The investigation was carried out at the Buri Goalini, Munshigonj and Gabura Unions under the Shyamnagar Upazila of Satkhira District, situated between 22°36′ and 22°24′ north latitudes and between 89°00′ and 89°19′ east longitudes (BBS, 2011). The 2011 Bangladesh census identifies a population base of 318,254 in Shyamnagar alone whilst 10% of the nationwide population, 14 million people, reside in the South-western coast (Szabo et al., 2016; Efretuei 2016; ibid). The main rivers of the region are the Kobadak, Sonai, Kholpatua, Morischap, Raimangal, Hariabhanga, Ichamati, Betrabati, Kalindi and Jamuna. Usual rainfall is 1, 68 mm with a day-to-day temperature fluctuating from 21 to 30°C. The yearly comparative moisture fluctuates between 7 and 80%. The standard deviation of annual precipitation around this expanse differs from 334.0 to 586.3 mm, (Kabir and Golder 2017) previously reported average maximum and minimum temperatures of 27.7 and 15.6°C during the dry season (November–February), 33.2 and 23.3°C during pre-monsoon season (March–May), and 31.7 and 25.5°C during monsoon season (June–October) for the South-western region of Bangladesh (Mukul et al., 2019).

The investigated region displays even topography, where the maximum expanse remains within 1 m from sea level. The soils are classified as histosols and are described as having grey, slightly calcareous, loamy soils on river banks and grey or dark grey, non-calcareous clays, mainly containing silts, in the extensive land area across the water bodies (Islam et al., 2012; Gorji et al., 2019; ibid). Histosols form when organic matter is generated fast than it is decomposed, this often occurs in areas prone to flooding, but not freezing, where there is poor drainage (Brady and Weil 2008a). Saline and exceedingly saline zones enclose approximately 3336.67 and 241.4 ha, corresponding to 41.46 and 36.6% of all the land of the study region (SRDI, 2012). The pH level usually varies from 5.4 to 7.44 (Islam et al., 2017; Shaibur et al., 2017).

Generally, Shyamnagar Upazila soils are highly dominated by silty clay and silty-clay loam texture (fine-textured and plastic in nature) where the presence of clay particles is much (Shaibur et al., 2017). Clay particles are mainly negatively charged which attract and adsorb positively charged particles to their surface (ibid) Also, lateral movement of water occurs in clay textured soil and that’s why waterlogging conditions develop. When these waterlogging conditions are saline, soil salinity develops (Naher et al., 2011). Cations responsible for salt production, such as Ca²⁺, K⁺, Na⁺, and Mg²⁺, in the soil, get adsorbed by clay particles. Islam et al. (2012) stated that the salinity level of the soil in Shyamnagar Upazila varies from moderate to high. Cation exchange capacity (CEC) of clay or clay loam type soils lie within 15–30 meq/100 g soil or above (Penn and Camberato 2019).

Naher et al. (2011) found that the cation exchange capacity (CEC) of the Shyamnagar Upazila soil varies from 12.0 to 27.6 meq/100 g soil. Also, Shaibur et al. depicted that the status of bicarbonate (HCO³⁻), sodium (Na), magnesium (Mg) and sulfur (S) were within 366–793 ppm, 7.50–13.50 ppm, 5.11–6.01 meq/100 g soil and 264–431 ppm respectively (Shaibur et al., 2017). Bangladesh’s coastal zones have undergone key alterations around the previous five decades, mainly due to recurrent and varied natural catastrophes with direct and indirect bearings on land assets and their various uses (Rasel et al., 2013). The land is despoiled and disappearing due to the impact of increasing salinity, flooding of low-lying swampy land, deluges, and land attrition due to involuntary and chaotic land exploitation by the inhabitants. This intense variation in land exploitation and alteration of the agricultural system has impeded normal crop production during the year (ibid).

