亚热带典型森林生产力及碳利用率的气候变化响应

doi:10.11707/j.1001-7488.LYKX20230622

摘要/Abstract

摘要：

目的: 研究不同森林生态系统的植被生产力及碳利用率的气候因子响应，对揭示陆地生态系统碳平衡变化规律具有重要意义，并为亚热带森林生态系统保护和管理提供科学依据。方法: 针对赣江流域常绿针叶林、常绿阔叶林、针阔混交林、竹林4种典型森林，利用参数本地化的Biome-BGC模型模拟了1970—2021年的总初级生产力（GPP）、净初级生产力（NPP），进而揭示了4种典型森林植被生产力和碳利用率在年际和月际尺度上对气候因子的响应。结果: 1）年GPP（g·m^–2a^–1）大小排序为常绿针叶林（2 514.6）>常绿阔叶林（2 467.9）>常绿针阔混交林（2 285.0）>竹林（2 040.1）；年际尺度上，竹林GPP与积温显著正相关（r = 0.41，P<0.01）；月际尺度上，4种典型森林GPP均受积温正向驱动（r > 0.99，P<0.01）。2）年NPP（g·m^–2a^–1）大小排序为常绿阔叶林（862.4）>竹林（739.2）>常绿针阔混交林（721.1）>常绿针叶林（681.3）；年际尺度上，常绿针叶林、常绿阔叶林和常绿针阔混交林的NPP受降水正向驱动（r > 0.32，P<0.05）；月际尺度上，常绿针叶林NPP与降水显著正相关（r = 0.59，P<0.05），常绿阔叶林NPP主要受积温正向驱动（r = 0.93，P<0.01）。3）碳利用率在年际和月际尺度上的大小排序均为竹林>常绿阔叶林>常绿针阔混交林>常绿针叶林。相比于NPP，碳利用率对气候变化的响应更强烈，年际和月际尺度上碳利用率均受积温负向驱动（r > 0.51，P<0.01）。结论: 总体上，积温是亚热带森林生态系统生产力和碳利用率的主要驱动因素；相较于常绿针叶林和常绿针阔混交林，常绿阔叶林和竹林的固碳能力更强，在气候变化背景下有更大的碳汇潜力。

关键词: 植被生产力, 碳利用率, 气候变化, Biome-BGC模型, 赣江流域

Abstract:

Objective: In this study, the responses of vegetation productivity and carbon use efficiency of different forest ecosystems to climate factors were investigated, which is of great significance for revealing the changes of carbon balance in terrestrial ecosystem and providing scientific basis for the protection and management of subtropical forest ecosystems. Method: Four typical forests, namely evergreen coniferous forest, evergreen broadleaf forest, evergreen conifer-broadleaf mixed forest, and bamboo forest, in the Ganjiang River Basin were targeted. The parameters localized Biome BGC model was used to simulate the gross primary productivity (GPP) and net primary productivity (NPP) from 1970 to 2021, revealing the response of productivity and carbon use efficiency of the four typical forest vegetation to climate factors at interannual and inter-monthly scales. Result: 1) The annual GPP (g·m^–2a^–1) was ranked as evergreen coniferous forest (2 514.6) > evergreen broadleaf forest (2 467.9) > evergreen conifer-broadleaf mixed forest (2 285.0) > bamboo forest (2 040.1). At the interannual scale, bamboo forest GPP was positively correlated with accumulated temperature (r = 0.41, P<0.01). At the inter-monthly scale, the GPP of the four typical forests was positively driven by accumulated temperature (r > 0.99, P<0.01). 2) The annual NPP (g·m^–2a^–1) was ranked as evergreen broadleaf forest (862.4) > bamboo forest (739.2) > evergreen conifer-broadleaf mixed forest (721.1) > evergreen coniferous forest (681.3). At the interannual scale, the NPP of evergreen coniferous forest, evergreen broadleaf forest and evergreen conifer-broadleaf mixed forest was positively driven by precipitation (r > 0.32, P<0.05). At the inter-monthly scale, there was a significant positive correlation between the NPP of evergreen coniferous forest and precipitation (r = 0.59, P<0.05). The NPP of evergreen broadleaf forest was mainly driven by accumulated temperature (r = 0.93, P<0.01). 3) The order of carbon use efficiency at the interannual and inter-monthly scales was sorted as bamboo forest > evergreen broadleaf forest > evergreen conifer-broadleaf mixed forest > evergreen coniferous forest. Compared to NPP, the carbon use efficiency had stronger response to climate change. The carbon use efficiency was negatively driven by accumulated temperature at both interannual and inter-monthly scales (r > 0.51, P<0.01). Conclusion: In summary, accumulated temperature is the main factor driving the productivity and carbon use efficiency of subtropical forest ecosystems. Compared to evergreen coniferous forest and evergreen conifer-broadleaf mixed forest, evergreen broadleaf forest and bamboo forest have stronger carbon sequestration capacity and greater carbon sink potential under the background of climate change.

