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The rise of Indian summer monsoon precipitation extremes and its correlation with long-term changes of climate and anthropogenic factors
The trends of extreme precipitation events during the Indian summer monsoon measured by two different indicators have been analyzed for the period of 1901–2020, covering the entire India in 9 regions segregated by a clustering analysis based on rainfall characteristics using the Indian Meteorologica...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283463/ https://www.ncbi.nlm.nih.gov/pubmed/35835829 http://dx.doi.org/10.1038/s41598-022-16240-0 |
Sumario: | The trends of extreme precipitation events during the Indian summer monsoon measured by two different indicators have been analyzed for the period of 1901–2020, covering the entire India in 9 regions segregated by a clustering analysis based on rainfall characteristics using the Indian Meteorological Department high-resolution gridded data. In seven regions with sufficiently high confidence in the precipitation data, 12 out of the 14 calculated trends are found to be statistically significantly increasing. The important climatological parameters correlated to such increasing trends have also been identified by performing for the first time a multivariate analysis using a nonlinear machine learning regression with 17 input variables. It is found that man-made long-term shifting of land-use and land-cover patterns, and most significantly the urbanization, play a crucial role in the prediction of the long-term trends of extreme precipitation events, particularly of the intensity of extremes. While in certain regions, thermodynamical, circulation, and convective instability parameters are also found to be key predicting factors, mostly of the frequency of the precipitation extremes. The findings of these correlations to the monsoonal precipitation extremes provides a foundation for further causal relation analyses using advanced models. |
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