基于模型数据融合的中国温带和亚热带典型森林生态系统碳通量模拟
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中国科学院地理科学与资源研究所,中国科学院地理科学与资源研究所

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中国科学院战略性先导科技专项(XDA05050600);国家科技支撑计划(2013BAC03B00);中国科学院科研信息化“科技领域云”项目(XXH12503-05-03)


Carbon flux simulation of typical temperate and subtropical forest ecosystems in China based on model-data fusion approach
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science,Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science

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    摘要:

    生态系统碳循环过程对水分响应的研究已成为全球变化关注的焦点问题之一。基于长白山温带针阔混交林与千烟洲亚热带人工针叶林观测站2003-2009年生长季的碳通量(NEE)和气象观测数据,综合考虑水分对光合、呼吸作用的影响,构建不同的NEE模型,并应用模型数据融合方法优化模型参数、遴选最适模型,系统分析了水分因子对不同森林生态系统碳循环的影响。结果表明:(1)优化后的模型参数均能被NEE实测数据较好约束。长白山生长季的光合、呼吸参数值均高于千烟洲,未考虑空气饱和水汽压差(VPD)的模型高估了千烟洲温度敏感性参数(Q10)值、低估了千烟洲基础呼吸速率参数(BR)值;(2)仅考虑VPD对光合作用影响的模型是长白山生长季碳通量模拟的最优模型,但模拟精度提高不显著。不同模型间碳通量组分模拟结果差异较小;(3)考虑VPD和土壤含水量对光合、呼吸作用共同影响的模型是千烟洲生长季碳通量模拟的最优模型,并且显著提高了模拟精度。未考虑水分的模型在生长季高估了总生态系统生产力(GEP)总量2.0%(21.85 g C/m2),同时更大幅度地高估了生态系统呼吸(RE)总量4.4%(38.02 g C/m2),从而导致NEE总量低估于实测值7.8%(18.55 g C/m2)。

    Abstract:

    Moisture effect on the carbon balance of terrestrial ecosystems is a key issue in global change research. It is crucial to accurately analyze the response of terrestrial ecosystem carbon cycle to moisture. However, the carbon flux models responding to environmental factors rarely consider the moisture effects on photosynthesis and respiration simultaneously; meanwhile there are still large uncertainties in model structures and parameters. Thus, this study was designed to (1) choose the optimal carbon flux model with accurate parameters for different ecosystems through model-data fusion approach, reducing the uncertainties of modeled results; (2) systematically analyze the influence of water factors on carbon flux simulation, including gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE). To consider the effects of moisture on both GEP and RE, we developed four different NEE models. Then, based on carbon flux and meteorological data during growing season from 2003 to 2009 in Changbaishan temperate mixed forest (CBS) and Qianyanzhou subtropical coniferous plantation (QYZ), Markov Chain Monte Carlo was employed to estimate model parameters, and Bayesian Information Criterion was applied to choose the optimal model for two forest ecosystems. The results showed that (1) the posterior values of model parameters were normally distributed, indicating that the parameters were well constrained by NEE. Photosynthetic and respiratory parameter values of CBS were higher than those of QYZ during the growing season. The model without vapor pressure deficit (VPD) overestimated the value of temperature sensitivity (Q10) and underestimated the value of basal respiration rate (BR) in QYZ; (2) the model considering VPD only was the optimal model for CBS,but its performance was not improved much. The modeled flux components were similar among the four models; (3) the model considering both VPD and soil water content (Sw) was the optimal model for QYZ, and its performance was improved significantly. The model ignored water factors overestimated 2% (21.85 g C/m2) of the total GEP, and 4.4% (38.02 g C/m2) of the total RE, and therefore, underestimated 7.8% (18.55 g C/m2) of the total measured NEE during the growing season.

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葛蓉,何洪林,任小丽,张黎,冯艾琳,王辉民,张军辉.基于模型数据融合的中国温带和亚热带典型森林生态系统碳通量模拟.生态学报,2017,37(5):1409~1420

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