Characterizing Indian meteorological moisture anomaly condition using long‐term (1901–2013) gridded data: a multivariate moisture anomaly index approach

Abstract

The long‐term (1901–2013) gridded rainfall and potential evapotranspiration (PET) data were utilized to develop a new index, multivariate moisture anomaly index (MMAI), for characterizing the meteorological moisture anomaly condition during monsoon season over Indian region. The 6‐month timescale standardized precipitation index (SPI) and standardized evapotranspiration index (SEI) were computed using time series rainfall and PET data using gamma and log‐logistic distribution, respectively. The long‐term seasonal SPI and SEI were converted into moisture anomaly magnitude and duration information, and their trends were analysed using Sen's slope and Mann–Kendall test, respectively. The standardized precipitation evapotranspiration index (SPEI) at 6‐month timescale was also analysed to find the impact of rainfall and PET on meteorological moisture anomaly. Both the trends and probability analysis showed that SPEI was mainly representing the trends and pattern of SPI only and was unable to capture the impact of PET. The MMAI was developed by fitting the time series SPI and SEI information into different joint probability distribution and by adapting the best model for each grid. The new index was able to consider the impact of both rainfall and PET. Based on the MMAI trend analysis, it was found that the overall moisture anomaly was decreasing in northwest (NW) India, whereas it was increasing in northeast (NE), central and peninsular India. The trends analysis of SPI and SEI depicted that in NW India the significant decrease in moisture anomaly may be due to both increase in rainfall and decrease in PET, whereas in NE India the significant increase in moisture anomaly may be due to both increase in PET and decrease in rainfall. In eastern and Gujarat coast the increase in moisture anomaly may be attributed to increase in PET whereas the significant increase in rainfall might lead to reduced moisture anomaly over western coast.

Spatiotemporal dynamics of temperature and precipitation with reference to COVID-19 pandemic lockdown: perspective from Indian subcontinent

“…During the month of monsoon, India receives 117 cm of rainfall which is about 80 percent of total rainfall (Sahai et al 2003 ). The average rainfall (% standard deviation) is 1324.6 (11%), 618.7(19%), 976.4 (14%), and 730.5 (15%) mm for NE, NW, central, and peninsular India, respectively (Das et al 2017 ). In this analysis, daily temperature and rainfall datasets have been extracted from the Climate Prediction Center (CPC) Global Daily Temperature Time Series ( https://psl.noaa.gov/data/gridded/ ) and Goddard Earth Sciences Data and Information Services Center (GES DISC) ( https://disc.gsfc.nasa.gov/ ), respectively.…”