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Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques

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Abstract

Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.

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Acknowledgements

The authors gratefully acknowledge the India Meteorological Department for providing the data in the open government data portal and also providing 0.25°×0.25° gridded daily rainfall data from 1901 to 2013 for all India to do the cluster analysis. The authors also acknowledge the open source software like NCL and R programming used in this manuscript to generate figures from the data. The authors acknowledge the Head, CSIR 4PI, for support and encouragement.

Funding

The research was not supported by any funding other than institutional support from CSIR, India.

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Authors and Affiliations

  1. CSIR Fourth Paradigm Institute, NAL Wind tunnel Road, Bangalore, 560037, India

    Gyanendranath Mohapatra, V. Rakesh & Smrati Purwar

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Gyanendranath Mohapatra, V. Rakesh & Smrati Purwar

  3. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    A. P. Dimri

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  1. Gyanendranath Mohapatra
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Contributions

G N Mohapatra contributed in conceptualization, design, and data analysis of manuscript. V Rakesh contributed in conceptualization, design, data analysis, and drafting the manuscript. Mrs. Smrati Purwar contributed in analyzing data and generating figures. A P Dimri contributed in manuscript editing and proof reading.

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Correspondence to V. Rakesh.

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Mohapatra, G., Rakesh, V., Purwar, S. et al. Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques. Theor Appl Climatol 145, 673–686 (2021). https://doi.org/10.1007/s00704-021-03644-7

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