Biome-BGC模型模拟阔叶红松林碳水通量的参数敏感性检验和不确定性分析

doi:10.17521/cjpe.2018.0231

摘要/Abstract

摘要：

生态过程模型的发展为研究者在长时间序列和区域尺度的研究提供了便利, 但模型模拟的准确性受到模型自身结构、模型参数估计合理性的影响。敏感性分析能够定量或定性筛选出对模型模拟结果影响较大的敏感参数, 是模型参数校准过程中的重要工具, 也是建模和应用的先决条件。该文以阔叶红松林为研究对象, 采用全局敏感性分析方法——傅里叶幅度灵敏度检验扩展法(EFAST)对Biome-BGC模型的生理生态参数进行了敏感性分析, 分别分析了红松(Pinus koraiensis)和阔叶树的净初级生产力(NPP)、蒸散(ET)对参数变化的敏感性。结果表明: (1)模拟红松NPP的不确定性高于阔叶树, 但二者的模拟ET的不确定性均较小。阔叶树的NPP和ET对生理生态参数的敏感性总体上都小于红松。(2)无论是红松、阔叶或其他植被类型, 模拟NPP均表现出对叶片碳氮比、细根碳氮比、比叶面积(SLA)和冠层截留系数的敏感性, 这4个参数的高敏感性主要是由模型自身结构所决定的, 与植被类型和研究地区的关系较小。对模拟ET而言, 细根与叶片碳分配比、新茎与新叶碳分配比和SLA均是影响红松和阔叶树ET的敏感参数, 但红松ET主要受参数与参数间的二阶或多阶交互作用的间接影响, 而阔叶树ET则主要是受到敏感参数直接效应的影响。(3)除了上述影响红松和阔叶树碳水通量的共性参数外, 诸如核酮糖-1,5-二磷酸羧化酶中叶氮含量、叶片与细根周转率、所有叶面积与投影叶面积之比等也是对模拟结果有影响的重要参数, 但是其敏感程度随物种不同和研究区不同而不同, 所以这类参数可以根据具体情况进行参数本地化, 对于其他不敏感参数则可以采用模型缺省值。

关键词: 敏感性分析, 傅里叶幅度灵敏度检验扩展法, 净初级生产力, 蒸散, Biome-BGC模型

Abstract:

Aims The emergence and application of ecosystem process models have provided useful tools for studying carbon and water balances of terrestrial ecosystems at large spatiotemporal scales, but the accuracy of model simulations is affected by the parameterization of key variables among many factors. Sensitivity analysis is commonly used to screen the critical parameters that have predominant influences on model simulations. The objective of this study was to identify the critical ecophysiological parameters in Biome-BGC model in simulating annual net primary productivity (NPP) and evapotranspiration (ET) of broadleaved-Korean pine forests in Northeast China.

Methods We simulated carbon and water fluxes of broadleaved-Korean pine forests with the Biome-BGC (version 4.2) at a daily time step based on site- and species-specific parameters. Daily meteorological data for the period 1958-2015 was obtained from the China Meteorological Administration. Initialization parameters such as geographical position, soil depth, and soil texture of the site were obtained from field measurements. Among the 43 ecophysiological parameters represented in the model, 30 were derived either from field measurements or from published data for the study sites in literature, and the default values were used for 13 of the parameters. The modeled forest NPP was compared with the tree-ring width index to test the model’s ability to simulate the inter-annual variations in forest productivity. The modeled NPP and ET were also compared with existing remote sensing products for the period 2000-2014 for validation purpose. Sensitivity analysis was conducted using a variance-based sensitivity analysis method—Extended Fourier Amplitude Sensitivity Test (EFAST) to acquire the first order and total order sensitivity index of the parameters.

Important findings Our locally parameterized Biome-BGC model well simulated the carbon and water fluxes of the broadleaved-Korean pine forests. The uncertainty of simulated NPP is higher for Korean pine trees than for broad-leaved trees, while that of ET was small for both tree types. Both NPP and ET of broad-leaved trees were generally less sensitive to ecophysiological parameters than Korean pine. Leaf carbon to nitrogen ratio, fine root carbon to nitrogen ratio, specific leaf area (SLA), and water interception coef?cient were among the highly sensitive parameters affecting the modeled NPP; while fine root carbon to new leaf carbon allocation, new stem carbon to new leaf carbon allocation and SLA were the highly sensitive parameters influencing ET. In addition, fraction of leaf N in Rubisco, leaf and fine root turnover, ratio of all sided to projected leaf area are also critical parameters affecting the output of Biome-BGC simulations. The degree of sensitivity of the critical parameters varied with species and sites, highlighting the need to adopt local parametrization of Biome-BGC model in simulating regional forest carbon and water fluxes. For other non-sensitive parameters, model default value can be readily used.

