Vulnerability Evaluation from 1995 to 2016 in Central Dry Zone Area of Myanmar

Abstract:

The central dry zone area of Myanmar is characterized as vulnerable area due to water stressed and one of the most food insecure regions in the country. In this region, the adverse effects of climate change are believed to be a major constraint to vulnerability. Theses extreme climatic events are likely increase in frequency and magnitude of serious drought periods and extreme floods. For vulnerability assessment we used remote sensing (RS) and geographical information system (GIS) technology and develop a numerical model, using spatial principle component analysis (SPCA) in ArcGIS software and evaluate two decade (1995, 2005 & 2016) vulnerability evaluation. The model contains following indicators: discharge change, climate moisture, drained area, flood risk, irrigation, evapotranspiration, precipitation, surface runoff, nitrogen load and population distribution. According to the numerical results, the vulnerability is classified into five levels: slight, light, medial, heavy and very heavy level by means of the cluster principle. The results show that vulnerability in the study area from 1995 to 2016 is at medial (25%) and heavy (25%) level and presents from south-west to north east direction. The vulnerability change trend show worst situation in 1995 (29.80) and best one in 2005 (17.45) but again vulnerability was increase in 2016 (21.58). In the study area the main driving forces for dynamic change in vulnerability is the intensive land use and high population density. This spatial approach allowed the analysis of different indicators, providing a platform for data integration as well as a visually powerful overview of the study area.

• Periodical: International Journal of Engineering Research in Africa (Volume 32)
• Pages: 139-154
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.32.139
• Online since: September 2017
• Authors: Mukesh S. Boori*, Komal Choudhary, Alexander Kupriyanov
• Keywords: GIS, Principle Component Analysis, Remote Sensing, Vulnerability

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