基于过程模型的气候变化对长白落叶松人工林净初级生产力的影响

doi:10.17521/cjpe.2016.0382

摘要/Abstract

摘要：

气候变化对净初级生产力(NPP)会产生显著的影响, 但影响的方向和程度存在较大的不确定性。过程模型是揭示气候变化对森林生产力影响的重要工具。该文以吉林省四平、临江、白山等地10个林区30块长白落叶松(Larix olgensis)人工林固定样地为研究对象, 基于气候、土壤、林分生长等观测数据, 运用3-PG模型模拟了长白落叶松人工林NPP在一个轮伐期(40 年)内随林龄的动态变化, 以及在未来不同气候情景条件下NPP的变化情况。结果表明: 通过本地参数化后的3-PG模型模拟的长白落叶松林NPP为272.79-844.80 g·m^-2·a^-1, 与基于样地实测的NPP具有很好的一致性, 平均相对误差和相对均方根误差均小于12%。在未来CO₂浓度、温度及降水同时增加的情景下, 长白落叶松林NPP明显增加。单独增加温度会减小长白落叶松林的NPP, 而降水及CO₂浓度增加能够在一定程度上促进NPP的增加, 但降水增加的正效应明显弱于温度升高的负效应。参数敏感性分析表明: 生长最适温度、林分比叶面积达(年龄为0时比叶面积+成熟叶比叶面积)/2时的林龄、每次霜冻导致生产力流失天数是模型的关键参数。因此, 3-PG模型可以准确地模拟长白落叶松的NPP, 模拟结果可为应对气候变化的长白落叶松经营管理提供依据。

关键词: 过程模型, 长白落叶松, 气候变化, 净初级生产力

Abstract:

Aims Climate change has significant effects on net primary productivity (NPP) in forests, but there is a large uncertainty in the direction and magnitude of the effects. Process-based models are important tools for understanding the responses of forests to climate change. The objective of the study is to simulate changes in NPP of Larix olgensis plantations under future climate scenarios using 3-PG model in order to guide the management of L. olgensis plantations in the context of global climate change.Methods Data were obtained for 30 permanent plots of L. olgensis plantations in Siping, Linjiang, Baishan, etc. of Jilin Province, and a process model, 3-PG model, was applied to simulate changes in NPP over a rotation period of 40 years under different climate scenarios. Parameter sensitivity was also determined. Important findings The locally parameterized 3-PG model well simulates the changes in NPP against the measured NPP data, with values between 272.79-844.80 g·m^-2·a^-1 and both mean relative error and relative root mean square error within 12%. The NPP in L. olgensis plantations would increase significantly with increases in atmospheric CO₂concentration, temperature and precipitation collectively. However, an increase in temperature alone would lead to a decrease in NPP, but increases in precipitation and atmospheric CO₂concentration would increase NPP; the positive effect of increasing precipitation appears to be weaker than the negative effect of increasing temperature. Sensitivity analysis shows that the model performance is sensitive to the optimum temperature, stand age at which specific leaf area equals to half of the sum of specific leaf area at age 0 (SLA₀) and that for mature leaves (SLA₁), and days of production loss due to frost.

Key words: process-based model, Larix olgensis, climate change, net primary productivity (NPP)

解雅麟, 王海燕, 雷相东. 基于过程模型的气候变化对长白落叶松人工林净初级生产力的影响. 植物生态学报, 2017, 41(8): 826-839. DOI: 10.17521/cjpe.2016.0382

Ya-Lin XIE, Hai-Yan WANG, Xiang-Dong LEI. Effects of climate change on net primary productivity in Larix olgensis plantations based on process modeling. Chinese Journal of Plant Ecology, 2017, 41(8): 826-839. DOI: 10.17521/cjpe.2016.0382