Key words: sensitivity analysis, extended fourier amplitude sensitivity test method, net primary production, evapotranspiration, Biome-BGC model

李旭华, 孙建新. Biome-BGC模型模拟阔叶红松林碳水通量的参数敏感性检验和不确定性分析. 植物生态学报, 2018, 42(12): 1131-1144. DOI: 10.17521/cjpe.2018.0231

LI Xu-Hua, SUN Osbert Jianxin. Testing parameter sensitivities and uncertainty analysis of Biome-BGC model in simulating carbon and water fluxes in broadleaved-Korean pine forests. Chinese Journal of Plant Ecology, 2018, 42(12): 1131-1144. DOI: 10.17521/cjpe.2018.0231

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2018.0231

https://www.plant-ecology.com/CN/Y2018/V42/I12/1131

图/表 11

参考文献 48

[1]	Cukier RI, Levine HB, Shuler KE ( 1978). Nonlinear sensitivity analysis of multiparameter model systems. Journal of Computational Physics, 26, 1-42. DOI URL
[2]	He LH, Wang HY, Lei XD ( 2016). Parameter sensitivity of simulating net primary productivity ofLarix olgensis forest based on BIOME-BGC model. Chinese Journal of Applied Ecology, 27, 412-420. DOI URL
	[ 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
[3]	Houborg R, Cescatti A, Migliavacca M ( 2012). Constraining Model Simulations of GPP Using Satellite Retrieved Leaf Chlorophyll. IEEE, Munich, Germany. 6455-6458.
[4]	Jiang YF ( 2013). Litter Decomposition and Function Role of Soil Fauna in Decomposition in a Pinus Koraiensis Mixed Broad-leaved Forest of Changbai Mountains. PhD dissertation, Northeast Normal University, Changchun.
	[ 蒋云峰 ( 2013). 长白山针阔混交林主要凋落物分解及土壤动物的作用. 博士学位论文, 东北师范大学, 长春.]
[5]	Kang MC ( 2016). Energy Partitioning and Modelling of Carbon and Water Fluxes of a Poplar Plantation Ecosystem in Northern China. PhD dissertation, Beijing Forestry University, Beijing.
	[ 康满春 ( 2016). 北方典型杨树人工林能量分配与碳水通量模拟. 博士学位论文, 北京林业大学, 北京.]
[6]	Kang S, Kimball JS, Running SW ( 2006). Simulating effects of fire disturbance and climate change on boreal forest productivity and evapotranspiration. Science of the Total Environment, 362, 85-102. DOI URL
[7]	Kumar M, Raghubanshi AS ( 2012). Sensitivity analysis of Biome-BGC model for dry tropical forests of Vindhyan highlands, India. Remote Sensing and Spatial Information Sciences, 38, 129-133. DOI URL
[8]	Li F ( 1984). A research of the bioproductivity of Korean pine broadleaf forest and its secondary forest of poplar-birch. Chinese Journal of Ecology, ( 2), 8-12.
	[ 李飞 ( 1984). 红松阔叶林及其次生杨桦林生物生产力的研究. 生态学杂志, ( 2), 8-12.]
[9]	Li XF, Han SJ, Hu YL, Zhao YT ( 2008). Decomposition of litter organic matter and its relations to C, N and P release in secondary conifer and broadleaf mixed forest in Changbai Mountains. Chinese Journal of Applied Ecology, 19, 245-251.
	[ 李雪峰, 韩士杰, 胡艳玲, 赵玉涛 ( 2008). 长白山次生针阔混交林叶凋落物中有机物分解与碳、氮和磷释放的关系. 应用生态学报, 19, 245-251.]
[10]	Li Y, Huang CL, Lu L ( 2014). Global sensitivity analysis of SEBS model parameters based on EFAST method. Remote Sensing Technology and Application, 29, 719-726.
	[ 李艳, 黄春林, 卢玲 ( 2014). 基于EFAST方法的SEBS模型参数全局敏感性分析. 遥感技术与应用, 29, 719-726.]
[11]	Li YH, Zhou L, Wu J, Zhou WM, Dai LM, Lu ZM, Huang LY ( 2017). Decomposition of mixed leaf litter ofLarix kaempferi plantation in eastern montane region of Liaoning Province. Chinese Journal of Ecology, 36, 3049-3055. DOI URL
	[ 李英花, 周莉, 吴健, 周旺明, 代力民, 卢正茂, 黄利亚 ( 2017). 辽东落叶松人工混交林凋落物混合分解特征. 生态学杂志, 36, 3049-3055.] DOI URL
[12]	Li YZ, Zhang TL, Liu QY, Li Y ( 2018). Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example. Chinese Journal of Applied Ecology, 29, 84-92.
	[ 李一哲, 张廷龙, 刘秋雨, 李英 ( 2018). 生态过程模型敏感参数最优取值的时空异质性分析—以BIOME-BGC模型为例. 应用生态学报, 29, 84-92.]
[13]	Liang XY, Liu SR, Wang H, Wang JX ( 2018). Variation of carbon and nitrogen stoichiometry along a chronosequence of natural temperate forest in northeastern China. Journal of Plant Ecology, 11, 339-350. DOI URL
[14]	Liu ZL, Jin GZ, Zhou M ( 2014). Measuring seasonal dynamics of leaf area index in a mixed conifer-broadleaved forest with direct and indirect methods. Chinese Journal of Plant Ecology, 38, 843-856. DOI URL
