Using Multi-index Model to Assess the Risk From Flash Flood: a Geospatial Approach on Sunamganj Haor (Wetland) Region, Bangladesh

doi:10.21203/rs.3.rs-2093330/v1

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Using Multi-index Model to Assess the Risk From Flash Flood: a Geospatial Approach on Sunamganj Haor (Wetland) Region, Bangladesh

https://doi.org/10.21203/rs.3.rs-2093330/v1

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Heavy downpours in Meghalaya and Assam regions are causing flash floods in Bangladesh's northeastern Haor region at regular intervals. As high volume of water enters into the Surma river through the downstream river network, the Haor region faces heavy damage almost every year. The major focus of the study was to assess the risk of flash floods in the greater Sylhet region. Literature review of past flood scenarios have shown that Sunamganj District directly faces the wrath of flash floods each year and witnesses tremendous damage. A geospatial multi-index model was developed which considered four major indices (hazard, exposure, sensitivity, and resilience) for this risk assessment. The model systematically accounted for various flood risk indicators related to the economic, social, and physical environment of the Sunamganj District. Geostatistical methods were used to calculate the risk from the indicators and Principal Component Analysis (PCA) was used for composite analysis. The result showed that the high- and very high-risk zones occupied almost 40% (~ 1452.51 Km²), while the very low- and low-risk zones covered about 45% (~ 1554.66 Km²) of the Sunamganj District. Based on the risk rank, Dharampasha upazila had high percentage (~ 60%) which followed by Sulla, Derai, Jamalganj, Daksin Sunamganj, and Tahirpur upazilas respectively. The proposed methods will help the researchers to identify the causes of flash floods and the results will be helpful for policymakers and developers to take steps for flash flood risks reduction.

Haor

Flash Flood

Multi-Index Model

Risk Assessment

More than 90% of disasters such as floods, droughts, hurricanes, and heat waves were caused by extreme weather between 1995 and 2015 in the world (UNISDR, 2015). Floods have become the most frequent and most devastating natural disaster. Among all these disaster, flash floods are the most severe natural disaster resulting in more than 5,000 deaths annually from a global perspective, along with social and economic losses (Bisht et al., 2018).

A flash flood is a hydrological event that occurs within a few hours of heavy rain, rapid snowmelt, a sudden glacial lake outburst, embankment failure, or the rapid break up of an ice block due to rapid temperature rise (Hossain et al., 2008). Flash flood usually happens in small areas. It is more common in the northeastern and southeastern regions of Bangladesh during the pre-monsoon months of April and May because of the surrounding hilly Assam and Meghalaya regions of India and the presence of the Haor basin (Mondal et al., 2021). The northeastern area becomes vulnerable since the upstream basin topography is relatively steep and the concentration time of the basin is relatively short (BWDB 2014; WMO 2003). The main cause of flash floods is heavy rains in the upstream area and the Haor region, which faces the highest rainfall with an average annual rainfall ranging from 2200 mm in the northwest to 5800 mm in the northeast and about 12,000 mm in India's adjacent state of Meghalaya (Kamal et al., 2018). The flash flooding scenario causes the following percentile of damage in the month of March (75%), April (70 to 90%), and May (15–40%) (Mondal et al., 2021).

During the pre-monsoon season (January to May), the Haor lands’ 80% coverage is occupied by Boro rice production which contributes to 18% of the total rice production of the country (Mondal et al., 2021). In Haor areas, the flash flood has a greater impact on agricultural production which resulted greater damage in 2000, 2002, 2007, 2010, 2017, 2019, and 2020 because of the maximum duration of the flood (Abedin & Khatun, 2020). Early flash flood in 2017 caused the most damage in the Sunamganj district (Kumar Biswas et al., 2020). Heavy downpours in the Meghalaya and Assam regions result in the unexpected rise of the water level and water flow speed in streams and rivers that bring a large number of debris, boulders, uprooted trees, obliteration of infrastructures and constructed buildings simultaneously and cause life loss, property damage, and infrastructure destruction in the majority of cases (Kumar Biswas et al., 2020). Flash flooding accelerates water quality degradation due to excessive oil leakage from number of boats. Flash flood victims face food scarcity during the disaster and post-disaster period which is a major impediment to long-term economic development (Kamruzzaman & Shaw, 2018).

