Understanding Flood Vulnerability Using Index-based Approach: A Study of Chilmari Upazila,Kurigram,Bangladesh

Abstract

The lives and livelihoods of the people of Bangladesh are regularly threatened by natural disasters and climate dangers. Floods are the most common occurrence that puts people and their properties at risk. This study was conducted to assess the flood vulnerability of Chilmari upazila, which is located in Kurigram district of Bangladesh. Using a random sampling technique, primary data was obtained from 395 households in six unions of Chilmari upazila through a structured questionnaire survey. The Flood Vulnerability Index (FVI), an index-based approach, has been used to assess flood vulnerability. A set of 23 indicators has been fixed to assess four components of floods: physical, economic, social and environmental. Following this procedure, an FVI score was calculated for each of the unions (administrative unit of upazila) of Chilmari. Ashtamir Char, Nayerhat and Chilmari were designated as ‘very vulnerable’ based on the FVI study. Since these unions have relatively low levels of adjustability, coping ability and resilience, their sensitivity and vulnerability are high. Two of the unions of Chilamri are found to be highly vulnerable to floods and another to be generally vulnerable to floods. The results of the FVI analysis have been used to produce a flood vulnerability map of Chilmari upazila. As FVI consists of multi-dimensional indicators, the analysis can be used to pursue specific flood-mitigation measures in Chilmari.

Keywords

Floods vulnerability flood vulnerability index vulnerability components

Introduction

Among the many types of natural catastrophes, floods are the most prevalent and have the greatest impact on human life (Balica, 2007; Kha et al., 2011; Wang et al., 2011). They can cause damage physically, socially and economically in urban and rural locations (Masood & Takeuchi, 2011).

Bangladesh, being an entirely river-based country, is infamous for its susceptibility to flooding due to its flat geography (Brouwer et al., 2007; Mirza et al., 2003). Because the country is at the intersection of several natural hazard risk zones, and is already poor, its government made a plea to the world community at the World Climate Conference 3 in Geneva in 2009 for greater technological and economic assistance to help them build more efficient flood-protection techniques (Younus, 2020). A lot of preventive measures, and structural and non-structural flood management tactics have already been implemented to help minimize the negative consequences and lower the likelihood of flood occurrence (Paul, 1995). Although floods are a serious problem in Bangladesh, it appears that no risk assessment based on indicators has been conducted there. Even though some researchers have looked into how to better protect a community against the threat of flooding (Brammer, 1990; Brouwer et al., 2007; Mirza, 2002, 2003; Mirza et al., 2003; Webster et al., 2010), additional work is needed to identify which houses or group of houses are the most vulnerable and how to get them back to a more stable situation.

Because of its significance in human-environment studies, the idea of vulnerability has changed over the years and has benefited from the input of numerous academic fields. As a result, there is a wide variety of interpretations, many of which seem at odds with one another (Cutter, 1996; Hebb & Mortsch, 2007). Flood vulnerability is defined as the extent to which a community is affected by a flood risk (UNESCO-IHE). Vulnerability may be stated as a measure of sensitivity to the flood (low, medium, high and very high). One definition of vulnerability is ‘the degree to which a system is vulnerable to damage from a hazardous occurrence such as flooding due to relative social or economic inadequacies or unfavourable conditions’ (Balica, 2007; Cardona 2003; Seventh Framework Programme, 2011). In order to develop a useful definition for this thesis, it is necessary to draw a line between resilience and the working definition. Therefore, ‘the preconditions of being injured owing to unfavourable situations, prior to the area flooding’ is the working definition of susceptibility in this study.

Balica (2007) defines exposure as the likelihood of a system being affected by a flooding event because of its proximity to the affected area. Exposure can also be thought of in terms of the characteristics that seem to be present in a given area that is vulnerable to flooding. Products, structures, historical artifacts, agricultural land and people are all examples of values. Generally speaking, exposure refers to the broad patterns and processes that estimate the magnitude and duration of a flood, which is a function of the amount to which a property is located within flood risk zones. Fuchs et al. (2011) define it as the connection between vulnerable elements and the threat, and Messner and Meyer (2006) use a similar definition.