Key words: vegetation productivity, carbon use efficiency, climate change, Biome-BGC model, Ganjiang River Basin

中图分类号:

黄云,徐黎亮,郑博福,宋旭,胡方清,朱锦奇,万炜. 亚热带典型森林生产力及碳利用率的气候变化响应[J]. 林业科学, 2025, 61(3): 121-134.

Yun Huang,Liliang Xu,Bofu Zheng,Xu Song,Fangqing Hu,Jinqi Zhu,Wei Wan. Responses of Productivity and Carbon Use Efficiency of Typical Subtropical Forests to Climate Change[J]. Scientia Silvae Sinicae, 2025, 61(3): 121-134.

图/表 12

图1

表1

不同类型森林相关生态生理参数①"

参数 Parameter	单位 Units	常绿针叶林 Evergreen coniferous forest （ECF）	常绿阔叶林 Evergreen broadleaf forest (EBF)	竹林 Bamboo forest (BF)
叶片和细根年周转率 Annual leaf and fine root turnover fraction	a^?1	0.26	0.5	0.675^b
活立木年周转率 Annual live wood turnover fraction	a^?1	0.7	0.7	0.7
整株植物死亡率 Annual whole-plant mortality fraction	a^?1	0.005	0.005	0.005
植物火烧死亡率 Annual fire mortality fraction	a^?1	0.005	0.002	0.005
细根与叶片碳分配比 (Allocation) fine root C∶leaf C	—	1.4	1.2^a	1
新茎与新叶碳分配比 New stem C∶new leaf C	—	2.2	2.2	1.8
新活木与新总木碳分配比 New live wood C∶new total wood C	—	0.071	0.16	0.1
新根与新茎的碳分配比 New root C∶new stem C	—	0.29	0.3	0.42
当前生长比例 Current growth proportion	—	0.5	0.5	0.5
叶片碳氮比 C∶N of leaves	—	44.86*	37.21*	24.30*
凋落物碳氮比 C∶N of leaf litter, after retranslocation	—	93	49	93
细根碳氮比 C∶N of fine roots	—	71.03*	59.90*	52.81*
活木质组织碳氮比 C∶N of live wood	—	71.03*	59.90*	52.81*
死木质组织碳氮比 C∶N of dead wood	—	702^*	480^*	729
凋落物中易分解物质比例 Labile proportion in leaf litter	—	0.31	0.32	0.32
凋落物中纤维素比例 Cellulose proportion in leaf litter	—	0.45	0.44	0.44
凋落物中木质素比例 Lignin proportion in leaf litter	—	0.24	0.24	0.24
细根中易分解物质比例 Labile proportion in fine root	—	0.34	0.3	0.3
细根中纤维素比例 Cellulose proportion in fine root	—	0.44	0.45	0.45
细根中木质素比例 Lignin proportion in fine root	—	0.22	0.25	0.25
死木质组织中纤维素比例 Cellulose proportion in dead wood	—	0.71	0.76	0.76
死木质组织中木质素比例 Lignin proportion in dead wood	—	0.29	0.24	0.24
冠层截留系数 Canopy water interception coefficient	LAI^?1d^?1	0.045	0.01^a	0.041
冠层消光系数 Canopy light extinction coefficient	—	0.51	0.7	0.269 8^c
叶面积与投影叶面积指数比 Leaf area to projected leaf area index ratio	—	2.6	2	2
冠层比叶面积 Canopy specific leaf area (projected area basis)	m²·kg^?1	5.20*	9.34*	17.18*
阴生叶和阳生叶的比叶面积比例 Ratio of shaded SLA∶sunlit SLA	—	2	2	2
Rubisco酶中叶氮含量 Leaf N content in Rubisco enzyme	—	0.08	0.06	0.06
最大气孔导度 Maximum stomatal conductance (projected area basis)	m·s^?1	0.006	0.005	0.006^d
表皮层导度 Cuticular conductance (projected area basis)	m·s^?1	0.000 06	0.000 01	0.000 01
边界层导度 Boundary layer conductance (projected area basis)	m·s^?1	0.09	0.01	0.01
气孔开始缩小时的叶片水势 Leaf water potential when stomata begin to close	MPa	–0.65	–0.6	–0.6
气孔完全闭合时的叶片水势 Leaf water potential when stomata are completely closed	MPa	–2.5	–3.9	–3.9
气孔开始缩小时的饱和水汽压差 VPD when stomata begin to close	Pa	610	1 800	930
气孔完全闭合时饱和水汽压差 VPD when stomata are completely closed	Pa	3 100	4 100	4 100

