Quantifying Carbon Use Efficiency: Unraveling the Impact of Climate Change and Ecological Engineering on Vegetation in the Three Rivers Source Region

文献类型： 外文期刊

第一作者： Luo, Yixia

作者： Luo, Yixia;Luo, Yixia;Pan, Jing;Chen, Jilong;Wu, Shengjun;Luo, Yixia;Pan, Jing;Chen, Jilong;Wu, Shengjun;Duan, Hengyi;Gao, Xue;Tan, Daming

作者机构：

关键词： carbon use efficiency; spatial and temporal variation; ecological engineering; climate change

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 16 期

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收录情况： SCI

摘要： Carbon use efficiency (CUE) was identified as a pivotal parameter for elucidating the carbon cycle within ecosystems. It signified the efficiency with which light energy was transformed into organic matter by vegetation. In light of the challenges posed by global warming, it was deemed essential to gain a comprehensive understanding of the fluctuations and determinants of CUE. Despite the significance of this topic, the current research on factors influencing CUE remained incomplete, notably lacking in exploration of the impacts of ecological engineering on CUE. The objective of this study is to elucidate the influences of climate change and ecological engineering on CUE, quantifying their effects using residual analysis. Additionally, it aims to analyze the primary factors contributing to the fluctuations in CUE. Our findings indicated an average CUE of 0.8536 (+/- 0.0026) with minor interannual variation. In the Three Rivers Source region, CUE is jointly influenced by ecological engineering (30.88%) and climate change (69.12%). Notably, climatic factors predominantly regulate CUE, accounting for approximately 90.20% of its regional variations, with over 44.70% of areas exhibiting contributions exceeding 80%. Moreover, the impact of evapotranspiration on CUE surpasses that of precipitation and temperature, while factors such as elevation and vegetation types also play significant roles. This study showed the quantification of climate change and ecological engineering effects on CUE, which would hold substantial implications for predicting and evaluating global carbon cycling.

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