Flood Prediction and Risk Assessment at the South-western Region of Bangladesh

doi:10.21203/rs.3.rs-1460563/v1

PDF

Research Article

Flood Prediction and Risk Assessment at the South-western Region of Bangladesh

https://doi.org/10.21203/rs.3.rs-1460563/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Although Bangladesh has been experiencing frequent flooding that has made colossal damages to lives and properties, overseeing floods remains a difficult task. Due to the complexity, scale, and multidisciplinary nature of flood management, it is a multisectoral activity. Khulna and Satkhira districts, located in the south-western region of Bangladesh, are considered as one of the most vulnerable areas to flooding due to their economic potential, demographic makeup, and adaptive capacity of the population. This research focused on finding the most vulnerable areas to flooding for each polder within the study area. Water level data from fourteen stations of seven rivers (Sibsa, Rupsa-Pasur, Kobadak, Bhadra, Kobadak, Ichamati (Western Border), Betna-Kholpetua, and Satkhira Khal) were analyzed to calculate water levels for 2, 5, 10, 25, and 100-year return period applying normal distribution, Extreme Value Type-I (EV-I), and Log Person Type- III (LP-III) distribution methods. The EV-I distribution method was showing the best fit. The study revealed that station SW243 (Rupsa-Pasur river) in the Dacope region has the most extreme water level, station SW259 (Sibsa river) has the second-highest water level, and station SW254.5 (Satkhira Khal) in Satkhira Sadar has the third-highest water level for the return period of 100 years. A flood inundation map was prepared using the EV-I method's 10-year return period value. The Analytic Hierarchy Process (AHP) was used to demonstrate the polders' vulnerability depending on several factors. Overall, polder15 (Ghubra, Satkhira) is the most vulnerable polder, while polder 33 and polder 32 respectively are the second and third most vulnerable polders for flooding, both located in the Dacope region.

Flood frequency analysis

Geographic Information System

Analytic Hierarchy Process

Flood vulnerability

Flood is a pervasive stage of the river when the adjoining areas inundate and human well-being is adversely affected. Damage in terms of loss of life, property, and economy caused by this natural disaster is well known to mankind. Flooding is so devastating that it accounts for approximately 40% of all sorts of natural disasters across the world (Chowdhury and Hassan 2017). In the last 30 years, floods cost 386 billion US dollars of economical losses (Wang et al. 2011). Several studies have suggested that the intensity and the occurrence rate of floods will increase in the coming years (Hoque et al. 2019). Moreover, factors like population growth, urbanization, economical development, and climate change will facilitate an increase in the magnitude of this natural phenomenon (Mojaddadi et al. 2017). Therefore, flood risk assessment and flood management are a must; otherwise, people, structure, economy, and environment are at significant risk in the near future. Bangladesh is a riverine country where about 230 rivers flow including one of the largest transboundary river basins in the world, the Ganges-Brahmaputra River Basin. The total catchment area of these rivers stands at 1.6 million sq. km of which only 7.5% lies within Bangladesh (Flood Forecasting and Warning Centre 2020). About 91% (1.07 trillion out of 1.8 trillion) of water flowing through the country are contributed by other countries and the rest are resulted because of precipitation (Rahman and Salehin 2013), which means the circulation of tremendous water through the basin into the Bay of Bengal from a very narrow space. As a result, flood is an unavoidable disaster for this country. High-magnitude floods occur regularly in this portion of the globe (Islam and Sado 2009). During monsoon, (June-September) 20–35% of the country is flooded because of the overflows of rivers (Rahman and Salehin 2013). Upstream contribution, local precipitation, navigability crisis of the rivers that slowed down the water discharge to the Bay of Bengal are the major contributing factors for flood in the country (Chowdhury and Hassan 2017).

Several methodologies have been tried to determine the link between flood magnitude and occurrence in the hydrological literature across time (Chen et al. 2019). It is difficult to model hydrological processes like floods analytically since they rely on many aspects like catchment characteristics, rainfall, and preceding conditions (Subramanya 2017). Flood frequency analysis (FFA) has long been a contentious issue due to its wide range of applications (Bobée and Rasmussen 1995). FFA estimates the frequency, amplitude, and return period of a hydrological event like floods (Wan Deraman et al. 2017). FFA is one of the possible tools for addressing design floods. This is especially noticeable in important engineering structures (Kidson and Richards 2016). It helps us calculate the magnitude and frequency of floods in a given area using different statistical characteristics.

The Normal Distribution, often known as the Gaussian Distribution, has several uses in statistics. This shows data scattering (Samantaray and Sahoo 2020). It is also known as the error function. Because many natural events occur regularly, their statistical data tend to be normally distributed. When data on flood spread and financial losses were unavailable, it has been demonstrated that this method beat extreme value, lognormal, and Weibull methods (Singh 1998). As a result, it was included in our study. Our inquiry examined the mechanism of EV-I dispersion. The EV-I distribution is frequently used in hydrology to forecast extreme hydrological phenomena such as floods (Bhagat 2017). Gumbel founded it in 1941, and it has since gained widespread acceptance among engineers and scientists in a variety of sectors (Subramanya 2017). Since then, it has been used to refer to severe phenomena in a variety of fields, including hydrology (Loaiciga and Leipnik 1999). Numerous hydrologists regard it as the most accurate tool for forecasting future floods (Bhagat 2017). This approach is frequently used to deal with extreme events such as flood pinnacles and volumes (Yue et al. 1999). Censored data are records of flood peaks that exceed a predetermined limit. Hald and Cohen (1949,1950,1976) invented them for the normal and lognormal distributions (Stedinger and Cohn 1986). In the late twentieth century, the LP-III distribution was developed. In 1967, the World Resources Council endorsed its use in hydrological studies. Additionally, it is the most widely used distribution method in the United States.

However, FFA has some setbacks. It requires a dataset over a longer period, e.g. at least 30 years and it is most reliable in the climate which stays uniform throughout the year (Samantaray and Sahoo 2020). Also, in LP-III distribution if the coefficient of skew is greater than 1.414 (Cs > 1.414) it will not show any valid values that are applicable for any analysis. Again, if the coefficient of skew Cs ≤ 1, the value will be much similar to lognormal distribution (Griffis and Stedinger 2007). So, the expected coefficient of skew will be less than 1.414 in this study. LP-III is being used in all sectors in USA. The U.S. Armed forces (Engineering crops) use LP-III in the field of flood recurrence investigation whereas, the National Oceanic and Atmospheric Administration is using it in the investigation of precipitation information. Moreover, and also the U.S. Naval force has been utilizing LP-III in the indication of plane score sink speed of non-transporter-based planes has been utilizing it. Another distribution like the Weibull distribution is suggested as well, but it doesn't fit normalization how the LP-III dispersion does (Peter Hovey & Thomas DeFiore 2003). Therefore, LP-III distribution was chosen for the analysis.