表1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表2

参考文献 0

	陈雪娇, 周　伟, 杨　晗. 2001—2017年三江源区典型草地群落碳源/汇模拟及动态变化分析. 干旱区地理, 2020, 43 (6): 1583- 1592.
	Chen X J, Zhou W, Yang H. Simulation and dynamic change of carbon source/sink in the typical grassland communities in the Three River Source area from 2001 to 2017. Arid Land Geography, 2020, 43 (6): 1583- 1592.
	陈雅敏, 张韦倩, 杨天翔, 等. 中国不同植被类型净初级生产力变化特征. 复旦学报(自然科学版), 2012, 51 (3): 377- 381.
	Chen Y M, Zhang W Q, Yang T X, et al. The Change characteristics of net primary production in different vegetation types in China. Journal of Fudan University (Natural Science), 2012, 51 (3): 377- 381.
	崔林丽, 杜华强, 史　军, 等. 中国东南部植被NPP的时空格局变化及其与气候的关系研究. 地理科学, 2016, 36 (5): 787- 793.
	Cui L L, Du H Q, Shi J, et al. Spatial and temporal pattern of vegetation NPP and its relationship with climate in the southeastern China. Scientia Geographica Sinica, 2016, 36 (5): 787- 793.
	戴尔阜, 李双元, 吴　卓, 等. 中国南方红壤丘陵区植被净初级生产力空间分布及其与气候因子的关系——以江西省泰和县为例. 地理研究, 2015, 34 (7): 1222- 1234.
	Dai E F, Li S Y, Wu Z, et al. Spatial pattern of net primary productivity and its relationship with climatic factors in Hilly Red Soil Region of southern China: a case study in Taihe County, Jiangxi Province. Geographical Research, 2015, 34 (7): 1222- 1234.
	封晓辉, 程瑞梅, 肖文发, 等. 基于LPJ-GUESS模型的鸡公山马尾松林生产力和碳动态. 林业科学, 2013, 49 (4): 7- 15. doi: 10.11707/j.1001-7488.20130402
	Feng X H, Cheng R M, Xiao W F, et al. Productivity and carbon dynamic of the Masson pine stands in Jigongshan region based on LPJ-GUESS model. Scientia Silvae Sinicae, 2013, 49 (4): 7- 15. doi: 10.11707/j.1001-7488.20130402
	郭丰生, 金佳鑫, 雍　斌, 等. 典型亚热带森林生态系统WUE对物候变化的响应——以浙江省森林生态系统为例. 中国科学: 地球科学, 2020, 50 (2): 305- 317.
	Guo F S, Jin J X, Yong B, et al. Responses of water use efficiency to phenology in typical subtropical forest ecosystems—a case study in Zhejiang Province. Scientia Sinica (Terrae), 2020, 50 (2): 305- 317.
	黄启民, 杨迪蝶, 高爱新. 毛竹光合作用的研究. 林业科学, 1989, 25 (4): 366- 369.
	Huang Q M, Yang D D, Gao A X. A study on photosynthesis of bamboo. Scientia Silvae Sinicae, 1989, 25 (4): 366- 369.
	黄启民, 杨迪蝶, 沈允钢, 等. 毛竹林的初级生产力研究. 林业科学研究, 1993, 6 (5): 536- 540.
	Huang Q M, Yang D D, Shen Y G. Studies on the primary productivity of bamboo (Phyllostachys pubescens) grove. Forest Research, 1993, 6 (5): 536- 540.
	黄晓云, 林德根, 王静爱, 等. 气候变化背景下中国南方喀斯特地区NPP时空变化. 林业科学, 2013, 49 (5): 10- 16. doi: 10.11707/j.1001-7488.20130502
	Huang X Y, Lin D G, Wang J A, et al. Temporal and spatial NPP variation in the Karst region in south China under the background of climate change. Scientia Silvae Sinicae, 2013, 49 (5): 10- 16. doi: 10.11707/j.1001-7488.20130502
	李春波, 张　园, 刘雅各, 等. 长白山自然保护区总初级生产力时空变化特征及其影响因素. 应用生态学报, 2023, 34 (5): 1341- 1348.
	Li C B, Zhang Y, Liu Y G, et al. Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China. Chinese Journal of Applied Ecology, 2023, 34 (5): 1341- 1348.
	李　慧. 2008. 福建省森林生态系统NPP和NEP时空模拟研究. 福州: 福建师范大学.
	Li H. 2008. On the spation-temporal simulation of forest ecosystem net primary productivity and net ecosystem productivity in Fujian Province. Fuzhou: Fujian Normal University. ［in Chinese］
