Enhancing stormwater drainage systems is paramount amid evolving climate dynamics, necessitating robust design and continual upgrades to address changing environmental conditions. The present work constructs the nonstationary Intensity-Duration-Frequency (IDF) curves for prominent urban areas of India. It develops 2313 nonstationary Generalized Extreme Value (GEV) models in annual and seasonal timeframes by integrating the influence of local and global climate-informed covariates, including time covariates. The work involves analyzing 1, 2, 3, 4, 6, 12, 24, 36, and 48 hourly maximum rainfall series with return periods of 2, 5, 10, 25, 50, and 100 years. Among the 16 urban areas examined, there's a significant shift from stationary to nonstationary extreme rainfall intensities, marked by a 38.7% increase in shorter duration series with a 5-year return period in New Delhi and Visakhapatnam. AMO, DMI, GTA, and LTA in New Delhi play significant roles. Similarly, in Visakhapatnam, SST in Niño 3.4 and DMI are significant covariates influencing nonstationarity. Recently, in the 2023 monsoon, the 25-year flood wreaked havoc in New Delhi, Rajkot, Surat, and Visakhapatnam. Generating nonstationary IDF curves for the annual and seasonal timeframes offers a comprehensive approach to stormwater design and infrastructure upgradation and effective adaptation strategies across sixteen Indian cities.

中文翻译：

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气候变化下印度城市地区的非平稳强度-持续时间-频率曲线建模


在不断变化的气候动态中，加强雨水排放系统至关重要，需要稳健的设计和持续升级，以应对不断变化的环境条件。目前的工作为印度主要城市地区构建了非平稳强度-持续时间-频率（IDF）曲线。它通过整合本地和全球气候相关协变量（包括时间协变量）的影响，开发了年度和季节时间范围内的 2313 个非平稳广义极值 (GEV) 模型。这项工作涉及分析 1、2、3、4、6、12、24、36 和 48 小时最大降雨量序列，重现期为 2、5、10、25、50 和 100 年。在所检查的 16 个城市地区中，极端降雨强度从固定到非固定的显着转变，新德里和维沙卡帕特南的 5 年一遇的较短持续时间序列增加了 38.7%。新德里的 AMO、DMI、GTA 和 LTA 发挥着重要作用。同样，在维沙卡帕特南，Niño 3.4 中的海温和 DMI 是影响非平稳性的显着协变量。最近，在2023年的季风中，长达25年的洪水对新德里、拉杰果德、苏拉特和维沙卡帕特南造成了严重破坏。生成年度和季节性时间范围内的非平稳 IDF 曲线为印度 16 个城市的雨水设计和基础设施升级以及有效的适应策略提供了全面的方法。