Though, flood mapping assumes a definitive part in flood risk and arranging, particularly in decreasing flood harm, especially in densely populated areas. But knowledge about vulnerability plays the most important challenge in any type of flood risk assessment. But, vulnerability assessment is a complicated interaction because it is dependent on many factors like social-economy states of the local area, capacity elevation from mean sea level, etc (Eini et al. 2020). AHP has great advantages and numerous implications for assessing human vulnerability from the social point of view. It was first developed by Saaty (1977) for setting priority and effective decision making. At present, AHP is comprehensively applied in sociological research and explicitly in peril and hazard analysis (Mondal et al. 2021).

The current global temperature is almost certainly higher than it was in the last 2000 years (Benadusi 2014). According to scientists and climatologists, nine out of ten natural hazards on the planet over the previous few decades have been caused by extreme weather and climate activities (Garai 2017). Warming temperatures raise ocean levels, putting coastal communities in jeopardy, particularly during natural disasters. The impacts of SLR (sea level rise) to the Bangladesh coast resemble long-lasting immersion, seepage clog in the polders, storm flood immersion, and saltiness interruption. As pointed out by many Global climate experts, including IPCC, global warming is expected to result in a rise in sea level of several tens of centimeters over the next 50 years and around one meter over the following century (Bhuiyan and Dutta 2012). This will result in a loss of up to 17% of the country's total land area (Choudhury et al. 2009). Therefore, a wide scope of effects ashore uses design and financial is expected. This research attempts to integrate two models for prioritizing hazards, vulnerability concerns, and difficulties in the community of Bangladesh's south-western areas. Community-based risk assessments are important tools for supporting the risk assessment and mitigation process, as well as identifying acceptable risk reduction choices such as land-use planning, early warning systems, readiness, awareness-raising initiatives, and appropriate adaption techniques (Islam et al. 2013).

Khulna and Satkhira Districts of the southwestern region were selected for this study in which most of the parts are hard-to-reach coastal zone, vulnerable to climate change and natural disasters like cyclones and floods. Even Khulna Metropolitan City has been identified as the 15th most vulnerable to climate change impact city (Esraz-Ul-Zannat and Md. 2012). The study area is most susceptible to tidal surges (Rahman and Salehin 2013) which eventually causes flooding. Two major factors are responsible for floods in this coastal area. Heavy rainfall and high siltation in the rivers, resulting in drainage problems known as drainage congestion and cyclonic storm surges that emerge from the Bay of Bengal (Islam et al. 2017). Also, global warming and SLR are posing a whole new threat to the coastal region. A relatively low magnitude flood can have a devastating impact in these areas. About 60–70% of the city region incorporates swamps, deserted channels, and dejections. The average height of these regions ranges from 0.5 to 5m (Hossain et al. 2014). Satkhira district is also part of the Khulna division. It is also one of Bangladesh's most impoverished areas (Fenton et al. 2017). The main occupation of most of the population is pisciculture (Azad et al. 2021).

About $200–300 million worth of fish and shrimp was washed away during the 2004 floods in Satkhira (Azad et al. 2021). The high magnitude flood is becoming more frequent; the region has experienced severe floods in 2009, 2010, 2013, 2015, 2017, 2019. This indicates a return period of the severe flood is 1–3 years at present which was 7 years and 33–50 years for severe floods and catastrophic floods respectively before (Ahmad and Ahmed 2003). This indicates the importance of flood assessment in this part of the country.

Seven rivers (14 stations) were chosen for this study, namely Sibsa, Rupsa-Pasur, Kobadak, Bhadra, Ichamati (Western Border), Betna-Kholpetua, and Satkhira Khal, as they encompass all of Khulna and Satkhira districts. Our analysis considers 17 polders based on the water station (Table 1).

Statistical information of historical data like mean values, skewness, standard deviations are calculated for the determination of FFA. Advanced Microsoft Excel programming was used for the FFA estimation and the value for the return period of 2, 5, 10, 25, 50, and 100 years of water level data was extracted. Straight-line regression was applied to extract the value of missing year data. Normal Distribution Method, EV-I Distribution and LP-III Distribution are used for the frequency analysis.

3.1 Normal distribution: The normal distribution is perhaps the main circulation in any measurable investigation since it fits numerous natural events. It has mainly two parameters, the mean and the standard deviation. The following equations are used to calculate a normal distribution for the return period of 2, 5, 10, 25, 50, and 100 years from the obtained data. The magnitude of the flood was obtained from Eq. 3.1.1

$${X}_{T}={P}_{avg}+KS$$ 3.1.1

Where K is the frequency factor of the specified return period, P_avg, and S are the average value and standard deviation of the data respectively. The frequency factor expressed as

$$K=W-\frac{2.515517+.802853W+.01032{W}^{2}}{1+1.432788W+.189269{W}^{2}+.001308{W}^{3}}$$ 3.1.2

Where,

$$W={\left[\text{ln}\left(\frac{1}{{p}^{2}}\right)\right]}^{\frac{1}{2}}0<P\le .5$$ 3.1.3

$P=\frac{1}{T}$ (T is the return period) (3.1.4)

3.2 Extreme Value Type-I Distribution (EV-I): The EV-I (Eq. 3.1.1), 5, 10, 25, 50, and 100 years return periods for every span period and requires few computations. With a predefined return period T (in year), the most extreme streamflow X_T (mm) is given by the accompanying condition.

$${X}_{T}={P}_{avg}+KS$$ 3.1.1

Where K signifies as Gumbel frequency factor and surrendered P_avg is the average of the most extreme streamflow compared to a particular period.

$$K=-\frac{\sqrt{6}}{\pi }\left[.5772+\text{ln}\left\{\text{ln}\left(\frac{T}{T-1}\right)\right\}\right]$$ 3.2.1

$${P}_{avg}=\frac{1}{n}\sum _{i=1}^{n}{P}_{i}$$ 3.2.2

Where is the P_i singular limit worth of streamflow and n is the number of occasions or the numbers of years. The standard deviation is determined by (3.2.3) processed utilizing the accompanying connection

$$S=\sqrt{\frac{1}{n}\sum _{i=1}^{n}{\left({P}_{I}-{P}_{avg}\right)}^{2}}$$ 3.2.3

3.3 Log Person Type- III Distribution (LP-III): LP-III distribution consists of the step of transforming the observed values by taking their logarithms. The mean and standard deviation of the logarithmically changed information are additionally determined. The recurring precipitation is conducted in a manner similar to Gumbel circulation by applying the LP-III approach. The improved-on articulation for this circulation is given as follows:

$${P}^{*}={\text{log}}_{10}({P}_{i})$$

3.3.1

$${P}_{t}={P}_{avg}^{*}+K{S}^{*}$$

3.3.2

$${P}_{avg}^{*}=\frac{1}{n}\sum _{i=1}^{n}{P}^{*}$$

3.3.3

$${S}^{*}={\left[\frac{1}{n-1}\sum _{i=1}^{n}{\left({P}^{*}-{P}_{avg}^{*}\right)}^{2}\right]}^{\frac{1}{2}}$$

