Study sites
We assessed livelihood vulnerability to climate variability and change in the fishing communities of Padma, Barguna District, and Kutubdia Para, Cox’s Bazar District in southern coastal Bangladesh (Fig. 1). These two districts are more affected by climatic phenomena such as cyclones, tidal fluctuation, and salinity intrusion than other coastal areas of Bangladesh (Agrawala et al. 2003). The two communities share some characteristics but also have different physiographic contexts and livelihood portfolios.
Padma is home to 4,204 people in 908 households. Most household heads are male with limited formal education. Most households (89 %) directly depend on fisheries; small-scale fishing in the Bay of Bengal is their main livelihood activity. Some households are involved in other livelihood activities such as fish drying, fish trading, net making and/or mending, boat making and repairs, shrimp post-larvae collection, daily labouring, firewood selling, grocery shop keeping, cattle rearing, investing money in informal loan systems, motorcycle driving, fish culture, and agriculture. Most men work as crews in small mechanised fishing boats. The fishing season runs from July to October (first season, within which a few days are excluded from fishing) and December to April (second season). Most fishing is done during the first season. A crew of 3–18 people work during a fishing operation that lasts 6–15 days.
Padma’s physical infrastructure is poor. Dirt roads become muddy during the rainy season and are dusty when it does not rain. Two cyclone shelters have a joint capacity of 3,000 people. One of the cyclone shelters serves as a primary school, the only formal education institution in Padma. There is no hospital or clinic but 2 pharmacies dispense medicines. People with medical needs visit the sub-district health complex in Patharghata about 8 km away. There is no access to the electricity grid or piped water supply. Filtered and alum-treated pond water of uncertain quality is used by households.
Livelihoods in Padma have been influenced by storm surge-induced flooding (hereafter refer to as flood), cyclones, sea-level rise, salinity intrusion, and land erosion (Table 1). The most devastating climatic shock in the past 40 years was the super cyclone Sidr (wind speed 230–270 km/h, surge height 20–25 feet) in 2007. A strong cyclone in the sea in 2005 and a flood caused by cyclone Aila in 2009 also had disastrous impacts on the community. Padma is <1 metre above the sea level and does not have a protective dike around it.
Table 1 Community exposure to climatic shocks and stresses
Kutubdia Para is home to 12,815 people in 2,015 households. Most households are climate disaster-driven migrants from the Kutubdia Island in the same district. The village came into existence in 1986 as an isolated neighbourhood, but it is now a ward in the district of Cox’s Bazar. Most household heads are male with little formal education.
Livelihoods in Kutubdia Para depend on fishery-related activities such as fishing in the sea, fish drying, fish transportation, and net mending. Fishing and fish drying support the livelihoods of about 92 % of the households. A few households depend on tailoring, grocery sales, and daily labouring in building construction for their livelihoods. Kutubdia Para’s physical infrastructure is poor and very similar to that of Padma, apart from all households have access to pure drinking water and electricity. It is 3 km from Cox’s Bazar airport and 6 km from Cox’s Bazar town.
Fishing practices in Kutubdia Para are similar to those of Padma, except that the second fishing season is extended for two more months and more fish is caught in this season. Fish are dried by traditional open-air method mainly (80 %) between November and February. The remaining 20 % of fish are dried in September, October, March, April, and May (extended drying period).
Since settling in Kutubdia Para, households have experienced two major cyclones and associated floods in 1991 (named Gorki) and 1997 (Table 1). They are also exposed to sea-level rise, temperature and rainfall variations, and little land erosion. Kutubdia Para is <1 m above sea level and <1 km away from the sea, and it does not have a protective dike around it. Its fish-drying field is close to sea and only a few centimetres above sea level.
The coastal region in which both communities lie will likely experience climate change impacts as predicted for Bangladesh as a whole, including increases in floods (Mirza 2003, 2011), temperature (MoEF 2005) and wind speed (Emanuel 1987), sea-level rise (MoEF 2005), and seasonal changes in rainfall (Agrawala et al. 2003). These impacts will have predominantly negative consequences for case study communities unless they adapt.
Indicators of vulnerability
Exposure, sensitivity, and adaptive capacity are the key factors that determine the vulnerability of households and communities to the impacts of climate variability and change (IPCC 2007). Indicators for each of these factors are therefore essential elements of a comprehensive vulnerability assessment. However, “many of these indicators cannot be quantified, and many of the component functions can only be qualitatively described” (Yohe and Tol 2002, p. 27). For instance, effective governance is important for adaptive capacity (Paavola 2008), but it is difficult to capture in an indicator (Vincent 2007). The most useful indicators of vulnerability have construct validity, are sensitive enough to capture variation, and broad enough to be transferable (Vincent 2007).
Exposure in the context of this study is the nature and degree to which a fishery-based livelihood system is exposed to significant climatic variations (modified from IPCC 2001, p. 987). Exposure indicators selected for this region characterise the frequency of extreme events, rate of land erosion and sea-level rise, and variations in temperature and rainfall (Tables 1, 2). The two communities have experienced similar variations in maximum temperature (Table 1) so no indicator on it was included in index calculation. Only retrospective data on indicator values were used; no future projections were attempted due to unavailability at community scale. This is sufficient for the purposes of this study, because the greater the level of exposure to climate variability (and change), the greater the relative propensity for communities and households to be impacted.
