1 Introduction

The Bay of Bengal is prone to tropical cyclones and accounts for 5.5 % of the global total cyclonic storms (Ali 1996, 1999). From 1797 to 1998, 67 major cyclone induced storms and tidal surges (Brammer 1999; Chowdhury 2002) struck the Bangladesh delta, including the highly destructive cyclones Sidar and Aila in November 2007 and May 2009 respectively (BUET 2008; Hasegawa 2008; Mutahara 2009).

The coastal resource system of Bangladesh consists of rich terrestrial and marine ecosystems, including vast mangroves (the Sundarbans) and a large number of estuaries (Islam 2004). The livelihood pattern of the coastal communities mainly depends on the availability of these resources in terms of ownership and access (Soussan and Datta 2002). In many countries, higher population density on the coast is accompanied by intensification of human activity, developments, and changes in land-use (Levy and Hall 2005). However, in Bangladesh, overcrowding in the mainland drives the poor and landless people to live in the coast where they are exposed to frequent cyclone and storm surges (IPCC 1996; Rahman 2004). Staying alive, and livelihood security is central to the welfare of the coastal communities (Mutahara et al. 2013); and increasingly perilous as the frequency of cyclonic storm-surges are increasing due to climate change (Emanuel et al. 2008).

This article represents a conceptual model to assess the household livelihood security against cyclone and storm-surge risks in the coastal area. The livelihood security model is generally a combination of three intervention strategies at the household level such as livelihood promotion (development oriented programming), livelihood protection (rehabilitation/mitigation oriented programming) and livelihood provisioning (relief-oriented programming) (Frankenberger and McCaston, 1998). Based on these strategies, the model assesses the livelihood protection and provision required for the coastal community vulnerable to storm surge. The livelihood security model developed here draws on the Socio-economic Vulnerability Index (SeVI) (Ahsan and Warner 2014), which measures socio-economic vulnerability to climate change disasters along the Bangladesh coast. It intends to bridge the gap between the necessities and priorities of communities at the micro level and policy variables at the meso level.

The current study focuses on the marginal livelihood groups and measures their household livelihood security to determine a comparative statistics of security level for different livelihood groups as well as different coastal settings. Livelihood security is an integrated concept, comprised of the capabilities, assets and activities required for a means of living. A livelihood system is sustainable if it can cope with and recover from stress and shocks (Charvet et al. 2014), maintain or enhance its capabilities and assets, and provide sustainable livelihood opportunities for the next generation (Chambers and Conway 1992). The Sustainable Livelihood Security Model defines dynamic livelihood systems, identifying the security options, synthesizing the security indicators (Goodin and Wright 1998; Saaty 1980, 1988) with participatory approaches and finally, integrating a Livelihood Security Index to quantify household livelihood security.

2 Coastal livelihoods in the Bangladesh delta

According to Edward and Frank (2001), a livelihood comprises “the assets (natural, physical, human, financial and social capital/resources), the activities, and the access to these (mediated by institutions and social relations) that together determine the living gained by the individual or household”. Livelihoods have differed as to their environmental, social, and institutional settings and often vary in terms of resource base, production relations, and marketing (PDO-ICZM 2002). In the coastal area, some people work independently (e.g. fry collector), some work as lessees or share croppers (e.g. salt farmers, shrimp farmers) and some are contracted labourers (Ahmad 2003; Rahman 2004). Some people make a living from the exploitation of natural resources (e.g. salt farmers, fry collectors, fisherman, honey collectors) and some live on skill-based human resources (e.g. boat-building carpentry, net making). We conducted this study on livelihood groups in the storm-surge affected areas in Bangladesh (PDO-ICZMP 2003). The storm-surge risk is the most severe for the marginal people who are fully dependent on the natural resources of the coast (Khalequzzaman 1988). The first step of the study entailed an analysis of existing information sources to determine the livelihood classes in the coastal areas of Bangladesh.