3.3.4

$${X}_{t}=antilog\left({P}_{t}\right)$$

3.3.5

Where ${X}_{t}$ is the magnitude of the water level of a specified return period.${P}^{*}$is the log value of initial data, K is the frequency factor, which is the capacity of coefficient of skew (C_S) and return period (T). The skewness coefficient is determined by utilizing (3.3.6).

$$K=f\left({C}_{s},T\right)$$

3.3.6

$${C}_{S}=\frac{n\sum _{i=1}^{n}{\left({P}^{*}-{P}_{avg}^{*}\right)}^{3}}{\left(n-1\right)\left(n-2\right){\left({S}^{*}\right)}^{3}}$$

3.3.7

The value of K can be acquired from tables in the hydrological book [Table 7.6 by Subramanya (2017) ]. The recurrence factor, K for the LPT-III Distribution can be derived after determining the recurrence interval. In case, the skew coefficient (C_S) is between two given skew coefficients in the table, linear interpolation (3.3.8) was used to get the appropriate value of K.

$$y={y}_{1}+\frac{{y}_{2}-{y}_{1}}{{x}_{2}{-x}_{1}}\left(x-{x}_{1}\right)$$

3.3.8

3.4 Flood Map Preparation and Inundation Analysis:

ArcGIS Platform has been utilized here to prepare flood map and inundation analysis considering the numerical value of EV-I distribution for 10-years return period as this distribution has not only been suggested by the Government of Bangladesh (Monirul Qader Mirza 2002), it also shows the best fit in comparison with other distributions. The SRTM (shuttle radar topography mission) DEM (digital elevation model) was used to prepare a flood map. The magnitude of the flood under different categories was brought out using the spatial analysis in ArcGIS. The method demonstrated below shows how GIS information is reconstructed from the base information and spatial analysis strategy that is applied to assess the level of effect for the situation concentrated on the region.

Posting flood depth markers in computable ArcGIS format.
The water height (calculated by EV-I) is generated using the inverse distance weighting (IDW) algorithm from flood depth markings. The simulated water height surface is the result of the interpolation.
Calculate inundated regions using map algebra by subtracting the DEM from the water height surface. The result of this algorithm is a map that displays the value of each point. Positive values correspond to flooded areas, whereas negative values correspond to unflooded areas.
Identifying flooding levels through reclassification of the inundation map into F0 (0-0.3m), F1 (0.31-0.9m), F2 (0.91-1.8m), F3 (1.8-3.2m), F4 (> 3.6m) zones. Then, using a structured query language (SQL) method, extract those classes to produce a raster inundation map.
Converting the raster inundation map to polygon format using the "raster to polygon" discussion tool. This phase generates an inundation map in the Into Polygon format.

3.5 AHP Modeling: The AHP (Analytic Hierarchy Process) is a comprehensive approach to dealing with dynamics that may be applied to a wide variety of scenarios, norms, and entertainers to resolve deeply complicated challenges. The AHP technique has been applied to a variety of sectors and projects, including environmental and natural resource studies, forestry, coastal management, and disaster and risk management (Schmoldt et al. 2001; Chen et al. 2009; Ryu et al. 2011; Samari et al. 2012). The AHP was used in this study to determine the standards and components that most accurately represent a disaster hazard in a coastal location. Seven parameters that describe the best risk factor at the local level were chosen for developing the AHP method (Fig. 2). Seven factors (Inundation level, Population density, Livelihood, Household structure, Settlement elevation, Literacy, and Vulnerable People) were selected for the development of the AHP approach in our studies. The parameters are carefully chosen from the distinct literature to ensure that they are sufficient to address the region's specific environment.

One of the traditional issues in choice hypothesis or multi-criteria investigation is the assurance of the general significance of every boundary. The general significance of boundaries vis-aá-vis the goal is typically addressed by a set of weights, and are standardized to a steady as:

$$\sum _{1}^{n}{W}_{i}=1$$

A significance scale is proposed for the pair-wise examination of criteria, because of countless analyses (Table 2). Experts' opinion has been used to prioritize these matrices. The pair-wise correlation network of n boundaries constructed based on Saaty's scaling (Table 2) proportions might be of the request (nxn) as follows in the eigen vector technique for ensuring the largest eigen value to gauge the loads:

$$A=\left[{a}_{ij}\right]i,j=\text{1,2},3\dots \dots n$$

3.5.1

Where,

$${a}_{ij}=\frac{{w}_{i}}{{w}_{j}} for all i and j$$

$${a}_{ij}=\frac{1}{{a}_{ij}}$$

And ${a}_{ij}={a}_{ik}/{a}_{jk }$for any I, j and k

Multiplying Eq. (3.5.1) with W (weighting factor) of (n$\times$l) size yields

(A-nI) W = 0 (3.5.2)

where I is an identity matrix of (nxn). According to matrix theory, if the comparison matrix A has the property of consistency, the system of equations has a trivial solution. The matrix A is; however, a judgment matrix and it may not be possible to determine the elements of A accurately to satisfy the property of consistency.

$${A}^{*}{W}^{*}={\lambda }_{max}{W}^{*}$$

where A^* is the estimate of A, and W* is the corresponding priority vector and ${{\lambda }}_{max}$is the largest eigenvalue for the matrix A. Eq. (3.5.2) yields the weights W which is normalized to unity. The consistency of each pairwise comparison matrix is calculated by consistency ratio (CR), which is equal to (CI/RI), where CI is the consistency index and is equal to (${{\lambda }}_{max}$-n)/(n-1) and RI is the average of the resulting consistency index depending on the order of the matrix. When CR is less than 0.10, the matrix has a reasonable consistency otherwise the matrix has to be changed (Palchaudhuri and Biswas 2016).

