初级生产
常绿
环境科学
沉积(地质)
每年落叶的
生态系统
碳纤维
氮气
固碳
植被(病理学)
碳循环
森林生态学
氮气循环
陆地生态系统
生态学
化学
生物
医学
古生物学
材料科学
有机化学
病理
沉积物
复合数
复合材料
作者
Tianxiang Wang,Li Zhang,Pan Li,Honglin He,Xiaoli Ren
标识
DOI:10.1016/j.agrformet.2023.109853
摘要
Nitrogen deposition has important effects on the forest carbon cycle, especially for forest ecosystems in China which experienced remarkable atmospheric nitrogen enrichment. However, the effects of nitrogen deposition on carbon allocation and carbon turnover time in China's forests on the national scale have not been well understood. In this study, we investigated how carbon allocation and vegetation carbon turnover time (τ veg ) change in China's forests during 1990 to 2015 and attribution to nitrogen deposition using the Community Land Model version 4.5. Results indicate that net primary productivity (NPP) allocated to leaves, fine roots, and wood, and τ veg show increasing trends, despite a decrease (-0.03% yr −1 ) in the fraction of NPP allocated to leaves and fine roots and an increase (0.06% yr −1 ) in the fraction of NPP allocated to wood. Modeled NPP allocated to three components and associated allocation coefficients were significantly correlated to N deposition. N deposition accounted for 46% and 40% of the trends of allocation coefficients of leaves or fine roots, and wood, respectively. N deposition promoted the increasing rate of τ veg in forests by 0.009 yr yr −1 . Forest ecosystems with a significant increasing trend of τ veg induced by N deposition covered about 76% of the forest area. The trends of τ veg induced by nitrogen deposition in deciduous forests were larger than that in evergreen forests. The increase in cumulative NPP (19.11%) induced by N deposition was larger than the carbon loss (14.70%), which suggested that more carbon was stored in the vegetation. Our results highlight the importance of the effects of nitrogen deposition on carbon allocation in promoting the carbon sink of forest, and recommend to enhance the long-term vegetation biomass observation, especially the fine-roots biomass and root litterfall to improve the model ability in predicting responses of carbon allocation and vegetation turnover processes to environmental changes.
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