GIS-based vulnerability mapping of the coastal stretch from Puri to Konark in Odisha using analytical hierarchy process

Abstract

The 485-km-long coastline of Odisha, a state in the northeastern part of the Indian peninsula, is potentially vulnerable to several disaster events that take place frequently. In addition to threats due to natural hazards, these coastal regions also face immense population and developmental pressures. The increase in the intensity and frequency of cyclones and accelerated sea level rise related to increased sea surface temperature have led to flooding, coastal erosion and shoreline retreat causing damage to coastal ecosystems and resources in these regions. In recognition of these risks, the present work demonstrates a GIS-based approach to assess the vulnerability of the 187-km stretch from Puri to Konark out of the total 485-km coastline using analytical hierarchy process (AHP). The present study focuses on computation of integrated coastal vulnerability index which is an integration of physical vulnerability index, geotechnical vulnerability index and social vulnerability index using AHP taking nine risk variables into consideration. An attempt has been made to demonstrate the state-of-the-art microzonation of the coastal stretch between Puri and Konark based on the vulnerability indices using geographical information system.

Vulnerability to watershed erosion and coastal deposition in the tropics

“…We considered the following environmental and anthropogenic factors in our assessment of erosional vulnerability: Mean Annual Precipitation, Land Cover Type, Mean Watershed Slope, Bedrock Lithology, Earthquake Intensity Probability, Soil Thickness, Agriculture, Grazing, Mining, and Development. We choose these factors based on the following: 1. various Vulnerability Indices 28,30,41,[49][50][51][52][53] 2. Soil Water Erosion Models (SWEM) such as the Revised Universal Soil Loss equation 54 and others e.g.…”

“…These classification breaks in Table 1 were determined based on the histogram distribution of each individual dataset, and when appropriate, other published vulnerability indices e.g. 28,30,40,41,[49][50][51][52][53][54][55] and many others. Below is the class break rationale for variables where breaks could not be determined by histogram distribution and published indices.…”

“…* email: trevor_browning@redlands.edu (RUSLE) or other soil water erosion models, primarily at the watershed scale [35][36][37] with few at a global scale 38,39 . Recent studies in India have focused on integrating risk indices 40,41 for a more holistic approach by using multiple factors (physical, social, and geo-technical) to quantify risk to a region affected by erosion.…”

Erosion and deposition vulnerability of small (<5,000 km2) tropical islands

“…Some focus on modeling potential watershed soil loss using the Revised Universal Soil Loss Equation (RUSLE) or other soil water erosion models, primarily at the watershed scale [ 38 – 40 ] with few at a global scale [ 41 , 42 ]. Recent studies in India have focused on integrating risk indices [ 43 , 44 ] for a more holistic approach by using multiple factors (physical, social, and geo-technical) to quantify risk to a region impacted by erosion.…”

Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem

“…In South Asia, the sea-level in the Ganges-Brahmaputra-Meghna delta of Bangladesh is likely to rise by 0.63 to 0.88 m by 2090 [12]. Frequent cyclones, together with increasing sea-levels, have resulted in flooding, coastal erosion, and recession of coastline in the region [13].…”