Livelihood: Disasters especially affect the least fortunate and most vulnerable individuals, creating social disparities and hurting economic growth. So, the income or livelihood pattern plays a significant role in assessing any disaster risk assessment. According to the population and housing census-2011 report, people living at the concerning area earn their livelihood by doing mainly 3 types of activity i.e. agriculture, business, and services. A three-point Likert scale is used in this study where 1 indicates most vulnerable and 3 indicates least vulnerable for calculation of AHP. As agriculture is the most vulnerable for flood it has been assigned with the number 1, and service is the least vulnerable for flood analysis it has assigned with number 3.
Literacy: Recently education has been emphasized for any Disaster risk reduction (DRR) planning. As pointed out by many studies, raising public awareness through the informal campaign is not enough to minimize the vulnerability of any localities. Rather, a scientific knowledge-based approach and public education should be applied in engaging the community in the DRR plan and to eradicate superstitions regarding the disaster (Benadusi 2014b). Hence, literacy has been selected as a component of the comparison matrix. Here, the average literacy percentage of the concerned polders has been divided into 5 classes where number 5 indicates the highest (81–100%) literacy percentage, and number 1 is assigned for the lowest literacy percentage (1–20%).
Population Density: A high population density can easily increase the disaster vulnerability of an area. Population density has been calculated by the total population of an area and dividing it with the total settlement area obtained from GIS analysis. The obtained result has been classified into five categories, where the number 5 indicates the lowest population density (1-250 individuals per square km) and 1 being the highest (1001–1250 individuals per square km). And the rest of the numbers fall between them.
Vulnerable people: In a disaster situation, seniors (65 + aged) are viewed as a weak populace similar to youngsters. As youngsters, the delicate old are regularly unfit to advocate for their advantages because of actual weaknesses, intellectual limits, or a combination of both. In comparison to a child, an older individual certainly relies on the assistance of an adult most of the time especially during in a situation. Two types of individuals have been considered as vulnerable people for the study, children under the age of 4 and people above the age of 65. The average percentages of both generations have been used for any single polder. We have classified those average percentages into 5 categories, where number 5 represent the lowest number percentages (0–2%) and number 1 represents the highest (8.1–10%).
Housing structure: There are mainly 4 types of housing structure found at the study area, i.e. Jhupri, Kutcha, Semi-pacca and Pacca. Where Jhupri has been assigned with number 1 as it is the most vulnerable housing structure for flooding and Pacca structure has been assigned with number 4 as it has the highest capacity to resist any natural hazards.
Settlement topographic elevation: Settlement topographic elevation plays a very significant role in flooding. From the GIS analysis the topographic elevation pf settlement has been calculated and are classified into 3 categories. Number 1 has been assigned with the least valued class (0-1.16m) and number 3 has assigned with the highest valued class (2.4m-3.5m).
Inundation The inundation level for each polder are determined from frequency analysis and GIS analysis. Polders have been assigned with numbers, where number 1 are given for the highest percentages (80–100%) of inundated polder and number 5 has been assigned with the least (0–20%) percentages of inundated polders.

The streamflow data for this study was received from the Bangladesh Water Development Board (BWDB) for the period 1980–2020. The flood map was developed using ArcGIS software (version 10.8.1) and data obtained BWDB's SRTM DEM at a resolution of 50m. All secondary data was gathered using data from the 2011 census. The most current government-authorized census report for obtaining demographic statistics is the Bangladesh Bureau of Statistics (BBS) 2011 report. There are still no updated census figures in Bangladesh. For this study, the 2011 census report provided data on vulnerable persons, housing structures, literacy, and livelihood.

5.1 Flood Occurrence: As the predicated magnitude shown in (Table 3), the EV-I distribution is showing the most extreme value as expected and the normal distribution is showing a relatively lower value than EV-I. The result depicts that station no SW243 (Rupsa-Pasur River) in the Dacope region will have the most extreme water level (4464.149mm-5496.602mm) for 100-year floods, while station no SW259 (Sibsa river) (4602.342mm-5603.337mm) will have the second-highest water level and the station no SW254.5 (Satkhira Khal) located in Satkhira Sadar will have the third-highest (4517.834mm-5156.07mm) water level.

5.2 Land Type based on land Elevation: Elevation of the study area is one of the key factors for flooding. The DEM data shows the topographic elevation in the study area. Based on the water station's location, 16 polders have been considered for this study (Fig. 3). The total average elevation of polders is 1.99m whereas polder16 has the highest average elevation (2.62m), polder3 and polder1 almost relatively have the same average elevation (2.37m & 2.36m).

However, looking at the settlement average topographic elevation, a significantly greater elevation averaging of 2.38m, with Polder1 settlements having the highest average elevation of 3.45m, Polder3 (3.31m) having the second-highest, and Polder16 (3.23m) having the third-highest is noticed.

5.3 Flood Simulation:

For flood simulation, predicated water level (EV-I for10-year return period) of each station is considered in this study. The stations were chosen based on their surrounding polder. Five categories of flood depths/inundated maps have been produced for each polder including the F0 zone (0m-0.3m), F1 zone (0.31m-0.9m), F2 zone (0.91m-1.8m), and F3 zone (1.8m-3.6m) and F4 zone (> 3.6m).

From the obtained results (Table 4), upper part of polder31 (93.79%), polder33 (91.86%), polder27-1 (92.58%), and polder23 (87.83%) has respectively the highest percentages of F3 zone. Whereas, polder16 (5%), polder15 (32.17%).

has respectively the lowest percentages of F3 zone. Although polder 29 has one of the lowest percentages (27.70%) of F3 zone but has the highest percentages (71.27%) of F2 zone, also polder15 has one of the lowest percentages of F3 zone and has one of the largest percentages (61.39%) of F2 zone. If we consider the F0, polder16 (37.40%), polder1 (9.10%), polder3 (6.39%) has respectively the largest percentages. And beside polder16 (13.34%), polder1 (11.66%) has the highest percentages of F1 zone. This indicates that polder 16 has the least flooding while the upper portion of polder 31 has the most flooding.

5.4 Variable Weightage Determination: In this study, 7 pairwise comparisons were created (Table 5). The measures recorded on the left are individually contrasted and every standard recorded on top is contingent upon which one is more significant regarding the objective. Experts’ opinion has been utilized to complete the pairwise compression section and the result is summarized in (Table 6).

Table 6 & Fig. 4 reveal that the polders15 obtained the least weightage value (3.307), thus the most vulnerable polder to flooding in the south-western region of Bangladesh. The upper part of polder31(polder31up: 4.457) and polder16 (4.506) has also obtained the second and third lowest value, so, can be marked as the second and third most vulnerable polders respectively. Whereas polder3 has obtained the highest value (5.562), the lower portion of polder31 (polder31down:5.279) and polder23 and polder10-12 (5.06) has also obtained the second and third highest value at our analysis. These polders (polder3, polder31down, and polder23 & polder10-12) are the least vulnerable polders to flood hazards.

