast, and calculating average rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife). In this work, an arbitrary baseline that was roughly parallel to the shorelines was digitized, and a transect interval of 100 m was selected based on judgments made by Refs. [24,25], and [26]. The DSAS calculated the changes in the landward and seaward of the shoreline over time (Fig. 2).

Fig. 2.

Fig. 2

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A part of the Central Coast's multi-temporal shorelines, illustrating thematic from DSAS and Parcel-based analyses.

2.5. Sea surface temperature (SST), surface current, and wind velocity data

Monthly average of Global SST was downloaded from Physical Oceanography Distributed Active Archive Center (PODAAC) portal in netCDF format. Our area of interest (AOI) for the Bay of Bengal was afterwards extracted from the global dataset. For our AOI, we used the National Virtual Ocean Data System (NVODS) portal for monthly averages of surface current and wind data. Wind data is selected for the east-west vector component (U) of the wind as ZONAL WIND COMPONENT and the north-south vector component (V) of the wind as MERIDIONAL WIND COMPONENT. Two (U-current and V-current) directions are used to calculate the surface current value. There is a u-current and v-current for each of the vectors, X-component and Y-component, with the X-axis representing longitude and the Y-axis representing latitude [27].

From 2005 to 2021, the dry and wet seasons, respectively, were determined using monthly average data for November to February and March to June.

2.6. Software

The topographic maps and the generalization of extracted shorelines from Google Earth Pro were digitalized by ArcGIS 10.4.1 software, and the host program from ArcView GIS 3.2a named Digital Shoreline Analysis System (DSAS) was used as the host software for the extensions to analyse shoreline changes. R-Studio 4.0.4 was used to analyse and illustrate the primary data (temperature, salinity, DO, chlorophyll). The SST, surface current, and wind data were plotted using QGIS 3.22.12 software, which was also used to create the final surface current and wind map overlaid on the SST map.

3. Results

For modeling shoreline change along Bangladesh’s central coast, corrected and generalized multi-temporal shorelines were used in the traditional transect-based analysis of shore-normal movement utilizing an ArcView GIS plugin called Digital Shoreline Analysis System (DSAS) [28]. Perpendicular to the reference baseline, DSAS generates transects that cross the shoreline at a user-specified interval alongshore [29]. The shoreline of 1985 was used as a baseline for the analysis of shoreline changes. For each shoreline intersection in a transect, the distance between the baseline and each shoreline intersection is measured using DSAS, which also incorporates date information as well as positional uncertainty for each shoreline. Based on the comparison of shoreline locations over time, DSAS generates a variety of statistical change measures. Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression Rate are some of the measures used (WLR). In order to calculate the following change metrics:

Distance measurements:

  • •

    Shoreline Change Envelope (SCE)

  • •

    Net Shoreline Movement (NSM)

Statistics:

  • •

    End Point Rate (EPR)

  • •

    Linear Regression Rate (LRR)

  • •

    Weighted Linear Regression Rate (WLR)

3.1. DSAS summary transects

All rates reported are in meters/year, and distance values are in meters.

3.1.1. SCE (Shoreline Change Envelope, m)

The overall change in shoreline movement was calculated by taking account of all observable coastline places and the distances reported without respect to specific dates. The central coast encountered a maximum distance of 1978.54 m at transect ID no. 1172 and a minimum distance of 1.03 m at transect ID no. 1614. (Table 1).

Table 1.

Results of shoreline change envelope (SCE).

Total number of transects Average distance (m) Maximum distance (m) Maximum distance transects ID Minimum distance (m) Minimum distance transects ID
5324 699.63 1978.54 1172 1.03 1614
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3.1.2. NSM (Net Shoreline Movement, m)

On the central coast, 5324 transects were examined using DSAS with an average distance of 19.76 m. Negative distance signifies eroding along the central coast shoreline at 2620 transects out of 5324. In contrast, 2704 transects with positive distance indicating accreting were identified. Negative distance transects made up 49.21% of all transects, whereas positive distance transects accounted for 50.79%. The highest negative and positive distances were 1913.09 m and 1795 m, respectively (Table 2).