	[ 刘志理, 金光泽, 周明 ( 2014). 利用直接法和间接法测定针阔混交林叶面积指数的季节动态. 植物生态学报, 38, 843-856.] DOI URL
[15]	Majkowski J, Ridgeway JM, Miller DR ( 1981). Multiplicative sensitivity analysis and its role in development of simulation models. Ecological Modelling, 12, 191-208. DOI URL
[16]	Mao HR, Chen JL, Jin GZ ( 2016). Effects of nitrogen addition on litter decomposition and nutrient release in typical broadleaf-Korean pine mixed forest. Journal of Beijing Forestry University, 38(3), 21-31. DOI URL
	[ 毛宏蕊, 陈金玲, 金光泽 ( 2016). 氮添加对典型阔叶红松林凋落叶分解及养分释放的影响. 北京林业大学学报, 38(3), 21-31.] DOI URL
[17]	Mei L ( 2006). Fine Root Turnover and Carbon Allocation in Manchurian Ash and Davurian Larch Plantations. PhD dissertation, Northeast Forestry University, Harbin.
	[ 梅莉 ( 2006). 水曲柳落叶松人工林细根周转与碳分配. 博士学位论文, 东北林业大学, 哈尔滨.]
[18]	Miao ZW, Lathrop RG, Xu M, La Puma IP, Clark KL, Hom J, Skowronski N, Tuyl SV ( 2011). Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands. Environmental Modelling and Software, 26, 1112-1122. DOI URL
[19]	Miao ZW, Trevisan M, Capri E, Padovani L, Del Re AAM ( 2004). Uncertainty assessment of the model RICEWQ in northern Italy. Journal of Environmental Quality, 33, 2217-2228. DOI URL PMID
[20]	Qi G, Chen H, Zhou L, Wang XC, Zhou WM, Qi L, Yang YH, Yang FL, Wang QL, Dai LM ( 2016). Carbon stock of larch plantations and its comparison with an old-growth forest in Northeast China. Chinese Geographical Science, 26, 10-21. DOI URL
[21]	Raj R, Hamm NAS, Tol CVD, Stein A ( 2014). Variance-based sensitivity analysis of BIOME-BGC for gross and net primary production. Ecological Modelling, 292, 26-36. DOI URL
[22]	Ren QW, Chen YB, Shu XJ ( 2010). Global sensitivity analysis of Xinanjiang model parameters based on Extend FAST method. Acta Scientiarum Naturalium Universitatis Sunyatseni, 49, 127-134.
	[ 任启伟, 陈洋波, 舒晓娟 ( 2010). 基于Extend FAST方法的新安江模型参数全局敏感性分析. 中山大学学报(自然科学版), 49, 127-134.]
[23]	Running SW, Coughlan JC ( 1988). A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes. Ecological Modelling, 42, 125-154. DOI URL
[24]	Saltelli A ( 2002). Sensitivity analysis for importance assessment. Risk Analysis, 22, 580-590. DOI URL PMID
[25]	Sang WG, Li JW ( 1998). Dynamics modeling of Korean pine forest in southern Lesser Xingan Mountains of China. Acta Ecologica Sinica, 18, 38-47. DOI URL
	[ 桑卫国, 李景文 ( 1998). 小兴安岭南坡红松林动态模拟. 生态学报, 18, 38-47.] DOI URL
[26]	Schmid S, Zierl B, Bugmann H ( 2006). Analyzing the carbon dynamics of central European forests: Comparison of Biome-BGC simulations with measurements. Regional Environmental Change, 6, 167-180. DOI URL
[27]	Sobol IM ( 1993). Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling Computational Experiments, 1, 407-414.
[28]	Su HX, Feng JC, Axmacher JC, Sang WG ( 2015). Asymmetric warming significantly affects net primary production, but not ecosystem carbon balances of forest and grassland ecosystems in northern China. Scientific Reports, 5, 9115-9122. DOI URL PMID
[29]	Tan JW ( 2017). Study on Parameter Sensitivity and Model Uncertainty Analysis of Crop Model. PhD dissertation, Wuhan University, Wuhan.
	[ 谭君位 ( 2017). 作物模型参数敏感性和不确定性分析方法研究. 博士学位论文, 武汉大学, 武汉.]
[30]	Tatarinov FA, Cienciala E ( 2006). Application of BIOME-BGC model to managed forests: 1. Sensitivity analysis. Forest Ecology and Management, 237, 267-279. DOI URL
[31]	Thornton PE, Law BE, Gholz HL, Kenneth LC, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger D, Falk M, Chen J, Sparks JP ( 2002). Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 113, 185-222. DOI URL
[32]	Tian HQ, Chen GS, Liu ML, Zhang C, Sun G, Lu CQ, Xu XF, Ren W, Pan SF, Chappelka A ( 2010). Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895-2007. Forest Ecology and Management, 259, 1311-1327. DOI URL
[33]	Wang L, Peng QY, Geng SB ( 2016). Study on tree layer biomass and productivity in forest in Lushuihe Forest Bureau of Changbai Mountains. Research of Soil and Water Conservation, 23, 277-281.
	[ 王亮, 彭琦云, 耿少波 ( 2016). 长白山露水河林区乔木层生物量及生产力研究. 水土保持研究, 23, 277-281.]
