Spatiotemporal characteristics of meteorological drought variability and trends (1981–2020) over South Asia and the associated large-scale circulation patterns

CLIMATE DYNAMICS(2022)

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摘要
The present study uses observation-based gridded air temperature and precipitation datasets to investigate the spatiotemporal changes in meteorological drought from 1981 to 2020 in South Asia (SA). The drought characteristics i.e., duration, area, frequency, intensity, and severity are calculated using Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) on annual and seasonal timescales. SA is divided into four homogeneous subregions using the k-means clustering algorithm, while the trends were estimated using Sen’s Slope estimator and modified Mann-Kendall (MMK) test. A state-of-the-art Bayesian Dynamic Linear (BDL) model was employed to assess the sub-regional drought variability and their possible links with large-scale atmospheric drivers such as the Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO). It is found that the SA winter season experienced a significant drying long-term trend, especially southwestern and northeastern parts of SA. Drought durations show dipolar patterns with longer drought duration in the southwestern compared to the northern parts of SA. Notably, arid and semi-arid regions have shown significant drying trends in terms of their area, frequency, and severity, implying that meteorological droughts are relatively more severe in those regions. Similarly, the relationship varies over time between drought variability and climate drivers. In particular, the IOD has an impact on drought episodes over southwest SA relative to the northern parts and is mostly affected by the Sea surface temperature (SST) variability. The study results contribute to a better understanding of the meteorological drought characteristics over SA, which is important for disaster risk managers and policymakers to mitigate drought impacts.
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关键词
Drought,Bayesian model,Climate change,Large-scale drivers,IOD,South Asia
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