Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index

Understanding the modulation of drought by large-scale ocean–atmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSO–IOD variability through composite, correlation, and mediation analyses. Results reveal that El Niño events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought frequency exceeding 30% and SPI < −1.5. Conversely, La Niña phases enhance monsoon rainfall and alleviate drought conditions across much of the subcontinent. Spatial correlations demonstrate that ENSO exerts a stronger, more coherent influence on both rainfall and SPI than the IOD, while positive IOD phases can partly offset El Niño-driven drought in limited regions. Mediation and wavelet coherence analyses confirm ENSO’s dominant control at interannual (4–8 year) timescales and reveal secondary, episodic modulation by the IOD. These findings highlight the complex, evolving dynamics between Pacific and Indian Ocean drivers in shaping India’s hydroclimate variability. The study underscores the need for integrated ENSO–IOD monitoring and inclusion of multi-ocean indicators in India’s drought early warning and seasonal forecast frameworks.

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Publication Details

Journal
Northeast Journal of Complex Systems
Published
2026-04-06
DOI
https://doi.org/10.63562/2577-8439.1140
Primary Topic
Climate variability and models
Type
article
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article

Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index

Satyaban B. Ratna, Tanu Sharma, R. Anil Kumar, Arpit Tiwari et al.
Northeast Journal of Complex Systems
Climate variability and models
article

Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index

Satyaban B. Ratna, Tanu Sharma, R. Anil Kumar, Arpit Tiwari, Preethi Nanjundan
article en

Abstract

Understanding the modulation of drought by large-scale ocean–atmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSO–IOD variability through composite, correlation, and mediation analyses. Results reveal that El Niño events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought frequency exceeding 30% and SPI < −1.5. Conversely, La Niña phases enhance monsoon rainfall and alleviate drought conditions across much of the subcontinent. Spatial correlations demonstrate that ENSO exerts a stronger, more coherent influence on both rainfall and SPI than the IOD, while positive IOD phases can partly offset El Niño-driven drought in limited regions. Mediation and wavelet coherence analyses confirm ENSO’s dominant control at interannual (4–8 year) timescales and reveal secondary, episodic modulation by the IOD. These findings highlight the complex, evolving dynamics between Pacific and Indian Ocean drivers in shaping India’s hydroclimate variability. The study underscores the need for integrated ENSO–IOD monitoring and inclusion of multi-ocean indicators in India’s drought early warning and seasonal forecast frameworks.

Northeast Journal of Complex SystemsVol. 8(1)
India Meteorological Department (IN), Christ University (IN)
Life below water
Openalex Percentile: Top 6%
Climate variability and models
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