Scholarlink

<p>Multiple with extreme rainfall induced flash flooding pose significant challenges in monsoon dominated regions of India, yet reliable near-real-time monitoring is limited due to sparse ground observations. This study highlights the use of a satellite-based rainfall index (RI), derived from multispectral infrared and water vapor observations, to map short-lived localized multiple with extreme rainfall events with application to the August 2019 flood in Karnataka India. The information of RI integrates brightness temperature information using Meteosat-8 with precipitation estimates from TRMM and GPM-IMERG and is validated independently by using district scale India Meteorological department (IMD) rainfall data. To understand the impact of climatological context on 2019 rainfall event, rainfall anomalies are examined alongside ENSO and Indian Ocean Dipole (IOD) indices using partial correlation analysis over the period of 1998 to 2019. Results attribute statistically consistent co-variability between large-scale climate models and monsoon rainfall variability, with ENSO showing a strong and significant association than IOD. These relationships are interpreted as background climatic modulation rather than direct causal forcing. The study advances satellite-based rainfall monitoring by leveraging high-resolution analysis rainfall episodes with long-term climatological context, offering a transferable frame work for flood early-warning support in data-scarce regions.</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

Satellite observations reveal the role of climate variability in the 2019 extreme rainfall and flood event in Karnataka, India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

查看全文
  • Mohammad Suhail Meer,
  • Anoop Kumar Mishra,
  • Vanganuru Nagaraju,
  • Balamurugan Paneerselvam,
  • Chaitanya Baliram Pande

摘要

Multiple with extreme rainfall induced flash flooding pose significant challenges in monsoon dominated regions of India, yet reliable near-real-time monitoring is limited due to sparse ground observations. This study highlights the use of a satellite-based rainfall index (RI), derived from multispectral infrared and water vapor observations, to map short-lived localized multiple with extreme rainfall events with application to the August 2019 flood in Karnataka India. The information of RI integrates brightness temperature information using Meteosat-8 with precipitation estimates from TRMM and GPM-IMERG and is validated independently by using district scale India Meteorological department (IMD) rainfall data. To understand the impact of climatological context on 2019 rainfall event, rainfall anomalies are examined alongside ENSO and Indian Ocean Dipole (IOD) indices using partial correlation analysis over the period of 1998 to 2019. Results attribute statistically consistent co-variability between large-scale climate models and monsoon rainfall variability, with ENSO showing a strong and significant association than IOD. These relationships are interpreted as background climatic modulation rather than direct causal forcing. The study advances satellite-based rainfall monitoring by leveraging high-resolution analysis rainfall episodes with long-term climatological context, offering a transferable frame work for flood early-warning support in data-scarce regions.