Framework for mapping the drivers of coastal vulnerability and spatial decision making for climate-change adaptation: A case study from Maharashtra, India

Abstract

The impacts of climate change are of particular concern to the coastal region of tropical countries like India, which are exposed to cyclones, floods, tsunami, seawater intrusion, etc. Climate-change adaptation presupposes comprehensive assessment of vulnerability status. Studies so far relied either on remote sensing-based spatial mapping of physical vulnerability or on certain socio-economic aspects with limited scope for upscaling or replication. The current study is an attempt to develop a holistic and robust framework to assess the vulnerability of coastal India at different levels. We propose and estimate cumulative vulnerability index (CVI) as a function of exposure, sensitivity and adaptive capacity, at the village level, using nationally comparable and credible datasets. The exposure index (EI) was determined at the village level by decomposing the spatial multi-hazard maps, while sensitivity (SI) and adaptive capacity indices (ACI) were estimated using 23 indicators, covering social and economic aspects. The indicators were identified through the literature review, expert consultations, opinion survey, and were further validated through statistical tests. The socio-economic vulnerability index (SEVI) was constructed as a function of sensitivity and adaptive capacity for planning grassroot-level interventions and adaptation strategies. The framework was piloted in Sindhudurg, a coastal district in Maharashtra, India. It comprises 317 villages, spread across three taluks viz., Devgad, Malvan and Vengurla. The villages in Sindhudurg were ranked based on this multi-criteria approach. Based on CVI values, 92 villages (30%) in Sindhudurg were identified as highly vulnerable. We propose a decision tool for identifying villages vulnerable to changing climate, based on their level of sensitivity and adaptive capacity in a two-dimensional matrix, thus aiding in planning location-specific interventions. Here, vulnerability indicators are classified and designated as 'drivers' (indicators with significantly high values and intervention priority) and 'buffers' (indicators with low-to-moderate values) at the village level. The framework provides for aggregation or decomposition of CVI and other sub-indices, in order to plan spatial contingency plans and enable swift action for climate adaptation.

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

“…The categorization of EI, SI, ACI, and ALVI into five classes of attributes (very low, low, moderate, high, and very high) at the spatial scale was accomplished by employing the equal interval approach under an ArcGIS environment [57,69]. Furthermore, the coefficient of correlation was run to find the relationships among EI, SI, ACI, and ALVI [26,56].…”

“…Since uncertainty is interlinked with climate change, exposure is beyond the reach of policy interventions. However, adopting suitable policy interventions for reducing sensitivity and enhancing adaptive capacity could reduce vulnerability [56]. To this end, district-wise sensitivity and adaptive capacity index values were plotted on the X and Y axes, respectively, of a scatter diagram, to develop a decision matrix that could help identify socioeconomically vulnerable areas so that effective interventions could be taken, on a priority basis.…”

“…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]. Notably, vulnerability assessment of the farming sector was restricted to a localized small area [46,55] and specialized fields like fisheries [27].…”

A Systematic Review of Coastal Vulnerability Assessment Studies along Andhra Pradesh, India: A Critical Evaluation of Data Gathering, Risk Levels and Mitigation Strategies

“…The majority of studies [32,[50][51][52][53] focused on basic factors as major drivers for coastal vulnerability along the Indian coast. Krishnan et al (2018) [74] proposed a cumulative vulnerability index (CuVI) framework to map the coastal vulnerability along the Maharashtra coast. The CuVI is a function of exposure, sensitivity and adaptive capacity and uses the exposure-index (EI), sensitivity-index (SI) and adaptive-capacity-index (ACI).…”

A Systematic Review of Coastal Vulnerability Mapping

“…Molinari et al [109] note that very few studies include validation of flood risk estimates and maps, which may be concerning considering that decision-makers rely on these products to make hazard mitigation investments and plans. In a more recent example, Krishnan et al [110] conducted a validation of individual indicators using multiple statistical procedures to ensure they are not overrepresented in the final Cumulative Vulnerability Index (CVI) for the Sindhudurg district in India.…”