Spatio-temporal change in rainfall over five different climatic regions of India
The present study deals with the estimation of dependences, spatio-temporal trends, change points, and stationarity in rainfall and rainy day series (1901–2013) for five (out of six) different climatic regions of India. Only one-fourth of the station rainfall and rainy day datasets exhibits long-ter...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-11-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/12/7/3124 |
| Summary: | The present study deals with the estimation of dependences, spatio-temporal trends, change points, and stationarity in rainfall and rainy day series (1901–2013) for five (out of six) different climatic regions of India. Only one-fourth of the station rainfall and rainy day datasets exhibits long-term dependence on an annual and seasonal basis. The presence of lag-one serial correlation is prominent for almost all the climatic regions of India. The significant decreasing trend is found mainly for the stations of semi-arid and humid sub-tropical regions. The magnitude of rainfall is decreasing for most parts of the study area by 10% for annual and monsoon seasons. The change point is presented in a smaller number of stations. Non-stationary behaviour is observed for the rainy day series of semi-arid and humid sub-tropical regions, which may increase the temporal variability of rainfall over the same regions. The findings of this study could be very useful for the planning and management of water resources of different climatic regions of India. HIGHLIGHTS
We analyse short-term and long-term dependences, spatio-temporal trends, change point, and stationarity in rainfall and rainy day datasets over five different climatic regions of India.;
Change point detection for different climatic regions over parts of India.;
The trend stationarity and non-stationarity is comparatively lower in rainfall datasets than the rainy day datasets for annual and monsoon seasons.; |
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| ISSN: | 2040-2244 2408-9354 |