[34]	White MA, Thornton PE, Running SW, Nemani RR ( 2000). Parameterization and sensitivity analysis of the BIOME- BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions, 4, 1-84. DOI URL
[35]	Wu YL, Wang XP, Ouyang S, Xu K, Hawkins BA, Sun Osbert JX ( 2017). A test of BIOME-BGC with dendrochronology for forests along the altitudinal gradient of Mt. Changbai in Northeast China. Journal of Plant Ecology, 10, 415-425. DOI URL
[36]	Xing HM, Xiang SY, Xu XG, Chen YJ, Feng HK, Yang GJ, Chen ZX ( 2017). Global sensitivity analysis of AquaCrop crop model parameters based on EFAST method. Scientia Agricultura Sinica, 50, 64-76. DOI URL
	[ 邢会敏, 相诗尧, 徐新刚, 陈宜金, 冯海宽, 杨贵军, 陈召霞 ( 2017). 基于EFAST方法的AquaCrop作物模型参数全局敏感性分析. 中国农业科学, 50, 64-76.] DOI URL
[37]	Xu CG, Hu YM, Chang Y, Jiang Y, Li XZ, Bu RC, He HS ( 2004). Sensitivity analysis in ecological modeling. Chinese Journal of Applied Ecology, 15, 1056-1062.
	[ 徐崇刚, 胡远满, 常禹, 姜艳, 李秀珍, 布仁仓, 贺红士 ( 2004). 生态模型的灵敏度分析. 应用生态学报, 15, 1056-1062.]
[38]	Yan M ( 2016). Multimodal Simulation and Dynamic Analysis of Forest Carbon Fluxes. PhD dissertation, Chinese Academy of Forestry, Beijing.
	[ 闫敏 ( 2016). 森林生态系统碳通量多模式模拟与动态分析. 博士学位论文, 中国林业科学研究院, 北京.]
[39]	Yan M, Tian X, Li ZY, Chen EX, Wang XF, Han ZT, Sun H ( 2016). Simulation of forest carbon fluxes using model incorporation and data assimilation. Remote Sensing, 8, 567-574. DOI URL
[40]	Yao GQ, Chi GQ, Dong ZQ, Guo DW ( 1986). Studies on productiveness of three typies of artificial stand forest of Korean pine in the mountainous areas of Liaoning Province. Journal of Northeast Forestry University, 14(4), 42-47.
	[ 姚国清, 池桂清, 董兆琪, 郭德武 ( 1986). 人工红松林三种林型生产力的研究. 东北林业大学学报, 14(4), 42-47.]
[41]	Yu DP, Zhou WM, Bao Y, Qi L, Zhou L, Dai LM ( 2015). Forest management of Korean pine and broadleaf mixed forest in Northeast China since the implementation of Natural Forest Protection Project. Acta Ecologica Sinica, 35, 10-17. DOI URL
	[ 于大炮, 周旺明, 包也, 齐麟, 周莉, 代力民 ( 2015). 天保工程实施以来东北阔叶红松林的可持续经营. 生态学报, 35, 10-17.] DOI URL
[42]	Zhang JX, Su W ( 2012). Sensitivity analysis of CERES-Wheat model parameters based on EFAST method. Journal of China Agricultural University, 17, 149-154. DOI URL
	[ 张静潇, 苏伟 ( 2012). 基于EFAST方法的CERES-Wheat作物模型参数敏感性分析. 中国农业大学学报, 17, 149-154.] DOI URL
[43]	Zhang L, Yu GR, Gu FX, He HL, Zhang LM, Han SJ ( 2012). Uncertainty analysis of modeled carbon fluxes for a broad-leaved Korean pine mixed forest using a process- based ecosystem model. Journal of Forest Research, 17, 268-282. DOI URL
[44]	Zhang LM, Wang CK ( 2010). Carbon and nitrogen release during decomposition of coarse woody debris for eleven temperate tree species in the eastern mountain region of Northeast China. Chinese Journal of Plant Ecology, 34, 368-374. DOI URL
	[ 张利敏, 王传宽 ( 2010). 东北东部山区11种温带树种粗木质残体分解与碳氮释放. 植物生态学报, 34, 368-374.] DOI URL
[45]	Zhang ZM, Wang XY, Li MT ( 2014). Uncertainty analysis of WASP based on global sensitivity analysis method. China Environmental Science, 34, 1336-1346. DOI URL
	[ 张质明, 王晓燕, 李明涛 ( 2014). 基于全局敏感性分析方法的WASP模型不确定性分析. 中国环境科学, 34, 1336-1346.] DOI URL
[46]	Zheng L, Song SK, Yuan XL, Dong JQ, Li LH ( 2017). Simulation of water and carbon fluxes in a broad-leaved Korean pine forest in Changbai Mountains based on Biome-BGC model and Ensemble Kalman Filter method. Chinese Journal of Ecology, 36, 1752-1760. DOI URL
	[ 郑磊, 宋世凯, 袁秀亮, 董嘉琪, 李龙辉 ( 2017). 基于Biome-BGC模型和集合卡尔曼滤波方法的阔叶红松林生态系统水碳通量模拟. 生态学杂志, 36, 1752-1760.] DOI URL
[47]	Zhou CH, Hao ZQ, He HS, Zhou DH ( 2008). Sensitivity of parameters in net primary productivity model of broadleaf- Korean pine mixed forest. Chinese Journal of Applied Ecology, 19, 929-935.
	[ 周春华, 郝占庆, 贺红士, 周丹卉 ( 2008). 阔叶红松林净初级生产力模型参数的敏感性. 应用生态学报, 19, 929-935.]
[48]	Zhu J ( 2013). Effects of Nitrogen Addition on Decomposition of Coarse Woody Debris in a Broad Leaved Korean Pine Forest in the Changbai Mountain. PhD dissertation, University of Chinese Academy of Sciences, Beijing.
	[ 朱江 ( 2013). 施N对长白山阔叶红松林粗木质残体分解的影响. 博士学位论文, 中国科学院大学, 北京.]