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2016.0382

https://www.plant-ecology.com/CN/Y2017/V41/I8/826

图/表 12

参考文献 44

[1]	Amichev BY, Hangs RD, van Rees KCJ (2011). A novel approach to simulate growth of multi-stem willow in bioenergy production systems with a simple process-based model (3-PG).Biomass and Bioenergy, 35, 473-488. DOI URL
[2]	Chen CG, Li XD, Zhang Z (1986). The research on the relationship between growth and ecological factors of Larix olgensis.Forest Science and Technology, 5(9), 6-8.(in Chinese)[陈传国, 李晓东, 张孜 (1986). 长白落叶松人工林的生长与生态因子相关关系的研究, 林业科技通讯,5(9), 6-8.] DOI URL
[3]	Chen Z, Zhang J, Xiong Z, Pan G, Müller C (2016). Enhanced gross nitrogen transformation rates and nitrogen supply in paddy field under elevated atmospheric carbon dioxide and temperature.Soil Biology & Biochemistry, 94, 80-87. DOI URL
[4]	Coops NC, Ferster CJ, Waring RH, Nightingale J (2009). Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States.Remote Sensing of Environment, 113, 680-690. DOI URL
[5]	Coops NC, Waring RH (2011). A process-based approach to estimate lodgepole pine (Pinus contorta Dougl.) distribution in the Pacific Northwest under climate change.Climatic Change, 105, 313-328. DOI URL
[6]	Dong D, Ni J (2011). Modeling changes of net primary productivity of karst vegetation in southwestern China using the CASA model.Acta Ecologica Sinica, 31, 1855-1866.(in Chinese with English abstract)[董丹, 倪健 (2011). 利用CASA模型模拟西南喀斯特植被净第一性生产力, 生态学报,31, 1855-1866.]
[7]	Dong SY, Gao XJ (2014). Long-term climate change: Interpretation of IPCC fifth assessment report.Progressus Inquisitiones de Mutatione Climatis, 10(1), 56-59.(in Chinese with English abstract)[董思言, 高学杰 (2014). 长期气候变化——IPCC第五次评估报告解读, 气候变化研究进展,10(1), 56-59.] DOI
[8]	Feikema PM, Morris JD, Beverly CR, Collopy JJ, Baker TG, Lane PNJ (2010). Validation of plantation transpiration in south-eastern Australia estimated using the 3-PG forest growth model.Forest Ecology and Management, 260, 663-678. DOI URL
[9]	Feng XF, Sun QL, Lin B (2014). NPP process models applied in regional and global scales and responses of NPP to the global change.Ecology and Environmental Sciences, 23, 496-503.(in Chinese with English abstract)[冯险峰, 孙庆龄, 林斌 (2014). 区域及全球尺度的NPP过程模型和NPP对全球变化的响应, 生态环境学报,23, 496-503.] DOI URL
[10]	Gonzalez-Benecke CA, Jokela EJ, Cropper WP, Bracho R, Leduc DJ (2014). Parameterization of the 3-PG model for Pinus elliottii stands using alternative methods to estimate fertility rating, biomass partitioning and canopy closure.Forest Ecology and Management, 327, 55-75. DOI URL
[11]	González-García M, Almeida AC, Hevia A, Majada J, Beadle C (2016). Application of a process-based model for predicting the productivity of Eucalyptus nitens bioenergy plantations in Spain. GCB Bioenergy, 8, 194-210. DOI URL
[12]	H?rk?nen S, Pulkkinen M, Duursma R, M Kel A (2010). Estimating annual GPP, NPP and stem growth in Finland using summary models.Forest Ecology and Management, 259, 524-533. DOI URL
[13]	Hao Y, Chen H, Wei Y, Li Y (2016). The influence of climate change on CO2 (carbon dioxide) emissions: An empirical estimation based on Chinese provincial panel data.Journal of Cleaner Production, 131, 667-677. DOI URL
[14]	He LH, Wang HY, Lei XD (2016). Parameter sensitivity of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model.Chinese Journal of Applied Ecology, 27, 412-420.(in Chinese with English abstract)[何丽鸿, 王海燕, 雷相东 (2016). 基于BIOME- BGC模型的长白落叶松林净初级生产力模拟参数敏感性, 应用生态学报,27, 412-420.] DOI URL
[15]	Hua LZ, Jiang XD, He XB (2007). Application of 3-PG model in Eucalyptus urophylla plantations of southern China.Journal of Beijing Forestry University, 29(2), 100-104.(in Chinese with English abstract)[花利忠, 江希钿, 贺秀斌 (2007). 3-PG模型在华南尾叶桉人工林的应用研究, 北京林业大学学报,29(2), 100-104.]