Coastal livelihood groups listed in Table 1 have been defined considering the following contexts:

Table 1 Marginal livelihood groups in Bangladesh coast
Full size table

  • Income time frame of coastal livelihood groups is influenced by the occurrences of cyclone induced storm-surges (generally occurring during the pre- and the post-monsoon) (Ganter 1996).

  • Cyclones and tidal surges cause loss of life and damage resources in various ways: For examples, agro-products, shrimp, and salt are washed away; fisherman cannot go out fishing; people cannot go outside for food, water, fuel, and daily needs; houses and sanitation systems are badly damaged.

3 Approach and methodology

Two case studies were selected for the current research. Coastal districts Cox’s Bazar and Satkhira (Fig. 1) are located near the southeast and south-west boundaries of the Bangladesh delta in the high and medium cyclone and surge risk zones (PDO-ICZMP 2003).

Fig. 1
figure 1

Study area map showing the study sites in the coastal zone of Bangladesh

Full size image

Livelihoods in rural Bangladesh are diversifying (Toufique and Turton 2002). Our field investigation confirms that this observation applies even more to the coastal zone in Bangladesh. Livelihood patterns in Cox’s Bazar and Satkhira are different due to different biophysical settings as well as available resource systems. Cox’s Bazar is located along the long open seashore and Satkhira is bounded by the largest mangrove forest in the world: the Sundarbans. The main methodological concept has been developed in a participatory approach (Huq 2001; Evan et al. 2005) followed in environmental and social research. It includes designing an indicator framework having a set of indicators for the security criteria in the livelihood resources system (Fig. 2) in the context of a developing country.

Fig. 2
figure 2

Schematic representation of indicator frame work development process

Full size image

Indicators were identified under natural capital/resources, human capital/resources, social capital/resources, physical capital/resources and financial capital/resources representing the main livelihood sub-systems in the coastal area. In each study sites, a two-step participatory approach was adopted. First, Focus Stakeholder Meetings (FSMs) (Mutahara 2009) were conducted to understand the local livelihood systems as well as to develop an indicator framework. Second, indicators’ responses towards specific livelihood security options were evaluated with a participatory approach using Analytical Hierarchy Process (AHP) (Saaty 1980, 1988); a multi-criteria decision making (MCDM) method commonly used in studies for risk-based environmental decision-making process (Tesfamariam and Sadiq 2006; Sadiq and Tesfamariam 2009). AHP provides a rational choice of different alternatives (the initially developed indicators) by identifying relevant criteria and evaluating a weighted score for each alternative that reflects its strength of preference (Goodwin and Wright 1998).

We used AHP to integrate subjective and personal preferences of indicators in performing the base analyses to develop the model. It is a systematic, explicit, and robust mechanism for eliciting and quantifying the subject judgment. Indicators were chosen from the initial indicator list under different livelihood-security aspects/options (Mutahara 2009): (1) Food security, (2) Income security, (3) Health and personal security, (4) Security of house and properties, and (5) Water security. Top-ranking indicators have been defined as the potential indicators to explore individual option of security which are the main inputs to the model.

In the second step, FSMs and individual household interviews were conducted to evaluate indicators for livelihood groups. Standard threshold values for the indicators were calculated from national and regional-level secondary information sources, including the Bangladesh Bureau of Statistics (BBS), the Local Government Engineering Department (LGED), Bangladesh; PDO-Integrated Coastal Zone Management Office; the Asian Development Bank; and the Center for Environmental and Geographical Information Services (CEGIS), Bangladesh. The model was verified through direct field observation and expert judgment. We also checked the validity of the application of the model to both field sites. For that, 10 households with approximately the same income level which had survived well through several storm-surges within the last two decades were selected randomly. We used an average value of livelihood indicators for those households to calculate the expected/standard household security level, to validate the livelihood security model developed here.

4 Model development for livelihood security

4.1 The conceptual model

The conceptual framework focuses on integrated assessment of the livelihood security required for livelihood protection and provision. The model broadly covers livelihood security against storm-surge risks and relates to the characteristics of the coastal livelihood systems in the Bangladesh Delta (Mutahara 2009; Mutahara et al. 2013).