Our predicated flood value for the 10-year return period (EV-I) has many similarities with the catastrophic event “Aila” in 2009 (Fig. 6). Cyclone Aila hit the south-western region of Bangladesh on May 25, 2009. Although, the death rate of 2009 cyclone Aila (190) is much lower in comparison of 2007 (5000 − 1000) catastrophic event (Chen et al. 2009). Consider the case (for this catastrophic event “Aila”) of a particular polder, such as polder 32, which is divided into two unions (Kamarkhola and Sutarkhali). For more than six months, the entire region of Polder 32 was entirely inundated in saline water. In 2010, water retreated from various areas of the Kamarkhola union due to natural drainage following the March 2010 closure (reconstruction of the damaged part of the polder). Due to the presence of numerous natural and man-made depressions in the Sutarkhali union, part of the depressed areas remained submerged in water. In 2011, the Kamarkhola union was completely dewatered, as was the majority of the Sutarkhali union, except for a minor quantity of dead storage in a few natural depressions. The polder’s rehabilitation and restoration required more than a year, during which time the area was regularly inundated by everyday tides (Kabir et al. 2016). This equates to a loss of 14.48 million BDT in total. Agriculture appears to be the main casualty of this devastating occurrence, as the majority of residents in this area are farmers. Agriculture accounts for 57.49 percent of the 14.48 million, and it has been anticipated that any future catastrophic occurrence would cost over 34.50 million BDT for agriculture only (Kibria et al. 2016).

In the total south-western region, this catastrophic event has destroyed 68,385 acres of harvesting land and affected 239,212 acres of land to some degree, and killed over 1 million animals by destroying shrimp ranches and residential areas, depriving people of their primary source of income as well as non-monetary revenues and resources. Furthermore, because the poor were the most severely impacted by Aila (nearly 90%), displaced event survivors were forced to relocate in large numbers (78 percent) to nearby urban areas such as Satkhira, Khulna, and Bagerhat for alternative livelihoods. This was because the poor were the most severely impacted, and movement was their only way to cope with the negative effects of the disaster (Schmoldt et al. 2001).

And we can observe that a catastrophic incident occurs on average every ten years. As with the 1998 flood, Aila (2009), and super cyclone Amphan (2020), all have inundated the coastal area, wreaking havoc on the local population. The polders are a type of erroneous assumption of safety. As previously noted, (2004, 2007, 2009, and so on), whenever a catastrophic disaster strikes our coastal region, polders disintegrate, resulting in inundation and suffering for the population.

The purpose of this study is to forecast the most vulnerable areas in the south-western coastal region to flooding using historical data. Our inundation map suggested that the concerned polders are highly flood-prone. Almost all polders have more than 90% of their land inundated by the F₃ zone, for a 10-year return period flood. Annual flooding affects the majority of the region. Additionally, Adnan et al., 2020 reported that the majority of our polders have an annual flood probability rating greater than 0.8, indicating the highest level of flood susceptibility, which concurs with our findings.

With a significant risk of flooding, the majority of individuals in our concern region are involved in agriculture, particularly aquaculture. As for polder 15, it has the lowest literacy rate, the biggest concentration of vulnerable people, and agricultural activities with the highest amount of flooding (F₂ zone), making it one of the most vulnerable polders to flood dangers. Polder 31 up is located 75 kilometers from the Bay of Bengal and is subject to diurnal tidal influence. The polder is flanked on four sides by rivers: the Upper Bhadra (west), the Jhapjhapia (east), the Manga (southeast), and the Mora Bhadra (southwest). Agriculture is the primary source of income in this polder. The polder's net cultivable area (NCA) is 1,853 hectares or 70% of the entire polder area. The majority of the people in the polder area work in agriculture (92 percent) (CEGIS, EIA on Rehabilitation of Polder 31-Part n.d.). With a high concentration of vulnerable persons and a high population density, our study concluded that this polder is one of the most vulnerable to flooding.

On the other hand, polder 3 may consider the most resilient polder for flood hazards. With most people linked with service rather than agriculture, the lowest population density, and maximum settlement elevation makes this polder resilient to flood risk. With the second-lowest population density and service as the most prevalent job status; in our analysis, polder 31down is regarded as the second most robust polder in terms of flood hazard.

While all of the parameters are critical, the study's findings indicate that population density, livelihood, and literacy rate have a vital role in determining vulnerability in the southwestern region. Because the polders' conditions are so similar, these three factors help distinguish the most vulnerable areas from the least vulnerable areas. The combination of these three conditions, a high illiteracy rate, and a lack of economic capacity to create flood-resistant infrastructure makes these polders very exposed to flood hazards. As numerous experts have noted, illiteracy is a significant factor affecting flooding vulnerability. Illiterate individuals and populations with minimal or no awareness of flood hazards are more vulnerable to the effects of flood hazards (Gwimbi 2007; Dufty Neil 2008; Hamidi et al. 2020). Scholars unanimously agree that increased population density also increases vulnerability to disasters. Areas with a larger population density are more prone to hazards and may be more difficult to evacuate, making them extremely vulnerable (Tascón-González et al. 2020). Additionally, a high population density has a detrimental effect on livelihood. A 2018 IPCC study details the anticipated implications in detail. Even if the Paris Agreement's objective of limiting global warming to 2°C is met, the frequency of tropical cyclones would grow, exposing a greater proportion of the world's land area to severe flood dangers. And Bangladesh has been designated as the country most vulnerable to sea-level rise and severe river flooding (2022). This would imply a loss of livelihood specifically, those living in the south-western region, as seen in (table 9), due to their association with agricultural activity.

The southwestern region is thought to be the most flood-prone area even though floods happen in almost every part of the country. Nevertheless, flood management remains a difficult task for its multifaceted and baffling nature. Therefore, a flood risk scenario is developed for Khulna and Satkira District of Bangladesh using FFA, Normal Distribution, EV I Distribution, and LPIII Distribution. The study includes socio-economic aspects in the analysis process to determine the most vulnerable zone for flooding. The study findings provide useful information for the respective authorities at the regional and local levels by identifying the most socially vulnerable areas to flooding, allowing them to take necessary steps in advance.

Acknowledgements:

The authors would like to express their gratitude to the BWDB authorities for supplying the water level data in a timely manner.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: All authors equally contributed to the study.