The three most vulnerable unions, Gabura, Burigoalini and Munshiganj located in the Shyamnagar Upazila were the focus areas of this study (Figure 1B). A total of 18 soil samples and 29 water samples were collected in these areas. Data was collected during rainy season in Bangladesh besides the rainy season in Bangladesh coincides with the summer monsoon season (June to mid-October), this season’s rainfall accounts for 75–80% of the total rainfall the country’s annual rainfall (Ahmed and Kim 2003).

Soil and water samples were collected between the 10th and 13th of July 2019 at the three unions. Samples were collected in bottles that were shaken overnight with 20% nitric acid and rinsed with deionized water to remove internal and external contaminants. Soil samples were collected using a manual auger at a depth of 10–15 cm deep (Soil Survey Staff, 2014).

Eighteen soil samples were collected within these areas from agricultural fields, ponds, riverbanks, and shrimp ghers. The coordinates and land use information for the soil samples are displayed in Table 1 and the water samples are displayed in Table 2. Photographs of the sites are also shown in Figures 2, 3 for soil and water samples respectively.

After the samples were collected, all samples were sent to the laboratory of the Soil Resources Development Institute (SRDI) within 21 h and kept in a refrigerator at a temperature below four degrees Celsius (Soil Survey Staff, 2014). Analysis of the soil and water samples was conducted from July 14 to August 21, 2019, at the SRDI. Soil samples were analyzed for pH, electrical conductivity (EC), calcium (Ca), organic matter (OM), total nitrogen (TN), potassium (K) and phosphorous (P). Water samples were analyzed for pH, EC, total dissolved solids (TDS), sodium (Na), bicarbonate (HCO₃) and chloride (Cl).

Twenty-nine water samples were also collected in these areas from tube wells, pond sand filters, and rainwater harvesting tanks. The coordinates and land use information for the soil samples are displayed in Table 2 and photographs of the collection sites are shown in Figure 3 below.

The major chemical constituents of soil and water and their quality factors were analysed using standard methodologies. Standard saturation paste method was used to determine the ECe where 350 g air-dried soil was taken for each sample to prepare the saturated paste, left it in room temperature for 24 h for equilibrium then the saturated paste extracts were collected by subsequently using Buchner funnel and applying suction (Rhoades, 1996; McBratney et al., 2014; Soil Survey Staff, 2014). In this research ECe soil sample was measured in saturated paste as ECe, dS/m at 25°C and for the EC water sample was measured as EC, dS/m at 25°C respectively. The EC was measured by means of the Jenway EC meter from the extracts (ibid). pH was measured using the Jenway pH metre as described by Tan (2005). The salt content (Total Dissolved Solids) was calculated using the following formula (Sparks 2003): Total Dissolved Solids ( TDS ) %   = 0.064 * EC

The Olsen Sodium Bicarbonate test was used to determine soil phosphorus. Soil potassium and sodium were determined separately by flame emission spectrophotometer (Jenway Model: PEP-7), using potassium and sodium filters, respectively, as outlined by Jackson (1973). Chloride and bicarbonate contents were determined by the titrimetric method described by Jackson (1973). The sample was determined by the Kjeldahl method described by Bremner (2009) and organic carbon was analysed using Walkley and Black (1934) wet oxidation method (Ghosh et al., 1983; ibid).

Additionally, historical data for electrical conductivity (EC) and chloride concentration was obtained from the Bangladesh Water Development Board (BWDB) for the South-western coastal region with data ranging from 1968 to 2019. Data from the BWDB was available from 3 stations: Kalaroa (SW 23; 2001–2018), Elarchar (SW 254.5; 2001–2018), Benarpota (SW 24; 1980–2018). The EC and chloride data from the BWDB was averaged on yearly basis for low and high-water levels. This data was plotted in Microsoft Office Excel 16 as the levels of chloride and EC at high and low tide over time in years.