[16]	Jia JY, Guo JP (2011). Characteristics of climate change in northeast China for last 46 years.Journal of Arid Land Resources and Environmentt, 25(10), 109-115.(in Chinese with English abstract)[贾建英, 郭建平 (2011). 东北地区近46年气候变化特征分析, 干旱区资源与环境,25(10), 109-115.]
[17]	Landsberg JJ, Waring RH (1997). A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning.Forest Ecology and Management, 95, 209-228. DOI URL
[18]	Liu K, Cao L, Wang GB, Shen X, Cao FL (2015). Estimating biomass components and LAI of Chinese fir plantation based on 3-PG model.Journal of Northwest A&F University, 43(9), 57-64.(in Chinese with English abstract)[刘坤, 曹林, 汪贵斌, 申鑫, 曹福亮 (2015). 基于3-PG模型的杉木人工林各器官生物量和LAI估算, 西北农林科技大学学报(自然科学版),43(9), 57-64.]
[19]	Ma HW, Xu CQ, Li HC (2008). Photosynthesis characteristics of Larix olgensis under different site conditions.Journal of Northeast Forestry University, 36(8), 4-7.(in Chinese with English abstract)[马华文, 许翠清, 李海朝 (2008). 立地条件对长白落叶松光合特性的影响, 东北林业大学学报,36(8), 4-7.] DOI URL
[20]	Medlyn BE, Duursma RA, Zeppel MJB (2011). Forest productivity under climate change: A checklist for evaluating model studies.Wiley Interdisciplinary Reviews: Climate Change, 2, 332-355. DOI URL
[21]	Paul KI, Booth TH, Jovanovic T, Sands PJ, Morris JD (2007). Calibration of the forest growth model 3-PG to Eucalyptus plantations growing in low rainfall regions of Australia.Forest Ecology and Management, 243, 237-247. DOI URL
[22]	Peng C, Zhou X, Zhao S, Wang X, Zhu B, Piao S, Fang J (2009). Quantifying the response of forest carbon balance to future climate change in Northeastern China: Model validation and prediction.Global and Planetary Change, 66, 179-194. DOI URL
[23]	Peng J, Dan L (2015). Impacts of CO2 concentration and climate change on the terrestrial carbon flux using six global climate—Carbon coupled models.Ecological Modelling, 304, 69-83. DOI URL
[24]	Potithep S, Yasuoka Y (2011). Application of the 3-PG model for gross primary productivity estimation in deciduous broadleaf forests: A study area in Japan.Forests, 2, 590-609. DOI URL
[25]	Sands PJ, Landsberg JJ (2002). Parameterisation of 3-PG for plantation grown Eucalyptus globulus.Forest Ecology and Management, 163, 273-292. DOI URL
[26]	Shvidenko AZ, Schepashchenko DG, Vaganov EA, Nilsson S (2008). Net primary production of forest ecosystems of Russia: A new estimate.Doklady Earth Sciences, 421, 1009-1012. DOI URL
[27]	Song L, Sun ZH (2012). Measurement of leaf area index of Larix olgensis plantations in hilly area of Sanjiang Plain.Journal of Northeast Forestry University, 40(9), 6-9.(in Chinese with English abstract)[宋林, 孙志虎 (2012). 长白落叶松人工林叶面积指数测定, 东北林业大学学报,40(9), 6-9.] DOI URL
[28]	Su W, Xu XX, Lü XZ, Fan MR, Zhang Y (2012). Study on the impact of climate change on net primary productivity of Larix principis-rupprechtii forest in Beijing mountain area. Guangdong Agricultural Sciences, 39(7), 69-72.(in Chinese with English abstract) [苏薇, 余新晓, 吕锡芝, 范敏锐, 张艺 (2012). 气候变化对北京山区华北落叶松林NPP影响研究, 广东农业科学,39(7), 69-72.] DOI URL
[29]	Subedi S, Fox T, Wynne R (2015). Determination of fertility rating (FR) in the 3-PG model for loblolly pine plantations in the Southeastern United States based on site index.Forests, 6, 3002-3027. DOI URL
[30]	Sun CM, Sun ZG, Liu T, Wang LJ, Chen YY, Guo DD, Tian T, Li JL (2015). Comprehensive estimation model of grassland NPP based on MODIS in China.Acta Ecologica Sinica, 35, 1079-1085.(in Chinese with English abstract)[孙成明, 孙政国, 刘涛, 王力坚, 陈瑛瑛, 郭斗斗, 田婷, 李建龙 (2015). 基于MODIS的中国草地NPP综合估算模型, 生态学报,35, 1079-1085.]
[31]	Sun ZH, Bi YJ, Mou CC, Cai TJ (2012). Using an ecosystem simulation model FORECAST to evaluate the effects of forest management strategies on long-term productivity of Korean larch plantations.Journal of Beijing Forestry University, 34(6), 1-6.(in Chinese with English abstract)[孙志虎, 毕永娟, 牟长城, 蔡体久 (2012). 基于FORECAST模型的长白落叶松人工林经营措施对长期生产力的影响, 北京林业大学学报,34(6), 1-6.]