Table 2 Indicators used to determine fishery-based livelihood vulnerability
Sensitivity in this context is the degree to which a fishery-based livelihood system is affected by or responsive to climate stimuli (note that sensitivity includes responsiveness to both problematic stimuli and beneficial stimuli) (adapted from IPCC 2007, p. 881). Sensitivity indicators characterise the first-order effects of stresses (IPCC 2001; Polsky et al. 2007). At the local level, exposure and sensitivity are almost inseparable, and it is challenging to characterise them (Smit and Wandel 2006). Sensitivity indicators include livelihood characteristics such as dependence of livelihoods on climate-sensitive activities and patterns of resource use (Smit and Wandel 2006; Eakin and Bojórquez-Tapia 2008). But many indicators of sensitivity are similar to those that influence a system’s adaptive capacity (Smit and Wandel 2006). In order to avoid using the same indicators for measuring sensitivity and adaptive capacity, only indicators of the dependence of livelihoods on climate-sensitive activities in the fisheries sector, for employment, income, and nutrition were used as sensitivity indicators (Macfadyen and Allison 2009; Allison et al. 2009) (Table 2). This assumes that households and communities with higher dependence on fisheries for employment, income, and nutrition are more likely to be impacted by climate variability and change (cf. Allison et al. 2009).
Adaptive capacity in the context of this study is the ability or capacity of the fishery-based livelihood systems to adjust to climate change (including variability and extremes), to take advantage of opportunities, or to cope with the consequences (modified from IPCC 2001, p. 982). However, there is little consensus about the characteristics and determinants of adaptive capacity at household, community, and national levels (Smit and Wandel 2006; Jones et al. 2010), because the exploration of adaptive capacity has only just begun (Vincent 2007). At the local level, adaptive capacity can be influenced by infrastructure, community structure and social groups, household structure and composition, knowledge, social capital (such as kinship networks and social support institutions), political influence, power relations, governance structures, managerial ability, and ability or inability to access livelihood assets, especially financial, technological, and information resources (Watts and Bohle 1993; Adams and Mortimore 1997; David 1998; Adger 1999; Handmer et al. 1999; Kelly and Adger 2000; Barnett 2001; Yohe and Tol 2002; Wisner et al. 2004; Haddad 2005; Ford et al. 2006; Smit and Wandel 2006; Tol and Yohe 2007; Vincent 2007; Paavola 2008; Sallu et al. 2010). Adaptive capacity is, however, context-specific varying across scales—countries, communities, social groups and households—and over time (Smit and Wandel 2006), and best determined by a given climatic exposure in which a particular system is exposed (Vincent 2007). Indicators of adaptive capacity for the fishery-based livelihoods should thus be developed considering the nature and type of exposure of households and communities. We chose to use adaptive capacity indicators covering a range of livelihood characteristics such as livelihood assets and strategies (Table 2), assuming that households and communities with more of these are better able to cope with and adapt to the impacts of climate variability and change.
Design of a composite livelihood vulnerability index
A composite vulnerability index approach was used in this study to assess relative exposure, sensitivity, and adaptive capacity. A composite index approach computes vulnerability indices by aggregating data for a set of indicators. An indicator represents a characteristic or a parameter of a system (Cutter et al. 2008) and it is an empirical, observable measure of a concept (Siniscalco and Auriat 2005, p. 7). The composite index approach can help to identify indicators or determinants for targeting interventions and programmes (Eakin and Bojórquez-Tapia 2008; Czúcz et al. 2009).
Using the suite of indicators described in Tables 1 and 2, we quantitatively assessed the vulnerability of fishery-based livelihood systems using the combination of individual indicators and aggregate indices shown in Table 2. Since each indicator was measured on a different scale, they were normalised (rescaled from 0 to 1) by using Eq. 1.
$${\text{index}}_{\text{Si}} = \frac{{S_{i} - S_{ \hbox{min} } }}{{S_{ \hbox{max} } - S_{ \hbox{min} } }} $$
(1)
where indexSi is a normalised value of an indicator of a household; S
i
is the actual value of the same indicator, and S
min and S
max are the minimum and maximum values, respectively, of the same indicator.
After normalisation the respective values were averaged to yield the three sub-indices for exposure, sensitivity, and adaptive capacity. As household scale exposure data were not available, the same exposure sub-index score was used to calculate intra-community livelihood vulnerability indices. This enabled us to gain insights into the determinants of livelihood vulnerability among similarly exposed households (Eakin and Bojórquez-Tapia 2008). The household-level sensitivity and adaptive capacity sub-indices were also normalised. The normalised adaptive capacity sub-index was inverted (1- index) for inclusion in the vulnerability index because the potential impact (which is a function of exposure and sensitivity) of climate variability and change may be offset, reduced or modified by adaptive capacity (IPCC 2007).
Sub-indices were combined to create a composite vulnerability index by using an additive (averaging) (Eq. 2) or multiplicative (Eq. 3) approach. We followed both procedures but, since they produced highly correlated vulnerability scores (Spearman’s ρ 0.97 for Padma and 0.98 for Kutubdia Para; p < 0.01), we highlight the results of the multiplicative approach because it better reflects low and high indicator and sub-index values (Hajkowicz 2006).
$$V \, = \, \left( {E \, + \, S \, + \, \left( {1 - {\text{AC}}} \right)} \right)/3 $$
(2)
$$V \, = \, E \, \times \, S \, \times \, \left( {1 - {\text{AC}}} \right) $$
(3)
Where V, E, S and AC represent vulnerability, exposure, sensitivity and adaptive capacity of a household, respectively.