Risk assessment approaches are designed to evaluate the loss and flood impact (Gigović et al., 2017) and develop risk maps for land use and infrastructure development (Komolafe et al., 2019; Zeleňáková, M.,2019). The index system techniques should be combined with GIS-based methodologies for successful monitoring and evaluation (Y.K. Qiao & Peng, 2017) because the system considers all components to measure the flash flood risk. In addition, GIS-based techniques are used in collaboration to produce spatial data (Abdelkarim & Gaber, 2019), whereas a multi-index approach is used to assess flash flood risk (Zhao, J.W.,2017), which requires various indicators and a large amount of data (Zhang et al., 2020). For this reason, the study used an index system method and a GIS-based approach to build a spatial multi-index model for flash flood risk assessment of the hoar region.

The study aims to assess the risk of flash floods and identify the risk zone in the Haor region of the Sunamganj district using a multi-index approach. Specifically, the study identifies the flash flood risk determination and various risk zones by developing a map.

Sunamganj, Habiganj, Netrokona, Kishoreganj, Sylhet, Moulavibazar and Brahmanbaria are the seven districts that make up the core northeast Haor area, which spans 1.99 million ha area and 19.37 million people (CEGIS, 2012). These districts contain about 373 Haors, covering an area of about 85,900 ha (859 km²), or about 43% of Bangladesh's total Haor zone (CEGIS, 2012) and 23 transboundary rivers have been identified that flow from India (Hossain et al., 2008). The maximum topography of Haor region is largely flat with maximum land surface elevation less than 10 meters. Due to the physiography, floodwater is moving into Haor region within 3-to-6-hour precipitation. The district of Sunamganj has been chosen as study area due to continuous flash flood occurrence over years. Previous studies showed that Sunamganj District faced most agricultural land damages due to 2017, 2019, 2020 flash flood. The district is covered by major Haor systems of about 95 Haors in the north-eastern region of Bangladesh (Mondal et al., 2021) and the physiography is accessible to the heavy rainfall and flood water from the upstream of Meghalaya hills which contributes to the flash flood occurrence nearly every year during pre-monsoon season (March-May).

Table 1

Major Flood History of Sunamganj District
Year	Flood Water entered the district	Inundation occurred till
1999	03 May	06 May
2000	28 April	30 April
2002	14 April	18 April
2004	13 April	15 April
2007	25 April	28 April
2010	17 April	19 April
2017	28 March	05 April
2019	04 July	10 July
2020	26 June	28 June
(Source: Abedin & Khatun, 2020)

To assess the flash flood risk in the Sunamganj District, a GIS based multi-index model was developed by this study. The multi-index system can be explained into the object layer, the index layer, and the indicator layer. The Sunamganj District flood risk assessment was the object layer; the index layer included the hazard index (H), Sensitivity index (V), exposure index (E) and Resilience index (R); and the indicator layer included 16 flood risk indicators. Data representing the 16 indicators were collected and pre-processed in the GIS environment.

Then, the data for each flood risk indicator were incorporated into the GIS. Finally, a risk distribution map of the Sunamganj District was generated by the combination of four indexes (hazard, exposure, sensitivity and resilience) and PCA (Principal Component Analysis) method.

3.1 Data Type and Source

The study is based on the secondary data and satellite images, water level data, rainfall data, demographic data were required for the outcome.

Table 2

Data Source and Type
Index Layer	Parameters	Data Types	Temporal Resolution	Source	Year
Hazar Index	Flash Flood	Water Level	Observed 3 hourly	BWDB	2002, 2007, 2017, 2019, 2020
Exposure Index	Precipitation	Rainfall	Daily	BWDB	2002, 2007, 2017, 2019, 2020
	Elevation	ALOS Word 3D – 30m		JAXA
	Drainage Pattern
	Soil Moisture
	Slope
Sensitivity Index	Demography	Population and Housing Census		BBS	2011
	Geomorphology	Sentinel 2A	10 Days	ESA	2020
	Wetland
	Vegetation
	Build Up Area
	Agricultural Land
Resilience	Literacy	Population and Housing Census		BBS	2011
	House Condition	Population and Housing Census		BBS	2011
	Economy	Economic Census			2020

3.3 Hazard Index Analysis

The intensity of flash flood in a given area is proportional to the hazard level. Water level data was used for flood depth extraction of Sunamganj district. BWDB 3-hourly water level data of 10 stations of the study area of five years (2002, 2007, 2017, 2019, 2020) during flash floods were taken into consideration for the flood depth analysis. The data were interpolated by Kriging method using “Kriging” tool in ArcGIS software for weights of these data. Then Decay Coefficient Simulation Model was applied by using ALOS 30m DEM data for finding out the flood depth.

