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<p>Early detection and monitoring of intense rainfall is essential to protect at-risk communities. This study aims to: (1) assess the use of satellite-derived Cloud Top Height (CTH) and Outgoing Longwave Radiation (OLR) in detecting extreme rainfall; (2) link these atmospheric parameters directly to rainfall intensity and flood impacts; and (3) establish a multi-sensor urban flood monitoring framework for data-sparse regions, using Patna, Bihar, as a primary case study. ERA-5 reanalysis product Convective Available Potential Energy (CAPE) was also taken into consideration to distinguish between active rainfall-producing convection and non-precipitating cloud systems. Using six gridded rainfall datasets (ERA5, INSAT-3D IMR/HEM, CHIRPS, GPM, GPCP) and Advanced Very High-Resolution Radiometer (AVHRR)-derived cloud parameters, the study captures the spatial–temporal variability of four major intense rainfall events (1997, 2007, 2013, 2019) that triggered severe flooding in Patna. Strong correlations were observed between atmospheric indicators-particularly CTH OLR, CAPE and rainfall intensity, addressing the limitations of earlier studies that relied on single rainfall products or short-term analyzes. To further validate this relationship, a comparative analysis has been conducted by integrating AVHRR based CTH and OLR across 25 Indian cities over a long-term period (1986–2019), representing a largely unexplored inter-city, long-term assessment in urban flood studies. The analysis indicates that CTH ≥ 17&#xa0;km and OLR ≤ 160 W/m<sup>2</sup> consistently preceded extreme rainfall events. Further, the 2019 Patna flood, which inundated ~ 30% of the city, was analyzed using a detailed multi-sensor approach integrating rainfall, atmospheric indicators, soil moisture, and Sentinel-1 based flood mapping. The findings demonstrate the effectiveness of combining atmospheric and land-surface parameters for detecting and characterising intense rainfall events and provide a scalable framework for satellite-based flood preparedness in data-scarce urban environments. These results strengthen the linkage between atmospheric behaviour, land-surface response, and flood impacts.</p>

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Monitoring and Assessing Intense Rainfall Episodes Over Patna Using Satellite Data

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Mala Sinha,
  • Swagata Payra,
  • Mili Ghosh Nee Lala,
  • Navneet Kumar

摘要

Early detection and monitoring of intense rainfall is essential to protect at-risk communities. This study aims to: (1) assess the use of satellite-derived Cloud Top Height (CTH) and Outgoing Longwave Radiation (OLR) in detecting extreme rainfall; (2) link these atmospheric parameters directly to rainfall intensity and flood impacts; and (3) establish a multi-sensor urban flood monitoring framework for data-sparse regions, using Patna, Bihar, as a primary case study. ERA-5 reanalysis product Convective Available Potential Energy (CAPE) was also taken into consideration to distinguish between active rainfall-producing convection and non-precipitating cloud systems. Using six gridded rainfall datasets (ERA5, INSAT-3D IMR/HEM, CHIRPS, GPM, GPCP) and Advanced Very High-Resolution Radiometer (AVHRR)-derived cloud parameters, the study captures the spatial–temporal variability of four major intense rainfall events (1997, 2007, 2013, 2019) that triggered severe flooding in Patna. Strong correlations were observed between atmospheric indicators-particularly CTH OLR, CAPE and rainfall intensity, addressing the limitations of earlier studies that relied on single rainfall products or short-term analyzes. To further validate this relationship, a comparative analysis has been conducted by integrating AVHRR based CTH and OLR across 25 Indian cities over a long-term period (1986–2019), representing a largely unexplored inter-city, long-term assessment in urban flood studies. The analysis indicates that CTH ≥ 17 km and OLR ≤ 160 W/m2 consistently preceded extreme rainfall events. Further, the 2019 Patna flood, which inundated ~ 30% of the city, was analyzed using a detailed multi-sensor approach integrating rainfall, atmospheric indicators, soil moisture, and Sentinel-1 based flood mapping. The findings demonstrate the effectiveness of combining atmospheric and land-surface parameters for detecting and characterising intense rainfall events and provide a scalable framework for satellite-based flood preparedness in data-scarce urban environments. These results strengthen the linkage between atmospheric behaviour, land-surface response, and flood impacts.