[32]	Sun ZH, Jin GZ, Mu CC (2009). Long-Term Productivity Maintenance of Monoculture Olga Hay Larch Timber Forest in Northeastern China. Science Press, Beijing. 113.(in Chinese)[孙志虎, 金光泽, 牟长城 (2009). 长白落叶松人工林长期生产力维持的研究. 科学出版社, 北京. 113.]
[33]	Turner DP, Ritts WD, Cohen WB, Gower ST, Running SW, Zhao M, Costa MH, Kirschbaum AA, Ham JM, Saleska SR, Ahl DE (2006). Evaluation of MODIS NPP and GPP products across multiple biomes.Remote Sensing of Environment, 102, 282-292. DOI URL
[34]	Wang L, Gong W, Ma Y, Zhang M (2013). Modeling regional vegetation NPP variations and their relationships with climatic parameters in Wuhan, China.Earth Interactions, 17(4), 1-20. DOI URL
[35]	Wang WF, Duan YX, Zhang LX, Wang B, Li XJ (2016). Effects of different rotations on carbon sequestration in Chinese fir plantations.Chinese Journal of Plant Ecology, 40, 669-678.(in Chinese with English abstract)[王伟峰, 段玉玺, 张立欣, 王博, 李晓晶 (2016). 不同轮伐期对杉木人工林碳固存的影响, 植物生态学报,40, 669-678.] DOI URL
[36]	Wang XY, Sun YJ, Ma W (2011). Biomass and carbon storage distribution of different density in Larix olgensis plantation.Journal of Fujian College of Forestry, 31(3), 221-226.(in Chinese with English abstract)[王秀云, 孙玉军, 马炜 (2011). 不同密度长白落叶松林生物量与碳储量分布特征, 福建林学院学报,31(3), 221-226.]
[37]	Wang YH, Zhou GS, Jiang YL, Yang ZY (2001). Estimating biomass and NPP of Larix forests using forest inventory data (FID).Acta Phytoecologica Sinica, 25, 420-425.(in Chinese with English abstract)[王玉辉, 周广胜, 蒋延玲, 杨正宇 (2001). 基于森林资源清查资料的落叶松林生物量和净生长量估算模式, 植物生态学报,25, 420-425.] DOI URL
[38]	Wu YL, Wang XP, Li QY, Sun Y (2014). Response of broad-leaved Korean pine forest productivity of Mt. Changbai to climate change: An analysis based on BIOME- BGC modeling.Acta Scientiarum Naturalium Universitatis Pekinensis, 50, 577-586.(in Chinese with English abstract)[吴玉莲, 王襄平, 李巧燕, 孙阎 (2014). 长白山阔叶红松林净初级生产力对气候变化的响应: 基于BIOME-BGC模型的分析, 北京大学学报(自然科学版),50, 577-586.]
[39]	Wu ZF, Jing YH, Liu JP, Shang LN, Zhao DS (2003). Response of vegetation distribution to global climate change in Northeast China.Scientia Geographica Sinica, 23, 564-570.(in Chinese with English abstract)[吴正方, 靳英华, 刘吉平, 商丽娜, 赵东升 (2003). 东北地区植被分布全球气候变化区域响应, 地理科学,23, 564-570.] DOI URL
[40]	Xu CL, Xun XM, Zhang SG (2012). Comparison in photosynthetic characteristics of Larix kaempferi, L. olgensis and their hybrids.Journal of Beijing Forestry University, 34(4), 62-66.(in Chinese with English abstract)[许晨璐, 孙晓梅, 张守攻 (2012). 日本落叶松与长白落叶松及其杂种光合特性比较, 北京林业大学学报,34(4), 62-66.]
[41]	Yin K, Tian YC, Yuan C, Zhang FF, Yuan QZ, Hua LZ (2015). NPP spatial and temporal pattern of vegetation in Beijing and its factor explanation based on CASA model.Remote Sensing for Land and Resources, 27(1), 133-139.(in Chinese with English abstract)[尹锴, 田亦陈, 袁超, 张飞飞, 苑全治, 花利忠 (2015). 基于CASA模型的北京植被NPP时空格局及其因子解释, 国土资源遥感,27(1), 133-139.] DOI URL
[42]	Zeng HQ, Liu QJ, Feng ZW, Wang XK, Ma ZQ (2008). GPP and NPP study of Pinus elliottii forest in red soil hilly region based on BIOME-BGC model.Acta Ecologica Sinica, 28, 5314-5321.(in Chinese with English abstract)[曾慧卿, 刘琪璟, 冯宗炜, 王效科, 马泽清 (2008). 基于BIOME-BGC模型的红壤丘陵区湿地松(Pinus elliottii)人工林GPP和NPP, 生态学报,28, 5314-5321.] DOI URL
[43]	Zhao M, Xiang W, Peng C, Tian D (2009). Simulating age-related changes in carbon storage and allocation in a Chinese fir plantation growing in southern China using the 3-PG model.Forest Ecology and Management, 257, 1520-1531. DOI URL
[44]	Zhou G, Wang Y, Jiang Y, Yang Z (2002). Estimating biomass and net primary production from forest inventory data: A case study of China’s Larix forests.Forest Ecology and Management, 169, 149-157. DOI URL