The results of the soil and water samples were analysed using Pearson’s correlation coefficient using the computer software IBM SPSS 25. Pearson’s correlation coefficient is a linear correlation model using two sets of data that produces a value, r, by accounting for the covariance and standard deviations within the data sets. The r vale produced indicts how highly one dataset is correlated or influenced by another with high values being more significant. The standard Pearson’s r is calculated as follows: r =   Σ ( x i − x ¯ ) ( y i − y ¯ ) Σ ( x i − x ¯ ) 2 Σ ( y i −   y ¯ ) 2

Standard deviation and other general calculations were also conducted. A standard linear regression, using historical data, was created to determine the R² value for chloride and EC at low and high tide for the Kalaroa, Elarchar, and Benarpota stations. This was done to identify variables that correlate with each other to find statistical patterns that might reveal evidence of the physical mechanism underlying them. Most calculations were conducted in Microsoft Excel 16 unless otherwise stated.

To calculate the areas affected by different seawater surges, three different storm surge heights were used, 1.5, 5.25, and 9 m as the minimum, mean and maximum surge height (Huq and Shoaib, 2013). A 90-m resolution Digital Terrain Model retrieved from (Jarvis et al., 2008) with a 1-m resolution altitude data were used. However, due to the 1-m altitude resolution of our data, the numbers were rounded to 1, 5, and 9 all of the areas within 1-m, 5-m and 9-m of mean sea level in the DEM were used to estimate the respective affected areas. The 1-m sea-level change was calculated using the same process, 1, 5, and 9-m surge 1-m above the current sea levels, similar to the methodology used in the study by Karim and Mimura (2008). This 1-m sea-level rise projection were taken from (ibid) where a study puts the sea-level change in 2100 between 30 and 100 cm, as well as the IPCC’s RCP8.5 estimating a rise of 52–98 cm in sea level by 2100, based on Mukul et al. (2019). Due to the constrained nature of the data resolution, a 1-m sea-level rise was used. Then, each affected area was counted by converting the raster DEM pixels into a vectorised matrix and selecting the pixels with the desired elevation values of 1 m, the sum of pixels from 1 to 5-m for the 5-m surge, and the sum of the pixels from 1 to 9 for the 9-m surge scenarios (Jarvis et al., 2008). This same technique was used with the 1-m sea-level rise scenario but taking the values from 1 m above, so by taking the pixel count from 2 m for the 1-m surge, the sum of 2–6 pixels for the 5-m surge, and the sum of 2–10 pixels for the 9-m surge. The pixel count then was multiplied by 8100 to calculate the area in square metres, and then divided by 1,000,000 to get the square kilometres (Huq and Shoaib, 2013; ibid).

The results of the soil analysis are displayed in Table 3 above.

ECe values of the soil samples are shown in Table 3, where the electrical conductivity of the soil samples varies between 1 and 32.7 dS/m. Again, the above results show high salinity statuses for all three respective sampling areas with more than 95% of the samples havilo1ng an ECe dS/m value higher than 8 dS/m across all the sampling areas. Soil salinity is often based on the direct measure of the electrical conductivity, soils with an ECe above 4 dS/m of which most of the samples were; furthermore some scientists have recommended that soils with an ECe above 2 dS/m be classified as saline as most crops will be harmed by salinity above ECe 2 dS/m (Ghosh et al., 1983; Sparks 2003; Brady and Weil 2008b). This will also have a large impact on agricultural production as only some salt-tolerant varieties of crops may produce a satisfactory yield in these areas (Alam et al., 2017; Van Tan and Thanh, 2021).

The highest pH values found for Gabura, Burigoalini and Munshiganj were 8.8, 6.8, and 8.4 whilst the lowest pH values were 6.5, 6.6, and 5.1, respectively. The results indicate that most of the areas of Gabura and Burigoalini have higher soil pH levels (mildly to strongly alkaline) as opposed to the neutral range (Ahmad and Rahman 2010). However, lower pH values were found in the Munshiganj site soils. The results confirm that agricultural and livestock production in Gabura and Burigaolini has been seriously affected due to the high pH levels (Alam et al., 2017). Salinity is known to have an impact on the pH of a soil, as salinity increases, pH decreases, in other words, the soil becomes more acidic. This serves to explain the lower pH soils having high salinity or TDS (Ghosh et al., 1983, Sparks, 2003).