参数 Parameter	符号 Symbol	红松基准值 Basic value of Korean pine	阔叶树基准值 Basic value of broadleaf species	单位 Unit	来源 Source
转移生长占生长季的比例 Transfer growth period	T_t	0.3	0.2	-	Biome-BGC V4.2
凋落过程占生长季的比例 Litterfall period	LFG	0.3	0.2	-	Biome-BGC V4.2
叶片与细根周转率 Annual leaf and fine root turnover fraction	LFRT	0.32	1.0	a^-1	Liu et al., 2014
活立木周转率 Annual live wood turnover fraction	LWT	0.7	0.7	a^-1	Biome-BGC V4.2
整株植物死亡率 Annual whole-plant mortality fraction	WPM	0.009 0	0.021 3	a^-1	Sang & Li, 1998
火灾死亡率 Annual fire mortality fraction	FM	0	0	a^-1	Set by us
细根与叶片碳分配比 New fine root C: leaf C	FRC:LC	1.2	0.9	-	Yao et al., 1986; Mei, 2006
新茎与新叶碳分配比 New stem C: leaf C	SC:LC	1.4	2.4	-	Li, 1984
活立木与所有木质组织碳分配比 New live wood C: total wood C	LWC:TWC	0.379	0.1	-	Wu et al., 2017
粗根与新茎碳分配比 New coarse root C: stem C	CRC:SC	0.29	0.23	-	White et al., 2000
当前生长比例 Current growth proportion	CGP	0.5	0.5	-	Biome-BGC V4.2
叶片碳氮比 C:N of leaves	C:N_leaf	34.30	17.55	kg·kg^-1	Measured by us
叶片凋落物碳氮比 C:N of falling leaf litter	C:N_litter	96.5	41.1	kg·kg^-1	Li et al., 2008; Mao et al., 2016; Li et al., 2017
细根碳氮比 C:N of ?ne roots	C:N_fr	56.4	47.4	kg·kg^-1	Liang et al., 2018
活立木碳氮比 C:N of live wood	C:N_lw	97.4	97.05	kg·kg^-1	Wu et al., 2017
死立木碳氮比 C:N of dead wood	C:N_dw	398	212	kg·kg^-1	Zhang & Wang, 2010
叶片凋落物易分解物质所占比 Leaf litter labile proportion	L_lab	0.45	0.53	-	Jiang, 2013
叶片凋落物纤维素所占比 Leaf litter cellulose proportion	L_cel	0.25	0.22	-	Jiang, 2013
叶片凋落物木质素所占比 Leaf litter lignin proportion	L_lig	0.30	0.25	-	Jiang, 2013
细根中易分解物质所占比 Fine root labile proportion	FR_lab	0.34	0.30	-	Biome-BGC V4.2
细根中纤维素所占比 Fine root cellulose proportion	FR_cel	0.44	0.45	-	Biome-BGC V4.2
细根中木质素所占比 Fine root lignin proportion	FR_lig	0.22	0.25	-	Biome-BGC V4.2
死立木中纤维素所占比 Dead wood cellulose proportion	DW_cel	0.73	0.76	-	Zhu, 2013
死立木中木质素所占比 Dead wood lignin proportion	DW_lig	0.27	0.24	-	Zhu, 2013
冠层截留系数 Water interception coef?cient	W_int	0.045	0.033	LAI^-1·d^-1	Wu et al., 2017
冠层消光系数 Light extinction coef?cient	k	0.50	0.58	-	Zhou et al., 2008
所有叶面积与投影叶面积之比 Ratio of all sided to projected leaf area	LAI_all:proj	2.6	2.0	-	White et al., 2000
冠层平均比叶面积 Average speci?c leaf area	SLA	16.4	54.2	m²·kg^-1	Measured by us
阴叶与阳叶比叶面积比 Ratio of shade SLA : sunlit SLA	SLA_shd:sun	2	2	-	White et al., 2000
Rubisco酶中叶氮含量 Fraction of leaf N in Rubisco	FLNR	0.080	0.075	-	Su et al., 2015
最大气孔导度 Maximum stomatal conductance	G_smax	0.006 0	0.006 5	m·s^-1	Su et al., 2015
表皮导度 Cuticular conductance	G_cut	0.000 06	0.000 01	m·s^-1	Su et al., 2015
边界层导度 Boundary layer conductance	G_bl	0.09	0.01	m·s^-1	White et al., 2000
气孔开始减小时叶片水势 Leaf water potential : start of g_sreduction	LWP_i	-0.65	-0.34	MPa	White et al., 2000
气孔停止减小时叶片水势 Leaf water potential : completion of g_s reduction	LWP_f	-2.5	-2.2	MPa	White et al., 2000
气孔开始减小时饱和水汽压差 Vapor pressure deficit : start of g_s reduction	VPD_i	610	1 100	Pa	White et al., 2000
气孔停止减小时饱和水汽压差 Vapor pressure deficit : completion of g_s reduction	VPD_f	3 100	3 600	Pa	White et al., 2000