The term ‘resilience’ can mean either the ability to adapt to change or the resistance to change. According to the Intergovernmental Panel on Climate Change (IPCC, 2001), resilience is ‘the capacity of a system to recover quickly from disturbances and to recover fully from the repercussions of adverse events’ (Chambers, 1989/2006; IPCC, 2001; Pelling, 2003). Resilience can be defined as ‘maintaining significant levels of efficiency in its components’, as explained by Balica (2007). And like Balica (2007), Cardona (2003) condenses the multiple causes of a lack of resilience into ‘the restrictions of access and mobilization of the resources’.

The goal of this study is to assess flood vulnerability in Chilmari upazila of Kurigram district, Bangladesh. Two questions have been explored in this research: (a) Which factors are responsible for flood vulnerability in Chilmari upazila? (b) Is there any difference in the extent of vulnerability in the six unions of Chinlmari upazila? This upazila is one of the areas in Bangladesh that has been hit the most by flooding. Because a large portion of the upazila is situated along the Brahmaputra river, much of it frequently experiences flooding and erosion. The six unions that make up this upazila are vulnerable to flooding because of the relatively low-lying terrain and deltas in the area, as well as the presence of a major river, the Brahmaputra (Bhuiyan, 2014). In this study, the Flood Vulnerability Index (FVI) method of assessing flood hazard vulnerability is employed in order to discover which regions within Chilmari upazila are most prone to flooding.

The main goal of a flood vulnerability assessment is to identify which regions are at the greatest danger of flooding and to create effective flood-mitigation plans. Flood vulnerability may be measured using three approaches: FVI, Vulnerability Curve (VC), and Historical Loss Data (HLD; Solin, 2012). FVI, the most popular strategy, is an efficient method used by planners and policymakers to help them decide where to place flood mitigation projects and to educate citizens about the location of dangerous areas and towns that are vulnerable to flooding (Balica et al., 2009). The indicator-based approach provides a more comprehensive picture of regional flood vulnerability than alternative methods. Vulnerability is the primary concern in flood risk management. One of the primary aims of flood vulnerability assessment is to forge a connection between abstract ideas about flood vulnerability and practical administrative tasks. With so many options for vulnerability assessments, it is up to authorities to pick the one that works best (Nasiri et al., 2018).

The article ‘Social Impacts and Flood Vulnerability in Bangladesh and Nepal’ (Dewan, 2015) examined flood vulnerability, impacts and coping strategies to propose a long-term mitigation strategy. Since 1970, Bangladesh and Nepal’s floods have increased in frequency, severity and length, causing human suffering, infrastructure damage, crop and agricultural loss, and severe economic impacts. Both countries’ disaster management bureaus have held many conferences, seminars and trainings to educate disaster managers and the public about scientific information and disaster preparedness. These countries have lived with flooding for centuries, using traditional/indigenous knowledge and other local adaptation practices. Indigenous, traditional and conventional traditions must be integrated into national and regional policy through a participatory process that includes policymakers and stakeholders (Dewan, 2015).

‘Flood Hazards and Vulnerability Assessment in a Riverine Flood Prone Region: A Case Study’ (Bhuiyan, 2014) uses remote sensing and GIS to assess riverine flood risks and vulnerabilities in a flood-prone area. Flood frequency analyses determine flood risk and vulnerability. Settlement and fisheries vulnerability maps display vulnerability. Vulnerability develops from depth harm. Settlement and fishing vulnerability functions generate a Raster-based vulnerability chart (Bhuiyan, 2014).

‘Community Responses to Flood Early Warning System: Case Study in Kaijuri Union, Bangladesh’ (Fakhruddin et al., 2015) examined community-based responses. Early warning reduces disaster risk. In recent decades, medium-range and intermittent forecasting has improved. This article proposes a medium-range flood forecasting framework that prioritizes agricultural users to reduce flood impacts on farmers. Group consultations determine danger and vulnerability. The study created a flood danger map and responses to probabilistic forecasts for farmers’ early warning needs. This report shows organizational decision-making using probabilistic forecasts. Focus group meetings, informal interviews, and investigations determined crop and livestock lead-time demands, impacts and management options. Flood risk mapping and early warning impacts were found (Fakhruddin et al., 2015).

