Simulation of Mixed Needleleaf and Broadleaf Forest Distribution in Changbai Mountain of China Using CLM5-FATES Model
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摘要:
长白山阔叶红松林是全球为数不多的大面积原始针阔混交林，对其开展模拟研究尤为必要.本研究基于植被功能性状的新一代动态全球植被模式CLM5-FATES（Community Land Model version 5-Functionally Assembled Terrestrial Ecosystem Simulator），选取25 ℃时最大羧化速率、比叶面积和叶寿命三个叶片特性参数，对长白山针阔混交林分布进行模拟，探讨模式中长白山针阔混交林分布的参数敏感性，检验模式对长白山针阔混交林分布的模拟能力.研究表明，不同的性状参数组合显著影响该地区两种植被类型分布的模拟结果，且25 ℃时最大羧化速率和比叶面积的影响大于叶寿命.适当的性状参数组合下，模式能再现观测结果中的长白山针阔混交林分布.本研究验证了该模式在长白山针阔混交林的适用性，可为进一步的气候植被相互作用研究提供重要支持.
Abstract:The broadleaved Korean pine forest in Changbai Mountain is one of the few large-area primary mixed needleleaf and broadleaf forest in the world, so it is particularly necessary to conduct simulation research on it. This study is based on the new generation dynamic vegetation model CLM5-FATES (Community Land Model version 5-Functionally Assembled Terrestrial Ecosystem Simulator), by selecting the maximum carboxylation rate at 25 ℃, specific leaf area, and leaf longevity to simulate the distribution of mixed needleleaf and broadleaf forest in Changbai Mountain. This paper explores the parameter sensitivity of the distribution of mixed needleleaf and broadleaf forest in the model, and investigates the model's ability to simulate the distribution of these forests. The study finds that different combinations of trait parameters significantly affect the distribution of vegetation types in the region. The maximum carboxylation rate at 25 ℃ and specific leaf area have a greater impact compared to leaf longevity. Under an appropriate combination of trait parameters, the model can reproduce the observed distribution of mixed needleleaf and broadleaf forest in Changbai Mountain. This study validates the applicability of the model to the mixed needleleaf and broadleaf forest in Changbai Mountain, providing crucial support for further research on climate-vegetation interactions.
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图 1 基于表 1中0~15组参数，GSWP3v1驱动CLM5-FATES模拟的40年中国东北长白山阔叶红松林永久样地的常绿针叶林和落叶阔叶林每平方米个体数量的逐月变化（单位：株/m2）
Fig. 1. Monthly variations in the number of individuals per square meter of evergreen needleleaf and deciduous broadleaf forests at the permanent sample plot of the broadleaved Korean pine forest in Northeast China's Changbai Mountain over 40 years, simulated by CLM5-FATES driven by GSWP3v1, based on parameter sets 0 to 15 from Table 1 (units: individuals/m2)
图 2 GSWP3v1驱动CLM5-FATES模拟的40年中国东北长白山阔叶红松林永久样地的常绿针叶林和落叶阔叶林每平方米个体数量的逐月变化（单位：株/m2）
a. 表 1中第0组参数组合；b. 第6组的$ {V}_{\mathrm{c}, \ \max, \ 25} $替换原始参数；c. 第6组的$ SLA $替换原始参数；d. 第6组的$ {L}_{\mathrm{l}} $替换原始参数；e. 第9组的$ {V}_{\mathrm{c}, \ \max, \ 25} $替换原始参数；f. 第9组的$ SLA $替换原始参数；g. 第9组的$ {L}_{\mathrm{l}} $替换原始参数
Fig. 2. Monthly variations in the number of individuals per square meter of evergreen needleleaf and deciduous broadleaf forests at a permanent sample plot of the broadleaved Korean pine forest in Northeast China's Changbai Mountain over 40 years, simulated by CLM5-FATES driven by GSWP3v1 (units: individuals/m2)
图 3 基于第0（a）、6（b）、7（c）、9（d）和11（e）组参数，使用GSWP3v1和CRUNCEPv7驱动CLM5-FATES模式在长白山阔叶红松林永久样地模拟100年的常绿针叶林和落叶阔叶林的逐年变化（单位：株/m2）
