Scholarlink

<p>The holy cave shrine of Amarnath is thronged annually by over three lakh devotees. However, the cryogenic-sensitive region is increasingly becoming vulnerable to anomalies in precipitation events and rising anthropogenic footprints. On the evening of 8 July 2022, a highly localized extreme rainfall event took place leading to a short-lived flash flood along with unsorted debris flow in the Amarnath valley, surmounting heavy loss of lives and local livelihoods. This article uses Earth Observation (EO) datasets to capture and understand the implications of using such data applications in mitigating disasters for remote and inaccessible areas. Despite the valuable insights provided by EO data, their applicability is often restricted by the temporal limitations, particularly those derived from open-source radar and optical satellite imagery, which are frequently incapable of capturing ephemeral or rapidly evolving phenomena. Some meaningful information about the present study was captured with the use of GPM (IMERG) satellite-based rainfall data, while others failed to gauge the situation. Eight topographical parameters have been examined to understand the local factors contributing to flash flood conditions in the Amarnath watershed. An AHP-based flash flood susceptibility zonation (FFSZ) was derived using Google Earth Engine (GEE) along with an interactive user interface was developed for visualization of the computed parameters. The FFSZ contains five classes with their areal percentages: Very Low (20.03%), Low (19.69%), Moderate (20.43%), High (20.14%) and Very High (19.71%) respectively. Our findings suggest the need for more ground-based automated weather stations (AWS) complementing satellite-based EO systems' limitations for providing high-precision regular interval observation. Finally, we propose a new ‘4S’ framework, namely, ‘source’, ‘setting’, ‘susceptibility’ and ‘solution’ for flash flood risk assessment. This framework has also been discussed in complementary to a cross-sectoral interface containing ‘science-governance-disaster risk reduction (DRR)-society’ aspects alongside major targets based on Global Goals (UN SDGs) and India’s national DRR agenda points.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring the Capability of Earth Observation Data and a New ‘4S’ Framework for Disaster Risk Reduction: An Experience from July 2022 Flash Floods in Amarnath Valley, India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

查看全文
  • Jayesh Mukherjee,
  • Anuva Chowdhury,
  • Surajit Ghosh

摘要

The holy cave shrine of Amarnath is thronged annually by over three lakh devotees. However, the cryogenic-sensitive region is increasingly becoming vulnerable to anomalies in precipitation events and rising anthropogenic footprints. On the evening of 8 July 2022, a highly localized extreme rainfall event took place leading to a short-lived flash flood along with unsorted debris flow in the Amarnath valley, surmounting heavy loss of lives and local livelihoods. This article uses Earth Observation (EO) datasets to capture and understand the implications of using such data applications in mitigating disasters for remote and inaccessible areas. Despite the valuable insights provided by EO data, their applicability is often restricted by the temporal limitations, particularly those derived from open-source radar and optical satellite imagery, which are frequently incapable of capturing ephemeral or rapidly evolving phenomena. Some meaningful information about the present study was captured with the use of GPM (IMERG) satellite-based rainfall data, while others failed to gauge the situation. Eight topographical parameters have been examined to understand the local factors contributing to flash flood conditions in the Amarnath watershed. An AHP-based flash flood susceptibility zonation (FFSZ) was derived using Google Earth Engine (GEE) along with an interactive user interface was developed for visualization of the computed parameters. The FFSZ contains five classes with their areal percentages: Very Low (20.03%), Low (19.69%), Moderate (20.43%), High (20.14%) and Very High (19.71%) respectively. Our findings suggest the need for more ground-based automated weather stations (AWS) complementing satellite-based EO systems' limitations for providing high-precision regular interval observation. Finally, we propose a new ‘4S’ framework, namely, ‘source’, ‘setting’, ‘susceptibility’ and ‘solution’ for flash flood risk assessment. This framework has also been discussed in complementary to a cross-sectoral interface containing ‘science-governance-disaster risk reduction (DRR)-society’ aspects alongside major targets based on Global Goals (UN SDGs) and India’s national DRR agenda points.