Adnan MSG, Talchabhadel R, Nakagawa H, Hall JW (2020) The potential of Tidal River Management for flood alleviation in South Western Bangladesh. Sci Total Environ 731:138747. https://doi.org/10.1016/J.SCITOTENV.2020.138747
Ahmad QK, Ahmed AU (2003) Regional Cooperation in Flood Management in the Ganges-Brahmaputra-Meghna Region: Bangladesh Perspective. Natural Hazards 2003 28:1 28:191–198. https://doi.org/10.1023/A:1021186203100
Azad AK, Pitol MdNS, RAKKIBU MDG (2021) Livelihood status of local communities around Sundarbans mangrove ecosystem in Shymnagar Upazila, Satkhira, Bangladesh. Asian J Forestry 5. https://doi.org/10.13057/ASIANJFOR/R050104
Benadusi M (2014a) Pedagogies of the Unknown: Unpacking ‘Culture’ in Disaster Risk Reduction Education. J Contingencies Crisis Manag 22:174–183. https://doi.org/10.1111/1468-5973.12050
Benadusi M (2014b) Pedagogies of the Unknown: Unpacking ‘Culture’ in Disaster Risk Reduction Education. J Contingencies Crisis Manag 22:174–183. https://doi.org/10.1111/1468-5973.12050
Bhagat N (2017) Nirman Bhagat. Flood Frequency Analysis Using Gumbel’s Distribution Method: A Case Study of Lower Mahi Basin. India J Water Resour Ocean Sci 6:51–54. https://doi.org/10.11648/j.wros.20170604.11
Bhuiyan MJAN, Dutta D (2012) Analysis of flood vulnerability and assessment of the impacts in coastal zones of Bangladesh due to potential sea-level rise. Nat Hazards 61:729–743. https://doi.org/10.1007/S11069-011-0059-3/TABLES/6
Bobée B, Rasmussen PF (1995) Recent advances in flood frequency analysis. Rev Geophys 33:1111–1116. https://doi.org/10.1029/95RG00287
hua Chen G, Tao L, Zhang H (2009) wen Study on the methodology for evaluating urban and regional disaster carrying capacity and its application. Safety Science 47:50–58. https://doi.org/10.1016/J.SSCI.2007.12.002
Chen WF, Toma S, Toma S et al (2019) Flood Frequency Analysis. Flood Frequency Analysis. https://doi.org/10.1201/9780429128813
Choudhury AM, Haque MA, Quadir DA (2009) Consequences of global warming and sea level rise in Bangladesh.
https://doi.org/101080/01490419709388092
20:13–31. https://doi.org/10.1080/01490419709388092
Chowdhury EH, Hassan QK (2017) Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh. Natural Hazards 2017 88:3 88:1805–1823. https://doi.org/10.1007/S11069-017-2947-7
Dufty N (2008) A New Approach to Community Flood Education | The Australian Journal of Emergency Management.The Australian Journal of Emergency Management23
Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. Int J Disaster Risk Reduct 50:101687. https://doi.org/10.1016/J.IJDRR.2020.101687
Esraz-Ul-Zannat Md (2012) Study on land use policies of khulna structure plan 2000–2020 in the light of climate change induced flood scenario
Fenton A, Paavola J, Tallontire A (2017) The Role of Microfinance in Household Livelihood Adaptation in Satkhira District, Southwest Bangladesh. World Dev 92:192–202. https://doi.org/10.1016/J.WORLDDEV.2016.12.004
Flood Forecasting and Warning, Centre BB (2020) Annual Flood Report 2019. WAPDA Building (8th Floor), Motijheel C/A, Dhaka-1000
Garai J (2017) Qualitative analysis of coping strategies of cyclone disaster in coastal area of Bangladesh. Nat Hazards 85:425–435. https://doi.org/10.1007/S11069-016-2574-8/TABLES/1
Griffis VW, Stedinger JR (2007) Log-Pearson Type 3 Distribution and Its Application in Flood Frequency Analysis. I: Distribution Characteristics. Journal of Hydrologic Engineering 12:482–491. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(482)
Gwimbi P (2007) The Effectiveness of Early Warning Systems for the Reduction of Flood Disasters: Some Experiences from Cyclone Induced Floods in Zimbabwe.Journal of Sustainable Development in Africa9
Hamidi AR, Wang J, Guo S, Zeng Z (2020) Flood vulnerability assessment using MOVE framework: a case study of the northern part of district Peshawar, Pakistan. Nat Hazards 101:385–408. https://doi.org/10.1007/S11069-020-03878-0/FIGURES/5
Hoque MAA, Tasfia S, Ahmed N, Pradhan B (2019) Assessing Spatial Flood Vulnerability at Kalapara Upazila in Bangladesh Using an Analytic Hierarchy Process. Sensors 2019, Vol 19, Page 1302 19:1302. https://doi.org/10.3390/S19061302
Hossain S, Setu NN, Saha R, Rahman M, Quality and Human Health Dhaka Water Supply Sector Development Project View project (2014) Natural Disasters Impact on the Water Cycle, Resources, Ground Water Management and Feasibility Study for 148 Pourashava having no-piped Water Supply: Mathematical Modelling for Safe Drinking Water Source Identification View project Natural Disasters Impact on the Water Cycle, Resources, Quality and Human Health. International Journal of Surface and Groundwater Management 01
Islam MM, Sado K (2009) Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. https://doi.org/101080/02626660009492334 45:337–355
Islam MMM, Hofstra N, Islam MA (2017) The Impact of Environmental Variables on Faecal Indicator Bacteria in the Betna River Basin, Bangladesh. Environ Processes 4:319–332. https://doi.org/10.1007/S40710-017-0239-6/TABLES/3
Islam MN, Malak MA, Islam • M, Nazrul et al (2013) Community-based disaster risk and vulnerability models of a coastal municipality in Bangladesh. 69:2083–2103. https://doi.org/10.1007/s11069-013-0796-6
Kabir T, Salehin M, Kibria MG (2016) Impacts of Post-Disaster Slow Rehabilitation of a Coastal Polder on Coastal Livelihoods: A Case Study on Aila. 506
Kibria MG, Shah M, Khan A, Gulam Kibria M (2016) Sectoral and Temporal Damage Variation Due to Cyclonic Storm Surge: The Case Study of Aila Performance Evaluation of a Hybrid Constructed Wetland for Treating Household Sewage Water View project South Asian Water (SAWA) Fellowship Project funded by IDRC, Canada View project Sectoral and Temporal Damage Variation Due to Cyclonic Storm Surge: The Case Study of Aila. J Mod Sci Technol 4:75–85
Kidson R, Richards KS (2016) Flood frequency analysis: assumptions and alternatives: http://dx.doi.org/101191/0309133305pp454ra 29:392–410. https://doi.org/10.1191/0309133305PP454RA
Loaiciga HA, Leipnik RB (1999) Analysis of extreme hydrologic events with Gumbel distributions: marginal and additive cases. Stochastic Environmental Research and Risk Assessment 1999 13:4 13:251–259. https://doi.org/10.1007/S004770050042
Mojaddadi H, Pradhan B, Nampak H et al (2017) &journalCode=tgnh20#VsXodSCLRhE 8:1080–1102. http://www.tandfonline.com/action/journalInformation?show=aimsScope https://doi.org/10.1080/19475705.2017.1294113. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS
Mondal M, Haldar S, Biswas A et al (2021) Modeling cyclone-induced multi-hazard risk assessment using analytical hierarchical processing and GIS for coastal West Bengal, India. Reg Stud Mar Sci 44:101779. https://doi.org/10.1016/J.RSMA.2021.101779
Monirul Qader Mirza M (2002) Global warming and changes in the probability of occurrence of floods in Bangladesh and implications. Glob Environ Change 12:127–138. https://doi.org/10.1016/S0959-3780(02)00002-X
Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district, India. Nat Hazards 84:1905–1920. https://doi.org/10.1007/S11069-016-2526-3/FIGURES/6
Peter Hovey & Thomas DeFiore (2003) Using Modern Computing Tools to Fit the Pearson Type III Distribution to Aviation Loads Data. Washington, DC 20591
Rahman R, Salehin M (2013) Flood Risks and Reduction Approaches in Bangladesh. 65–90. https://doi.org/10.1007/978-4-431-54252-0_4
Ryu J, Leschine TM, Nam J et al (2011) A resilience-based approach for comparing expert preferences across two large-scale coastal management programs. J Environ Manage 92:92–101. https://doi.org/10.1016/J.JENVMAN.2010.08.020
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Saaty TL, Sodenkamp M (2008) Making decisions in hierarchic and network systems. Int J Appl Decis Sci 1:24–79. https://doi.org/10.1504/IJADS.2008.017952
Samantaray S, Sahoo A (2020) Estimation of flood frequency using statistical method: Mahanadi River basin, India. H2Open J 3:189–207. https://doi.org/10.2166/H2OJ.2020.004
Samari D, Azadi H, Zarafshani K et al (2012) Determining appropriate forestry extension model: Application of AHP in the Zagros area, Iran. For Policy Econ 15:91–97. https://doi.org/10.1016/J.FORPOL.2011.10.006
Schmoldt DL, Kangas J, Mendoza GA (2001) Basic Principles of Decision Making in Natural Resources and the Environment. 1–13. https://doi.org/10.1007/978-94-015-9799-9_1
Singh VP (1998) Entropy-Based Parameter Estimation in Hydrology. 30. https://doi.org/10.1007/978-94-017-1431-0
Stedinger JR, Cohn TA (1986) Flood Frequency Analysis With Historical and Paleoflood Information. Water Resour Res 22:785–793. https://doi.org/10.1029/WR022I005P00785
Subramanya K (2017) Engineering Hydrology. McGraw-Hill, New Delhi
Tascón-González L, Ferrer-Julià M, Ruiz M, García-Meléndez E (2020) Social Vulnerability Assessment for Flood Risk Analysis. Water 2020, Vol 12, Page 558 12:558. https://doi.org/10.3390/W12020558
The Center for Environmental and Geographic Information Services (CEGIS) M of WRBWDB Environmental Impact Assessment (EIA) on Rehabilitation of Polder 31-Part. House-5, road 23/C, Ghulshan-1, Dhaka 1212
Wan Deraman WHA, Abd Mutalib NJ, Mukhtar NZ (2017) Determination of return period for flood frequency analysis using normal and related distributions. Journal of Physics: Conference Series 890:012162. https://doi.org/10.1088/1742-6596/890/1/012162
Wang Y, Li Z, Tang Z, Zeng G (2011) A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China. Water Resour Manage 2011 25:13. https://doi.org/10.1007/S11269-011-9866-2
Yue S, Ouarda TBMJ, Bobée B et al (1999) The Gumbel mixed model for flood frequency analysis. J Hydrol 226:88–100. https://doi.org/10.1016/S0022-1694(99)00168-7
Climate Change and Community Resilience. Climate Change and Community Resilience. https://doi.org/10.1007/978-981-16-0680-9

