Geophysical Research abstracts, EGU General Assembly, Vienna, Austria, 2013
Vulnerability to natural hazards is an outcome of composite and dynamic interactions of physical,... more Vulnerability to natural hazards is an outcome of composite and dynamic interactions of physical, socioeconomic , environmental and administrative factors. To assess vulnerability of settlements, the identification of appropriate vulnerability indicators is an important step towards creating an integrated vulnerability index from a set of indicators. Indicators for measuring vulnerability are powerful tools to identify and monitor temporal and spatial vulnerability of settlements. It prioritizes strategies for improving resilience and for determining the usefulness of those strategies. The study area includes the settlements along the coast of Chennai in the Tamil Nadu state, southeast coast of India. The coastal settlements of Chennai comprises residential areas, commercial places, tourist resorts, ports, hotels, fishing villages and other important infrastructure which have experienced an increasing threat from storms, cyclones, and tsunami over the past few years. In 2004, the city of Chennai was devastated by the catastrophic tsunami and other recent cyclonic events like Nisha cyclone in 2008 and Nilam cyclone in 2012 leading to loss of life and property. The study examines an integrated vulnerability of Chennai's coastal settlements to natural hazard with special focus on Tropical Cyclone (TC) and Storm Surge (SS) events. Recently, Coastal Vulnerability Index (CVI) has been developed by Indian National Center for Ocean Information Services (INCOIS) based on eight physical parameters i.e. shoreline change rate, mean sea level change rate, regional elevation, bathymetric data, mean tidal range, significant wave height, geomorphology, and extreme storm surge and return periods which has only provided a physical vulnerability index. The CVI does not include socioeconomic and administrative indicators. In this regard the present study is an effort to fill this gap in the vulnerability study of the Chennai city, by developing a methodology to aggregate socioeconomic , environmental and administrative indicators with physical parameters which resulted in an integrated composite index of vulnerability (ICVI). Data used for the study includes qualitative and quantitative data, remote sensing imageries which has been analyzed and processed in a GIS framework. Hotspots of vulnerability to cyclones and storm surge events have been established with the help of ICVI. The index has identified a low, medium and high range of coastal settlement's vulnerability which has been mapped for spatial analysis. The results indicate that vulnerability of settlements along the coast is highly distinguished and subjected by a range of social, economic, administrative and physical indicators. Thus, integrated vulnerability index and map prepared for the coastal settlements of Chennai can be utilized by the various organizations involved in disaster management and mitigation. The index can also be incorporated in the regional planning process at national, state and local level.
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Sky classification for the site was done based upon the ‘all weather model’ by Perez. As revealed from the primary survey, factors identified as important in influencing artificial lighting during daytime are sky conditions, age, work hours, education, income and housing typology. A binary logistic regression applied to the database to predict whether people would switch on a light at daytime in the living room, revealed that the need was least in the Angular apartments and highest in the Bungalows. A similar model for kitchen revealed highest daytime artificial illumination requirement for duplexes with lowest for angular apartments.
Sky classification for the site was done based upon the ‘all weather model’ by Perez. As revealed from the primary survey, factors identified as important in influencing artificial lighting during daytime are sky conditions, age, work hours, education, income and housing typology. A binary logistic regression applied to the database to predict whether people would switch on a light at daytime in the living room, revealed that the need was least in the Angular apartments and highest in the Bungalows. A similar model for kitchen revealed highest daytime artificial illumination requirement for duplexes with lowest for angular apartments.