	李海奎, 雷渊才. 2010. 中国森林植被生物量和碳储量评估. 北京: 中国林业出版社.
	Li H K, Lei Y C. 2010. Estimation and evaluation of forest biomass carbon storage in China. Beijing: China Forestry Publishing House. ［in Chinese］
	李双元. 2014. 基于CASA模型的赣江流域植被净初级生产力估算研究. 兰州: 兰州交通大学.
	Li S Y. 2014. Estimation of the vegetation net primary productivity based on CASA model in Gan River Basin. Lanzhou: Lanzhou Jiaotong University. ［in Chinese］
	刘洋洋, 王　倩, 杨　悦, 等. 2000—2013年中国植被碳利用效率(CUE)时空变化及其与气象因素的关系. 水土保持研究, 2019, 26 (5): 278- 286.
	Liu Y Y, Wang Q, Yang Y, et al. Spatiotemporal dynamic of vegetation carbon use efficiency and its relationship with climate factors in China during the period 2000–2013. Research of Soil and Water Conservation, 2019, 26 (5): 278- 286.
	吕振刚, 李文博, 黄选瑞, 等. 气候变化情景下基于潜在NPP的河北省华北落叶松生长适宜性. 林业科学, 2019, 55 (11): 37- 44. doi: 10.11707/j.1001-7488.20191105
	Lü Z G, Li W B, Huang X R, et al. Larix principis-rupprechtii growth suitability based on potential NPP under climate change scenarios in Hebei Province. Scientia Silvae Sinicae, 2019, 55 (11): 37- 44. doi: 10.11707/j.1001-7488.20191105
	马泽清, 刘琪璟, 王辉民, 等. 中亚热带人工湿地松林(Pinus elliottii)生产力观测与模拟. 中国科学: 地球科学, 2008, 38 (8): 1005- 1015.
	Ma Z Q, Liu Q J, Wang H M, et al. Observation and simulation of productivity of Pinus elliottii plantation in central subtropical region. Scientia Sinica (Terrae), 2008, 38 (8): 1005- 1015.
	申贵仓, 张旭东, 张　雷, 等. 蜀南苦竹林生态系统碳储量与碳汇能力估测. 林业科学, 2013, 49 (3): 78- 84. doi: 10.11707/j.1001-7488.20130311
	Shen G C, Zhang X D, Zhang L, et al. Estimating the carbon stock and carbon sequestration of the Pleioblastus amarus forest ecosystem in southern of Sichuan. Scientia Silvae Sinicae, 2013, 49 (3): 78- 84. doi: 10.11707/j.1001-7488.20130311
	王小红, 刘宪锋, 孙高鹏, 等. 2001—2020年秦巴山区植被生产力对干旱的响应. 应用生态学报, 2022, 33 (8): 2105- 2112.
	Wang X H, Liu X F, Sun G P, et al. Response of vegetation productivity to drought in the Qinling-Daba Mountains, China from 2001 to 2020. Chinese Journal of Applied Ecology, 2022, 33 (8): 2105- 2112.
	杨元合, 石　岳, 孙文娟, 等. 中国及全球陆地生态系统碳源汇特征及其对碳中和的贡献. 中国科学: 生命科学, 2022, 52 (4): 534- 574.
	Yang Y H, Shi Y, Sun W J, et al. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality. Scientia Sinica (Vitae), 2022, 52 (4): 534- 574.
	叶许春, 杨晓霞, 刘福红, 等. 长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子. 生态学报, 2021, 41 (17): 6949- 6959.
	Ye X C, Yang X X, Liu F H, et al. Spatio-temporal variations of land vegetation gross primary production in the Yangtze River Basin and correlation with meteorological factors. Acta Ecologica Sinica, 2021, 41 (17): 6949- 6959.
	曾慧卿, 刘琪璟, 冯宗炜, 等. 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, 2008, 28 (11): 5314- 5321. doi: 10.3321/j.issn:1000-0933.2008.11.013
	Zeng H Q, Liu Q J, Feng Z W, et al. GPP and NPP study of Pinus elliottii forest in red soil hilly region based on BIOME-BGC model. Acta Ecologica Sinica, 2008, 28 (11): 5314- 5321. doi: 10.3321/j.issn:1000-0933.2008.11.013
	祝景彬, 贺慧丹, 李红琴, 等. 祁连山南麓高寒灌丛GPP变化特征及对生长季积温的响应. 草业科学, 2021, 38 (2): 221- 230. doi: 10.11829/j.issn.1001-0629.2020-0330