Table 2.

Results of net shoreline movement (NSM).

Total number of transects Average distance (m Percent of all transects that have a negative distance Maximum negative distance (m) Percent of all transects that have a positive distance Maximum positive distance (m)
5324 19.76 49.21% −1913.09 50.79% 1795
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3.2. Statistical summary of DSAS

3.2.1. EPR (End Point Rate, m/year)

The End Point Rate revealed that 49.21% of all transects were erosional, with statistically significant erosion occurring in 47.9% of the transects. On the other hand, transects of accretional transects accounted for 50.79% of all transects, with 49.85% had statistically significant accretion. Maximum erosion occurred at 185.83 m/year, with an average of 22.57 m/year, whereas maximum accretion occurred at 195.42 m/year, with an average of 22.45 m/year (Table 3).

Table 3.

Results of EPR, LRR and WLR.

Statistics Percentage/Number of erosional transects Percent of all transects that have statistically significant erosion Maximum value of erosion (m/year) Average of all erosional rates (m/year) Number of accretional transects Percent of all transects that have statistically significant accretion Maximum value of accretion (m/year) Average of all accretional rates (m/year)
End Point Rate 49.21% 47.9% −185.83 −22.57 50.79% 49.85% 195.42 22.45
Linear Regression Rate 46.75% 0.48% −89.52 −17.77 53.25% 0.43% 81.43 17.88
Weighted Linear Regression 2051 0.48% −89.52 −17.77 2336 0.43% 81.43 17.88
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*Total number of transects = 4387.

*Number of independent transects (EPR) = 600.

*Number of independent transects (LRR, WLR) = 174.

3.2.2. LRR (Linear Regression Rate, m/year)

According to LRR, 46.75% of transects were eroding, with 0.48% eroding statistically significantly. Transects with accretion account for 53.25% of all transects, with 0.43% having statistically significant accretion. The central coast experienced maximum erosion of 89.52 m/year, with an average of 17.77 m/year. In comparison, the maximum value of accretion was 81.43 m/year, with an average of 17.88 m/year (Table 3).

3.2.3. WLR (Weighted Linear Regression, m/year)

Weighted Linear Regression showed that 2051 out of 4387 transects were erosional, while 2336 were accretional. Moreover, 0.48% and 0.43% of transects, showed statistically significant erosion and accretion, respectively. Additionally, it indicated that the coast experienced maximum erosion of 89.52 m/year, with an average of 17.77 m/year, and maximum accretion of 81.43 m/year, with an average rate of accretion of 17.88 m/year (Table 3).

3.3. Analysis of coastline changes based on transects

Fig. 3 shows the erosion and accretion affected area along the central coast. Most of the upper (northern) section appears eroding, whereas the lower (southern) section appears accreting. Specific areas include Bhola Island, Hatiya, Nijhum Dwip, and Char Monpura. The coastal areas of the Bhola and Noakhali-Feni, closer to the active river mouths, have become increasingly influenced by the rivers. Significant river/estuary bank erosion was detected in these regions, although local accretion in a downriver direction was observed at The Bhola coastal zone was the section of the mainland coast that had the most extensive erosion.

Fig. 3.

Fig. 3

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Erosion and accretion along the Central Coastal zone of Bangladesh.

Even though accretion occurs in practically every section of the central coast, the chars of Noakhali are the most rapidly accreting. About 50% accretion occurred in Noakhali-Feni, the headland between the Feni River estuary, which was also found to have a fast accretion rate of 195.42 m/yr. The island of Urirchar, Swarna Dweep, Bhashan Char, and Sandwip had a higher accretion rate, and each had a different setting from the other areas. Southward erosion and northward accretion were seen in these areas.

