林业科学 ›› 2020, Vol. 56 ›› Issue (2): 12-23.doi: 10.11707/j.1001-7488.20200202
冯鑫炜1,张志强1,*,许行1,律江2,张海泉2,孟祥雪2
收稿日期:
2018-12-24
出版日期:
2020-02-25
发布日期:
2020-03-17
通讯作者:
张志强
基金资助:
Xinwei Feng1,Zhiqiang Zhang1,*,Hang Xu1,Jiang Lü2,Haiquan Zhang2,Xiangxue Meng2
Received:
2018-12-24
Online:
2020-02-25
Published:
2020-03-17
Contact:
Zhiqiang Zhang
摘要:
目的: 定量研究欧美杨人工林生态系统净碳交换(NEE)对环境因子响应的时滞现象,为提高生态系统碳源/汇量的估算准确性提供科学依据。方法: 应用涡度相关技术,结合微气象观测系统,对北京市顺义区欧美杨人工林NEE和环境因子进行连续动态观测,获取2014年生长季(4-10月)NEE、空气温度(Ta)、5 cm深处土壤温度(T5)、光合有效辐射(PAR)、饱和水汽压差(VPD)和25 cm深处土壤体积含水量(VWC25)观测数据,采用回归分析与小波互相关分析相结合的方法定量研究NEE与环境因子的时滞关系;以决定系数(R2)、林氏调和系数(LCCC)和均方根误差(RMSE)作为评价指标,利用偏相关分析和主成分分析方法分析时滞现象对NEE与环境影响因子之间关系的影响。结果: PAR是影响NEE变化的主导因子;由于该生态系统地下水位高,水分供给充足,VWC25对NEE没有显著影响(P=0.151 5);白天NEE与PAR同步变化,比Ta、T5和VPD分别提前2.5、2和2.5 h达到峰值(时滞),夜间NEE与环境因子没有确定的时滞关系;消除时滞现象后,NEE与T5的线性关系由不显著(P=0.224 8)变为极显著(P=0.005 1);主成分分析表明,对数回归模型可以更准确地描述NEE与环境因子间关系,消除时滞后模型的R2和LCCC分别提高8.1%和2.0%,RMSE降低0.417 μmol·m-2s-1。结论: 欧美杨人工林生态系统NEE日动态变化受Ta、T5、PAR和VPD控制,白天时滞现象明显,消除时滞现象可以有效提高NEE模型拟合精度。
中图分类号:
冯鑫炜,张志强,许行,律江,张海泉,孟祥雪. 欧美杨人工林生态系统净碳交换对环境因子响应的时滞[J]. 林业科学, 2020, 56(2): 12-23.
Xinwei Feng,Zhiqiang Zhang,Hang Xu,Jiang Lü,Haiquan Zhang,Xiangxue Meng. Time-Lag Responses of Net Ecosystem Carbon Exchange to Environmental Factors in a Populus×euramericana Plantation[J]. Scientia Silvae Sinicae, 2020, 56(2): 12-23.
表1
净生态系统碳交换(NEE)与Ta、T5、PAR和VPD的偏相关系数"
分析变量 Analyzed variable | 控制变量 Control variable | 消除时滞前 Before eliminating time-lag | 消除时滞后 After eliminating time-lag | |||
偏相关系数 Partial correlation coefficient | P | 偏相关系数 Partial correlation coefficient | P | |||
NEE-Ta | T5, PAR, VPD | -0.117 | < 0.000 1 | -0.196 | < 0.000 1 | |
NEE-T5 | Ta, PAR, VPD | 0.020 | 0.224 8 | 0.069 | 0.005 1 | |
NEE-PAR | Ta, T5, VPD | -0.531 | < 0.000 1 | -0.480 | < 0.000 1 | |
NEE-VPD | Ta, T5, PAR | 0.200 | < 0.000 1 | 0.074 | 0.002 5 |
表2
Ta、T5、PAR和VPD 4个环境因子各主成分的特征值和贡献率"
主成分 Principal component | 消除时滞前Before eliminating time-lag | 消除时滞后After eliminating time-lag | |||||
特征值 Eigenvalue | 贡献率 Contribution rate(%) | 累计贡献率 Cumulative contribution rate(%) | 特征值 Eigenvalue | 贡献率 Contribution rate(%) | 累计贡献率 Cumulative contribution rate(%) | ||
1 | 2.996 8 | 74.92 | 74.92 | 3.033 9 | 75.85 | 75.85 | |
2 | 0.706 0 | 17.65 | 92.57 | 0.684 9 | 17.12 | 92.97 | |
3 | 0.237 1 | 5.93 | 98.50 | 0.217 5 | 5.44 | 98.41 | |
4 | 0.060 1 | 1.50 | 100.00 | 0.063 7 | 1.59 | 100.00 |
表3
Ta、T5、PAR和VPD 4个环境因子的主成分载荷"
主成分 Principal component | 消除时滞前Before eliminating time-lag | 消除时滞后After eliminating time-lag | |||||||
Ta | T5 | PAR | VPD | Ta | T5 | PAR | VPD | ||
1 | 0.523 | 0.533 | 0.419 | 0.517 | 0.508 | 0.524 | 0.465 | 0.502 | |
2 | 0.446 | 0.390 | -0.772 | -0.228 | 0.524 | 0.426 | -0.618 | -0.402 | |
3 | 0.223 | 0.207 | 0.477 | -0.824 | -0.244 | -0.606 | 0.754 | ||
4 | 0.691 | -0.722 | 0.681 | -0.696 | 0.184 | -0.133 |
表4
净生态系统碳交换(NEE)与综合环境因子(C和C’)的回归分析"
回归模型 Regression model | 消除时滞前 Before eliminating time-lag | 消除时滞后 After eliminating time-lag | |||||
回归方程Equation | R2 | P | 回归方程Equation | R2 | P | ||
线性模型Linear model | NEE=-0.064 C-4.328 | 0.377 | < 0.000 1 | NEE=-0.060 C’ - 1.640 | 0.505 | < 0.000 1 | |
对数模型Logarithmic model | NEE=-8.692ln C + 27.317 | 0.450 | < 0.000 1 | NEE=-8.272ln C’ + 27.773 | 0.531 | < 0.000 1 | |
双曲线模型 Hyperbolic model | NEE=504.708/C - 20.335 | 0.346 | < 0.000 1 | NEE=472.913/C’ - 17.500 | 0.371 | < 0.000 1 |
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