排放情景 Emission scenarios	CO₂浓度 CO₂concentration (mg·L^-1)	CO₂浓度基准值 CO₂concentration reference value (mg·L^-1)	气温增加 Air temperature increment (℃)	气温增加基准值 Air temperature increment reference value (℃)	降水量增加 Precipitation increment (%)	降水量增加基准值 Precipitation increment reference value (%)
RCP 2.6	440-480	460	0.3-1.7	1.0	0.3-5.1	2.7
RCP 6.0	510-570	540	1.4-3.1	2.3	1.4-9.3	5.4
RCP 8.5	560-630	595	2.6-4.8	3.7	2.6-14.4	8.5

排放情景 Emission scenarios	CO₂浓度 CO₂concentration (mg·L^-1)	CO₂浓度基准值 CO₂concentration reference value (mg·L^-1)	气温增加 Air temperature increment (℃)	气温增加基准值 Air temperature increment reference value (℃)	降水量增加 Precipitation increment (%)	降水量增加基准值 Precipitation increment reference value (%)
RCP 2.6	440-480	460	0.3-1.7	1.0	0.3-5.1	2.7
RCP 6.0	510-570	540	1.4-3.1	2.3	1.4-9.3	5.4
RCP 8.5	560-630	595	2.6-4.8	3.7	2.6-14.4	8.5

