Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

Wanqiang Han; Jingyun Guan; Jianghua Zheng; Yujia Liu; Xifeng Ju; Liang Liu; Jianhao Li; Xurui Mao; Congren Li

doi:10.3389/fpls.2023.1143863

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

Front Plant Sci. 2023 Mar 16:14:1143863. doi: 10.3389/fpls.2023.1143863. eCollection 2023.

Authors

Wanqiang Han^{1

2}, Jingyun Guan^{1

2

3}, Jianghua Zheng^{1

2}, Yujia Liu^{1

2}, Xifeng Ju^{1

2}, Liang Liu^{1

2}, Jianhao Li^{1

2}, Xurui Mao^{1

2}, Congren Li^{1

2}

Affiliations

¹ College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China.
² Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China.
³ College of Tourism, Xinjiang University of Finance & Economics, Urumqi, China.

Abstract

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.

Keywords: climate regions; drought stress; grassland types; influencing factors; vulnerability probability.

Grants and funding

This study was supported by the Response Mechanism of Net Primary Productivity of Xinjiang Grassland to Extreme Drought Project of Xinjiang Uygur Autonomous Region (NO.2022D04009), and the Tianshan Cedar Project of Xinjiang Uygur Autonomous Region (No. 2020XS04).