参数 Parameter	符号 Symbol	红松基准值 Basic value of Korean pine	阔叶树基准值 Basic value of broadleaf species	单位 Unit	来源 Source
转移生长占生长季的比例 Transfer growth period	T_t	0.3	0.2	-	Biome-BGC V4.2
凋落过程占生长季的比例 Litterfall period	LFG	0.3	0.2	-	Biome-BGC V4.2
叶片与细根周转率 Annual leaf and fine root turnover fraction	LFRT	0.32	1.0	a^-1	Liu et al., 2014
活立木周转率 Annual live wood turnover fraction	LWT	0.7	0.7	a^-1	Biome-BGC V4.2
整株植物死亡率 Annual whole-plant mortality fraction	WPM	0.009 0	0.021 3	a^-1	Sang & Li, 1998
火灾死亡率 Annual fire mortality fraction	FM	0	0	a^-1	Set by us
细根与叶片碳分配比 New fine root C: leaf C	FRC:LC	1.2	0.9	-	Yao et al., 1986; Mei, 2006
新茎与新叶碳分配比 New stem C: leaf C	SC:LC	1.4	2.4	-	Li, 1984
活立木与所有木质组织碳分配比 New live wood C: total wood C	LWC:TWC	0.379	0.1	-	Wu et al., 2017
粗根与新茎碳分配比 New coarse root C: stem C	CRC:SC	0.29	0.23	-	White et al., 2000
当前生长比例 Current growth proportion	CGP	0.5	0.5	-	Biome-BGC V4.2
叶片碳氮比 C:N of leaves	C:N_leaf	34.30	17.55	kg·kg^-1	Measured by us
叶片凋落物碳氮比 C:N of falling leaf litter	C:N_litter	96.5	41.1	kg·kg^-1	Li et al., 2008; Mao et al., 2016; Li et al., 2017
细根碳氮比 C:N of ?ne roots	C:N_fr	56.4	47.4	kg·kg^-1	Liang et al., 2018
活立木碳氮比 C:N of live wood	C:N_lw	97.4	97.05	kg·kg^-1	Wu et al., 2017
死立木碳氮比 C:N of dead wood	C:N_dw	398	212	kg·kg^-1	Zhang & Wang, 2010
叶片凋落物易分解物质所占比 Leaf litter labile proportion	L_lab	0.45	0.53	-	Jiang, 2013
叶片凋落物纤维素所占比 Leaf litter cellulose proportion	L_cel	0.25	0.22	-	Jiang, 2013
叶片凋落物木质素所占比 Leaf litter lignin proportion	L_lig	0.30	0.25	-	Jiang, 2013
细根中易分解物质所占比 Fine root labile proportion	FR_lab	0.34	0.30	-	Biome-BGC V4.2
细根中纤维素所占比 Fine root cellulose proportion	FR_cel	0.44	0.45	-	Biome-BGC V4.2
细根中木质素所占比 Fine root lignin proportion	FR_lig	0.22	0.25	-	Biome-BGC V4.2
死立木中纤维素所占比 Dead wood cellulose proportion	DW_cel	0.73	0.76	-	Zhu, 2013
死立木中木质素所占比 Dead wood lignin proportion	DW_lig	0.27	0.24	-	Zhu, 2013
冠层截留系数 Water interception coef?cient	W_int	0.045	0.033	LAI^-1·d^-1	Wu et al., 2017
冠层消光系数 Light extinction coef?cient	k	0.50	0.58	-	Zhou et al., 2008
所有叶面积与投影叶面积之比 Ratio of all sided to projected leaf area	LAI_all:proj	2.6	2.0	-	White et al., 2000
冠层平均比叶面积 Average speci?c leaf area	SLA	16.4	54.2	m²·kg^-1	Measured by us
阴叶与阳叶比叶面积比 Ratio of shade SLA : sunlit SLA	SLA_shd:sun	2	2	-	White et al., 2000
Rubisco酶中叶氮含量 Fraction of leaf N in Rubisco	FLNR	0.080	0.075	-	Su et al., 2015
最大气孔导度 Maximum stomatal conductance	G_smax	0.006 0	0.006 5	m·s^-1	Su et al., 2015
表皮导度 Cuticular conductance	G_cut	0.000 06	0.000 01	m·s^-1	Su et al., 2015
边界层导度 Boundary layer conductance	G_bl	0.09	0.01	m·s^-1	White et al., 2000
气孔开始减小时叶片水势 Leaf water potential : start of g_sreduction	LWP_i	-0.65	-0.34	MPa	White et al., 2000
气孔停止减小时叶片水势 Leaf water potential : completion of g_s reduction	LWP_f	-2.5	-2.2	MPa	White et al., 2000
气孔开始减小时饱和水汽压差 Vapor pressure deficit : start of g_s reduction	VPD_i	610	1 100	Pa	White et al., 2000
气孔停止减小时饱和水汽压差 Vapor pressure deficit : completion of g_s reduction	VPD_f	3 100	3 600	Pa	White et al., 2000