气候变化情景 Climate change scenarios	排放情景 Emission scenarios	CO₂浓度 CO₂concentration	气温 Air temperature	降水量 Precipitation
C₀T₀P₀		不变 No change	不变 No change	不变 No change
	RCP 2.6	460 mg·L^-1	不变 No change	不变 No change
C₁T₀P₀	RCP 6.0	540 mg·L^-1	不变 No change	不变 No change
	RCP 8.5	595 mg·L^-1	不变 No change	不变 No change
	RCP 2.6	不变 No change	+1.0 ℃	不变 No change
C₀T₁P₀	RCP 6.0	不变 No change	+2.3 ℃	不变 No change
	RCP 8.5	不变 No change	+3.7 ℃	不变 No change
	RCP 2.6	不变 No change	不变 No change	+2.7%
C₀T₀P₁	RCP 6.0	不变 No change	不变 No change	+5.4%
	RCP 8.5	不变 No change	不变 No change	+8.5%
	RCP 2.6	不变 No change	+1.0 ℃	+2.7%
C₀T₁P₁	RCP 6.0	不变 No change	+2.3 ℃	+5.4%
	RCP 8.5	不变 No change	+3.7 ℃	+8.5%
	RCP 2.6	460 mg·L^-1	+1.0 ℃	不变 No change
C₁T₁P₀	RCP 6.0	540 mg·L^-1	+2.3 ℃	不变 No change
	RCP 8.5	595 mg·L^-1	+3.7 ℃	不变 No change
	RCP 2.6	460 mg·L^-1	不变 No change	+2.7%
C₁T₀P₁	RCP 6.0	540 mg·L^-1	不变 No change	+5.4%
	RCP 8.5	595 mg·L^-1	不变 No change	+8.5%
	RCP 2.6	460 mg·L^-1	+1.0 ℃	+2.7 %
C₁T₁P₁	RCP 6.0	540 mg·L^-1	+2.3 ℃	+5.4 %
	RCP 8.5	595 mg·L^-1	+3.7 ℃	+8.5 %

气候变化情景 Climate change scenarios	排放情景 Emission scenarios	CO₂浓度 CO₂concentration	气温 Air temperature	降水量 Precipitation
C₀T₀P₀		不变 No change	不变 No change	不变 No change
	RCP 2.6	460 mg·L^-1	不变 No change	不变 No change
C₁T₀P₀	RCP 6.0	540 mg·L^-1	不变 No change	不变 No change
	RCP 8.5	595 mg·L^-1	不变 No change	不变 No change
	RCP 2.6	不变 No change	+1.0 ℃	不变 No change
C₀T₁P₀	RCP 6.0	不变 No change	+2.3 ℃	不变 No change
	RCP 8.5	不变 No change	+3.7 ℃	不变 No change
	RCP 2.6	不变 No change	不变 No change	+2.7%
C₀T₀P₁	RCP 6.0	不变 No change	不变 No change	+5.4%
	RCP 8.5	不变 No change	不变 No change	+8.5%
	RCP 2.6	不变 No change	+1.0 ℃	+2.7%
C₀T₁P₁	RCP 6.0	不变 No change	+2.3 ℃	+5.4%
	RCP 8.5	不变 No change	+3.7 ℃	+8.5%
	RCP 2.6	460 mg·L^-1	+1.0 ℃	不变 No change
C₁T₁P₀	RCP 6.0	540 mg·L^-1	+2.3 ℃	不变 No change
	RCP 8.5	595 mg·L^-1	+3.7 ℃	不变 No change
	RCP 2.6	460 mg·L^-1	不变 No change	+2.7%
C₁T₀P₁	RCP 6.0	540 mg·L^-1	不变 No change	+5.4%
	RCP 8.5	595 mg·L^-1	不变 No change	+8.5%
	RCP 2.6	460 mg·L^-1	+1.0 ℃	+2.7 %
C₁T₁P₁	RCP 6.0	540 mg·L^-1	+2.3 ℃	+5.4 %
	RCP 8.5	595 mg·L^-1	+3.7 ℃	+8.5 %

