Coastal Multi-Hazard Vulnerability Mapping: A Case Study Along The Coast of Nellore District, East Coast of India

Mahendra, R. S.; Mohanty, P. C.; Kumar, T. Srinivasa; Shenoi, S. S. C.; Nayak, Shailesh

doi:10.5721/itjrs20104235

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…The coastal physical vulnerability due to inundation by oceanogenic disasters was estimated following Mahendra et al (2010Mahendra et al ( , 2011. The flood line mapping was carried out based on sea level trend, shoreline change rate, contours, extreme water level and their return periods in 100 years.…”

Section: Multi-hazard Mappingmentioning

confidence: 99%

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

Krishnan

Ananthan

Jeevamani

et al. 2018

Ambio

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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.

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Section: Multi-hazard Mappingmentioning

confidence: 99%

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

Krishnan

Ananthan

Jeevamani

et al. 2018

Ambio

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“…In the North Arabian Sea, we have observed the shift from diatom to green dinoflagellates (Gomes et al 2008). The increase in extreme rainfall also increases river run-off, polluting coastal the multi-hazard and risk maps for part of the tsunamiaffected area on the East coast of India (Mahendra et al 2011(Mahendra et al , 2010b.…”

Section: Water Qualitymentioning

confidence: 90%

Coastal zone management in India − present status and future needs

Nayak¹

2017

Geo-spatial Information Science

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The coastal zone is a region where land, ocean and atmosphere interact and hence it is dynamic in nature. India has a long coastline which was not adequately monitored until the advent of the satellite remote sensing era in the 70s. India has a very robust remote sensing program that the Indian Remote Sensing Satellite (IRS) series of satellites were effectively used to monitor coastal habitats, landforms, shoreline, water quality, etc., and changes were identified during the last 40 years. The classification system for coastal habitats and the classification and geometric accuracies of products were standardized. Detailed information for mangroves communities and characteristics of coral reefs were generated. The high and low tide lines were delineated seamlessly for the entire coastline using satellite data. All these data were organized in a GIS and the coastal database for the entire country was created. Impacts of various hazards on such as cyclones, tsunami and sea level changes on coastal habitats were documented. Based on topography, shoreline changes and tides, coastal multi-hazard vulnerability maps were characterized by employing the elevation data derived from satellite data and were prepared for the coastline of India. The information on ocean color and sea surface temperature was used to generate potential fishery advisories, which are provided daily to fishermen. The coastal database was utilized effectively to identify coastal regulation zones, marine protected areas, vulnerable zones, etc. Various services for tsunami, fishery and coral reef bleaching were generated for societal benefits. It is planned to develop models for the coastal zone, so that impeding dangers and likely changes in the coastal zone can be predicted and suitable actions can be undertaken. It is necessary to integrate socioeconomic data with the knowledge database of coastal zone to understand the impact of anthropogenic activities and the changing climate on the coastal zone.

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“…Within the research articles, documents with quantitative analysis prevail with 57%. (Wipulanusat, Nakrod, & Prabnarong, 2009), (Mahendra, Prakash, Srinivasa Kumar, Shenoi, & Shailesh, 2010), (Lozoya, Sardá, & Jiménez, 2011), (Kappes, Keiler, von Elverfeldt, & Glade, 2012), (Kappes, Gruber, et al, 2012), (Marzocchi, Garcia-Aristizabal, Gasparini, Mastellone, & Ruocco, 2012), (Kameshwar & Padgett, 2014), (Johnson, Depietri, & Breil, 2016), (Gill & Malamud, 2017), (Stults, 2017), (Villegas-González, Ramos-Cañón, González-Méndez, González-Salazar, & De Plaza-Solórzano, 2017), (Bonacho & Oliveira, 2018), (Furlan, Torresan, Critto, & Marcomini, 2018), (Hagenlocher, Renaud, Haas, & Sebesvari, 2018), (Hernández, Carreño, & Castillo, 2018), (Kwag & Hahm, 2018), (K. Liu, Wang, Cao, Zhu, & Yang, 2018), (Mukherjee, Nateghi, & Hastak, 2018), (Pilone & Demichela, 2018), (Reniers, Khakzad, Cozzani, & Khan, 2018), (Sahoo & Bhaskaran, 2018), (Viavattene et al, 2018), (Zimmaro, Stewart, Brandenberg, Kwak, & Jongejan, 2018) As mentioned above, at the level of research articles, quantitative analyses predominate, and this is a trend that is repeated under the type of analysis level (see Figure 10). Descriptive analyses represent 23% of the 30 analyzed documents, and the other ones contain qualitative, semi-quantitative, and mixed analysis approaches (e.g., combining qualitative, semiquantitative and quantitative analyses).…”

Section: Analysis Of Publications Based On Multi-hazard Analysismentioning

confidence: 99%