Since knowing how vulnerable a community is to extreme flooding is crucial for its survival and for adapting to climate change, several investigations have been carried out on flood danger, including flood vulnerability assessments. Most of the flood vulnerability assessments carried out in Bangladesh are more theoretical than practical (Aktar et al., 2021). After a comprehensive review of the literature, it was identified that assessment of flood vulnerability in Chilmari upazila using the FVI method would contribute to this literature gap. In order to determine which areas are most at risk of flooding, the FVI is used to rank them. In other words, FVI bridges the gap between academic estimates of flood vulnerability and practical flood control.

Research Methods

The study has incorporated a mixed methodology. Both primary and secondary data have been collected to complete the research. The data required for vulnerability assessment has been collected by conducting focus group discussions (FGDs), and a household questionnaire survey. The demographic data has been collected from the upazila parishad (sub-district council) and union parishad (council) office of Chilmari upazila.

Study Area Profile

The location of study is an upazila (sub-district) in Bangladesh that has been hit particularly hard by the floods. The Chilmari upazila is located in the district of Kurigram, which is located in the country’s northernmost region. A total of 224.97 square kilometres make up the upazila, which has a population of 140,165 and a total of 31,689 households as of 2012 according to the Bangladesh national web portal. Half of the people living in the upazila are literate. The majority of the population relies on farming for their livelihood. Chilmari is located at 25°56.67′ N 89°69.17′ E, situated close to the border between India and Bangladesh. Cutting across the upazila is the great Brahmaputra river. Six different unions exist in Chilmari upazila: Ashtamir Char, Chilmari, Thanahat, Nayerhat, Ramna and Raniganj. A map of Chilmari upazila is presented in Figure 1.

Figure 1.

Map of Chilmari Upazila.

Properties and Calculation of FVI

Performing a vulnerability assessment and, more specifically, putting together an FVI is a difficult undertaking. This is because there is a certain loss of knowledge that occurs when complicated information about traits is reduced to indicators, indicators are reduced to factors, and factors are reduced to an index of only one number. Nonetheless, simplification is essential, and it is possible to achieve this with the Gross National Product and the Human Development Index, both of which are extensively applied and recognized metrics (Germanwatch, 2004).

Vulnerability is the result of the combination of exposure, susceptibility and resilience. There is a positive correlation between susceptibility and exposure, which in turn has a positive correlation with vulnerability. On the other hand, resilience has an inverse correlation with vulnerability. If vulnerability is understood to mean a lack of resilience, then resilience has the potential to have a beneficial effect on vulnerability.

The risk equation was given priority by Villagrán de León (2006), who characterized the relation between vulnerability and its variables as follows:

FVI = \frac{E x p o s u r e \times S u s c e p t i b i l i t y}{R e s i l i e n c e}

This equation has been used in this study to calculate flood vulnerability.

Selection of Flood Vulnerability Indicators Under Vulnerability Factors

Developing a goal is a necessary step before beginning the process of picking indicators. This purpose serves as the foundation for developing a list of system characteristics that are required to be evaluated as part of the research project. There is a strong connection that can be made between the indication and the characteristic of the system. The articulated goal, which is necessary in order to arrive at a set of indicators that are founded on science, serves as the point of departure. The characteristics of flood susceptibility are evaluated with the help of this set of indicators. The evaluation of the characteristic is always the primary concern, but there is a tight connection with the indicator. This is due to the fact that the quality of the indicator is established by how well it can address the system’s characteristics (Birkmann, 2006).

It is difficult to assess the risk of flooding since it is dependent on a wide range of components, some of which include social issues, economic aspects, external conditions, physical aspects, and even political ones. When conducting an indicator-based evaluation of vulnerability, the first step is always to identify the indications to use. In this research, we used ‘exposure’, ‘sensitivity’ and ‘adaptive capacity’ to assess a community’s level of vulnerability.

