An investigation of coastal vulnerability and internal consistency of local perceptions under climate change risk in the southwest part of Bangladesh

Rakib, Muntaha; Sasaki, Jun; Pal, Sosimohan; Newaz, Md. Asif; Bodrud-Doza, Md.; Bhuiyan, Mohammad Amir Hossain

doi:10.1016/j.jenvman.2018.10.054

Cited by 64 publications

(40 citation statements)

References 31 publications

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“…Studies focusing on vulnerability assessment have so far not been comprehensive or appropriately delineated, especially in the coastal region of Bangladesh. Although a large number of vulnerability studies [27,34,35,38,42,44,45,46,47,48,49,50,51,52,53,54,55] are being conducted, almost none of the previous studies addressed integrated agriculture (i.e., crops, fisheries, and livestock) or took into account the connections between different spatial scales, addressed multiple hazards, or led to the formulation of intervention planning to enhance adaptation capacity. These latter two issues comprise a global research gap [56].…”

Section: Introductionmentioning

confidence: 99%

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

Hoque

Cui

et al. 2019

IJERPH

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The adverse impacts of climate change exert mounting pressure on agriculture-dependent livelihoods of many developing and developed nations. However, integrated and spatially specific vulnerability assessments in less-developed countries like Bangladesh are rare, and insufficient to support the decision-making needed for climate-change resilience. Here, we develop an agricultural livelihood vulnerability index (ALVI) and an integrated approach, allowing for (i) mapping out the hot spots of vulnerability distribution; (ii) identifying key factors of spatially heterogeneous vulnerability; and (iii) supporting intervention planning for adaptation. This study conceptualized vulnerability as a function of exposure, sensitivity, and adaptive capacity by developing a composite index from a reliable dataset of 64 indicators comprising biophysical, agro-ecological, and socioeconomic variables. The empirical studies of coastal Bangladesh revealed that Bhola, Patuakhali, and Lakshmipur districts, around the mouth of the deltaic Meghna estuaries, are the hot spot of vulnerability distribution. Furthermore, the spatially heterogeneous vulnerability was triggered by spatial variation of erosion, cyclones, drought, rain-fed agriculture, land degradation, soil phosphorus, crop productivity, sanitation and housing condition, infant mortality, emergency shelters, adoption of agro-technology. The integrated approach could be useful for monitoring and evaluating the effectiveness of adaptation intervention by substituting various hypothetical scenarios into the ALVI framework for baseline comparison.

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Section: Introductionmentioning

confidence: 99%

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

Hoque

Cui

et al. 2019

IJERPH

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“…Components and factors are interpreted by examining loadings. Contributions of original variables to each principal component (PC) can be conveniently divided into three classes based on the loading scores: Strong (>0.75), moderate (0.75-0.50), and weak (0.50-0.30), respectively [40].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models

et al. 2019

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Process-based land surface models are important tools to study the historical and future effects of climate change and land use change. The planting date has a considerable effect on crop growth and consequently on dynamic parameters used in land surface models, for example albedo and actual evapotranspiration. If planting dates can be related to climate, scenarios can use this relation to estimate planting dates. Such a relation is expected to differ according to agro-ecological zone. In this study, spring and summer maize planting date observations at 188 agricultural meteorological experiment stations of China, as well as monthly weather records, over the years 1992–2010 were used as the data source. In order to quantify the relation between planting dates and climate parameters, growing season monthly average minimum temperature (Tmin), mean temperature (T), and precipitation (P) were used. The time trend analysis of planting dates and weather data, principal component analysis (PCA) of weather data, and multivariate regression of planting dates as affected by weather data were used. Both Tmin and T increased during this period in most zones, whereas precipitation showed no trend. In southwest and northwest China, maize planting dates advanced significantly for both spring and summer maize. However, in the north China plain (summer maize) and northeast China (spring maize), the planting date was significantly delayed. Ordinary least squares multivariate regression models were able to explain 33% and 59% of the variance of planting dates in the southwest China (i.e., the humid subtropics zone) for spring and summer maize, respectively. However, only 3% could be explained in the Loess Plateau. Thus, adjusting planting dates in scenario analysis using land surface models is indicated for some zones, but not others, where socioeconomic factors are dominant.

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“…Until now, household's agricultural vulnerability analysis studies, particularly in the south-west coastal region of Bangladesh, have not been extensively or adequately characterized. Though there is a substantial number of vulnerability studies (Ahsan & Warner, 2014;Bhuiyan et al, 2017;Hoque et al, 2019;Islam et al, 2014;Islam et al, 2013;Mullick et al, 2019;Rabby et al, 2019;Rakib et al, 2019;Uddin et al, 2019;Younus, 2017;Younus & Kabir, 2018) have found but there is hardly a single study to assess the agricultural vulnerability of household's to climate induced disasters in the south-western coastal Bangladesh. Therefore, this study was focused on assessing agricultural vulnerability to climate induced disasters of south-west coastal Bangladesh using agricultural vulnerability indicators under the umbrella of climate induced disasters vulnerability components of exposure, sensitivity and adaptive capacity which have been widely used and recognized by Intergovernmental Panel on Climate Change (IPCC) and recent studies on climate change.…”

Section: Introductionmentioning

confidence: 99%

Household’s Agricultural Vulnerability to Climate Induced Disasters: A Case on South-west Coastal Bangladesh

Khan

Kabir

Hasan

et al. 2021

Preprint

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The purpose of this study was to assess household’s agricultural vulnerability to climate induced disasters and to identify the indicators of adaptive capacity that determine the vulnerability of south-western coastal household’s in Bangladesh. The vulnerability was calculated by taking the Intergovernmental Panel on Climate Change (IPCC) concept through an Agricultural Vulnerability Index (AVI). Then the ordered logit model was employed in order to identify key determinants of agricultural vulnerability to climate induced disasters. A survey of 346 household’s head from the two settlements (181 household’s head from Sutarkhali and 165 household’s head from Nalian) of Sutarkhali Union of Dacope Upazila under Khulna District was used in this study. Findings revealed that the mean score of Sutarkhali was 0.703 (high) in exposure, 0.724 (high) in sensitivity, 0.341 (low) in adaptive capacity and finally, the agricultural vulnerability index (AVI) was 0.695 (high). On the same fashion, Nalian was an average score of 0.697 (high) in exposure, 0.721 (high) in sensitivity, 0.386 (low) in adaptive capacity, and finally 0.677 (high) in agricultural vulnerability index (AVI). Annual savings, formal education, ownership of livestock, improved seeds supply, access to irrigation pump, improved crop diversification/ High Yielding Variety (HYV) crops, access to large farm size and access to farm credit were found to be statistically significant indicators of adaptive capacity that determine agricultural vulnerability of the both study sites. Finally it is recommended that the intervention required for coastal adaptation of agriculture should be initiated by respective authorities.

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An investigation of coastal vulnerability and internal consistency of local perceptions under climate change risk in the southwest part of Bangladesh

Cited by 64 publications

References 31 publications

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models

Household’s Agricultural Vulnerability to Climate Induced Disasters: A Case on South-west Coastal Bangladesh

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