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Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis
Author(s) -
Jha Srinidhi,
Das Jew,
Goyal Manish Kumar
Publication year - 2020
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.6935
Subject(s) - climatology , precipitation , environmental science , extreme value theory , context (archaeology) , climate change , pacific decadal oscillation , return period , indian ocean dipole , generalized extreme value distribution , north atlantic oscillation , scale (ratio) , sea surface temperature , geography , meteorology , geology , mathematics , statistics , flood myth , oceanography , archaeology , cartography
The variability in the extreme rainfall events is of growing concern in the context of climate change. Several high rainfall events have occurred in India in recent years and simulations from the Intergovernmental Panel on Climate Change suggest a rise in extremes. The low‐frequency global‐scale modes/oscillations are widely considered as the significant drivers of inter‐annual variability of the Indian rainfall pattern and extreme rainfall events. To account for climate external forcings, we assessed the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation on extreme precipitation over 24 major river basins of India using the nonstationary extreme value analysis. Moreover, the uncertainty in the parameters of the fitted nonstationary extreme value distribution is assessed using Bayesian inference. It was found that extreme precipitation events in the country are dominated by these oscillations, especially in central India. Moreover, the return levels of high rainfall were found to be intensifying with increasing return period. We also observed that uncertainty in return levels was significant in almost every river basin. The results presented here contribute to a better understanding of the large‐scale climate variability and its impact on high rainfall pattern, which would provide an essential understanding of the rainfall‐induced hazard prevention and enhance the risk management strategy.