3.4 Exposure Index Analysis

This analysis is used to identify life and property which are exposed to the flood hazard. Five parameters were included to analyze the exposure index of this study.

3.4.1 Elevation

The topographic profile was prepared based on ALOS Word 3D DEM of 30-meter spatial resolution and the interpolate line of “3D analyst” tool of ArcMap was used for preparing the topographic profile where 10 cross sections were taken. Then the “profile graph” tool was applied to create topo profile graph that revealed the elevation and distance of cross section line.

3.4.2 Slope

In this study slope map was created directly from the DEM in ArcGIS environment by using newer implementation “Slope” tool.

3.4.3 Drainage Density (DD)

This describes the ratio of the total length of rivers in each area to the area's size which means higher drainage density indicates more surface runoff that arises flood risk (Elkhrachy, 2015).

ALOS DEM were downloaded from the JAXA website for the density calculation. The study area consisted of two mosaic elevation models, “N024E091” and “N024E090” which were converted into a single raster and bounded by the Area of Interest (AOI), the boundary of the study area. The “Hydrology” tool and Line Density method were used for the drainage density in ArcGIS 10.8.

3.4.4 Rainfall Distribution

Rainfall data is considered as significant components for flood risk assessments by academics worldwide (Mahmoud & Gan, 2018). BWDB daily rainfall data from 10 stations of Sunamganj district of five years (2002, 2007, 2017, 2019, 2020) during flash floods were taken into consideration.

10 days rainfall data during the flash flood of 2002, 2007, 2017, 2019 and 2020 were analyzed to understand the rainfall pattern of Sunamganj district. A standard deviation value was calculated for frequency and uncertainty of the rainfall pattern condition of this specific time. The standard deviation equation was:

$$\sigma =\sqrt{\frac{\sum {\left(x-\mu \right)}^{2}}{n-1}}$$

Where, σ = Standard Deviation; x = Each value; µ = Variables Average Value; n = Number of Values in the Sample

The standard deviation data were imported in ArcMap for interpolation by using “Kriging” tool. Finally, the rainfall pattern map of Sunamganj District was produced.

3.4.5 Topographic Wetness Index (TWI)

The essential factor TWI for flash flood research quantifies topographic control on hydrological processes. It is related to the soil moisture that influences rapid runoff and flash floods.

ALOS World 3D DEM data were projected for flow direction and flow accumulation. The slope of the area was calculated. As the border pixels have zero flow accumulation value, value 1 was added with flow accumulation since. If the value 1 wasn’t added, the watershed showed division which had no flow accumulation would have a zero value and (0) will be undefined.

$$TWI=\frac{Flow acuumulation}{\text{tan}slope}$$

3.5 Sensitivity Index Analysis

Sensitivity is related to the degree of threat to a particular population or the capacity of a system, which can suffer and respond harmfully during the occurrence of any kind of hazardous event. It is useful for risk reduction, hazard and disaster management and climate change areas. Six indicators were included to assess the sensitivity index.

3.5.1 Geomorphology

Blue, Green, Red and Infrared these four bands of sentinel 2A images and ALOS world 3D DEM data were used for geomorphology analysis. The Blue and Green bands were composite for DOP and Water Attenuation to calculate bathymetry. NDVI, Maximum Likelihood supervised classification images, ISO Cluster unsupervised classification were done by DEM, Bathymetry were conducted for geomorphological map of Sunamganj District.

3.5.2 Water Ratio Index (WRI)

Water Ratio Index was calculated using the following formula (Sajjad et al., 2021):

$$\text{W}\text{R}\text{I}= \frac{\text{G}\text{R}\text{E}\text{E}\text{N}+\text{R}\text{E}\text{D}}{\text{N}\text{I}\text{R}+\text{S}\text{W}\text{I}\text{R}}$$

Where, $\text{G}\text{R}\text{E}\text{E}\text{N}$= Green Band, $\text{R}\text{E}\text{D}$= Red Band, $\text{N}\text{I}\text{R}$= Near Infrared Band, $\text{S}\text{W}\text{I}\text{R}$= Short-wave Infrared Band.

