Modelling Coastal Flood Hazard Using ArcGIS Spatial Analysis tools and Satellite Image
Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 8)Publication Date: 2014-08-05
Authors : Sailesh Samanta; Cathy Koloa;
Page : 961-967
Keywords : Remote Sensing; GIS; Frequency analysis; Inundation; Risk element; Return period;
Abstract
The objectives of the study are flood hazard mapping and infrastructure and settlement/population vulnerability assessment in a low laying coastal flood prone area of Lae coast for different flood magnitudes. Different variables like coastlines data, High resolution LiDAR satellite image (20cm) and Digital Elevation Model (1 m resolution), historic tidal gauge sea levels, future projections of global mean sea level rise, Infrastructure elements and demographic information along the Lea coastal track are used to modelled and develop the coastal flood hazard data base. Maximum of hourly peak sea level is 1.96 m in Lae coast according to historic tidal gauge sea levels. Frequency analysis is carried out to estimate extreme sea levels for coastal flood hazard mapping for the basis of specified flood frequencies or return periods. of 3 m and 4.5 m is considered (1: 50 and 1: 100 years). Hazards maps are developed to determine the water level and inundated area for 50-years (3 m) and 100-year (4.5 m) return periods flood. Vulnerability functions of risk elements and flood hazard maps are analyzed in ArcGIS environment to develop maps for vulnerable area, population and infrastructural elements. The result shows that an area of 38.19 sq km area is under the risk of inundation in the middle of this century (50 years return period) and 48.08 sq km in the end of this century (100 years return period). A majority of urban populations in the region live in coastal areas, inundation would also likely cause large costs for infrastructure relocation. These hazard maps can be developed for any other coastal areas of Papua New Guinea to manage coastal hazards in the context of regional and local knowledge, using data gathered by site-specific tide-gauges and other relevant technology.
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