Abstract
Since the middle of the last century, the so-called acceleration of the water cycle, due to global warming, has led to increased rainfall variability worldwide. In this research, change point detection in the monsoon rainfall of the Narmada River basin (central India) was analysed. This analysis, based on seven rainfall stations at an annual scale during the 1901–2015 period, utilised a combination of the Bayesian approach (BA) with the discrete wavelet transform (DWT). The analysis indicates a shift towards drier conditions starting in the 1960s, with a long-term trend beginning as early as the 1920s. It was revealed that the high variability of monsoon rainfall can be attributed to the dominance of intra-annual, multi-annual, and less-than-decadal cyclical phenomena (short- and medium-term phenomena), which mask existing change points, thus creating difficulties for the BA in identifying them. Overall, the BA-DWT methods effectively detected the change and multi-change points in the studied monsoon rainfall time series, thereby outperforming the BA method when applied to the original series.
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Acknowledgements
The first author expresses profound gratitude to the Directorate General for Scientific Research and Technological Development of Algeria. Appreciation is also extended to the India Meteorological Department (IMD) for the provided data. The sixth author's contribution was partly funded by the National Council for Scientific and Technological Development, Brazil (313358/2021-4).
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BZ, UVP and MC conceived the framework of this research, processed data, designed the experiments, plots, map preparation, validated the processing results, and wrote the manuscript. ZA, CAGS, and DF gave feedback on the written manuscript, helped in analyses and editing the manuscript and added technical improvements. All authors read and approved the final manuscript.
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Zerouali, B., Pawar, U.V., Elbeltagi, A. et al. Change-point detection in monsoon rainfall of Narmada River (central India) during 1901–2015. J Earth Syst Sci 132, 133 (2023). https://doi.org/10.1007/s12040-023-02140-y
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DOI: https://doi.org/10.1007/s12040-023-02140-y