3.4. Coastal classification according to erosion accretion rate

The shoreline movement signature over time was represented (Fig. 4) as a series of change values obtained sequentially by DSAS for all periods at a transect location. Classes were manually selected from the dendrogram by studying the distance measure to locate groups of coastal areas with comparable erosion-accretion patterns throughout the years. The periodic erosion-accretion processes on the Central Coast have resulted in a wide classification of the region into six categories: (1) Very high erosion, (2) High erosion, (3) Moderate erosion, (4) Moderate accretion, (5) High accretion, and (6) Very High accretion.

Fig. 4.

Fig. 4

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Classification of Central Coast according to erosion and accretion.

A combination of erosion and accretion can be seen along the central coast of the study area on the map (Fig. 5). Coastline migration rates from 1985 to 2021 showed the northward movement eroding and southward movement accumulating over this period. Shoreline data analysis using GIS reveals a complex pattern of localized and periodic erosion and accretion along the Central Coast.

Fig. 5.

Fig. 5

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The rates of shoreline displacement along the Central Coast at 100 m intervals over a period of time (1985–2021).

3.5. Ocean hydrodynamics

3.5.1. Ocean current

During the dry season (November–February), the Bay of Bengal showed a similar pattern of wind direction and surface current from north to south (Fig. 6). The sea surface temperature (SST) was low (19 °C) near the estuary and gradually rises as the surface is influenced by wind and current. The surface current direction in Fig. 6 showed that the majority of the islands in the Meghna Estuary were being impacted on the northern side (i.e. Bhola, Hatiya, Nijhum dwip). DSAS analysis showed changes in islands in the eastern part of the estuary (Noakhali, Urirchar, Swarna Dweep, Bhashan Char, and Sandwip). Based on the direction of the currents, these regions appeared to be less influenced by the ocean current.

Fig. 6.

Fig. 6

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Direction of ocean surface current and wind direction overlaid on SST in dry season. The white rectangle focuses on the study area (Data courtesy: SST from PODAAC portal and surface current and wind velocity from NVODS).

Changes in current directions and wind pattern were seen during the wet seasons (March–June) (Fig. 7). The wind direction observed from ocean to landward, modifying surface current by dividing northwest and northeast. A clockwise surface current circulation and a high temperature (30 °C) were found in the Bay of Bengal. The high river discharge during the wet season was observed to have an effect on the SST.

Fig. 7.

Fig. 7

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Direction of ocean surface current and wind direction overlaid on SST in wet season. The white rectangle focuses on the study area (Data courtesy: SST from PODAAC portal and surface current and wind velocity from NVODS).

3.5.2. Residual flow

Table 4 shows the discharges from the Tetulia River, the Shahbazpur Channel, the Hatia-Sandwip cross-section, and the Sandwip Channel. Based on the Dry season data, with only a little river discharge, it is found that the Sandwip Channel’s northward flow is mostly influenced by a mix of tide and bathymetry, i.e. it is not significantly influenced by river discharge. The Sandwip Channel, on the other hand, has a stronger northward flow during the rainy season than in the dry season. The Sandwip Channel has a northward flow of 10,000 to 15,000 m³ s−1. Tetulia River, Shahbazpur Channel, Hatia-Sandwip have southward flow of 3000 to 17,000 m³ s−1, 14,000 to 60,000 m³ s−1 and 7000 to 39,000 m³ s−1 respectively.

Table 4.

Net discharges (positive towards the sea) through Tetulia River, Shahbazpur Channel, Hatia-Sandwip cross-section and Sandwip Channel.

Tetulia (m³ s−1) Shahbazpur (m³ s−1) Hatia-Sandwip (m³ s−1) Sandwip (m³ s−1) Total (m³ s−1)
Dry season 3288 14,307 7425 −10 538 14,482
Wet season 9321 33,677 18,126 - 14,761 46,363
Dry (high) 17,348 58,681 39,532 - 13,633 101,964
Dry (low) 3625 16,845 7502 - 15,841 12,131
Wet (high) 15,375 60,156 31,892 2642 110,065
Wet (low) 16,017 59,134 19,850 2272 96,773
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Source: Ministry of Water Resources of Bangladesh.

