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In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Xinjiang grasslands play a crucial role in regulating the regional carbon cycle and maintaining ecosystem stability, and grassland net primary productivity (NPP) is highly vulnerable to drought. Drought events are frequent in Xinjiang due to the impact of global warming. However, there is a lack of more systematic research results on how Xinjiang grassland NPP responds to drought and how its heterogeneity is characterized. In this study, the CASA (Carnegie Ames Stanford Application) model was used to simulate the 1982–2020 grassland NPP in Xinjiang, and the standardized Precipitation Evapotranspiration Index (SPEI) was calculated using meteorological station data to characterize drought. The spatial and temporal variability of NPP and drought in Xinjiang grasslands from 1982 to 2020 were analyzed by the Sen trend method and the Mann-Kendall test, and the response characteristics of NPP to drought in Xinjiang grasslands were investigated by the correlation analysis method. The results showed that (1) the overall trend of NPP in Xinjiang grassland was increasing, and its value was growing season > summer > spring > autumn. Mild drought occurred most frequently in the growing season and autumn, and moderate drought occurred most frequently in spring. (2) A total of 64.63% of grassland NPP had a mainly concurrent effect on drought, and these grasslands were primarily located in the northern region of Xinjiang. The concurrent effect of drought on NPP was strongest in plain grassland and weakest in alpine subalpine grassland. (3) The lagged effect is mainly in the southern grasslands, the NPP of alpine subalpine meadows, meadows, and alpine subalpine grasslands showed mainly a 1-month time lag effect to drought, and desert grassland NPP showed mainly a 3-month time lag effect to drought. This research can contribute to a reliable theoretical basis for regional sustainable development.

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