参数符号 Parameter symbol	红松取值范围 Value range of Korean pine	阔叶树取值范围 Value range of broadleaved species	单位 Unit
LFRT	[0.256, 0.384]		a^-1
LWT	[0.56, 0.84]	[0.56, 0.84]	a^-1
WPM	[0.0072, 0.0108]	[0.017, 0.0256]	a^-1
FRC:LC	[0.96, 1.44]	[0.72, 1.08]	-
SC:LC	[1.12, 1.68]	[1.92, 2.88]	-
LWC:TWC	[0.303, 0.455]	[0.08, 0.12]	-
CRC:SC	[0.232, 0.348]	[0.184, 0.276]	-
CGP	[0.4, 0.6]	[0.4, 0.6]	-
C:N_leaf	[27.44, 41.16]	[14.04, 21.06]	kg·kg^-1
C:N_litter	[77.2, 115.8]	[32.88, 49.32]	kg·kg^-1
C:N_fr	[45.12, 67.68]	[37.92, 56.88]	kg·kg^-1
C:N_lw	[77.92, 116.88]	[77.64, 116.46]	kg·kg^-1
C:N_dw	[318.4, 477.6]	[169.6, 254.4]	kg·kg^-1
L_cel	[0.2, 0.3]	[0.176, 0.264]	-
L_lig	[0.24, 0.36]	[0.2, 0.3]	-
FR_cel	[0.352, 0.528]	[0.36, 0.54]	-
FR_lig	[0.176, 0.264]	[0.2, 0.3]	-
DW_lig	[0.216, 0.324]	[0.192, 0.288]	-
W_int	[0.036, 0.054]	[0.0264, 0.0396]	LAI^-1·d^-1
k	[0.4, 0.6]	[0.464, 0.696]	-
LAI_all:proj	[2.08, 3.12]	[1.6, 2.4]	-
SLA	[13.12, 19.68]	[43.36, 65.04]	m²·kg^-1
SLA_shd:sun	[1.6, 2.4]	[1.6, 2.4]	-
FLNR	[0.064, 0.096]	[0.06, 0.09]	-
G_smax	[0.0048, 0.0072]	[0.0052, 0.0078]	m·s^-1
G_cut	[0.000048, 0.000072]	[0.000008, 0.000012]	m·s^-1
G_bl	[0.072, 0.108]	[0.008, 0.012]	m·s^-1
LWP_i	[-0.78, -0.52]	[-0.408, -0.272]	MPa
LWP_f	[-3, -2]	[-2.64, -1.76]	MPa
VPD_i	[488, 732]	[880, 1320]	Pa
VPD_f	[2480, 3720]	[2880, 4320]	Pa