参数 Parameter	值 Value	分类 Category	来源 Source
生物量的分配关系和比例 Allometric relationships and partitioning
胸径2 cm树叶与干分配比 Foliage: stem partitioning ratio when DBH = 2 cm	1.00	A	本文拟合 Fitted in this study
胸径20 cm树叶与干分配比 Foliage: stem partitioning ratio when DBH = 20 cm	0.5	A	本文拟合 Fitted in this study
干生物量与胸径关系中常数值 Constant in the stem biomass and DBH relationship	0.007 3	A	本文拟合 Fitted in this study
干生物量与胸径关系中幂值 Power in the stem biomass and DBH relationship	3.409	A	本文拟合 Fitted in this study
净初级生产量分配给根的最大值 Maximum fraction of net primary productivity to roots	0.95	A	本文拟合 Fitted in this study
净初级生产量分配给根最小值 Minimum fraction of net primary productivity to roots	0.5	A	本文拟合 Fitted in this study
气温修正因子 Air temperature modifier
生长最低气温 Minimum air temperature for growth (℃)	-25	L	Xu et al., 2012
生长最适气温 Optimum air temperature for growth (℃)	17	L	Sun et al., 2009
生长最高气温 Maximum air temperature for growth (℃)	27	L	Xu et al., 2012
霜冻修正因子 Frost modifier
每次霜冻导致生产力流失天数 Production lost days per frost day (d)	1	C	默认参数 Default parameters
冠层结构和过程 Canopy structure and process
比叶面积 Specific leaf area (SLA)
年龄为0时比叶面积 Specific leaf area at age 0 (m²·kg^-1)	12.93	L	Song & Sun, 2012
成熟叶比叶面积 Specific leaf area for mature leaves (m²·kg^-1)	5	L	Song & Sun, 2012
年龄为(SLA₀+ SLA₁)/2比叶面积 Age at which specific leaf area = (SLA₀ + SLA₁)/2	8	L	Song & Sun, 2012
光截获 Light interception
消光系数 Extinction coefficient	0.5	L	Amichev et al., 2011
郁闭度年龄 Age at canopy cover (a)	5	L	Gonzalez-Benecke et al., 2014
从林冠降水蒸发的最大比例 Maximum proportion of rainfall evaporated from canopy	0.15	C	默认参数 Default parameters
最大降水截留时叶面积指数 Leaf area index for maximum rainfall interception	5	C	默认参数 Default parameters
光合生产和呼吸 Photosynthesis production and respiration
冠层量子效率 Canopy quantum efficiency (mol·mol^-1)	0.035	L	Ma et al., 2008
净初级生产力/总初级生产力 Ratio of net primary productivity to gross primary productivity	0.47	L	Liu et al., 2015
树枝在干中的比例 Fraction of stem biomass as branch and bark	0.15	L
林分初生时树枝占干生物量的比例 Fraction of branch and bark at age = 0	0.15	L	Coops & Waring, 2011
林分成熟时树枝占干生物量的比例 Fraction of branch and bark for mature stands			Coops & Waring, 2011
树枝占平均值时的林龄 Age at which fraction = (Branch and bark fraction at age = 0+Branch and bark fraction for mature stands)/2	1.5	L	Coops & Waring, 2011
立地初始化条件 Stand initialization
初始种植年 Years of initial plantation	1973-1983	M	本研究测定 Measurements in this study
初始密度 Initial stocking (trees·hm^-2)	3300	M	本研究测定 Measurements in this study
海拔 Altitude (m)	230-751	M	本研究测定 Measurements in this study
纬度 Latitude (°)	41.61-43.88	M	本研究测定 Measurements in this study
肥力等级 Fertility rating	0.7 ± 0.1	M	本研究测定 Measurements in this study
土壤质地类型 Soil texture	Clay loam	M	本研究测定 Measurements in this study