Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

Article type: Research Article

Authors: Agrawal, Rajat | Singh, Suraj Kumar | Kanga, Shruti | Sajan, Bhartendu | Meraj, Gowhar | Kumar, Pankaj^{; *}

Affiliations: Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur – 302017, Rajasthan, India | Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, Bhatinda – 151401, Punjab, India | Department of Ecosystem Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo – 113-8654, Japan | Institute for Global Environmental Strategies, Hayama – 240-0115, Japan

Correspondence: [*] Corresponding Author. kumar@iges.or.jp

Abstract: This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

Keywords: Flood hazard assessment, Google earth engine, Multi-layered analysis, Decision support system, Disaster management

DOI: 10.3233/JCC240007

Journal: Journal of Climate Change, vol. 10, no. 1, pp. 47-60, 2024