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<p>The indices Rx1Day, Rx3Day, and Rx5Day represent accumulated rainfall over one, three, and five days, respectively. The likelihood of flooding varies by region, influenced by climatic and hydrological conditions. Research on the relationship between rainfall intensity, atmospheric circulation, and flooding is limited. A comprehensive assessment is crucial to understanding the teleconnections between monsoons and atmospheric indices. This study investigates high-intensity rainfall events from 2001 to 2024, focusing on rainfall intensity and percentile indices. It employs wavelet coherence and correlation methods to examine teleconnections and uses Extreme Value Theory for optimal results to estimate return periods, and assesses volumetric floodwater generation using the SCS-CN method. Trends indicate a significant decrease in high-intensity rainfall during June, July, and August, with Rx1Day rainfall showing a decline of 0.29&#xa0;mm per month. Indices R90pTOT, R95pTOT, and R99pTOT also show decreasing trends in early monsoon months, while extreme rainfall events increase in October. July shows the most substantial decline at 1.223&#xa0;mm per month, contrasted by a positive change of 0.377&#xa0;mm per month in October. The coherence of Rx1Day, Rx3Day, and Rx5Day indicates a strong correlation with climate oscillation indices, particularly inter-seasonally. While intra-seasonal coherence from 2013 to 2017 is higher. It is less persistent, indicating dynamic climate influences. For Rx1Day, stations exhibit positive Generalized Extreme Value shape parameters, with P4 at 0.218. The study finds that rainfall exceeding 130&#xa0;mm in a day can lead to hazards, with significant events likely to cause downstream flooding. The findings highlight the importance of understanding atmospheric influences on extreme weather for effective planning and response strategies amid climate variability.</p> Graphical Abstract <p></p>

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Revealing the Relationship between Rainfall Extremes, Atmospheric Changes and Potential Flooding using Wavelet and Extreme Value Models

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Sribas Kanji,
  • Subhasish Das

摘要

The indices Rx1Day, Rx3Day, and Rx5Day represent accumulated rainfall over one, three, and five days, respectively. The likelihood of flooding varies by region, influenced by climatic and hydrological conditions. Research on the relationship between rainfall intensity, atmospheric circulation, and flooding is limited. A comprehensive assessment is crucial to understanding the teleconnections between monsoons and atmospheric indices. This study investigates high-intensity rainfall events from 2001 to 2024, focusing on rainfall intensity and percentile indices. It employs wavelet coherence and correlation methods to examine teleconnections and uses Extreme Value Theory for optimal results to estimate return periods, and assesses volumetric floodwater generation using the SCS-CN method. Trends indicate a significant decrease in high-intensity rainfall during June, July, and August, with Rx1Day rainfall showing a decline of 0.29 mm per month. Indices R90pTOT, R95pTOT, and R99pTOT also show decreasing trends in early monsoon months, while extreme rainfall events increase in October. July shows the most substantial decline at 1.223 mm per month, contrasted by a positive change of 0.377 mm per month in October. The coherence of Rx1Day, Rx3Day, and Rx5Day indicates a strong correlation with climate oscillation indices, particularly inter-seasonally. While intra-seasonal coherence from 2013 to 2017 is higher. It is less persistent, indicating dynamic climate influences. For Rx1Day, stations exhibit positive Generalized Extreme Value shape parameters, with P4 at 0.218. The study finds that rainfall exceeding 130 mm in a day can lead to hazards, with significant events likely to cause downstream flooding. The findings highlight the importance of understanding atmospheric influences on extreme weather for effective planning and response strategies amid climate variability.

Graphical Abstract