Fig. 3. Yearly variations in the number of individuals of evergreen needleleaf and deciduous broadleaf forests at the permanent sample plot of broadleaved Korean pine forest in Changbai Mountain over 100 years, simulated by the CLM5-FATES model driven by GSWP3v1 and CRUNCEPv7, based on the parameters from Set 0 (a), 6 (b), 7 (c), 9 (d), and 11 (e) in Table 1 (units: individuals/m2)
图 4 基于观测资料1991年至2010年平均的长白山地区的常绿针叶林（ENF）和落叶阔叶林（DBF）面积百分比（单位：%）（a）.第0、6、7、9和11组参数下，使用GSWP3v1（b~f）和CRUNCEPv7（g~k）驱动CLM5-FATES模式模拟的长白山地区第81~100年平均的常绿针叶林和落叶阔叶林的空间分布（单位：株/m2）
蓝色区域为长白山国家级自然保护区
Fig. 4. Percentage of forest area (a) covered by evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) in the Changbai Mountain region based on observation averaged over the period from 1991 to 2010 (units: %). The simulated spatial distribution of evergreen needleleaf forests and deciduous broadleaf forests in the Changbai Mountain region averaged over years 81 to 100, using CLM5-FATES model driven by GSWP3v1 (b‒f) and CRUNCEPv7 (g‒k) based on parameters from Set 0, 6, 7, 9, and 11 (units: individuals/m2)
表 1 25 ℃时最大羧化速率（$ {V}_{\mathrm{c}, \ \max, \ 25} $）、比叶面积（$ SLA $）和叶寿命（$ {L}_{\mathrm{l}} $）三个属性的参数组合
Table 1. Parameter combinations for the maximum carboxylation rate at 25 ℃($ {V}_{\mathrm{c}, \ \max, \ 25} $), specific leaf area (SLA) and leaf longevity ($ {L}_{\mathrm{l}} $)
参数组合序号 25 ℃时最大羧化速率（$ {V}_{\mathrm{c}, \ \max, \ 25} $）
（$ \mathtt{μ}\mathrm{m}\mathrm{o}\mathrm{l}\ \mathrm{C}{\mathrm{O}}_{2}{\mathrm{m}}^{-2}\cdot {\mathrm{s}}^{-1} $）比叶面积（$ SLA $）
（$ {\mathrm{m}}^{2}\cdot \mathrm{g}\cdot {\mathrm{C}}^{-1} $）叶寿命（$ {L}_{\mathrm{l}} $）
（年）常绿针叶 落叶阔叶 常绿针叶 落叶阔叶 常绿针叶 落叶阔叶 0 65 58 0.010 0.030 4.000 0 1.000 0 1 84 68 0.002 0.010 2.062 6 0.325 8 2 44 74 0.004 0.008 2.382 4 0.535 7 3 34 42 0.006 0.014 0.758 5 0.642 7 4 49 47 0.003 0.017 4.115 5 0.149 8 5 71 69 0.003 0.010 1.367 8 0.424 1 6 47 61 0.006 0.021 3.170 4 0.299 4 7 108 49 0.002 0.017 1.967 1 0.201 9 8 65 97 0.003 0.006 2.202 5 0.303 5 9 101 58 0.003 0.021 5.384 2 0.322 2 10 47 79 0.004 0.010 1.640 3 0.395 2 11 17 38 0.006 0.024 3.993 2 0.266 6 12 87 79 0.003 0.010 2.761 3 0.538 4 13 80 32 0.002 0.013 3.824 9 0.458 6 14 1 42 0.004 0.018 1.469 7 0.321 4 15 103 43 0.002 0.016 0.683 9 0.276 1 注：参数组合序号0为CLM5-FATES模式原始参数，1~15依据 Fisher et al.（2015） 的参数计算得到.
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表 2 仅用表 1的第6组或第9组中的$ {V}_{\mathrm{c}, \ \max, \ 25} $、$ SLA $或$ {L}_{\mathrm{l}} $参数替换CLM5-FATES原始参数的参数组合
Table 2. Parameter combinations replacing the original parameters of CLM5-FATES with $ {V}_{\mathrm{c}, \ \max, \ 25} $, SLA or $ {L}_{\mathrm{l}} $ from Set 6 or Set 9 in Table 1
参数组合序号 $ {V}_{\mathrm{c}, \ \max, \ 25} $（$ \mathtt{μ}\mathrm{m}\mathrm{o}\mathrm{l}\ \mathrm{C}{\mathrm{O}}_{2}{\mathrm{m}}^{-2}\cdot {\mathrm{s}}^{-1} $） $ SLA $（$ {\mathrm{m}}^{2}\cdot \mathrm{g}\cdot {\mathrm{C}}^{-1} $） $ {L}_{\mathrm{l}} $（年） 常绿针叶 落叶阔叶 常绿针叶 落叶阔叶 常绿针叶 落叶阔叶 0 65 58 0.010 0.030 4.000 0 1.000 0 6 47 61 0.006 0.021 3.170 4 0.299 4 61 47 61 0.010 0.030 4.000 0 1.000 0 62 65 58 0.006 0.021 4.000 0 1.000 0 63 65 58 0.010 0.030 3.170 4 0.299 4 9 101 58 0.003 0.021 5.384 2 0.322 2 91 101 58 0.010 0.030 4.000 0 1.000 0 92 65 58 0.003 0.021 4.000 0 1.000 0 93 65 58 0.010 0.030 5.384 2 0.322 2
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