Organic Matter (OM), nitrogen (N) and potassium (K) showed statistical significance in their sample abundance (Table 4). Most of the samples displayed a potassium concentration that exceeded the recommended 2.0 m_eq/100 g range and could be detrimental to certain species. The reason for this high levels of K could be due to the alternating wetting and drying of the soils that can be brought about during extreme weather events, it is known that alternating wetting and drying periods enhances the K fixation in the soil, making it more available (Weil and Brady, 2017). Above this threshold, luxurious potassium intake in plants, retains elevated levels causing slow plant growth, furthermore excess K is toxic for human and animal consumption (He and Chen 2013; Alam et al., 2017). The abundance in total nitrogen (TN) and organic matter was observed for all the samples in all study areas (Xue and An 2018). Inundated soil is rich in organic matter and nitrogen content, which explains the proportionally higher rates of these two variables, especially in water-filled soils such as ponds and ghers (Fulton et al., 2011). External organic and non-organic additions may change the ratio of total nitrogen and available nitrogen, as nitrogen may be unavailable to plants in certain compounds (Bingham and Cotrufo 2015). The deficiency of phosphorous (P) in the soil samples is visible in all of the study areas, this is to be expected as high organic matter in soils leads to lower levels of phosphate fixation (Weil and Brady, 2017). However, there is a higher concentration of phosphorous in shrimp ghers as compared to agricultural lands, likely due to biological excretions by the shrimp. This is a problem, as the phosphorous concentrations are too low for agricultural production, which has the potential to lead to phosphorus deficiency in plants, resulting in the need to use phosphorous fertilizers (Lawlor et al., 2004; Sharma et al., 2013). The low phosphorous in these soils is likely due to the high level of organic matter in the soil which has likely arisen due to the nature of heavy flooding in the area, soils that are not aerated tend to have higher OM contents as less decomposition occurs, this in turn also reduces the soils P fixation capacity (Weil and Brady, 2017). Similarly, calcium (Ca) concentrations in all the samples are way above the recommended 5–10 m_eq/100 g soil (Van Tan and Thanh, 2021). This excess of calcium inhibits seed germination and reduces growth rates dramatically (Lawlor et al., 2004). A statistical correlation was conducted between the soil nutrients with soil pH, electrical conductivity (EC) and organic matter (OM) was conducted and the results are shown in Table 4.

The Pearson’s correlation coefficient study in Table 4 showed a significant positive correlation between ECe dS/m and OM (0.555*), OM and TN (0.752**) and OM and K (0.560*). Other correlations were found to be nonsignificant, indicating no influence between the parameters. This correlation study illustrated that organic matter concentration is positively correlated with electrical conductivity (EC), total nitrogen (TN) and potassium (K) and vice versa. The correlation with TN is expected as OM is essentially dead plant and animal material which tend to be mainly made of carbon, nitrogen, phosphorous, and hydrogen. Similarly the correlation between OM and K is expected as an increase in OM increase the CEC of soils, making K fixation higher (Weil and Brady, 2017). The correlation between OM and ECe dS/m is interesting as an increase in ECe dS/m directly reduces the decomposition of OM in the soil (Iranmanesh and Sadeghi 2019). A decrease in OM decomposition will also result in a decrease of available P which further explains the results in Table 3. In summary, the soils in the area are high in OM due to the nature of flooding in the area; the flooding in the area also increases the overall TDS and in turn ECe dS/m of the soil. The increase in ECe dS/m in the soil will decrease OM decomposition which will further decrease P availability (Ghosh et al., 1983). This will continue in cycles with every extreme weather event.