	Zhu J B, He H D, Li H Q, et al. Effect of growing season degree days on gross primary productivity and its variation characteristics in alpine shrubland at the southern foot of Qilian mountains. Pratacultural Science, 2021, 38 (2): 221- 230. doi: 10.11829/j.issn.1001-0629.2020-0330
	Cao M K, Woodward F I. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature, 1998, 393 (5): 249- 252.
	Chen S L, Jiang H, Cai Z J, et al. The response of the net primary production of Moso bamboo forest to the On and Off-year management: a case study in Anji County, Zhejiang, China. Forest Ecology and Management, 2018, 409, 1- 7. doi: 10.1016/j.foreco.2017.11.008
	Chen S L, Jiang H, Jin J X, et al. Changes in net primary production in the Tianmu Mountain Nature Reserve, China, from 1984 to 2014. International Journal of Remote Sensing, 2017, 38 (1): 211- 234.
	Chen Y R, Xiao W F. Estimation of forest NPP and carbon sequestration in the Three Gorges Reservoir area, using the biome-BGC model. Forests, 2019, 10 (2): 149.
	Collalti A, Prentice I C. Is NPP proportional to GPP? Waring’s hypothesis 20 years on. Tree Physiology, 2019, 39 (8): 1473- 1483. doi: 10.1093/treephys/tpz034
	Deng Y, Wang X H, Wang K, et al. Responses of vegetation greenness and carbon cycle to extreme droughts in China. Agricultural and Forest Meteorology, 2021, 298/299, 108307. doi: 10.1016/j.agrformet.2020.108307
	Feng X, Liu G, Chen J M, et al. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. Journal of Environmental Management, 2007, 85 (3): 563- 573. doi: 10.1016/j.jenvman.2006.09.021
	Gong X Y, Schäufele R, Lehmeier C A, et al. 2017. Atmospheric CO₂ mole fraction affects stand-scale carbon use efficiency of sunflower by stimulating respiration in light. Plant, Cell & Environment, 40(3): 401−412.
	Ichii K, Suzuki T, Kato T, et al. Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations. Biogeosciences, 2010, 7 (7): 2061- 2080. doi: 10.5194/bg-7-2061-2010
	IPCC. 2021. Climate Change 2021: the physical science basis. contribution of working group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
	Ji Y H, Zhou G S, Luo T X, et al. Variation of net primary productivity and its drivers in China’s forests during 2000–2018. Forest Ecosystems, 2020, 7 (1): 1- 11. doi: 10.1186/s40663-019-0212-0
	Kang F F, Li X J, Du H Q, et al. Spatiotemporal evolution of the carbon fluxes from bamboo forests and their response to climate change based on a BEPS Model in China. Remote Sensing, 2022, 14 (2): 366. doi: 10.3390/rs14020366
	Kang S, Kimball J S, Running S W. Simulating effects of fire disturbance and climate change on boreal forest productivity and evapotranspiration. Science of the Total Environment, 2006, 362 (1/2/3): 85- 102.
	Kelliher F M, Leuning R, Raupach M R, et al. Maximum conductances for evaporation from global vegetation types. Agricultural and Forest Meteorology, 1995, 73 (1/2): 1- 16.
	Li T, Li M Y, Ren F, et al. Estimation and spatio-temporal change analysis of NPP in subtropical forests: a case study of Shaoguan, Guangdong, China. Remote Sensing, 2022, 14 (11): 2541. doi: 10.3390/rs14112541
	Liu J, Chen J M, Cihlar J, et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 1997, 62 (2): 158- 175. doi: 10.1016/S0034-4257(97)00089-8
