Flood susceptibility mapping using a geomorphometric approach in South Australian basins

Alaa Ahmed (), Guna Hewa and Abdullah Alrajhi
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Alaa Ahmed: UniSA STEM, University of South Australia
Guna Hewa: UniSA STEM, University of South Australia
Abdullah Alrajhi: King Abdulaziz City for Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 106, issue 1, No 28, 629-653

Abstract: Abstract Watershed characteristics and their hydrological responses can have severe effects on the occurrence and extent of floods. Therefore, this study focuses on the integration of geospatial techniques and remote sensing data to identify watershed terrain characteristics and evaluate the influence of these characteristics on flood susceptibility in South Australia. Data from the Shuttle Radar Topography Mission (SRTM) and geologic and topographic maps and a geographic information system (GIS) were used to delineate drainage basins, measure morphometric parameters and link different parameters to evaluate the degree of flood vulnerability. Depending on their relations to the flood hazards, the morphometric parameters were categorized into two groups; then, a rank score was assigned to each group. Finally, the flood susceptibility of the basins was visualized, and the basins were classified into low, intermediate and high flood hazard areas. The results show that approximately 45.7, 44.7 and 9.7% of the study area is at risk of high, medium and low degrees of flooding, respectively. The results were validated through secondary data relating to historic floods. The causes of flooding were analysed using rainfall and road density data, while the consequences of flooding were verified by the population distribution across the study area. The findings of this study can be used to support decision-makers in planning and investing in mitigation measures, especially in highly susceptible areas of South Australian basins.

Keywords: Flood; Morphometric analysis; GIS; Remote sensing; South Australia (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-020-04481-z

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