Further comparisons were conducted between pH and phosphorous (P) and are graphically displayed in Figure 4 below.

Comparing phosphorous content with pH shows that at a pH of 7.0–8.3, samples had P content greater than 12 ppm (Jackson, 1973). At higher and lower pH values (pH > 8.3; pH < 7.0) the P content of the samples was mostly below 10 ppm. However, no significant correlation exists between these two variables, indicating either the absence of coupled physical processes. In soil, both acidic and alkaline conditions increase phosphorus concentrations, which explains their relatively low abundance outside the neutral pH range. Thus, maximum P availability occurs at near-neutral pH, confirming the findings of Penn and Camberato (2019). However, the input of synthetic phosphorous in agriculture and shrimp farms may skew the results, so no statistically significant relationship can be found. This means that if pH was to fluctuate above or below a neutral pH, it will rapidly be threatened phosphorous levels.

The results of the water analysis are displayed in Table 5 below where the EC values varies from 0.1 to 41.3 dS/m. The results show that most of the samples have exceeded the permissible limit of salinity in the sampling areas, particularly in river water (>30 dS/m) (Alam et al., 2017). Increased shrimp production using brackish water causes artificial salinity in the water and affects adjacent agricultural fields and drinking water reservoirs (Mahmuduzzaman et al., 2014; World Health Organization, 2017). Continuous use of saline water can be a great threat to human consumption and irrigation.

As seen above, G1–G4 had much higher Na values than compared to the other soil samples in the area. Furthermore, six samples from the Buri Goalini area and one from the Munshigani union had similar high Na concentrations. This is a concern as high Na levels can cause sodicity in soil which makes soil less permeable and more prone to being flooded (Sparks 2003). In turn, the soil in these flooded areas will develop higher organic matter concentrations (histosol formation).

Approximately 93% of the water samples showed a sodium (Na) concentration beyond the recommendation of 200 mg/L as well as chloride above 250 mg/L, these concentrations are unsuitable for drinking water (World Health Organization, 2017). As these two elements are associated with salt, the samples containing NaCl are much higher in TDS than the standard values (Tavakkoli et al., 2011; ibid). The results furthermore represent a direct connection between pH and bicarbonate (HCO³⁻). The higher the bicarbonate, the higher the pH (alkaline condition) (ibid). Most of the water samples from the Gabura and Burigoalini sites showed moderately to highly alkaline conditions exceeding the permissible limit for human consumption (Islam et al., 2017). Saline water can also corrode metal pipes of supply water and increase metal concentration in drinking water which can pose a further threat to human health. Among the water samples, the concentration of bicarbonate (HCO^3-) was highest at 384.3 mg/L for sample B-15 and lowest at 30.5 mg/L for sample B-11; despite this, none of the samples was above the standard limit of 600 mg/L (World Health Organization, 2017). This trend follows a similar relationship with pH, which was verified by the very strong statistical correlation in Table 6.

The Pearson’s correlation coefficient study of water parameters in Table 6 illustrated a highly significant and positive correlation between pH and bicarbonate (0.79**), EC and Na (0.99**), EC and Cl⁻ (0.91**) and Na and Cl⁻ (0.89**) (Tavakkoli et al., 2011). Other correlations were found to be non-significant. Overall, the correlation study of the study areas showed that in most cases, the concentrations of EC, Na, and Cl⁻ were highly and positively correlated with each other (Tavakkoli et al., 2011; Alam et al., 2017; Islam et al., 2017). On the other hand, pH showed a weak and negative correlation with EC and other parameter concentrations besides bicarbonate. The pH and bicarbonate show strong positive correlations, which are related to the bicarbonate buffer system (Rhoades, 1996). A Pearson correlation was conducted between different soil and water parameters.

