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Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India
Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India
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Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India
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Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India
Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India
Journal Article

Impact assessment of sea level rise over coastal landforms: a case study of Cuddalore coast, south-east coast of India

2019
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Overview
Densely populated south-east coast of India is susceptible to disasters such as tsunami, coastal flooding storm-surge and shoreline erosion. Apart from episodic events, the gradual sea-level rise (SLR) has got more attention to coastal researchers recently relating to the potentially impacted coastal zone, its anthropogenic/environment associations, and the possible future scenarios. Global average SLR rate has increased in recent decades from 1.7 mm year−1 1901 to 2010, 3.1 mm year−1 from 1993 to 2003 and 3.12 mm year−1 from 1993 to 2012. The present study is an aim to assess the impact of future sea-level rise along Pondicherry—Chidambaram coast using Bruun Rule and Modified Bruun Rule. Eight satellite-derived data sets were used to study the shoreline change trends during 1990–2015. 25 years of shoreline change trend reveals that ~ 49% of the coastline is under erosion. Shoreline retreat to an increase in local sea level was also mapped by Bruun Rule. Bruun Rule has some limitations, and hence Modified Bruun Rule was used to analyze the inundation factor. The horizontal inundation of the study area was estimated as ~ 1.1 km (Bruun Rule) and ~ 1.6 km (Modified Bruun Rule). The impacts of SLR in the study area were determined by integrating inundation data with geomorphological and land use/land cover data. The study reveals that about 16.08 sq.km area of geomorphological features is likely to be highly affected, while 17.5 sq.km of the area likely to be affected on land use/land cover features. This study provides an interactive means to identify the vulnerable zone. The output maps can be used to visualize the affected areas spatially.