To some extent, the residual circulation confines river water within the Meghna Estuary and therefore extends residence time, which is one of the reasons for the estuary’s relatively low salinity even during the dry season. Thus, if the residual flow is terminated, the low saline water masses discharging into the estuary, which also transport sediment into the estuary, will redirect and transit through the estuary in a considerably shorter southward direction.

4. Discussion

The shoreline of central coast was observed to be changing dramatically over time (Fig. 3). According to a DSAS analysis, most of the central coast is experiencing erosion and accretion. Bhola Island, Hatiya, Nijhum Dweep, and Char Monpura are examples of regions where the upper (northern) section appears to be eroding, and the lower (southern) half appears to be accreting (Fig. 4).

Bhola coastal zone was the most eroded area [30] of the mainland coast, with a maximum value of erosion of −185.83 m/year. Most of the total accretion occurred in Noakhali-Feni, on the headland between the Feni River estuary and the main rivers estuaries, where the most rapid rates of accretion exceeded 195.42 m/yr. Southward erosion and northward accretion were detected on the eastern part of the central coast which includes island of Urirchar, Swarna Dweep, Bhashan Char, and Sandwip. Both in the dry season (Fig. 6) and the wet season (Fig. 7), river flow was driven towards this eastern part of the central coast by the influence of wind and surface current. The residual flow of the Tetulia River, Shahbazpur Channel, and Hatia-Sandwip cross-section (Table 4) was found positive both in the dry and rainy seasons, and this can be referred to the cause of the erosional activity of the central coast.

Central coast hydrodynamic models suggest an anticyclonic circulation with a northward flow in the Sandwip Channel and a southern flow in the Tetulia River and the area between Hatia and Sandwip. Similar observations were reported by Ref. [31] that sediments that enter into Hatiya channel from river discharge are trapped by the counter-clockwise circulation before settling or being transferred out of the Meghna estuary.

The Sandwip Channel has a northward flow of 10,000 to 15,000 m³ s−1 (Table 4). The flow is equal to the river discharge during the dry season and one-third to one-sixth of the river discharge during the rainy season. The circulation has an effect on the salinity and morphological conditions in the area, although the effect is unknown.

The effects of SST, surface current, and wind on the central coast are significant [32]. Warmer SST in the wet season is associated with anticyclonic circulation and downwelling, while a weaker surface current is associated with local wind forcing [33]. All the forces combined have less influence on residual flow, which is the cause of northward erosion and southward accretion of the central coast.

To a certain extent, residual circulation holds river water within the Meghna Estuary, extending residence duration and contributing to the estuary’s comparatively low salinity even during the dry season [34]. If the residual flow is interrupted, low salinity water masses discharging into the estuary-which also carry sediment input-will redirect and pass the estuary in a considerably shorter southerly direction [35].

In-situ data were plotted to better understand the hydrodynamics of the central coast (Fig. 8). During the dry season, vertical salinity distribution was observed higher in the river mouth than in the wet season (Fig. 8). Very low salinity (<1) was detected in wet season as the rivers carry heavy flow in rainy season from the upstream [36]. Temperature showed the reverse scenery than salinity for both seasons. Higher temperature (>27 °C) was observed at the river mouth in dry season. While temperature was observed less impacted in wet season.

Fig. 8.

Fig. 8

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Vertical distributions of salinity, temperature, Phosphate concentrations, Nitrate Concentrations, Silicate concentrations and Total Suspended Matter (TSM) concentration.

The concentrations of phosphate, Nitrate, and silicate were measured from ocean surface water and interpolated. Phosphate concentrations were found to be less than 1.4 mg/L in the dry season and increased above 1.6 mg/L at the river mouth in the wet season. Nitrate concentration was observed to be high (1.800 mg/L) at the bottom of the river in the dry season, and it increased (1.825 mg/L) more in the wet season (Fig. 8). Like Nitrate, similar conditions were observed in silicate concentration, 5.775 mg/L in the dry season and 5.825 mg/L in the wet season (Fig. 8). As the phosphate concentration increased during the rainy season, this indicates that the monsoon flow has an effect on it. Additionally, the river’s bed flow has an effect on the concentrations of silicate and Nitrate.

