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<p>Tropical cyclones (TCs) in the North Indian Ocean (NIO) constitute only 6% of global TCs but cause over 80% of cyclone-related fatalities. Conventional wind speed-based assessments often underestimate the compound nature of TC hazards. We develop a multivariate framework integrating wind speed and precipitation to characterize TC compound hazards in India (1951–2020). Using multivariate dependence modeling, we estimate joint return periods, conduct district-level exposure characterization, and assess non-stationarity. Advanced metrics, including the Shannon surprise, various statistical distance metrics, and fraction attributable risk, reveal regions with heightened susceptibility and the climate change influence on cyclone risks, especially notable among districts of Odisha, Tamil Nadu, and Andhra Pradesh. This methodologically simple and computationally efficient framework, based on a quasi-Lagrangian perspective, while demonstrated for India, offers a scalable methodology for assessing compound TC hazards to cyclone-prone regions globally. It supports targeted adaptation strategies, contributing to the broader goals of sustainable development and climate resilience.</p>

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Comprehensive multivariate characterization of tropical cyclone and its district-level exposure characteristics over India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Ravi Ranjan,
  • Vittal Hari,
  • Subimal Ghosh,
  • Subhankar Karmakar

摘要

Tropical cyclones (TCs) in the North Indian Ocean (NIO) constitute only 6% of global TCs but cause over 80% of cyclone-related fatalities. Conventional wind speed-based assessments often underestimate the compound nature of TC hazards. We develop a multivariate framework integrating wind speed and precipitation to characterize TC compound hazards in India (1951–2020). Using multivariate dependence modeling, we estimate joint return periods, conduct district-level exposure characterization, and assess non-stationarity. Advanced metrics, including the Shannon surprise, various statistical distance metrics, and fraction attributable risk, reveal regions with heightened susceptibility and the climate change influence on cyclone risks, especially notable among districts of Odisha, Tamil Nadu, and Andhra Pradesh. This methodologically simple and computationally efficient framework, based on a quasi-Lagrangian perspective, while demonstrated for India, offers a scalable methodology for assessing compound TC hazards to cyclone-prone regions globally. It supports targeted adaptation strategies, contributing to the broader goals of sustainable development and climate resilience.