Figure 3 conceptually shows the model for coastal livelihood security with its three major elements: (a) contexts, (b) livelihood system and strategy, and (c) livelihood security dimensions/outcomes. Contextual factors situate in the household and community. The model is constructed to identify the level of (in) security of the coastal people/household exposed to storm-surge hazards. In that sense storm-surge and its destructive actions is defined as the key contextual factor affecting the livelihoods.

Fig. 3
figure 3

Components of the model of livelihood security against storm-surge hazards in the coastal area

Full size image

The coastal livelihoods and their stakeholders are the basic elements of the model (CEGIS 2007). It has been defined as the element of vulnerability in that study field (Chadwick 2003; CEGIS 2007). In the model, the affected party i.e. the coastal livelihood groups have been introduced including their household activities, resources, and strategies. Here, the aim of analyzing livelihood system and strategy was to understand the typical accessibility of human, social, economic, and natural capital in households and the nature of production, income, and exchange activities. Livelihood security indicators are the analytical inputs to the model, which were defined for the household unit in the livelihood system of a coastal community. The identified indicators are listed in Table 2.

Table 2 Primarily identified security indicators for livelihoods in the coastal community
Full size table

In the model, the standard threshold value of a livelihood indicator is used to analyze the security level. The threshold level could be a constant value or could vary by month, season, or year (Fleig et al. 2006). Table 2 shows the security standard (threshold value of livelihood security indicators) has been shown according to national/regional statistics (yearly) in Bangladesh (BBS 2001, 2011; NWRD 2010). The security level was calculated for individual livelihood groups. Analytically, the model produces a Livelihood Security Index (SI) which is a combination of the parameters defined in Table 3.

Table 3 Indicator parameters and symbols used in the model
Full size table
Table 4 Scale for security scoring in individual indicators
Full size table
Table 5 Calculation for security scoring of five indicators (farmers in Cox’s Bazar area)
Full size table
Table 6 Individual security level (%) for livelihood groups in Cox’s Bazar area
Full size table
Table 7 Individual security level (%) for livelihood groups in Satkhira area
Full size table

4.2 Designing a livelihood security index from the conceptual model

The developed model is a scientific tool for assessing household security for any livelihood group in the coastal areas exposed to storm-surge hazard. The following steps were followed in developing the Security Index.

  • Step 1 Two types of values for each selected indicator have been calculated through analyzing secondary data, FGDs and mostly household interviews in the coastal area. Here, change between the present value and standard value was calculated for each individual indicator which is shown as percentage of unit difference. Change in Individual indicator was calculated under an individual security aspect by the following equation:

    $$\left| { \, {\mathbf{I}}_{{\mathbf{d}}} } \right| \, = \, \left\{ {\left( {{\mathbf{I}}_{{\mathbf{p}}} - \, {\mathbf{I}}_{{\mathbf{s}}} } \right)/ \, \left( {{\mathbf{I}}_{{\mathbf{p}}} + \, {\mathbf{I}}_{{\mathbf{s}}} } \right)} \right\} \times {\mathbf{100}}$$
    (1)

    Here, Ip is the Present value of individual indicator, Is is the Standard value of individual indicator, Id is the Percentage of unit difference between the present value of indicator and the standard value of individual indicator.

  • Step 2 A value exchange scale is defined in this step to identify the security score from the result of Step 1 because the value of Id may represent alternative directions, i.e. either positive (+) or negative (−). Here, the positive direction shows security and negative direction shows insecurity.

    In this model development process, we used only positive scores because conceptually this model is able to measure security at the household level. Insecurity level for the same household can be identified directly and easily using the model upshot.

  • Step 3 Security of household (in percentage) for individual livelihood security aspects/options which is at risk of storm-surges in the coast has been measured by the index defined below. The security level for household in individual security aspects/option (j) can be calculated by using security scores of indicators (i = 1, 2,…, n) those respond to such security aspects j in the following equation:

    $${\text{SI}}_{{\mathbf{j}}}\, { = }\,\left\{ {\sum\limits_{{{\mathbf{i}} = 1}}^{{\mathbf{n}}} {{\text{X}}_{\text{ij}} } /{\text{M}}_{\text{j}} } \right\} \times 100$$
    (2)

    where, SIj is the Security level under jth individual aspect, Xij is the Positive score of ith indicators under jth aspect.