Total Suspended Matter (TSM) was measured at 150 mg/L near the ocean in the dry season and 200 mg/L at the mouth. The highest TSM concentration, more than 300 mg/L measured in the wet season (Fig. 8). During the wet season, the estuary flows more sediments, and the mouth holds more sediments than the ocean. As ocean currents reorganize the sediments, the concentration of sediments decreases.

According to a study conducted on some central coast islands, increased salinity is associated with a high accretion rate [37]. In our study, islands in the eastern part of the coast (Urirchar, Swarna Dweep, Bhashan Char, and Sandwip) have similar characteristics. Accretion is associated with dissolved nutrients and low wave energy since they promote organic carbon storage capacity and the biotic system in the sediment [38]. The concentration of TSM has a significant impact on the biochemical properties of the ocean and has a direct relationship with coastal erosion and accretion [39]. In wet season TSM concentration was observed the highest (300 mg/L), which influenced by the Meghna River system [28].

5. Conclusion

Over fifty percent of the world’s population now resides within 60 km of a coastline; by 2025, this percentage is expected to have increased to more than 75%. The coasts are extremely dynamic, undergoing substantial changes as a result of both natural and man-made impacts. A thorough understanding of coastal morphological changes is critical for integrated coastal management and long-term coastal development. The Meghna Estuary is a coastal plain estuary off the coast of Bangladesh. Bathymetry, tides, and outflow from the Meghna River are key driving forces for flow in the Estuary [40].

The relation between central coast hydrodynamics and morphology is revealed in this study. The central coast is shaped in a unique way by the Meghna River’s residual flow, ocean currents, and tides. Bhola island was mostly eroded (−185.83 m/year) whereas the Sandwip island was accreting (195.42 m/yr) and the direction of erosion and accretion are completely different.

Nutrient analysis from the water sample reveals that the concentration of the nutrient (i.e. silicate, nitrate, phosphate) increases with the increased flow of river in wet season. 5.825 mg/L, 1.825 mg/L and 1.6 mg/L respectively. During the dry season, TSM was 150 mg/L near the ocean. During the rainy season, TSM concentrations exceed 300 mg/L. During the rainy season, the estuary flows more sediments than the ocean. Sediment concentration drops when ocean currents reorganize sediments.

The total land area of the coastal zone changed very little despite fast erosion and accretion (a net land gain of 8.66 km2). Overall, the coast accreted 315.5 km2 and was eroded 306.8 km2 [8]

The Sandwip Channel has a northward flow of 10,000 to 15,000 m³ s−1. Tetulia River, Shahbazpur Channel, Hatia-Sandwip have southward flow of 3000 to 17,000 m³ s−1, 14,000 to 60,000 m³ s−1 and 7000 to 39,000 m³ s−1 respectively. And these residual flows cause southward accretion of the central coast. In both the dry and wet seasons, the ocean currents flow in various directions. During the dry season, currents move in a circular pattern from east to west, accelerating erosion on the northern area of the shore.

Author contribution statement

Tania Sultana: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Md. Tariqul Islam; Subrata Sarker: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

MD Shajjadur Rahman: Contributed reagents, materials, analysis tools or data. Wrote the paper.

Abu Bokkar Siddique: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

A.N.M. Samiul Huda: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Data availability statement

Data will be made available on request.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Acknowledgement

The authors acknowledge Ministry of Science and Technology, government of republic Bangladesh; NANO-DOAP Global project (https://nf-pogo-alumni.org/projects/global/) and University Research Center, Shahjalal University of Science and Technology (grant no: 2021/PS/009) for proving fund to conduct this study.

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