参数符号 Parameter symbol	红松取值范围 Value range of Korean pine	阔叶树取值范围 Value range of broadleaved species	单位 Unit
LFRT	[0.256, 0.384]		a^-1
LWT	[0.56, 0.84]	[0.56, 0.84]	a^-1
WPM	[0.0072, 0.0108]	[0.017, 0.0256]	a^-1
FRC:LC	[0.96, 1.44]	[0.72, 1.08]	-
SC:LC	[1.12, 1.68]	[1.92, 2.88]	-
LWC:TWC	[0.303, 0.455]	[0.08, 0.12]	-
CRC:SC	[0.232, 0.348]	[0.184, 0.276]	-
CGP	[0.4, 0.6]	[0.4, 0.6]	-
C:N_leaf	[27.44, 41.16]	[14.04, 21.06]	kg·kg^-1
C:N_litter	[77.2, 115.8]	[32.88, 49.32]	kg·kg^-1
C:N_fr	[45.12, 67.68]	[37.92, 56.88]	kg·kg^-1
C:N_lw	[77.92, 116.88]	[77.64, 116.46]	kg·kg^-1
C:N_dw	[318.4, 477.6]	[169.6, 254.4]	kg·kg^-1
L_cel	[0.2, 0.3]	[0.176, 0.264]	-
L_lig	[0.24, 0.36]	[0.2, 0.3]	-
FR_cel	[0.352, 0.528]	[0.36, 0.54]	-
FR_lig	[0.176, 0.264]	[0.2, 0.3]	-
DW_lig	[0.216, 0.324]	[0.192, 0.288]	-
W_int	[0.036, 0.054]	[0.0264, 0.0396]	LAI^-1·d^-1
k	[0.4, 0.6]	[0.464, 0.696]	-
LAI_all:proj	[2.08, 3.12]	[1.6, 2.4]	-
SLA	[13.12, 19.68]	[43.36, 65.04]	m²·kg^-1
SLA_shd:sun	[1.6, 2.4]	[1.6, 2.4]	-
FLNR	[0.064, 0.096]	[0.06, 0.09]	-
G_smax	[0.0048, 0.0072]	[0.0052, 0.0078]	m·s^-1
G_cut	[0.000048, 0.000072]	[0.000008, 0.000012]	m·s^-1
G_bl	[0.072, 0.108]	[0.008, 0.012]	m·s^-1
LWP_i	[-0.78, -0.52]	[-0.408, -0.272]	MPa
LWP_f	[-3, -2]	[-2.64, -1.76]	MPa
VPD_i	[488, 732]	[880, 1320]	Pa
VPD_f	[2480, 3720]	[2880, 4320]	Pa

	红松 Korean pine		阔叶树 Broadleaved species
	NPP (g C·m^-2·a^-1)	ET (mm·a^-1)	NPP (g C·m^-2·a^-1)	ET (mm·a^-1)
平均值 Mean	498.4	677.6	656.1	678.0
标准差 SD	76.9	0.013	63.5	0.19
变异系数 CV	15.4	0.002	9.7	0.03