    The value of X for the different indicators (i = 1 to n) has been calculated by counting the numbers of positive (+) signs. n is the Number of individual indicators sensitive for individual aspect, Mj is the Total score of responsive indicators under jth aspect, j is the Different security aspects (1–5)

    Now the overall livelihood security at the household level of a coastal community against the hazard (storm-surge) can be calculated through combining the security scores under all denoted security aspects. The composite Security Index consisting of different aspects has been expressed as follows:

    $${\mathbf{SI = }}\sum\limits_{{{\mathbf{j}} = 1}}^{{\mathbf{N}}} {{\mathbf{SI}}_{{\mathbf{j}}} /{\mathbf{N}}}$$
    (3)

    where, SI level of livelihood security for household (in percentage), N number of security aspects considered in the composite index.

5 Model application

The assessment of security level may have to deal with multiple sources of uncertainty that the model can consider automatically as per its analytical approach. In this model, uncertainty factors are directly related to the the storm-surge charecteristics: its action, scope of defenses etc. and also human behavior. It may also have to deal with the ecosystem conservation knowledge as well as institutional capacity. All those factors and their relevance were studied and justified using expert’s opinion in indicator development process. Therefore, we are confident that the indicator selection and scoring procedure will work sufficiently to identify and resolve such uncertainty. We applied the livelihood security model against storm-surge hazards in two selected areas; a high storm-surge risk area in Cox’s Bazar and a medium storm-surge risk area in Satkhira (PDO-ICZMP 2004).

5.1 Assessment of livelihood security indicators

We used the indicators for constructing a model for both qualitative and quantitative requirements. The indicator values have been analyzed under specific units or scales such as percentage, number, degree and binary options (shown in the Table 2). Some values have been calculated from the relevant data-base and some have been defined from direct household interview in the study areas. Appendix Tables 8 and 9 shows the present measured value of indicators (Ip) for different livelihood groups in the study areas (a) the Cox’s Bazar and (b) Satkhira. During evaluation of indicators from data analysis (results shown in Appendix Tables 8 and 9), we found two major categories: 1) common/same values for livelihood groups and 2) different values for individual group in each area. The first type of indicator shows the collective security status that means the same value for overall community households in the defined area and the second type actually indicates the value especified as individual household basis for different groups. For example, the indicator “performance of hospital/health center” shows the same measured unit value for all livelihood groups living in the same area where the “Rate of production” shows different value for different groups in such area.

Table 8 Input data for livelihood groups in Cox’s Bazar area
Full size table
Table 9 Input data for livelihood groups in Satkhira area
Full size table

5.2 Security scoring for individual indicators

We used AHP methods to make the decision for priority of indicators under the security options, and these can then be taken up in quantitative surveys. The priority-scored indicators have been used for measuring security level under individual security options such as food security, income security and so on for each livelihood group. Priority selection is shown in Appendix Table 10. The security score under individual indicators has been estimated from the comparative analysis between present field survey data (Ip) (Appendix Tables 8 and 9) and standard threshold values (Is) (Table 2) according to national average value (from BBS year books, NWRD and Local Government Organizations) by using Eq. 1 described in Sect. 4.2. From the difference of individual indicator’s values the security scores have been found under different security options. For better understanding of security scoring process, we used a sample calculation where we used the limited number of indicators (n = 5) with only 2 security options for one livelihood group.

