Dynamic variations of terrestrial ecological drought and propagation analysis with meteorological drought across the mainland China

Ecological drought is a complex comprehensive process in which the water conditions for normal growth and development of vegetation are changed due to insufficient water supply. In this study, based on the remotely sensed vegetation health index (VHI) and the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) datasets from 1982 to 2020 in China, the Breaks For Additive Seasons and Trend algorithm (BFAST) was used to analyze the dynamic variations of ecological drought, the standardized regression coefficient method was applied to identify the primary drivers of ecological drought, and the regression analysis was adopted to reveal the coupling effect of atmospheric circulation factors on ecological drought. The results indicated that: (1) the ecological drought showed an overall decreasing trend during 1982-2020 in China, with a negative mutation point that occurred in April 1985; (2) spring drought and summer drought were more likely to occur in the South China, and autumn drought and winter drought were more likely to appear in the Sichuan Basin; (3) the propagation time from meteorological to ecological drought was shorter in summer (2.67 months) and longer in winter (7 months), with average r values of 0.76 and 0.53, respectively; (4) the Trans Polar Index (TPI), Arctic Oscillation (AO) and El Nino-Southern Oscillation (ENSO) had important impacts on ecological drought, which can be used as input factors of drought early warning system to improve the accuracy of drought prediction.

Automated summary

In this study, the dynamic variations of ecological drought in China from 1982 to 2020 were analyzed using the VHI and FLDAS datasets. The results showed a decreasing trend of ecological drought in China, with spring and summer drought more likely to occur in the South China and autumn and winter drought more likely to appear in the Sichuan Basin. The TPI, AO, and ENSO were identified as important drivers of ecological drought, which can improve the accuracy of drought prediction in early warning systems.