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<p>This study diagnoses the processes and identifies the footprints of the 2025 June-July multi-hazard events in the western Himalayas using remote sensing observations (e.g., Sentinel-1A SAR data) within the Google Earth Engine platform. The early monsoon of 2025 brought extreme precipitation anomalies (+ 195% in Himachal Pradesh, + 96% in Uttarakhand), triggering intense rainfall events (&gt; 100&#xa0;mm/day), cloudbursts, floods, and landslides across vulnerable districts such as Mandi, and Chamoli. Extreme rainfall footprints (5.66–53&#xa0;km) overlapped with terrain-instability zones, amplifying slope failure risks. Spatial analysis revealed significant flood heterogeneity, with high-altitude Lahaul-Spiti recording the maximum inundation (170.17&#xa0;ha), contrary to assumptions that southern districts would be most affected. Minimal flooding in Bilaspur, Mandi, and Shimla highlights the terrain-controlled nature of flood hazards. These findings underscore the utility of integrating remote sensing and cloud computing for rapid, physiographically adaptive hazard assessment, while emphasizing the need for multi-hazard early warning systems tailored to Himalayan geomorphic and climatic complexity.</p>

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Cascading impacts of 2025 June-July multi-hazard episodes in the Western Himalayas: Evidence through remote sensing observations and Google Earth Engine

  • 期刊论文

  • 发表日期：

    2025年9月27日

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  • Rahul Deopa,
  • Hrishikesh Singh,
  • Vaibhav Tripathi,
  • Mayank Tyagi,
  • Mohit Prakash Mohanty

摘要

This study diagnoses the processes and identifies the footprints of the 2025 June-July multi-hazard events in the western Himalayas using remote sensing observations (e.g., Sentinel-1A SAR data) within the Google Earth Engine platform. The early monsoon of 2025 brought extreme precipitation anomalies (+ 195% in Himachal Pradesh, + 96% in Uttarakhand), triggering intense rainfall events (> 100 mm/day), cloudbursts, floods, and landslides across vulnerable districts such as Mandi, and Chamoli. Extreme rainfall footprints (5.66–53 km) overlapped with terrain-instability zones, amplifying slope failure risks. Spatial analysis revealed significant flood heterogeneity, with high-altitude Lahaul-Spiti recording the maximum inundation (170.17 ha), contrary to assumptions that southern districts would be most affected. Minimal flooding in Bilaspur, Mandi, and Shimla highlights the terrain-controlled nature of flood hazards. These findings underscore the utility of integrating remote sensing and cloud computing for rapid, physiographically adaptive hazard assessment, while emphasizing the need for multi-hazard early warning systems tailored to Himalayan geomorphic and climatic complexity.