For WRI analysis Sentinel 2A images were used for WRI equation and the specific bands for Sentinel 2A for the equation was:

$$\text{W}\text{R}\text{I}= \frac{\text{B}\text{a}\text{n}\text{d} 3+\text{B}\text{a}\text{n}\text{d} 4}{\text{B}\text{a}\text{n}\text{d} 8+\text{B}\text{a}\text{n}\text{d} 12}$$

3.5.3 Normalize Difference Vegetation Index (NDVI)

According to this formula, the density of vegetation at a certain point of the image is equal to the difference in the intensities of the reflected light in the red and infrared range divided by the sum of these intensities. This Index defines values from − 1.0 to + 1.0 which is basically representing the greens, where negative values are mainly formed from clouds, water, and snow. For NDVI calculation, Sentinel 2A images were used and the general equation was:

$$\text{N}\text{D}\text{V}\text{I}= \frac{\text{N}\text{I}\text{R}-\text{R}\text{E}\text{D}}{\text{N}\text{I}\text{R}+\text{R}\text{E}\text{D}}$$

Where, $\text{R}\text{E}\text{D}$= Red Band, $\text{N}\text{I}\text{R}$= Near Infrared Band and specifically for Sentinel 2A, the bands were:

$$\text{N}\text{D}\text{V}\text{I}= \frac{\text{B}\text{a}\text{n}\text{d} 8-\text{B}\text{a}\text{n}\text{d} 4}{\text{B}\text{a}\text{n}\text{d} 8+\text{B}\text{a}\text{n}\text{d} 4}$$

3.5.4 Normalized Difference Built-up Index (NDBI)

For NDBI, the value ranges from − 0.1 to 0.1 for barren rocks, sand and snow; from 0.2 to 0.5 for shrubs, grasslands and senescing crops, from 0.6 to 0.9 for dense vegetation and tropical rainforest; -1 for deep water. The following equation was used for NDBI (Khokhar et al., 2021):

$$\text{N}\text{D}\text{B}\text{I}= \frac{\text{S}\text{W}\text{I}\text{R}-\text{N}\text{I}\text{R}}{\text{S}\text{W}\text{I}\text{R}+\text{N}\text{I}\text{R}}$$

Where, $\text{N}\text{I}\text{R}$= Near Infrared Band, $\text{S}\text{W}\text{I}\text{R}$= Short-wave Infrared Band. For Sentinel 2A, the specific bands were:

$$\text{N}\text{D}\text{B}\text{I}= \frac{\text{B}\text{a}\text{n}\text{d} 11-\text{B}\text{a}\text{n}\text{d} 4}{\text{B}\text{a}\text{n}\text{d} 11+\text{B}\text{a}\text{n}\text{d} 4}$$

3.5.5 Agricultural Land

Maximum Likelihood Supervised Classification method was applied for land use land cover (LULC) map of the study area to extract agricultural land. Sentinel-2A, Level 1C (ID = L1C_T46RCN_A028444_20201202T043140 and ID = L1C_T45RZH_A028444_20201222T0432140) acquired within the time of December 2020, were downloaded from the United States Geological Survey (EarthExplorer, n.d.). Two images were used for mosaics and 10 bands (2–8, 8A,11 and 12) of these two images were composited and the Sunamganj shapefile was clipped from the map. 1000 random points were taken and points were classified into five classes (vegetation, water body, barren land, agricultural land, and built-up area) for creating proper signature file. Among all classes, agricultural land was extracted and compared with DEM data for understanding the elevation of Agricultural land.

3.6 Resilience Index

Literacy rate, housing structure, poverty rate – these 3 parameters were taken under consideration for resilience index. These data were collected from BBS 2011 “Community Book” of Sunamganj district and the “Poverty Maps of Bangladesh 2016” by the collaboration of the Bangladesh Bureau of Statistics (BBS) and the World Food Program (WFP). Then these data were input into the upazila shapefile of Sunamganj district using ArcGIS. After that “conversion” tool of ArcGIS 10.8 was used and raster images were produced for each parameter for further PCA analysis.

