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<p>We unravel the response of terrestrial ecosystems in India to Tropical Cyclones (TCs) originating in the North Indian Ocean (NIO). We find that about 34.6% of TCs drove greening and 65.4% caused browning response of vegetation during 2000–2020. TC-induced greening is more likely for TCs originated in pre-monsoon (100%) or monsoon (62.5%) than post-monsoon with large browning response (94%). Rainfall by TCs increases soil moisture (SM) and reduces climatic water deficit (CWD) for a moisture-stressed region, and its effective utilisation by vegetation triggers the greening response. Granger Causality reveals that TC-induced rain and greening response exhibit a maximum temporal lag of 40 days. The favourable vegetation response to TCs is a new insight as it sheds light on the complex Atmosphere-Land-Ocean (ALO) interactions on a regional scale. The findings can aid to improve climate models for better policy decisions aimed at climate adaptation and sustainability on both regional and global scales.</p>

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Tropical cyclones enhance photosynthesis in moisture-stressed regions of India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月21日

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  • Rahul Kashyap,
  • Jayanarayanan Kuttippurath

摘要

We unravel the response of terrestrial ecosystems in India to Tropical Cyclones (TCs) originating in the North Indian Ocean (NIO). We find that about 34.6% of TCs drove greening and 65.4% caused browning response of vegetation during 2000–2020. TC-induced greening is more likely for TCs originated in pre-monsoon (100%) or monsoon (62.5%) than post-monsoon with large browning response (94%). Rainfall by TCs increases soil moisture (SM) and reduces climatic water deficit (CWD) for a moisture-stressed region, and its effective utilisation by vegetation triggers the greening response. Granger Causality reveals that TC-induced rain and greening response exhibit a maximum temporal lag of 40 days. The favourable vegetation response to TCs is a new insight as it sheds light on the complex Atmosphere-Land-Ocean (ALO) interactions on a regional scale. The findings can aid to improve climate models for better policy decisions aimed at climate adaptation and sustainability on both regional and global scales.