Table 10 Priority calculation under different security options (selected indicators by AHP)
Full size table

Table 5 shows a sample input data calculation for the livelihood security measurement of farmer households in Cox’s Bazar applying steps 1 and 2 of the model described in Sect. 4.2. Here, in the second row of the Table 5, individual indicator i = 1 was selected under the food security (j1) aspect for the farmer group in Cox’s Bazar. The present value of i1 is 0 where the security standard (defined in Table 2) is 1. Now the value difference (Iq) is about 100 % with negative direction that means i1 shows insecurity in food with score 3 according to the security scale defined in Table 4. In the same process, i = 2 and i = 3 were investigated where i = 2 was not responding for food security according to the AHP analysis (Appendix Table 10). So, i2 is not scored under food security, however it scored 1 for income security (j2) in the negative direction i3 is scored for both security options as 1 in the negative direction. However, i = 4 and i = 5 indicators have shown in scores 1 and 2, respectively food security and income security was relatively in the positive direction. Here, the calculated score under food security aspect/option (j = 1) is 3, whereas the total score is 7 (M1 = 7). So, in the model, ∑X1 = 3.

5.3 Calculation of security level for individual security options

The levels of different security options have been measured by using Eq. 2 under Step 3.

$$\begin{aligned} {\text{SI}}_{ 1} \, &= \,\bigg(\sum {\text{X}}_{ 1} /{\text{M}}_{ 1} \bigg)\, \times \, 100\, = \,\left( { 3/ 7} \right)\, \times \, 100\quad {\text{j }} = { 1},\,{\text{defines}}\,{\text{food}}\,{\text{security}} \hfill \\ & = 42.86\,\% \end{aligned}$$

Therefore, the calcutated food security for the sample indicators is 42.86 % (sample calculation partially using only 5 indicators, it is not the complete scenario). Tables 6 and 7 show the complete measured value of security (as a percentage) under the individual security option (SIj) for the selected livelihood groups in the study areas.

In Table 6, security levels under individual options have been presented for the defined livelihood groups in Cox’s Bazar area. These results were measured by using Eq. 2 of the model. The same process was followed in Satkhira area; the results are shown in Table 7. The values shown in Tables 6 and 7 are the input data for Eq. 3 of the model.

5.4 Calculation of security level of livelihood groups

The overall security level of the coastal livelihood groups were calculated using Eq. 3 in the third step of the Livelihood Security Model. For example, in theCox’sBazar area, the security level of the farmer group is calculater as follows:

$${\text{SI}}_{\text{Farmer}} = \, \left( {{\text{SI}}_{ 1} + {\text{SI}}_{ 2} + {\text{SI}}_{ 3} + {\text{SI}}_{ 4} + {\text{SI}}_{ 5} } \right)_{\text{Farmer}} / 5= \, \left( { 4 1. 6 7+ 4 4. 6 8+ 3 8. 30 + 4 5. 6 5+ 3 9. 1 3} \right)/ 5= { 41}. 8 9 { }\left( \% \right)$$

Figures 4 and 5 show the overall model results.

Fig. 4
figure 4

Computation of security level at Cox’s Bazar for the period of 2013 (Source Mutahara and Haque 2011; Mutahara et al. 2013)

Full size image
Fig. 5
figure 5

Computation of security level at Satkhira area for the period of 2013 (Source Mutahara and Haque 2011; Mutahara et al. 2013)

Full size image

6 Results and discussion

Figures 4 and 5 present the model results for Cox’s Bazar and Satkhira areas, respectively. In both areas, the results have determined the livelihood security of individual groups. The lowest security level 14.96 % was found for fry collectors (Fig. 4). In the Cox’sBazar area, the fry collectors live at a very marginal level, with access to but not ownership of marine resources. Women and children are mostly involved in fry collection using very traditional instruments. In most cases they lost their instruments and cannot go to sea during and also long time after a storm-surge. Wage labourer group is also less secure (17.88 %) because of limited scope of work during and after a storm-surge. However, they have some access to rehabilitation work with other groups like agriculture, salt farmer or dry fisher. On the other hand, the highest security was found for salt farmer group in Cox’s Bazar. They have ownership to land which they use for salt farming. They have seasonal investment and income. We found that farmers can preserve the produced salt in the field giving mud cover during the occurence of a storm-surge. Farmer, fisherman and dry fisher groups were also at relatively higher security levels.