3.7 Principal Component Analysis (PCA)

The data in the input bands are transformed from the input multivariate attribute space to a new multivariate attribute space whose axes are rotated with respect to the original space using the Principal Components tool after the indices are analyzed. The axes (attributes) in the new space are uncorrelated. The main reason to transform the data in a principal component analysis is to compress data by eliminating redundancy (Azotea et al., 2015; Gómez-Palacios et al., 2017; Zeng et al., 2012).

This was run using ArcGIS 10.8 to figure out the principal components of the indices. By using the raster calculator tool, the percent of eigenvalues were taking into account of the raster with continuous data to find out the total sum of products of the percent eigenvalues and raster. Hence, the weighted value of the parameters gave output to the composite indices map.

After conducting PCA for Exposure index, Sensitivity index and Resilience index, the Eigen values for each parameter were received. The Eigen value of indices were given bellow:

Table 3

Summary of PCA Results
PC Layer	Eigen Value	% Of Eigen Values
Exposure Index
Elevation	1.0739	34.2332	34.2332
Slope	0.89236	28.4459	62.6791
Drainage Density	0.83548	26.6329	89.312
Rainfall Pattern	0.20311	6.4746	95.7866
TWI	0.13217	4.2134	100
Sensitivity Index
Geomorphology	3.35414	55.0629	55.0629
WRI	0.83307	13.6761	68.739
NDVI	0.64827	10.6422	79.3813
NDBI	0.45949	7.5432	86.9244
Agricultural Land	0.32352	5.3111	92.2355
Population Density	0.30005	4.9258	97.1613
Dependent Population	0.17292	2.8387	100
Resilience Index
Literacy Rate	0.68241	53.5042	53.5042
Housing Structure	0.50092	39.2744	92.7786
Poverty	0.0921	7.2214	100

3.8 Flash Flood Risk Analysis

The Exposure Index map, Sensitivity Index map and Resilience index map were produced by PCA and the Hazard Index map was produced from the flood depth data. Flash Flood Risk (FFR) of Sunamganj District was redefined by calculating the risk map with the assistance of “raster calculator” tool in ArcGIS. The applied equation was given below:

Flash Flood Risk (FFR) = {Hazard * (Exposure + Sensitivity)} – Resilience

From the previous studies it was found that most of the researchers developed the flood susceptibility class boundaries based on their own expert opinion and there was no specific rule for classification. In this study, the resultant flood susceptibility map was classified into very low risk, low risk, moderate risk, high risk, very high risk zones based on the natural braking method in the ArcGIS environment(Das, 2019; Mahmoud & Gan, 2018; Ullah & Zhang, 2020).

Four types of indexes were studied for the risk assessment. The results of the indexes are:

4.1 Hazard Index Assessment

The flood depth of the years of 2002, 2007, 2017, 2019 and 2020 were analyzed and the result showed that an average flood depth of 3.61 to 8.74 meter was considered as high-risk zone found in the western part of the upazilas. Tahirpur, Dharmapasha, Jamalganj, Derai, Sulla upazilas had the majority area in high-risk and very high-risk zone because of the presence of large Haors densely and comparatively lowland area which made the area highly vulnerable to flash floods.

4.2 Exposure Index Assessment

The exposure index was calculated by studying elevation, slope, drainage density, average rainfall, TWI. The undulating topography of Sunamganj showed higher elevation (up to 10 meters) in the northern part in the Meghalaya border of India but land elevation below 8 meters was found in rest of the area. The elevation and flood had inverse relationship which resulted higher elevation had lower exposure. The elevation map marked that most of the Sunamganj region was below 5 meters with very low gradient slope (0 to 3.03 degrees) though the slope gradient varied from 0 to 55.3. The surrounding hilly area with the low gradient slopes such as flat terrains in northwest appearance was highly vulnerable to flash flood occurrences compared to higher gradient slopes. Again, the Surma river flowing through the study area had higher drainage density (> 2 km²) which indicated more surface runoff associated with high-risk and very high-risk of the flash flood zones. Rainfall occurred more frequently in current years was found by analyzing 5-year rainfall distribution map. The higher standard deviation values of northern part showed heavy downpour due to the presence of cyclonic circulation over Sub-Himalayan West Bengal which made the area high-risk and very high-risk zones by orographic rainfall phenomenon. The higher topographic wetness index (TWI) areas were more susceptible to flash floods than lower TWI areas because higher TWI was found in higher density of deep depression. The TWI map illustrated the majority study area had higher TWI values turned the area into high-risk and very high-risk zones because the higher TWI value represented the rapid runoff and flash flood.

