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

Analyzing Spatiotemporal Variations and Driving Factors of Grassland in the Arid Region of Northwest China Surrounding the Tianshan Mountains

“…Prior modeling studies have indicated that indirect effects are more significant in arid regions than in humid areas [68,69]. In our study, SEM revealed that topographic variations and the consequent changes in water-heat conditions were identified as the primary factors affecting vegetation growth in the Xinjiang region [70,71]. Therefore, the discrepancy in moisture input caused by the mountains may result in notable differences in the arid climate of the northern and southern Tianshan Mountains, especially in terms of temperature, precipitation, and soil, thereby establishing a natural ecosystem in the plains consisting of alpine meadows, mid-mountain humid forests, semiarid grasslands, and arid deserts [72].…”

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020

“…In Xinjiang, the northern part is dominated by the Altai Mountains, the southern part is characterized by the Kunlun Mountain range, and the central part is the Tianshan Mountains. These mountain ranges divide Xinjiang into the Tarim Basin in the south and the Junggar Basin in the north ( Han et al., 2023 ). The geographical environment in Xinjiang gives rise to a typical temperate continental climate, where maritime air masses have limited reach.…”

Concurrent and lagged effects of drought on grassland net primary productivity: a case study in Xinjiang, China

“…Different grassland types have different physiological characteristics and different response characteristics to drought. Han et al (2023) explored the differences in the probability of drought stress in Xinjiang from different grassland types, but the hysteresis characteristics of different grassland types to drought need to be further explored. Soil moisture is a primary limiting factor for vegetation growth in arid and semiarid regions.…”

“…More meteorological data should be integrated for analysis in the future. Meanwhile, to explore the response characteristics of different grassland types NPP to drought, this study used the GLC2000 dataset to classify grassland types, which has a high spatial resolution and detailed classification and is widely used in grassland studies (Wang et al, 2016;Liu et al, 2019;Han et al, 2023). However, as land use/cover is dynamic and its changes may have some impact on grassland NPP and drought (Shen et al, 2020;Ma et al, 2022), future studies can integrate multiple land cover datasets to reflect grassland distribution more accurately.…”