The Pearson correlation coefficient study between the soil and water parameters depicted a significant negative correlation between the ECe dS/m of soil and the EC of water (−0.47*); the ECe dS/m of soil and the Na of water (−0.50*) and a positive correlation between the OM of soil and the Na of water (0.50*) (Akter et al., 2016; Alam et al., 2017). Other correlations, both positive and negative were found to be non-significant. Surprisingly, a negative but significant correlation is visible between the ECe dS/m of soil and the EC of water (−0.47*). This indicates that if water salinity increases, soil salinity decreases. This can be explained by the dissolution of salts from the soil into the water when it is waterlogged (Islam et al., 2017). Other nonsignificant correlations are visible between chloride, calcium and potassium, which are all responsible for the increase of the salinity in soil and water by producing calcium chloride (CaCl₂) and potassium chloride (KCl⁻) salts. However, these correlations are too weak to draw any conclusions.

Historical data was collected and analysed overtime to try to determine and/or predict future patterns and correlations. The relationship between chloride and EC at different tide levels at the Benaporta station is shown in Figure 5.

The Benaporta station shows a matching relationship between Cl⁻ and EC concentrations at low and hide tides, but Cl⁻ at high tide shows a more attenuated relationship (Ahmad and Rahman 2010). At the same time, there was a massive spike in chloride in 1982, with EC levels reaching over 10,000 (dS/m), a smaller spike is visible from 2006 to 2007 with salinity levels of >3,000 dS/m and chloride levels of >1,000 ppm, and another spike from 2009 to 2011. Thereafter, a more intense increase in both salinity levels of 3,000 to 4,000 dS/m and chloride concentrations of 1,000 to 2,000 ppm, from 2009 to 2011. This increase is followed by minor spikes from 2012 to 2018.

The relationship between chloride and EC at different tide levels at the Benaporta station is shown in Figure 6.

The Kalaroa station clearly shows a spike of both chloride and EC from 2001 to 2003, with a salinity level of >4,000 dS/m and a chloride concentration of >1,000 ppm at high tides. These levels are similar to the same spike at Benaporta station. From 2006 to 2011, there is a massive increase in salinity (Pal et al., 2016). The SIDR Disaster caused salinity intrusions in the south-western parts of Bangladesh–with levels fluctuating between 5,000 and 6,000 dS/m at high and low tides, and chlorine levels fluctuating between 2,000 and 3,000 ppm. These values are more than double the spike recorded between 2001 and 2005 and are higher than the same anomaly observed at Benaporta station. Finally, from 2013 to 2015 a minor spike can be seen, however, much weaker than the two others. Lastly, the relationship between chloride and EC at different tide levels at the Elarchar station is shown in Figure 7.

In Figure 7, a trend similar to that observed at the Kalaroa station is repeated at Elarchar, where an increase in chloride and EC is observed from 2001 to 2003 with a salinity level of >8,000 dS/m and a chloride concentration of >2,000 ppm at high tides. Surprisingly, these values are twice as high as those seen in Kalaroa. Along with this, a massive increase in salinity, with levels well above 12,000 dS/m at high and low tides, and chlorine levels above 6,000 ppm is recorded from 2007 to 2011 (SRDI, 2012; World Health Organization, 2017). These values are again twice as high as those recorded at the Kalaroa station. Table 7 reflects the correlations between the chloride concentrations at high tide between the three stations.

Every correlation is higher than R² = 0.2 except for the Benarpotas high tide with the Kalaroa’s high and low tide. This indicates that most data sets are affected quite similarly by the same environmental process. The same correlations were done using the EC data in Table 8.

All the correlations had values greater than R² = 0.2, indicating that these data values respond in a similar way to the same environmental process.

The correlation analysis from Table 7 shows the chloride concentrations of the three stations at low and high tide, except for the correlation between Benarpota’s high tide and Kalaroa high and low tide data. Similarly, Table 8 shows that all had a significant correlation above R² = 0.2. Both tables show correlations above R² = 0.5. The two most predominant spikes from 2001 to 2003 and 2006/7 to 2011 are therefore statistically supported by these correlations. This means that the physical and environmental processes behind these spikes are affecting trends in salinity levels of the Betna-Kholpetua River in a similar and fairly uniformly manner.

