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

IF 0.7 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES
Rajat Agrawal, Suraj Kumar Singh, S. Kanga, Bhartendu Sajan, Gowhar Meraj, Pankaj Kumar
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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.
通过综合灾害绘图推进洪水风险评估:基于谷歌地球引擎的综合科学分析和决策支持方法
本研究采用一种全面、多层次的方法来评估特定区域的洪水易发性,整合了各种环境数据集,如 JRC 全球地表水、Landsat 8 图像和 SRTM 高程数据。GEE FMA 是一款利用谷歌地球引擎功能的强大工具,该分析采用了 GEE FMA,涵盖了水发生、永久水、高程、水域距离、地形危险评分和植被指数(NDVI 和 NDWI)。水域出现层建立了对水体分布与洪水脆弱性相关性的基本认识,而永久水域则完善了这一认识。水域距离图层用于衡量目标风险评估的临近程度,而海拔高度图层则根据地形确定易受影响的区域。GEE FMA 将这些图层综合到洪水灾害易感性地图中,将脆弱性分为极低、低、中、高和极高。这种细致入微的理解对于确定干预措施的优先次序至关重要。GEE FMA 的快速处理速度使其成为洪水灾害管理中短期决策支持的宝贵工具,为明智决策和弹性基础设施开发提供洞察力。地形灾害评分可提供有关地形如何影响洪水风险的信息,而湿度灾害评分则对湿度条件进行分类,以确定洪水易发地点。决策者可以依靠这些数值快速、准确地评估洪水易发性。在气候不确定和城市化的时代,GEE FMA 成为决策、减轻洪水影响和制定有效洪水风险管理战略的可靠工具。
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来源期刊
Journal of Climate Change
Journal of Climate Change METEOROLOGY & ATMOSPHERIC SCIENCES-
自引率
16.70%
发文量
18
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