Qinghai Province (Tibetan Plateau): Quantifying the Influence of Climate Change and Human Activities on Vegetation Net Primary Productivity and Livestock Carrying Capacity Growth Potential.

Quantitative exploration of shifts in regional vegetation net primary productivity (NPP) and their driving factors holds immense importance in unraveling the mechanisms steering vegetation alterations, comprehending the impact of climate variations and human interventions on NPP, and guiding ecological management. Despite this significance, there is a scarcity of research reports on Qinghai Province. The aim is to dissect the influences of climate change and human activities on Qinghai's vegetation NPP and to estimate the growth potential of livestock carrying capacity. This study addresses the gap by juxtaposing the characteristics of climate-induced potential NPP changes, computed using the Zhou Guangsheng model, with actual NPP changes, calculated via the CASA model. Our findings underscore climate factors as the predominant drivers of Qinghai's vegetation NPP, accounting for 64.6% of the total area. Regions influenced by human activities contribute 34.3%, while unchanged areas constitute 2%. Climate emerges as the primary catalyst for increased vegetation NPP in Qinghai, encompassing 87% of the total area, with 73% attributed to climate factors across all counties. Conversely, human activities predominantly lead to decreased NPP, affecting 11% of the total area. Notably, 99% of the reduced NPP is attributable to human activities, concentrated in Golmud, Mangya, and Dulan counties in the northwest. Examining the growth potential of livestock carrying capacity from 1982 to 2018 reveals a consistent upward trajectory in Qinghai Province. The average annual growth potential per unit area escalates from 0.38 SHU/ha in 1982 to 0.56 SHU/ha in 2018. By 2018, regions exhibiting positive growth potential encompass 95% of the province, with areas exceeding 1 SHU/ha constituting 9%, primarily situated in the eastern part of Qinghai Province.