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Assessment Of Vulnerability Of Arunachal Pradesh (india) To Floods

Congress: 2015
Author(s): Aditi Bhadra, Arnab Bandyopadhyay, Sanayanbi Hodam
North Eastern Regional Institute of Science and Technology1

Keyword(s): Sub-theme 11: Key vulnerabilities and security risks,
Article:
AbstractWater resource systems are vulnerable to floods due to three main factors; hazard, exposure, and adaptive capacity. Hazard is defined as a physical manifestation of climate change. Exposure can be understood as the values that are present at the location where floods can occur. Adaptive capacity is the ability of an entity -- a country, a community, or an individual -- to take action to cope better with current or potential adverse conditions brought about by hazards. India being the worst flood affected country next to Bangladesh accounts one fifth of the global deaths by flood every year. Arunachal Pradesh, due to its location near the two river systems (the Brahmaputra and Barak), unique location in the fragile geo-environment of eastern Himalayan periphery and poor adaptive capacity, is very much vulnerable to flood. So, vulnerability assessment of this state to flood is very important. The objectives of this study were: * To assign weights to indicators for hazard, exposure, and adaptive capacity for estimation of vulnerability indices for floods. * To determine vulnerable districts for floods in Arunachal Pradesh. The present study area, Arunachal Pradesh, is situated between 26º 30' to 29º 28' N latitudes and 91º 25' to 97º 24' E longitudes. It covers an area of 83,700 sq. km and consists of 16 districts. The average annual rainfall varies from 1,380 to 5,500 mm. In each district a set of indicators were selected for each of the three component of vulnerability. Values of those indicators were collected from Directorate of Economics and Statistics, Government of Arunachal Pradesh, Itanagar for 2010. UNDP's Human Development Index (HDI) method ((UNDP, 2006) was followed to normalize indicators. In order to obtain standardized values of indicators without any units, first they need to be normalized so that they all lie between zero and one. Before doing this, it is important to identify the functional relationship between the indicators and vulnerability. Two types of functional relationship are possible: vulnerability increases with increase in the value of the indicator or vulnerability increases with decrease in the value of the indicators. In this study, method of normalization took into account the above functional relationship which was important in the construction of the indices. The value one corresponded to the district with maximum vulnerability and zero corresponded to the district with minimum vulnerability. Weights corresponding to the selected indicators of hazard, exposure, and adaptive capacity were assigned using the unequal weight method defined by Iyengar and Sudershan (1982). In Iyengar and Sudarshan's method the weights are assumed to vary inversely with the variance over the regions in the respective indicators of vulnerability. The vulnerability index so computed lies between zero and one. The different components of vulnerability (hazard, exposure, and adaptive capacity) were analyzed separately, but same ranking approach was also used to assess composite vulnerability of the districts. Weights for hazard indicators were calculated and further, using these weights and the normalised indicators, vulnerability indices and ranks of 16 districts for hazard were also calculated. The vulnerable districts in terms of hazard having vulnerability index more than 0.5 are Papum Pare, Changlang, Lower Dibang valley, Upper Siang, West Siang, Upper Subansiri, and East Kameng. Similarly, the assigned weights and vulnerability indices for exposure and adaptive capacity were also calculated. The vulnerable districts in terms of exposure having vulnerability index more than 0.5 are Lower Subansiri, Changlang, and Tirap. The vulnerable districts in terms of adaptive capacity having vulnerability index more than 0.5 are Dibang valley, Anjaw, Upper Siang, Tirap, Tawang, West Kameng, East Kameng, Lower Subansiri, Kurung Kumey, Upper Subansiri, Lower Dibang Valley and Changlang. For calculating the composite vulnerability index, the collected normalized indicators of hazard, exposure, and adaptive capacity were arranged together in the form of rectangular matrix with rows representing districts and columns representing indicators. The vulnerable districts with composite vulnerability index more than 0.5 are Upper Siang, Anjaw, Dibang Valley , Lower Dibang Valley, Changalng, Lower Subansiri, Tirap, East Kameng, Kurung Kumey, and West Kameng. From the results, it can be seen that there are six districts with vulnerability index between 0.6 and 0.8 (Upper Siang, Anjaw, Dibang Valley, Lower Dibang Valley, Chanlang, and Lower Subansiri), highest being 0.67 for Upper Siang. There are nine districts with vulnerability index in the range from 0.4 to 0.6 (Tirap, East Kameng, Kurung Kumey, West Kameng, Tawang, West Siang, Upper Subansiri, and Lohit). Only Papum Pare district has the index in between 0.2 and 0.4 (0.31), which is the lowest among all districts. Following conclusions were drawn from the study: 1. Considering indicators for hazards, seven districts, viz., Papum Pare, Changlang, Lower Dibang Valley, Upper Siang, West Siang, Upper Subansiri, and East Kameng are vulnerable to flood. 2. Considering indicators for exposure, three districts, viz., Lower Subansiri, Changlang, and Tirap are vulnerable to flood. 3. Considering indicators for adaptive capacity, 12 districts, viz., Dibang valley, Anjaw, Upper Siang, Tirap, Tawang, West Kameng, East Kameng, Lower Subansiri, Kurung Kumey, Upper Subansiri, Lower Dibang Valley and Changlang are vulnerable to flood. 4. Considering all indicators, 10 districts, viz., Upper Siang, Anjaw, Dibang Valley, Lower Dibang Valley, Changlang, Lower Subansiri, Tirap, East Kameng, Kurung Kumey, and West Kameng are vulnerable to flood. Finally it can be concluded that in Arunachal Pradesh, vulnerability to flood is mainly because of poor adaptive capacity followed by considerable hazard and exposure. 1. Iyengar, N.S. and Sudarshan, P. (1982) A Method of Classifying Regions Multivariate Data. Economic and Political Weekly, Special Article, 2048-2052. 2. UNDP. (2006) Human Development Report 2006: Beyond Scarcity–Power, Poverty and the Global Water Crisis. Basingstoke, United Kingdom, Palgrave Macmillan.
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