Figure 8 below displays two maps with projected impacts of potential storm surges at different distances inland in the Bay of Bengal. Figure 8A is at current sea level and Figure 8B is with a 1-m rise in sea level.

These maps show that a potential storm surge of 9 m has the capacity, in both scenarios, to cover over a third of the country [Self-made with data retrieved from Jarvis et al. (2008)]. The results from the different storm surge flooding extents with current sea levels indicate that even a small cyclone that creates a 1-m surge (Dasgupta et al., 2014; Hossain and Mullick 2020) has the potential to affect 1,492 km² or about 1% of the area of Bangladesh and could reach the city of Khulna. However, a more severe cyclone creating a 5-m storm surge has the potential to flood 17,724 km² of the coastal area, or about 12% of the country’s territory, reaching the outskirts of Chittagong and Dhaka. Lastly, as a worst-case scenario, a very extreme cyclone, with a 9-m storm surge could flood an area of 48,491 km² or about a third of the country (Karim and Mimura 2008; IPCC 2014), having a considerable impact on Dhaka and Chittagong.

Based on the maps above it is evident that the most vulnerable areas are the rivers. Many streams enter deep into Bangladesh’s territory, which means that even a moderate cyclone (1-m surge), can affect river environments and freshwater supplies far from the coast. These results may explain the correlations between the Kalaroa, Benarpota and Elarchar stations in Tables 7, 8, as these are within the range of a 1-m storm surge. Also, in Figures 5–7, these stations reflect an increase in salinity over the years, where cyclones have led to an intrusion of saltwater (IPCC 2014).

As seen in Figure 8B, the map indicates the same seawater surge scenario but based on the scenario of a projected 1-m rise of sea level, according to the IPCC’s RCP8.5 from Mukul et al. (2019). This indicates that a 1-m surge can reach as far as the north-western border with India, because the surge can penetrate deep into the country through the channels of the rivers that run through it, covering 1.2%. A 5-m surge will cover a great part of the southwestern portion of Bangladesh and the surrounding floodplain areas of river systems with the potential to affect 17% of the country’s area (IPCC 2014). An extreme 9-m surge event is predicted to cover more than one-third of the country and has the potential to affect the entire southern portion, having a considerable impact on all three major cities. Similarly, the Bangladeshi Sundarbans, will be highly affected, as the southwestern portion (Dasgupta et al., 2014), will be mostly inundated by a 9-m water surge.

Figure 9 displays the maps from two different storm surge scenarios for the Shyamnagar Upazila region. Map A is a projection of the area affected by surges of 1, 5, and 9 m due to a cyclone or a tropical storm, with current sea levels. Map B shows the same area but with a 1-m rise in sea level, similar to Figure 8.

The maps above show that a 9-m storm surge can cover most of the area (>90%) in the Upazila [Self-made with data retrieved from Jarvis et al. (2008)]. The first scenario shows that a 1-m surge will affect 79 km² of the area or around 6% of the Upazila, however, a 5-m water surge can affect more than half of the area, and a potential 9-m surge scenario will flood almost the entire area or 97% of the Upazila.

Comparatively, Figure 9B shows the same scenario, but with a baseline of 1 m above sea level, indicating that 5.6% of the area or 79 km² will be submerged by 2100 (IPCC 2014). Here, the most significant change can be seen in the 1-m surge, as it will cover 9% of the area, mainly affecting a large part of the mainland around Shyamnagar town. The 5-m surge will now cover almost three-quarters of the Upazila, affecting areas in the southern Sundarbans that were only affected by a 9-m surge. Finally, a 9-m surge will cover less area compared to the current scenario but is expected to affect almost the entire Upazila.