Observed changes in seasonal drought characteristics and their possible potential drivers over Pakistan

INTERNATIONAL JOURNAL OF CLIMATOLOGY(2022)

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Abstract
Long-term drought monitoring and its assessment are of great importance for meteorological disaster risk management. The recurrent spells of heat waves and droughts have severely affected the environmental conditions worldwide, including Pakistan. The present work sought to investigate the spatiotemporal changes in drought characteristics over Pakistan during Rabi and Kharif cropping seasons. The role of large-scale circulation and interannual mode of climate variability is further explored to identify the physical mechanisms associated with droughts in the region. Monthly precipitation and temperature data (1983-2019) from 53 meteorological stations were used to study drought characteristics, using the standardized precipitation evapotranspiration index (SPEI). The nonparametric Mann-Kendall, Sen's Slope, and Sequential Mann-Kendall tests were applied on the drought index to determine the statistical significance and magnitude of the historical trend. The state-of-the-art Bayesian Dynamic Linear model was further used to analyse large-scale climate drivers of droughts, revealed an increase in drought severity, mostly over arid to semiarid regions for both cropping seasons. While temperature played a significant role in defining droughts over dry and hot seasons, rainfall is influential over the western disturbances influenced region. The analysis of atmospheric circulation patterns revealed that large-scale changes in wind speed, air temperature, relative humidity, and geopotential height anomalies are the likely drivers of droughts in the region. We found that Nino4, sea surface temperature, and multivariate El Nino-Southern Oscillation (ENSO4.0) Index are the most influential factors for seasonal droughts across Pakistan. Overall, the findings provide a better understanding of drought-prone areas in the region, and this information is of potential use for mitigating and managing drought risks.
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Key words
Bayesian dynamic linear model, climate change, drought, large-scale drivers, Pakistan
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