植被(病理学)
环境科学
降水
气候变化
生态系统
绿化
高原(数学)
生长季节
自然地理学
全球变暖
光合有效辐射
气候学
生态学
地理
地质学
医学
数学分析
光合作用
植物
数学
病理
气象学
生物
作者
Shangyu Shi,Ping Wang,Zhan Xiaoyun,Jianqiao Han,Minghang Guo,Fei Wang
出处
期刊:Catena
[Elsevier]
日期:2023-09-04
卷期号:233: 107483-107483
被引量:6
标识
DOI:10.1016/j.catena.2023.107483
摘要
The Qinghai-Tibet Plateau (QTP) has a fragile ecosystem that is sensitive to climate change. Due to the amplifying effect of climate change, the QTP has experienced rapid warming and shifting precipitation in recent decades, profoundly impacting the local ecosystem. However, the specific details of how vegetation responds to these changes were unclear, and the corresponding contributions were poorly quantified. Here, we employed an elastic net regression model to investigate the sensitivity of vegetation to climate factors across multiple time scales and various seasons. The vegetation activity was represented by the enhanced vegetation index (EVI), while climate change was represented by temperature, precipitation, photosynthetically active radiation (PAR), and soil moisture fraction (SMF). During 2000–2020, approximately 50 % of the QTP area showed greening, mainly concentrated in the northern region. Climate change explained approximately 70 % of the variation in vegetation during the growing season, 39 % in spring and 66 % in autumn. Grasslands exhibited the highest sensitive to climate change, with a relative contribution of 83 %, followed by mixed forests (70 %), forests (53 %) and deserts (52 %). Both temperature and precipitation significantly affected vegetation, with relative contributions of 29 % and 22 %, respectively, during the growing season. PAR and SMF had less impact on vegetation, with relative contributions of 8 % and 12 %, respectively. In the greening region, precipitation (26 %) was more important for vegetation growth compared to temperature (25 %). These findings emphasize the importance of precipitation on vegetation on the QTP, providing valuable insights for improving regional ecosystem assessment model and promoting the restoration of fragile ecosystems.
科研通智能强力驱动
Strongly Powered by AbleSci AI