Distinct vegetation response to drying and wetting trends across an aridity threshold. (12th March 2021)

• Record Type: Journal Article
• Authors: Zhao, Wei; Yu, Xiubo; Liu, Yu; Xu, Li; Chen, Zhi; Li, Shenggong
• Is Part Of: Environmental research communications. Volume 3:Number 2(2021)
• Publication Date: 2021-03-12
• Subjects: aridity index -- drying and wetting trends -- threshold -- drylands -- vegetation index trends; Environmental sciences -- Periodicals; 333.705
• DOI: 10.1088/2515-7620/abe8e3
• Languages: English
• ISSNs: 2515-7620

Abstract

Abstract: Aridity regulates the terrestrial ecosystem productivity in water-limited regions. The aridity index (AI) is often defined as the ratio of annual precipitation to annual potential evapotranspiration. However, how the drying and wetting influence the vegetation response and its characteristic along aridity gradient remains unclear. Here, we examined trends of the AI and normalized difference vegetation index (NDVI) in the drylands of East Asia from 1982 to 2015, which denoted the drying and wetting trends and vegetation response, respectively. The results show that the variability in land area and AI from 1982–2015 was lower in the whole dryland than its subtypes including hyper-arid, arid, semi-arid, and dry sub-humid regions. Drying and wetting trends were observed in each AI interval along a spatial aridity gradient. Wetting trends are prevalent owing to their area and magnitude were twice those of drying trends. Spatial variation of aridity shaped the pattern of trends in vegetation response to drying and wetting trends; drier regions had smaller and narrower ranges of variation in NDVI trends relative to wetter regions. A shift in AI trends and NDVI trends along the spatial aridity gradient occurred at 0.4 of AI. Distinct patterns of vegetation response to aridity change were observed across the aridity threshold, and the transition region was identified in the studied drylands. The results suggest that changes in the subtypes might be masked by the entire drylands and then leading to failure in recognizing the transformation of the subtypes. This implies that terrestrial carbon storage variability prediction should consider the spatial aridity changes to avoid the uncertainties due to the divergent vegetation response to AI trends at different aridity levels.