Scholarlink

<p>This study presents a comprehensive, data-driven assessment of rainfall variability across six major Indian cities, Hyderabad, Delhi, Mumbai, Chennai, Kolkata, and Bangalore, through the integration of Bayesian regression diagnostics and Fourier spectral analysis. Bayesian regression revealed city-specific temporal trends, with Bangalore exhibiting a statistically significant positive slope (+ 0.31&#xa0;mm/year), indicating localized intensification of rainfall. Fourier analysis identified dominant annual cycle components (0.0021–0.0028&#xa0;yr<sup>−1</sup>), corresponding to periodicities of ~ 1.0–1.3&#xa0;years, which accounted for 65–73% of total variance across cities, reflecting the expected dominance of seasonal monsoon cycles rather than evidence of long-term climatic control. Phase shift estimation uncovered lead–lag structures of up to ± 1.7&#xa0;months, while coherence scores exceeded 79% for all regions, confirming synchronized seasonal cycles. Although large-scale climate drivers such as ENSO and the Indian Ocean Dipole may contribute, attribution remains tentative without direct index analysis or causality testing. From a measurement perspective, these quantified spectral and temporal features highlight the predominance of seasonal monsoon drivers in shaping rainfall dynamics and underscore the importance of uncertainty quantification, calibration, and diagnostic reliability in environmental meteorology. The combined methodology strengthens forecasting precision and provides a reproducible framework for climate-resilient water resource planning and urban adaptation in monsoon-dependent ecosystems.</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

Decomposing rainfall dynamics across Indian metros: Bayesian trends, spectral cycles, and climatic coherence

  • 期刊论文

  • 发表日期：

    2026年6月14日

查看全文
  • Guhan Velusamy,
  • Sheshakumar Goroshi,
  • Dharma Raju Akasapu,
  • Nagaratna Kopparthi

摘要

This study presents a comprehensive, data-driven assessment of rainfall variability across six major Indian cities, Hyderabad, Delhi, Mumbai, Chennai, Kolkata, and Bangalore, through the integration of Bayesian regression diagnostics and Fourier spectral analysis. Bayesian regression revealed city-specific temporal trends, with Bangalore exhibiting a statistically significant positive slope (+ 0.31 mm/year), indicating localized intensification of rainfall. Fourier analysis identified dominant annual cycle components (0.0021–0.0028 yr−1), corresponding to periodicities of ~ 1.0–1.3 years, which accounted for 65–73% of total variance across cities, reflecting the expected dominance of seasonal monsoon cycles rather than evidence of long-term climatic control. Phase shift estimation uncovered lead–lag structures of up to ± 1.7 months, while coherence scores exceeded 79% for all regions, confirming synchronized seasonal cycles. Although large-scale climate drivers such as ENSO and the Indian Ocean Dipole may contribute, attribution remains tentative without direct index analysis or causality testing. From a measurement perspective, these quantified spectral and temporal features highlight the predominance of seasonal monsoon drivers in shaping rainfall dynamics and underscore the importance of uncertainty quantification, calibration, and diagnostic reliability in environmental meteorology. The combined methodology strengthens forecasting precision and provides a reproducible framework for climate-resilient water resource planning and urban adaptation in monsoon-dependent ecosystems.