The models result from Satkhira area is shown in Fig. 5. In Satkhira the wage labour group was found as the least secure livelihood group. This area is highly dependent on culture fisheries (shrimp culture). The labourers mainly work in the shrimp field on a daily basis. Therefore, they do not have independent access to income generation. Fry collectors are also in a less secure zone. The highest security level (33.99 %) was found for farmers in Satkhira. In this coastal area farmers cultivate rice and vegetables. Currently they use high yielding varities of rice. Crop rotation also make them secure against the loss from storm surges. The forest extractors were also found to have a relatively higher security level because of their seasonal income opportunity. However they are still vulnerable in their dependancy on forest resources only.

In Figs. 4 and 5, the standard household security level has also been determined. The standard level is used for the justification of model application. The standard method of model validation could not be followed properly for the model in such a very rural coastal area. With this limitation, we checked the model with a pre-defined standard security (degree of safety) level for households in each coastal district, as perceived by the community. In both areas(Cox’s Bazar and Satkhira), the local communities responded positively to the defined possible standard security level as they expected. The standard livelihood security value is about 66.01 % in the Cox’s Bazar area. Following the same methodology, the standard level of security value may be as high as 68.23 % in Satkhira. Figures 4 and 5 shown that marginal livelihood groups have very low levels of livelihood security. Even the security levels of the livelihood groups having the highest security levels, e.g. salt farmers in Cox’s Bazar (45.13 %) and farmers in Satkhira (33.99 %), are low compared to the standard level of security.

The model results indicate another important finding. We can easily draw a comparative assessment among the commom livelihood groups in different cases. In this study, we found four common groups (farmer, fisherman, fry collector, and wage labourers) in two study areas. Figure 6 shows the variation in household security level among theses common livelihood groups in Cox’s Bazar and Satkhira.

Fig. 6
figure 6

Comparative analysis of livelihood security in two study sites (Mutahara et al. 2013)

Full size image

In our findings, the major difference is shown in the fisherman group. The fisherman group in Cox’s Bazar (39.89 %) is more secure than in Satkhira (24.14 %). This is likely due to the long open seashore in Cox’s Bazar and fishermen have more finiancial and logistical support in Cox’s Bazar (Mutahara et al. 2013). The level of security for farmers in Cox’s Bazar is 41.89 % whereas in Satkhira it is 33.99 %. The farmers in Cox’s Bazar are more secure than in Satkhira due to land use pattern. In Sathkhira, farmers generally cultivate rice in shrimp fields during the dry season. However, in Cox’s Bazar, we found separate fields for shrimp and rice production. The level of security of fry collectors is better in Satkhira (16.14 %) than in Cox’s Bazar (14.96 %). The fry collectors mainly access the rives and khals (tidal channels) in Satkhira whereas in Cox’s Bazar they mostly use the open sea.

7 Conclusion

In this study, seven (7) marginal livelihood groups have been identified including their specific livelihood opportunities and resources in two study areas (Cox’s Bazar and Satkhira) in Bangladesh. In specific, six (6) groups were living in Cox’s Bazar area and five (5) were in Satkhira. However, four (4) livelihood groups (farmer, fisherman, fry collector, and wage labourer) were common in both sites.

Livelihood security is an impotrant issue in the strom-surge affected areas of the Bangladesh coast. It is not only due to physiographic and socio-economic conditions but also due to climate change vulnerability. In our study, the livelihood security model has two main outcomes. First, it introduced a holistic analytical approach for assessing livelihood security levels. Second, it contributed a tool of livelihood protection and system development for the coastal area. The livelihood Security Index (SI) calculated the overall household security level (in  %) for livelihood groups against the risk of storm surges. The model result shows the livelihood security levels for the marginal livelihood groups in both coastal areas. It also shows a comparative view of livelihood security in common livelihood groups in the different coastal area of Bangladesh.

This study can contribute to future coastal resource management and livelihood development programs. It could play a vital role in the sustainable planning for disaster risk reduction and adaptation management in the Bangladesh coast. Although this model has been developed and applied in the Bangladesh delta, it can also be applied in the coastal zones of other deltas for developing sustainable coastal zone management planning.