Quantifying the spatiotemporal variability of rainfall is the principal component for the assessment of the impact of climate change on the hydrological cycle. A better understanding of the quantification of variability and its trend is vital for water resources planning and management. Therefore, a multitude of studies has been dedicated to quantifying the rainfall variability over the years. Despite their importance for modelling rainfall variability, the studies mainly focused on the amount of rainfall and its spatial patterns. The studies investigating the spatial and temporal variability of rainfall across Central India, in general, and at multiscale, in particular, are limited. In this study, we used a Standardized Variability Index (SVI), based on information theory to investigate the spatiotemporal variability of rainfall. The proposed measure is independent of the temporal scale, the length of the data and therefore, is able to compare the rainfall variability at multiple timescales. Distinct spatial patterns were observed for information entropies at the monthly and seasonal scale. Stations with statistically significant trends were observed and vary from monthly to seasonal scale. There is an increase in the variability of precipitation amount from South to North, indicating that spread of the rainfall is high in the South when compared to North of Central India. Trend analysis revealed there is changing behaviour in the rainfall amount as well as rainy days, showing an increase in variability of rainfall over Central India, hence the high probability of occurrence of extreme events in near future.
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Investigation of Precipitation Variability and Extremes Using Information Theory
Published: 13 November 2020 by MDPI in The 3rd International Electronic Conference on Atmospheric Sciences session Climatology
Keywords: standardized variability index; spatiotemporal variability; precipitation; extremes; Shannon entropy; India