The spatial distribution of Sunamganj’s flash flood risk exposure was high in the northwestern part and low in the northeastern part (Fig. 5). The high drainage density, low elevation, low gradient slope, higher TWI were found in high exposure area. Dowarabazar and Bishwambarpur upazilas showed a very low-risk exposure due to the presence of higher elevation and vast portion of Dharmapasha, Tahirpur, Derai, Jamalganj areas indicated high-risk and very high-risk zones for flash flooding.

Table 4

Correlation Metrix of Exposure Index
PC Layer	Elevation		Drainage Density	Rainfall Pattern	TWI
Elevation	1
Slope	0.38497**	1
Drainage Density	0.25987**	0.06408**	1
Rainfall Pattern	-0.3416	-0.00056	-0.0215	1
TWI	0.3201**	0.5772**	0.07131*	0.0099	1
Here, “*” = Highly Significant, “” = Significant

4.3 Sensitivity Index Assessment

Sensitivity index assessment was discussed with the help of geomorphology, WRI, NDVI, NDBI, agricultural land, population density and percentage of dependent population. Six geomorphological features such as deep depression, shallow depression, floodplain, active channel, low land and plain land were studied for geomorphological map in this study. The map revealed that high density of deep depression, shallow depression, floodplain and active channel were responsible for high-risk zone. Because heavy rainfall in active river or channel resulted rapid runoff, sometimes overflowed on the riverbank and onrush of upstream river floods were the reasons of inundating more areas. The Sunamganj had crisscrossed by numerous rivers coming down from the hills of India and considerable number of tributaries and distributaries carried out a huge amount of runoff water which caused flash floods frequently. Numerous distributaries, tributaries, rivers and Haors in most of the unions of Sunamganj showed high mean WRI value which indicated the deep water. The higher WRI occurred when the floodwater overflowed very quickly to the surrounding areas which made the area more sensitive to flash floods. Tahirpur upazila’s three unions named Dakshin Sreepur, Uttar Sreepur and Tahirpur and other three upazlias named Dharmapash, Derai, and Jamalganj showed higher mean WRI value. Higher NDVI value indicated dense vegetation which reduced the intensity or speed runoff water of flash floods. But higher density of waterbody, shallow and deep depression resulted lower NDVI. According to the topographic profile, the northeastern and the eastern part of Sunamganj had the upland than the western part and this upland showed high NDVI value. Based on the NDVI map, low land and depression areas with less NDVI value was in very high-risk zone for flash flood. A positive correlation between NDBI and flash flood was observed where the value ranged from 0.0873 to 0.996 which indicated built-up area. The downstream and adjacent to the river network area showed higher NDBI value because the riverbank area had been filling with settlement and the high NDBI containing areas were exposed to the flood risk. According to the agricultural map, majority agricultural land was lowland and situated beside shallow, deep depression and this agricultural land submerged during monsoon. This low elevated agricultural land in Sunamganj was most vulnerable to flash floods because of the rapid flood water movement from the upland to lowland which resulted middle and western parts of Sunamganj very high-risk zone for potential damage of agricultural land.

The sex ratio of the study area was almost equal which meant more than 50% of population could have high risk because women and children could face more vulnerability during flood occurrence. The study found that flash floods had greater effects on densely populated areas than sparsely populated areas and Chatak, Sunamganj Sadar and Dowarabazar upazilas showed higher population density. Lastly, dependent population refers age group from 0 to 14 and above 65 years and dependent population is considered most vulnerable for flash flood. The study area showed the percentage of the dependent population valued from 42.20–54.30% and the graph revealed that the northern upazilas had higher dependent population.

The sensitivity index was geomorphology and WRI dominating. The map showed that western part of Sunamganj was more sensitive to flash flood than eastern part (Fig. 8). The higher WRI value containing areas such as Dharmapash, Tahirpur, Jamalganj and Sulla were in high-risk and very high-risk zones to flash flood. Because the Haor orientation accelerated more sensitivity to flash flood in these upazilas.

