Revealing alarming changes in spatial coverage of joint hot and wet extremes across India
Abstract Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observe...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f9926b06b4404cafacd0cae301d45cb6 |
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| Sumario: | Abstract Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observed records across Indian mainland. Analysis shows an alarming rate of change in the spatial extent of some of the joint extreme phenomena, tending to remain above normal. For example, above normal hot nights and wet days events expands at a rate of 0.61% per year considering entire Indian mainland. If the historical trend continues at the same rate, consecutive cold and wet day events will drop below the threshold of mean value observed in the base line period (1981–2010) everywhere in the country by the end of the twenty-first century. In contrast, the entire country will be covered by hot nights and wet days events only (frequency of occurrence will cross the threshold of mean value observed in the base line period). This observation is also supported by the CMIP6 climate model outputs. It is further revealed that extremes of any single variable, i.e. either precipitation or temperature (e.g., Extreme Wet Days, Consecutive Wet Days, Hot Nights, and Cold Spell Duration Index), do not manifest such an alarming spatial expansion/contraction. This indicates that the consideration of the joint indices of hydroclimatic variables is more informative for the climate change impact analysis. |
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