植物生态学报 ›› 2015, Vol. 39 ›› Issue (12): 1156-1165.DOI: 10.17521/cjpe.2015.0112

所属专题: 碳水能量通量

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长白山阔叶红松林生态系统光能利用率的动态变化及其主控因子

张雷明1,,A;*, 曹沛雨1,2, 朱亚平3, 李庆康1, 张军辉4, 王晓凌3, 戴冠华4, 李金功5   

  1. 1中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室, 北京 100101
    2中国科学院大学, 北京 100049
    3河南科技大学农学院, 河南洛阳 471003
    4中国科学院沈阳应用生态研究所, 沈阳 110016
    5长白山自然保护管理中心, 吉林延边 133613
  • 出版日期:2015-12-01 发布日期:2015-12-31
  • 通讯作者: 张雷明
  • 作者简介:

    # 共同第一作者

  • 基金资助:
    中国科学院战略性先导科技专项项目(XDA05050208和XDA05050602)和国家自然科学基金(31170422和31570446)

Dynamics and regulations of ecosystem light use efficiency in a broad-leaved Korean pine mixed forest, Changbai Mountain

ZHANG Lei-Ming1,*, CAO Pei-Yu1,2, ZHU Ya-Ping3, LI Qing-Kang1, ZHANG Jun-Hui4, WANG Xiao-Ling3, DAI Guan-Hua4, LI Jin-Gong5   

  1. 1Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3College of Agricultural, Henan University of Science and Technology, Luoyang, Henan 471003, China
    4Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
    and 5The Administration Center of Changbai Mountain National Nature Reserve, Yanbian, Jilin 133613, China
  • Online:2015-12-01 Published:2015-12-31
  • Contact: Lei-Ming ZHANG
  • About author:

    # Co-first authors

摘要:

生态系统光能利用率(LUE)反映了植被通过光合作用利用光能吸收和固定大气中CO2的能力, 是表征生态系统生产力的重要指标。选取长白山温带阔叶红松(Pinus koraiensis)林生态系统为研究对象, 利用涡度相关通量观测数据, 采用直角双曲线方程获取了生态系统光合作用的表观量子效率(ε); 基于总生态系统初级生产力(GEP)与下垫面入射光合有效辐射(Q)的比值得到生态光能利用率(LUEeco)。研究表明: 在季节尺度上, εLUEeco均表现出显著的单峰变化特征, 并主要受到土壤温度和归一化植被指数(NDVI)的调控, 同时, εLUEeco都受到GEP的显著影响, 而与Q的相关性较弱或无显著相关关系, 但散射辐射的增加在一定程度上有助于提高生态系统的LUEεLUEeco存在显著的线性正相关关系, 但ε明显高于LUEeco。2003-2005年, εLUEeco每年最大值的平均值分别为(0.087 ± 0.003)和(0.040 ± 0.002) μmol CO2·μmol photon-1, 年际间变异度分别为4.17%和4.25%, 而不同年份之间最大差异均达到8%或8%以上, 从而对模型模拟结果产生明显影响。因此, 在基于光能利用率模型的模拟研究中, 最大LUE的年际变异需要在参数反演和优化中给予重要考虑。

关键词: 生态系统碳交换, 光能利用率, 最大光能利用率, 年际变异, 植被指数

Abstract:

Aims Ecosystem light use efficiency (LUE) reflects the ability of CO2 uptake and light utilization via photosynthesis, which is a key parameter in ecosystem models to evaluate ecosystem productivity. The objectives of this study were to: (1) compare the differences of LUE derived from different methods; (2) elucidate the seasonal dynamics of LUE and its regulatory factors; and (3) evaluate the maximum LUE (LUEmax) and its variability based on eddy-covariance (EC) flux.Methods Using the flux data from an EC tower during 2003-2005 at a broad-leaved Korean pine (Pinus koraiensis) mixed forest, Changbai Mountain, two types of LUE indicators were generated from: 1) the apparent quantum yield (ε) estimated with rectangular hyperbolic curve, and 2) the ecological light use efficiency (LUEeco) calculated as the ratio between gross ecosystem productivity (GEP) and photosynthetically-active radiation (Q).Important findings The seasonal variation of ε and LUEeco appeared a unimodal pattern within a year, with the variations significantly dominated by soil surface temperature and Normalized Difference Vegetation Index (NDVI). A positive correlation between GEP and LUE was found for both ε and LUEeco, with the effect of Q on LUE relatively weak. The increase in diffusion radiation appeared favorable for enhanced LUE. Generally, there was a significant positive relationship between ε and LUEeco, while ε was higher than LUEeco, especially during the mid-season. The annual maximum value of ε and LUEeco was (0.087 ± 0.003) and (0.040 ± 0.002) μmol CO2·μmol photon-1 over the three years, respectively. The interannual variability of LUEmax for ε and LUEeco was 4.17% and 4.25%, respectively, with a maximum difference of >8%, likely resulted from considerable uncertainty in model simulations. Our results indicated that the inversion and optimization of maximum LUE should be taken seriously in the application of LUE models.

Key words: ecosystem carbon exchange, light use efficiency, ecosystem maximum light use efficiency, interannual variability, vegetation index