Table 5

Correlation Metrix of Sensitivity Index
PC Layer	Geomorphology	WRI	NDVI	NDBI	Agricultural Land	PD	DP
Geomorphology	1
WRI	0.23995**	1
NDVI	0.14607*	0.83035**	1
NDBI	-0.24454	-0.713	-0.61216**	1
Agricultural Land	-0.18487	-0.48125	-0.43057	0.33811**	1
Population Density (PD)	-0.13532	-0.37563	-0.35624	0.27354**	0.3286**	1
Depending Population (DP)	-0.10259	-0.16632	-0.15509	0.1602*	0.16503*	0.20009**	1
Here, “*” = Highly Significant, “” = Significant

4.4 Resilience Index Assessment

Literacy rate, poverty rate and housing structure of the study area were taken under consideration for the assessment. Lack of educational institutions, significant amount of inundated area during monsoon resulted very low literacy rate of about 35% in average. The upazilas of Tahirpur, Dharmapasha, Jamalganj had the lowest literacy rates valued 30.4%, 29.2% and 32.5% respectively. The poverty rate scenario of the study area showed that Tahirpur, Jamalganj, Derai, and Sulla upazilas had the highest rates of poverty at over 22.61% and specially the Sulla union had highest percentage of 60.9%. The overall poverty condition indicated that 26% inhabitants of Sunamganj district lived just below of the upper poverty line.

The housing structure of the area were made of four types such as Pucca, Semi-pucca, Kancha and Jhupi whereas three of the structures were considered highly vulnerable to flash flood except Pucca construction. According to BBS report of 2011, more than 60% housing structure was Kancha and Jhupi of this district. Specially Sulla, Dharmapasha, Tahirpur, Jamalganj, Derai, Dakshin Surma had above 80% rate of vulnerable housing structure but Pucca and Semi-puccha constructed house percentile were very low in this whole district and Jagannathpur and Chhatak had higher percentage of Pucca house of about 17.1% and 16.1% respectively and other upazilas showed the existence of Pucca house less than 8%.

The lower literacy rate in mass level resulted unconsciousness of the severity of flash flood and less willingness of adopting precautions against flash floods. The significant amount of people below poverty line showed their inability to fight with flash floods in constructive and non-constructive level and lastly, the vulnerable housing structure made the weaker resilience to flash flood.

Table 6

Correlation Metrix of Resilience Index
PC Layer	Literacy Rate	Poverty	Housing Structure
Literacy Rate	1
Poverty	-0.15031	1
Housing Structure	0.3564**	-0.07734	1
Here, “*” = Highly Significant, “” = Significant

The resilience index map showed that Jamalganj, Tahirpur, Dharamapasha and Sulla upazilas showed very low resilience which made the area high-risk and very high-risk zones for flash flood (Fig. 9). Because these upazilas were Haor based region and the economic system and ecology resulted less availability of basic right of proper shelter, food and educational facilities.

4.5 Flash Flood Risk Assessment

The flash flood risk map was generated by integration of four indexes map (hazard, exposure, sensitivity, resilience). The study revealed that 39% area of the Sunamganj district had very high probability of flash flood while moderate, low, and very low vulnerable areas were 18.25%, 4.13%, and 38.14% respectively. The risk assessment map indicated that western part of the study area was highly susceptible to flash flood than eastern part (Fig. 10). Because the western part had characteristics of very low elevation, lower degree of slope, high drainage density, low economic status and poor housing structure. The geomorphological feature and vulnerability of human’s social conditions caused the higher risk in western zone. Besides, the flash flood risk map showed that the zone of high density of deep and shallow depressions existing places faced the highest number of historical floods.

Table 7

Area wise Flash Flood Risk
Upazila	Very Low	Low	Moderate	High	Very high
Bishwambarpur	141.89	3.65	18.39	15.84	9.84
Chhatak	295.89	19.45	61.07	46.23	8.46
Dakshin Sunamganj	61.44	14.66	66.79	109.60	55.36
Derai	65.58	18.71	91.76	113.46	117.14
Dharampasha	72.11	11.17	118.98	137.16	156.37
Dowarabazar	207.84	5.19	30.37	39.90	9.72
Jagannathpur	212.04	19.27	67.84	44.36	24.78
Jamalganj	78.85	18.60	54.23	87.32	93.52
Sulla	27.85	13.29	65.32	88.14	64.66
Sunamganj Sadar	147.70	20.55	42.11	49.19	18.64
Tahirpur	91.65	7.27	54.51	59.17	103.64
Total Area	1402.85	151.81	671.36	790.37	662.14
Area (%)	38.14	4.13	18.25	21.49	18.00

The table showed that Dharmapasha, Tahirpur, Jamalganj, Derai, Sulla and Dakshin Sunamganj upazilas showed the majority portion of the area at high risk and very high-risk zones due to low land and high density of deep and shallow depression.