This study calculated exposure using ten different variables that included proximity to rivers, housing type, household assets, agricultural dependence, per capita income, population density, the number of disabled people in a given area, population growth, land use pattern and annual rainfall. The number of people who currently reside in an area that is prone to flooding due to a nearby river is represented by the indicator of closeness to the river. The housing pattern, especially the katcha (not permanent, weak) houses creates greater exposure to flood (Qasim et al., 2017). Agricultural dependency to a great extent, small per capita income, high percentage of disabled people, high population growth rate, greater amount of rainfall and inappropriate land use can create high exposure to flood vulnerability (Aktar et al., 2021). The family’s income also has an impact on flood vulnerability. With significantly more money, people can build houses in safer regions and utilise flood-resistant materials to construct their homes. As a result, the higher a person’s income, the less exposure is created to flood vulnerability.

Flat topography, low employment rate, lower percentage of literacy rate, higher amount of child mortality and higher frequency of floods make any community more susceptible to them (Aktar et al., 2021). Education is an essential variable because educated people are less prone to disasters (Dufty, 2008). So, we used these indicators to measure susceptibility in the FVI calculation.

We selected eight indicators to measure adaptive capacity, including flood shelter, flood insurance, flood awareness, flood warning system, emergency service, indigenous flood coping practices and flood embankments in the study area.

The appropriate indicators are shown in Table 1 and are categorised according to four components; nevertheless, it is essential that the selection of vulnerability indicators be directly linked to the setting of the study as a whole.

Table 1.

FVI Components, Factors and Indicator Design.

Components	Indicators
Components	Exposure	Acronym	Susceptibility	Acronym	Resilience	Acronym
Physical	Closeness to river	C_R	Topography	T_P	Shelter	S_T
Physical	Housing pattern	H_P	Topography	T_P	Communication system	C_S
Economic	Household assets	H_A	Employment rate	E_R	Flood insurance	F_I
	Agricultural dependency	A_D
	Per capita income	P_I
Social	Population density	P_D	Literacy rate	L_R	Awareness/ preparedness	A_P
	Disabled people	D_P	Literacy rate	L_R	Warning system	W_S
	Population growth	P_G	Child mortality	C_M	Emergency service	E_S
	Population growth	P_G	Child mortality	C_M	Indigenous practice	I_P
Environmental	Land use	L_U	Flood frequency	F_F	Embankment	E_M
Environmental	Rainfall	R_F	Flood frequency	F_F	Embankment	E_M

Collection of Data

The data for the research has been collected by using a mixed method approach. Both primary and secondary data have been collected. Primary data has been collected thorough a household questionnaire survey.

Population and Sample of Research: The residents of Chilmari upazila are the focus of the research and sample size was determined using the Yamane formula with a 95% confidence level, 10% precision and 50% prevalence.

n = N / (1 + N e^{2})

Where n = Sample size

N = Household size

E = Margin of error

Here, household size = 31689

The margin of error = 0.005, and the sample size is 395 households.

n = 31689 / (1 + 31689 * {(0.005)}^{2}) = 395.01

Preparation of Questionnaire: A semi structured questionnaire was designed for collecting the data. The questionnaire covers all the 23 indicators under four components of FVI. The semi-structured questions were set to identify the exposure, susceptibility and resilience for flood vulnerability assessment in the unions of Chilmari upazila.

Processing and Normalization of the Indictors

To calculate the vulnerability score of all the four components, the indicators are assessed within a scale of 1 to 5, where 1 represents the lowest vulnerability and 5 represents the highest vulnerability. The collected value of each indicator has been normalised using the following formula:

Normalisation = \frac{X - M i n i m u m}{M a x i m u m - M i n i m u m}

The normalised value represents the extent of vulnerability of each component as shown in Table 2.

Table 2.

Category, Weight and Normalization Value.

Category	Weight	Normalized Value
Very low vulnerable	1	0
Low vulnerable	2	0.25
Moderate vulnerable	3	0.50
High vulnerable	4	0.75
Very high vulnerable	5	1

Source: Aktar et al. (2021).