Table 1

The names, ids, and positions of selected water stations, as well as the surrounding Upazilas and rivers where the stations are located
Serial No	District	Upazila	River	Station Id	Station Name	Latitude	Longitude
1	Khulna	Dacope	Sibsa	SW259	Nalianala_Hadda	22.46	89.42
2	Khulna	Paikgachha	Sibsa	SW258	Paikgacha	22.58	89.32
3	Khulna	Dacope	Rupsa-Pasur	SW243	Chalna	22.6	89.53
4	Khulna	Khulna Sadar	Rupsa-Pasur	SW241	Khulna	22.82	89.58
5	Khulna	Koyra	Kobadak	SW165	Kobadak Forest Office	22.22	89.31
6	Khulna	Paikgachha	Kobadak	SW164	Chandkhali	22.52	89.25
7	Khulna	Dacope	Bhadra	SW29	Sutarkhali_Forest Office	22.51	89.43
8	Khulna	Dumuria	Bhadra	SW28	Dumuria	22.8	89.42
9	Satkhira	Tala	Kobadak	SW163	Tala Magura	22.73	89.27
10	Satkhira	Shyamnagar	Ichamati (Western Border)	SW130	Kaikhali	22.19	89.08
11	Satkhira	Kaliganj	Ichamati (Western Border)	SW129	Basantapur	22.46	89.03
12	Satkhira	Debhata	Ichamati (Western Border)	SW128	Shakra	22.63	88.95
13	Satkhira	Assasuni	Betna-Kholpetua	SW26	Protapnagar	22.39	89.19
14	Satkhira	Satkhira Sadar	Satkhira Khal	SW254.5	Elarchar	22.66	89.05

Table 2

Importance scale (Saaty and Sodenkamp 2008)
Intensity of importance	Definition	Explanation
1	Parameters are of equal importance	Two parameters contribute equally to the objective
3	Parameter i is of more importance compared to parameter j	Experience and judgement strongly favor i over j
5	Essential or strong importance of i compared to j	Experience and judgement strongly favor i over j
7	Very strong or demonstrated importance	Criteria i is very strongly favored over j and its dominance is demonstrated in practice
9	Absolute importance	The evidence of favoring i over j to the highest possible order of affirmation
2,4,6,8	Intermediate values between two adjacent judgement	Judgement is not precise enough to assign values of 1, 3, 5, 7 and 9 (compromise is needed)