	Mao F J, Li P H, Zhou G M, et al. Development of the BIOME-BGC model for the simulation of managed moso bamboo forest ecosystems. Journal of Environmental Management, 2016, 172, 29- 39.
	Parton W J, Stewart J W B, Cole C V. Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry, 1988, 5 (1): 109- 131.
	Running S W, Hunt Jr E R. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BCG, and an application for global-scale models//Ehleringer J R, Field C B. eds. Scaling physiological processes: leaf to globe. New York: Academic Press, 141-158.
	Song X, Zheng B F, Hu F Q, et al. Divergent responses of NPP to climate factors among forest types at interannual and inter-monthly scales: an empirical study on four typical forest types in subtropical China. Forests, 2023, 14 (7): 1474. doi: 10.3390/f14071474
	Thornton P E, Law B E, Gholz H L, et al. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 2002, 113 (1/4): 185- 222.
	Thornton P E, Running S W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 1999, 93 (4): 211- 228. doi: 10.1016/S0168-1923(98)00126-9
	White M A, Thornton P E, Running S W, et al. Parameterization and sensitivity analysis of the BIOME–BGC terrestrial ecosystem model: net primary production controls. Earth Interactions, 2000, 4 (3): 1- 85. doi: 10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2
	Xie M L, Zhu Y, Liu S G, et al. Simulating the impacts of drought and warming in summer and autumn on the productivity of subtropical coniferous forests. Forests, 2022, 13 (12): 2147. doi: 10.3390/f13122147
	Yan M, Tian X, Li Z Y, et al. A long-term simulation of forest carbon fluxes over the Qilian Mountains. International Journal of Applied Earth Observation and Geoinformation, 2016, 52 (2): 515- 526.
	Yu G R, Chen Z, Piao S L, et al. High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proceedings of the National Academy of Sciences, 2014, 111 (13): 4910- 4915. doi: 10.1073/pnas.1317065111
	Zhang J, Chu Z Y, Ge Y, et al. TRIPLEX model testing and application for predicting forest growth and biomass production in the subtropical forest zone of China’s Zhejiang Province. Ecological Modelling, 2008, 219 (3/4): 264- 275.
	Zhang L, Ren X L, Wang J B, et al. Interannual variability of terrestrial net ecosystem productivity over China: regional contributions and climate attribution. Environmental Research Letters, 2019, 14 (1): 014003. doi: 10.1088/1748-9326/aaec95
	Zhang Y J, Xu M, Chen H, et al. Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate. Global Ecology and Biogeography, 2009, 18 (3): 280- 290.
	Zhang Y J, Yu G R, Yang J, et al. Climate-driven global changes in carbon use efficiency. Global Ecology and Biogeography, 2014, 23 (2): 144- 155. doi: 10.1111/geb.12086
	Zhao F, Yang P, Li R Q, et al. Spatio-temporal assessing of natural vegetation regulation on SO₂ absorption coupling ecosystem process model and OMI satellite data. Environmental Research Letters, 2022, 17 (3): 034044.
	Zhao M S, Running S W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 2010, 329 (5994): 940- 943. doi: 10.1126/science.1192666