Calculation of FVI

The general formula for FVI is calculated by classifying the component in three groups of indicators: exposure (E), susceptibility (S) and resilience (R):

FVI = \frac{E \times S}{R}

(1)

In terms of the variables that make up the equation, the following ones have been derived from it (Balica, 2012):

F V I_{P h y s i c a l} = \frac{C_{R} * H_{P} * T_{P}}{S_{T} + C_{S}}

(2)

F V I_{E c o n o m i c} = \frac{H_{A} * A_{D} * P_{I} * E_{R}}{F_{I}}

(3)

F V I_{S o c i a l} = \frac{P_{D} * D_{P} * P_{G} * L_{R} * C_{M}}{A_{P} + W_{S} + E_{S} + I_{P}}

(4)

F V I_{E n v i r o n m e n t a l} = \frac{L_{U} * R_{F} * F_{F}}{E_{M}}

(5)

The total FVI for any area can be calculated by adding up these four indicators, which are based on the FVI (Equations 2–5). This index value provides an indication of the degree of susceptibility, as seen in Table 3.

Table 3.

Insight into the Vulnerability of Flooding.

Index Value	Description
<0.01	Very small vulnerability to floods
0.01–0.25	Small vulnerability to floods
0.25–0.50	Vulnerable to floods
0.50–0.75	High vulnerability to floods
0.75–1.00	Very high vulnerability to floods

Source: Aktar et al. (2021).

Data Analysis and Findings

Physical Vulnerability

The vulnerability of a location to flooding depends in part on its physical circumstances: whether those conditions are natural or man-made. There are five indicators under the physical components of flood vulnerability representing exposure, susceptibility and resilience. Using the formula discussed in the methods earlier, the FVI for physical components has been calculated and shown in Table 4.

Table 4.

Union-wise FVI Score of Physical Vulnerability.

Union	FVI Physical
Raniganj	0.24
Thanahat	0.21
Ramna	0.28
Nayerhat	0.28
Ashtamir Char	0.32
Chilmari	0.32

Economic Vulnerability

The economic dimension provides a representation of the region under the study’s level of wealth and prosperity. These measurements offer information regarding the capacity to produce and distribute goods and services that are susceptible to flooding. Developing countries, for instance, are defined by a low income per capita, a paucity of human resources, a lack of investment and financing, and a lack of robust infrastructure. Developed countries, on the other hand, are distinguished by a number of factors, including large investments in preventive and counter-measures, a high life expectancy rate, flood insurance and urban planning. If the rate of economic expansion picks up, there will almost certainly be an increase in the likelihood of flooding (Balica & Wright, 2010). The indicators of economic vulnerability to floods for Chilmari upazila have been chosen since they are used as a determinant of vulnerability. The index for the risk of flooding will consist of these five different indicators. The FVI for economic components is shown in Table 5.

Table 5.

Union-wise FVI Score of Economic Vulnerability.

Union	FVI Economic
Raniganj	0.14
Thanahat	0.09
Ramna	0.14
Nayerhat	0.19
Ashtamir Char	0.19
Chilmari	0.19

Social Vulnerability

Indicators that represent the context, capacity, skills, knowledge, attitudes, beliefs and behaviour of individuals, households, organizations and communities at various geographic sizes are included in the social dimension. Indicators of social well-being are often employed in order to evaluate the status quo of social conditions or the degree to which social objectives have been met in areas such as human health, levels of education, recreational possibilities and concerns regarding social equality (Balica & Wright, 2010). There are nine indicators for assessing social vulnerability for Chilmari upazila. The FVI for social components is shown in Table 6.

Table 6.

Union-wise FVI Score of Social Vulnerability.

Union	FVI Social
Raniganj	0.06
Thanahat	0.05
Ramna	0.09
Nayerhat	0.11
Ashtamir Char	0.11
Chilmari	0.09

Environmental Vulnerability

Indicators that relate to the environmental damage caused by floods or encroachment caused by human beings that could enhance the vulnerability of certain places are included in the environmental aspect of the project. It has been demonstrated that certain activities, such as farming, urban expansion and forestry increase sensitivity to flooding, which can contribute to an increase in pollution (Balica & Wright, 2010). There are four indicators for assessing environmental vulnerability for Chilmari upazila. The FVI for environmental components is shown in Table 7.

Table 7.

Union-wise FVI Score of Environmental Vulnerability.

