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<p>This study downscaled the future precipitation extreme indices over the smart cities of the Gangetic Plain using the Statistical Downscaling Model (SDSM). The analysis focused on three Global Climate Models (GCMs): CanESM5, NorESM2-MM, and MPI-ESM1-2&#xa0;h, under two Shared Socioeconomic Pathways (SSP245 and SSP585). Historical precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (1981–2014) and National Center NCEP gridded data were used to calibrate and validate the model. The study examined extreme precipitation indices such as RX1day (Maximum 1-day precipitation), RX5day (Maximum consecutive 5-day Precipitation), R20mm (Annual count of days when PRCP &gt; = 20&#xa0;mm), and R95P(Annual total PRCP when RR &gt; 95th percentile), which quantify the intensity, frequency, and contribution of extreme rainfall events. The results reveal distinct trends in extreme precipitation across the cities under both SSP245 and SSP585. Under SSP245, moderate increases in extreme precipitation indices were observed, particularly in CanESM5, with significant trends emerging under SSP585. Capital cities Delhi, Lucknow, Patna, and Kolkata showed strong increases in extreme precipitation indices under SSP585, highlighting the potential exacerbation of urban flooding risks and infrastructure strain in the future. The CanESM5 model generally showed higher sensitivity and trends, indicating a higher frequency and intensity of extreme rainfall events under SSP585 scenarios. The study provides valuable projections of future climate extremes, offering insights into potential climate risks for urban infrastructure, water management, and disaster preparedness in the smart cities of the Gangetic Plain.</p>

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Assessment of future urban precipitation extremes over smart cities of the gangetic plains using statistically downscaled CMIP6 projections

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Prabhat Kumar,
  • Archisman Barat,
  • P. Parth Sarthi,
  • Vikram Raj

摘要

This study downscaled the future precipitation extreme indices over the smart cities of the Gangetic Plain using the Statistical Downscaling Model (SDSM). The analysis focused on three Global Climate Models (GCMs): CanESM5, NorESM2-MM, and MPI-ESM1-2 h, under two Shared Socioeconomic Pathways (SSP245 and SSP585). Historical precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (1981–2014) and National Center NCEP gridded data were used to calibrate and validate the model. The study examined extreme precipitation indices such as RX1day (Maximum 1-day precipitation), RX5day (Maximum consecutive 5-day Precipitation), R20mm (Annual count of days when PRCP > = 20 mm), and R95P(Annual total PRCP when RR > 95th percentile), which quantify the intensity, frequency, and contribution of extreme rainfall events. The results reveal distinct trends in extreme precipitation across the cities under both SSP245 and SSP585. Under SSP245, moderate increases in extreme precipitation indices were observed, particularly in CanESM5, with significant trends emerging under SSP585. Capital cities Delhi, Lucknow, Patna, and Kolkata showed strong increases in extreme precipitation indices under SSP585, highlighting the potential exacerbation of urban flooding risks and infrastructure strain in the future. The CanESM5 model generally showed higher sensitivity and trends, indicating a higher frequency and intensity of extreme rainfall events under SSP585 scenarios. The study provides valuable projections of future climate extremes, offering insights into potential climate risks for urban infrastructure, water management, and disaster preparedness in the smart cities of the Gangetic Plain.