森林类型 Forest type	研究区域 Study area	时间 Period	模型 Model	NPP/(g·m^?2a^–1)	参考文献 Reference
常绿针叶林 Evergreen coniferous forest （ECF）	江西千烟洲 Qianyanzhou, Jiangxi	1985—2005	Biome-BGC	343.3~906.4	马泽清等, 2008
	江西千烟洲 Qianyanzhou, Jiangxi	1993—2004	Biome-BGC	453~828	曾慧卿等, 2008
	赣江流域 Ganjiang River Basin	1998—2012	CASA	657	李双元等, 2014
	三峡库区 Three Gorges Reservoir area	1981—2014	Biome-BGC	262.9~807.7	Chen et al., 2019
	赣江流域 Ganjiang River Basin	1970—2021	Biome-BGC	357.2~916.9	本研究This study
常绿阔叶林 Evergreen broadleaf forest （EBF）	江西省 Jiangxi Province	2001	BEPS	620.1~1 273.4	Feng et al., 2007
	江西泰和县 Taihe County, Jiangxi	1998—2012	CASA	985	戴尔阜等, 2015
	浙江天目山 Tianmu Mountains, Zhejiang	1984—2014	CASA	739	Chen et al., 2017
	赣江流域 Ganjiang River Basin	1970—2021	Biome-BGC	662.9~1 036.1	本研究This study
针阔混交林 Evergreen conifer-broadleaf mixed forest （MIX）	浙江省 Zhejiang Province	1999	TRIPLEX	784.5	Zhang et al., 2008
	中国 China	1993—2011	—	330~920	陈雅敏等, 2012
	中国东南部 Southeast China	2001—2010	—	656.6	崔林丽等, 2016
	赣江流域 Ganjiang River Basin	1970—2021	Biome-BGC	596.8~871.2	本研究This study
竹林 Bamboo forest （BF）	福建省 Fujian Province	2005	Biome-BGC	739.6	李慧等, 2008
	浙江天目山 Tianmu Mountains, Zhejiang	1984—2014	CASA	740	Chen et al., 2017
	浙江安吉 Anji, Zhejiang	2011—2014	TRIPLEX	747~911	Chen et al., 2018
	赣江流域 Ganjiang River Basin	1970—2021	Biome-BGC	579.3~849.1	本研究This study