Table 8

Area wise Flash Flood Risk Rank
Risk Rank	Upazila	Area (%)
1	Dharampasha	59.21
2	Sulla	58.94
3	Derai	56.71
4	Jamalganj	54.38
5	Dakshin Sunamganj	53.58
6	Tahirpur	51.48
7	Sunamganj Sadar	24.38
8	Jagannathpur	18.77
9	Dowarabazar	16.94
10	Bishwambarpur	13.54
11	Chhatak	12.69

It was found that more than 50% area of these upazilas (Dharamapasha, Sulla, Derai, Jamalganj, Dakshin Sunamganj and Tahirpur) were in risk zone. Comparatively lower appearance of deep and shallow depression, higher elevation made Bishwambarpur, Chattak, Dowarabzar, Jaganathpur and Sunamganj Sadar upazilas less vulnerable to flash flood.

The geographical setting of Sunamganj district resulted frequent flash flood. To assess the flash flood risk at Sunamganj district four indices were used and Principal Composite Analysis (PCA) was applied to composite all the indicators. The key findings of the study were:

The hazard index assessment showed that the western part of the Sunamganj District was mostly at high and very high risk of flash flooding.

The exposure index exhibited that the lower elevation land, lower slope, and high drainage density areas were responsible for high risk and very high-risk zones.

The sensitivity index showed the presence of deep and shallow depressions was high, the WRI value was high and the percentage of lower agricultural land was high. All these described portions were basically in high and very high-risk zones for flash floods.

The resilience index explained that the eastern Sunamganj part had higher elevation and little presence of deep depression which showed high resilience and a very low and low risk zones for flash floods.

The risk map showed that high and very high-risk zones occupied 39.49% (~ 1452.51 Km²) of the whole Sunamganj District, while the lowest risk zones and very low and low risk zones was around 42.27% (~ 1554.66 Km²). Again, the western and northwestern part of Sunamganj District were identified as very high and high flood susceptibility zones.

The area-wise risk ranks were Dharmapasha upazila (59.21%) was in high and very high-risk zone and other identified high risk zones were Sulla (58.94%), Derai (56.71%), Jamalganj (54.38%), Daksin Sunamganj (53.58%), and Tahirpur (51.48%).

Low elevation, very gradual slope and high drainage density, presence of high amount of deep depression made Sunamganj district more vulnerable. On the contrary, eastern parts upazilas of Sunamganj District were classified in zones of very low to low flood susceptibility. The study would have much more accuracy if sufficient water level data, precipitation station data in BWDB, updated BBS data were available. However, the study will be useful for developing susceptibility maps in flash flood- prone regions and identify more possible causes to handle the flash flood risk at local level.

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No competing interests reported.

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Using Multi-index Model to Assess the Risk From Flash Flood: a Geospatial Approach on Sunamganj Haor (Wetland) Region, Bangladesh

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Abstract

Figures

1. Introduction

2. Study Area

3. Methodology

3.1 Data Type and Source

3.3 Hazard Index Analysis

3.4 Exposure Index Analysis

3.4.1 Elevation

3.4.2 Slope

3.4.3 Drainage Density (DD)

3.4.4 Rainfall Distribution

3.4.5 Topographic Wetness Index (TWI)

3.5 Sensitivity Index Analysis

3.5.1 Geomorphology

3.5.2 Water Ratio Index (WRI)

3.5.3 Normalize Difference Vegetation Index (NDVI)

3.5.4 Normalized Difference Built-up Index (NDBI)

3.5.5 Agricultural Land

3.6 Resilience Index

3.7 Principal Component Analysis (PCA)

3.8 Flash Flood Risk Analysis

4. Result And Discussion

4.1 Hazard Index Assessment

4.2 Exposure Index Assessment

4.3 Sensitivity Index Assessment

4.4 Resilience Index Assessment

4.5 Flash Flood Risk Assessment

5. Conclusion

References

Additional Declarations

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