Union	FVI Environmental
Raniganj	0.19
Thanahat	0.13
Ramna	0.19
Nayerhat	0.19
Ashtamir Char	0.19
Chilmari	0.19

Flood Vulnerability Index (FVI) for Chilmari Upazila

Using the FVI scores of physical, economic, social and environmental components the total FVI has been calculated by using the following formula:

F V I_{U n i o n} = F V I_{P h y s i c a l} + F V I_{E c o n o m i c} + F V I_{S o c i a l} + F V I_{E n v i r o n m e n t a l}

(vi)

The result in Figure 2 shows that three unions of the upazila are most vulnerable to floods according to the FVI score. These unions are Ashtamir Char, Chilmari and Nayerhat. Ramna and Raniganj unions have high vulnerability to floods while Thanahat union is generally vulnerable to floods. Using the FVI score, a flood vulnerability map is prepared to compare the vulnerability among the unions. The map is presented in Figure 3.

Figure 2.

Union-wise Flood Vulnerability Score in Chilmari Upazila.

Figure 3.

Flood Vulnerability Map of Chilmari Upazila.

Discussion

The outcome from this research gives the total scenario of flood vulnerability in Chilmari upazila. From the FVI calculations, the physical, economic, social and environmental factors of vulnerability can be easily understood through the FVI score. Among the six unions of the upazila, Ashtamir Char, Nayerhat and Chilmari unions are char land (land surrounded by the river) and closely connected with the river Brahmaputra. For this reason, the physical and environmental vulnerabilities of these unions are on the highest side. The physical vulnerability of these unions can be reduced by preparation and use of a master plan for the upazila. The flood restricted zone should be preserved, and new construction and settlements should be prohibited in the flood restricted zone, which can be a strong mitigation measure.

Most of the people of Chilmari upazila are farmers and they depend highly on agriculture for their livelihood. Economically, the people of Raniganj and Ashtamir Char unions are more vulnerable than the others as there are fewer income-generating opportunities and they are less familiar with the latest crop diversification techniques. Both diversification of crops and jobs can reduce the economic vulnerability of the unions.

Because of the progress in the education and health sectors in rural areas over the last 15 years in Bangladesh, the literacy rate has increased significantly and child mortality has decreased. People use their traditional knowledge to combat the floods every year. For these reasons the social vulnerability to floods is relatively low in all the unions compared to the other factors of flood vulnerability. Nevertheless, the awareness building and training programmes on flood resistant cultivation as well as an effective flood warning system can enhance the resilience of the unions to floods.

There have been other studies for flood vulnerability assessment using FVI. In some, the indicators are selected in general to identify the vulnerability of an area to floods as a whole. In this study, the indicators of vulnerability are divided into physical, economic, social and environmental factors. A wide range of indicators have been used, so that the detailed context of vulnerability can be understood. Extensive measures can be taken to overcome the current situation by understanding the strength and weakness of physical, economic, social and environmental factors of flood vulnerability.

Conclusion

The Kurigram district is but one of the areas of Bangladesh that is prone to flooding. This study focuses on flood-susceptible unions in the Chilmari upazila of the district, and it presents an indexing method that can be used to determine the vulnerability of a location to flooding. In addition to that, the findings of this study provide an overall scenario of the FVI framework assessment methods for flood vulnerability. The idea behind the FVI as a whole is based on the fact that Chilmari upazila is subject to a four-dimensional risk of flooding (physical, economic, social and environmental). This study includes a comprehensive analysis and understanding of flood-vulnerability measures, including exposure, sensitivity and resilience. The comparative vulnerability analysis performed by each of the six unions that make up the upazila constitutes the findings of the study. The FVI suggests that Ashtamir Char, Chilmari and Nayerhat unions are the most vulnerable because of high exposure and susceptibility and low resilience or adaptive capacity. With better adaptive capacity, Thanahat union is the least vulnerable union among the six unions of the upazila. The study provides decision-makers and concerned authorities with a useful tool for identifying and prioritising specific risky locations, as well as taking action to decrease present flood vulnerabilities and prepare for future flood risk reduction. This study also establishes a point of reference for evaluating the efficacy of flood risk reduction strategies over time, against which subsequent studies can be compared. Hazard assessment is as important as vulnerability assessment in obtaining a complete picture of flood risk in the selected unions.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The research has been conducted using the funds provided by the University Grant Commission, Bangladesh.

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