Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Jingxuan Su; Liangxin Fan; Zhanliang Yuan; Zhen Wang; Zhijun Wang

doi:10.1016/j.scitotenv.2023.168688

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Sci Total Environ. 2024 Feb 1:910:168688. doi: 10.1016/j.scitotenv.2023.168688. Epub 2023 Nov 20.

Authors

Jingxuan Su¹, Liangxin Fan², Zhanliang Yuan¹, Zhen Wang¹, Zhijun Wang¹

Affiliations

¹ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China.
² School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China. Electronic address: fanliangxin@126.com.

PMID: 37992825
DOI: 10.1016/j.scitotenv.2023.168688

Abstract

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Keywords: Drought sensitivity; Grassland NDVI; Northwest China; SPEI; Time-cumulative effect; Time-lag effect.