[1]	崔妞妞,庞建壮,张祎帆,许行,张勤,张志强. 海河典型流域气候变化和植被恢复对水文的影响——以张家口清水河流域为例[J]. 林业科学, 2025, 61(3): 38-49.
[2]	张雨田,石军南,张怀清,吴炳伦. 洞庭湖湿地植被时空动态及其驱动力分析[J]. 林业科学, 2024, 60(8): 1-13.
[3]	朱教君,王高峰,张怀清,高添. 关于“气候智慧林业”研究的思考[J]. 林业科学, 2024, 60(7): 1-7.
[4]	管崇帆,高翔,李志鹏,胡晓创,胡美均,张劲松,孟平,蔡金峰,孙守家. 辽西地区樟子松人工林生产力和水分利用效率对气候变化的响应及预测[J]. 林业科学, 2024, 60(7): 28-39.
[5]	李琪,孙睿,柏佳,张静宇,张赫林. 聚集指数和最大羧化速率对基于遥感产品的植被生产力估算的影响[J]. 林业科学, 2024, 60(6): 25-36.
[6]	赵杼祺, 胡振宏, 何鲜, 黄志群. 森林木质残体微生物群落构建机制研究进展[J]. 林业科学, 2024, 60(2): 106-117.
[7]	申佳艳,范泽鑫,张慧,彭新华,李金花,余潇,杨文雄,李云芳,李新宇,刘悦宁,苏建荣. 云南3种松树径向生长的气候因子响应异质性[J]. 林业科学, 2024, 60(11): 48-62.
[8]	葛婉婷,刘莹,赵智佳,张珅,李洁,杨桂娟,曲冠证,王军辉,麻文俊. 不同气候情景下黄心梓木在我国的潜在适生区预测[J]. 林业科学, 2024, 60(11): 63-74.
[9]	刘磊,赵立娟,刘佳奇,张辉盛,张志伟,黄瑞芬,高瑞贺. 基于优化的MaxEnt模型预测气候变化下松褐天牛在我国的潜在适生区[J]. 林业科学, 2024, 60(11): 139-148.
[10]	薛盼盼,缪宁,岳喜明,陶琼,张远东,冯秋红,毛康珊. 青藏高原东缘岷江冷杉径向生长对升温响应分异的坡向和海拔差异[J]. 林业科学, 2023, 59(7): 65-77.
[11]	韦雪蕾,张国钢,贾茹,姬云瑞,徐红英,杨泽玉,刘化金,刘宇霖,杨培宇. 黑龙江兴凯湖水鸟多样性变化及其影响因素[J]. 林业科学, 2023, 59(6): 118-129.
[12]	王亚,王军辉,王福德,刘逸夫,谭灿灿,袁艳超,聂稳,刘建锋,常二梅,贾子瑞. 末次间冰期以来及未来气候情景下红皮云杉适生分布区模拟[J]. 林业科学, 2023, 59(12): 1-12.
[13]	张淑宁,陈俊兴,敖敦,红梅,张雅茜,包福海,王淋,乌云塔娜,白玉娥,包文泉. 气候变化背景下我国长柄扁桃潜在适生区预测[J]. 林业科学, 2023, 59(12): 25-36.
[14]	司莉青,王明玉,陈锋,舒立福,赵凤君,李伟克. 雷电分布特征与雷击森林火预警[J]. 林业科学, 2023, 59(10): 1-8.
[15]	贾畅,王丽娜,唐亚坤. 利用Biome-BGC模型模拟黄土区沙棘人工林碳通量时的生理生态参数敏感性[J]. 林业科学, 2022, 58(11): 49-60.