Table 3

Predicted Magnitude of the selected water stations
	Return period (Years)
Serial no	River name	Station No	Methods	2	5	10	25	50	100
1	Sibsa	SW259	Normal Distribution (m)	2.83	3.47	3.80	4.16	4.39	4.60
			Gumbel Distribution(m)	2.70	3.38	3.82	4.38	4.80	5.22
			Log Person 3 Distribution(m)	2.64	2.64	3.75	4.43	4.99	5.60
		SW258	Normal Distribution(m)	3.38	3.80	4.03	4.26	4.42	4.99
			Gumbel Distribution(m)	3.29	3.74	4.03	4.41	4.69	4.97
			Log Person 3 Distribution(m)	3.63	4.09	4.25	4.37	4.58	4.80
2	Rupsa-Pasur	SW243	Normal Distribution(m)	3.53	4.05	4.33	4.63	4.82	4.99
			Gumbel Distribution(m)	3.43	3.98	4.34	4.81	5.15	5.50
			Log Person 3 Distribution(m)	3.63	4.09	4.25	4.37	4.43	4.46
		SW241	Normal Distribution(m)	3.13	3.39	3.53	3.68	3.77	3.86
			Gumbel Distribution(m)	3.08	3.35	3.54	3.77	3.94	4.11
			Log Person 3 Distribution(m)	3.34	3.50	3.54	3.57	3.57	3.58
3	Kobadak	SW165	Normal Distribution(m)	2.64	3.03	3.23	3.44	3.58	3.70
			Gumbel Distribution(m)	2.57	2.97	3.24	3.57	3.82	4.07
			Log Person 3 Distribution(m)	2.56	2.96	3.24	3.59	3.86	4.14
		SW164	Normal Distribution(m)	3.27	3.66	3.86	4.08	4.22	4.35
			Gumbel Distribution(m)	3.20	3.60	3.87	4.22	4.47	4.73
			Log Person 3 Distribution(m)	3.32	3.46	3.78	4.27	4.69	5.16
4	Bhadra	SW29	Normal Distribution(m)	3.33	3.63	3.78	3.95	4.05	4.15
			Gumbel Distribution(m)	3.27	3.58	3.78	4.04	4.23	4.42
			Log Person 3 Distribution(m)	3.23	3.53	3.76	4.08	4.33	4.59
		SW28	Normal Distribution(m)	3.08	3.43	3.61	3.81	3.93	4.05
			Gumbel Distribution(m)	3.02	3.38	3.62	3.93	4.15	4.38
			Log Person 3 Distribution(m)	3.31	3.37	3.32	3.27	3.24	3.21
5	Kobadak	SW163	Normal Distribution(m)	2.52	3.08	3.37	3.68	3.88	4.06
			Gumbel Distribution(m)	2.42	3.00	3.38	3.87	4.23	4.59
			Log Person 3 Distribution(m)	2.57	3.12	3.37	3.58	3.70	3.78
6	Ichamati (Western Border)	SW130	Normal Distribution(m)	3.60	3.94	4.11	4.30	4.42	4.53
			Gumbel Distribution(m)	3.54	3.89	4.12	4.41	4.63	4.85
			Log Person 3 Distribution(m)	3.50	3.85	4.11	4.46	4.73	5.01
		SW129	Normal Distribution(m)	3.87	4.07	4.18	4.30	4.37	4.44
			Gumbel Distribution(m)	3.83	4.04	4.19	4.37	4.51	4.64
			Log Person 3 Distribution(m)	3.85	4.07	4.19	4.34	4.44	4.52
		SW128	Normal Distribution(m)	3.72	4.03	4.19	4.37	4.48	4.58
			Gumbel Distribution(m)	3.66	3.99	4.20	4.47	4.68	4.88
			Log Person 3 Distribution(m)	3.69	4.02	4.21	4.43	4.60	4.72
7	Betna-Kholpetua	SW26	Normal Distribution(m)	3.07	3.40	3.58	3.77	3.89	4.00
			Gumbel Distribution(m)	3.00	3.35	3.59	3.88	4.10	4.32
			Log Person 3 Distribution(m)	2.99	3.36	3.59	3.90	4.13	4.36
8	Satkhira Khal	SW254.5	Normal Distribution(m)	3.48	3.93	4.17	4.42	4.58	4.72
			Gumbel Distribution(m)	3.40	3.87	4.17	4.57	4.86	5.16
			Log Person 3 Distribution(m)	3.52	3.95	4.14	4.33	4.43	4.52

Table 4

Flood zonation percentages for each polder using EV-I method (%)
Polder	F0	F1	F2	F3	F4
Polder27-1	0.28	0.72	6.41	92.58	0.00
Polder33	0.93	1.05	5.95	91.86	0.20
Polder31up	0.98	1.15	3.40	93.79	0.68
Polder-26	0.01	1.65	48.57	49.77	0.00
Polder-23	0.64	1.60	9.87	87.83	0.06
Polder10-12	1.28	1.78	10.46	86.30	0.18
Polder32	0.94	1.99	23.56	73.41	0.10
Polder29	0.29	0.74	71.27	27.70	0.00
Polder31down	0.87	1.54	38.75	58.81	0.04
Polder-4	2.84	5.48	24.16	67.52	0.00
Polder-5	2.48	4.71	26.86	65.70	0.25
Polder7-2	3.05	5.46	31.34	60.15	0.00
Polder-1	9.10	11.66	19.15	58.91	1.18
Polder-3	6.39	9.61	28.79	53.23	1.99
Polder-15	2.06	4.38	61.39	32.17	0.00
Polder-16	37.40	13.34	43.90	5.37	0.00

Table 5

Pairwise comparison matrix of the main criteria with respect to the Parameters
Parameters	Livelihood	Inundation	Population Density	Settlement elevation	House structure	Literacy	Vulnerable People	Weights
Livelihood	1	7	0.5	7	7	0.25	5	0.18
Inundation	0.14	1	0.14	5	5	0.14	0.14	0.06
Population Density	2	6.99	1	7	7	0.33	5	0.21
Settlement elevation	0.14	0.2	0.14	1	1	0.14	5	0.06
House structure	0.14	0.2	0.14	1	1	0.2	0.25	0.02
Literacy	4	6.99	3	6.99	5	1	0.25	0.27
Vulnerable People	0.2	6.99	0.2	0.2	4	4	1	0.16
CI 0.08065, RI 0.0611, CR 0.08065 < 0.10. Hence the matrix is consistent

Table 6

Analyzed weighted for each polder
Polder name	Population density	Housing structure	Settlement Elevation(M)	Inundation (%)	Livelihood	Literacy	Vulnerable People	Calculated Weights
Polder33	3	2	2	1	1	3	2	4.69
Polder32	3	2	2	1	1	3	2	4.69
Polder31down	4	2	2	1	3	3	2	5.27
Polder31up	1	2	2	1	3	3	1	4.45
Polder29	3	2	2	1	3	3	2	5.06
Polder27	3	2	2	1	3	3	2	5.06
Polder-26	3	2	2	1	1	3	2	4.69
Polder-23	3	2	2	1	3	3	2	5.06
Polder-16	1	2	3	2	1.67	3	2	4.506
Polder-15	2	2	2	1	1	2	1	3.307
Polder13-14/1–2	2	2	2	1	3	3	2	4.84
Polder10-12	3	2	2	1	3	3	2	5.06
Polder7-2	2	2	2	1	3	3	2	4.84
Polder-5	2	2	3	1	3	3	2	4.90
Polder-4	2	2	2	1	3	3	2	4.84
Polder-3	5	2	3	1	3	3	2	5.56
Polder-1	1	2	3	1	3	3	2	4.68

PDF

Reviews received at journal
24 Mar, 2022
Reviewers invited by journal
24 Mar, 2022
Editor assigned by journal
17 Mar, 2022
First submitted to journal
16 Mar, 2022

You are reading this latest preprint version

Flood Prediction and Risk Assessment at the South-western Region of Bangladesh

Status:

Version 1

Abstract

Figures

1. Introduction

2. Study Area

3. Materials And Methods

3.4 Flood Map Preparation and Inundation Analysis:

4. Data Collection

5. Results

5.3 Flood Simulation:

6. Discussion

7. Conclusion

Declarations

References

Tables

Status:

Version 1