Han Wanqiang, Guan Jingyun, Zheng Jianghua, Liu Yujia, Ju Xifeng, Liu Liang, Li Jianhao, Mao Xurui, Li Congren
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China.
Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China.
Front Plant Sci. 2023 Mar 16;14:1143863. doi: 10.3389/fpls.2023.1143863. eCollection 2023.
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.
在气候变暖过程中，干旱增加了生态系统的脆弱性。由于草原对干旱极为敏感，草原干旱胁迫脆弱性评估已成为当前亟待解决的问题。首先，利用相关性分析确定研究区域内草原归一化植被指数（NDVI）对多尺度干旱胁迫（SPEI-1~SPEI-24）的标准化降水蒸散指数（SPEI）响应特征。然后，采用共轭函数分析对不同生长时期草原植被对干旱胁迫的响应进行建模。利用条件概率探讨不同干旱胁迫水平（中度、重度和极端干旱）下草原NDVI下降到较低百分位数的概率，并进一步分析不同气候区和草原类型的干旱脆弱性差异。最后，确定了不同时期草原干旱胁迫的主要影响因素。研究结果表明，新疆草原干旱响应时间的空间格局具有明显的季节性，非生长季1月至3月、11月至12月呈增加趋势，生长季6月至10月呈下降趋势。8月是草原干旱胁迫最脆弱的时期，草原损失概率最高。当草原经历一定程度的损失时，它们会制定策略减轻干旱胁迫的影响，从而降低落入较低百分位数的概率。其中，半干旱草原以及平原草原和高山亚高山草原的干旱脆弱性概率最高。此外，4月和8月的主要驱动因素是温度，而9月最显著的影响因素是蒸散。该研究结果不仅将加深我们对气候变化下草原干旱胁迫动态的理解，还将为未来应对干旱的草原生态系统管理和水资源分配提供科学依据。
