环境科学
草原
生态系统呼吸
生态系统
生长季节
气候变化
高原(数学)
植被(病理学)
初级生产
土壤呼吸
大气科学
碳循环
土壤碳
陆地生态系统
气候学
自然地理学
农学
生态学
土壤水分
地理
土壤科学
生物
地质学
数学分析
数学
医学
病理
作者
Tonghong Wang,Xufeng Wang,Songlin Zhang,Xiaoyu Song,Yang Zhang,Junlei Tan,Zhiguo Ren,Tongren Xu,Tao Che,Yanpeng Yang,Zain Nawaz
标识
DOI:10.1016/j.scitotenv.2024.172039
摘要
Alpine grassland is the main vegetation on the Qinghai-Tibetan Plateau (QTP) and exhibits high sensitivity to extreme weather events. With global warming, extreme weather events are projected to become more frequent on the QTP. However, the impact of these extreme weather events on the carbon cycle of alpine grassland remains unclear. The long-term in-situ carbon fluxes data was collected from 2013 to 2022 at an alpine grassland site to examine the impact of extreme low air temperature (ELT) and reduced moisture (including air and soil) on carbon fluxes during the growing season. Our findings indicated that a significant increase in net ecosystem production (NEP) after 2019, with the average NEP increasing from 278.91 ± 43.27 g C m−2 year−1 during 2013–2018 to 415.45 ± 45.29 g C m−2 year−1 during 2019–2022. The ecosystem carbon use efficiency (CUE) increased from 0.38 ± 0.06 during 2013–2018 to 0.62 ± 0.11 during 2019–2022. By combining concurrently measured environmental factors and remote sensing data, we identified the factors responsible for the abrupt change in the NEP after 2019. This phenomenon was caused by an abrupt decrease in ecosystem respiration (Reco) after 2019, which resulted from the inhibition imposed by ELT and reduced moisture. In contrast, gross primary production (GPP) remained stable from 2013 to 2022, which was confirmed by the remotely sensed vegetation index. This study highlights that combined extreme weather events associated with climate change can significantly impact the NEP of alpine grassland, potentially affecting different carbon fluxes at different rates. These findings provide new insights into the mechanisms governing the carbon cycle of alpine grassland.
科研通智能强力驱动
Strongly Powered by AbleSci AI