Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province

doi:10.12302/j.issn.1000-2006.202211005

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (5): 11-20.doi: 10.12302/j.issn.1000-2006.202211005

Special Issue: 专题报道：自然保护地森林生态系统研究

Previous Articles Next Articles

Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province

HUANG Luyao¹(), DU Shanfeng¹, JI Xiaofang², GUAN Xin¹, LIU Shenglong³, YE Limin⁴, JIANG Jiang¹^,^*()

1. Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key Laboratory of Soil and Water Conservation and Ecological Restoration,College of Forestry and Grassland, College of Soil and Water Conservation, Nanjing Forestry University, Nanjing 210037, China
2. Institute of Agricultural Resources and Regional Planing, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. Longquan Conservation Center of Qianjiangyuan-Baishanzu National Park, Longquan 323700, China
4. Jingning She Autonomous County Ecological Forestry Development Center, Jingning 323500, China

Received:2022-11-04 Revised:2023-03-28 Online:2024-09-30 Published:2024-10-03
Contact: JIANG Jiang E-mail:hly123066@163.com;jiangjiang@njfu.edu.cn

Abstract

Abstract:

【Objective】This study aims to investigate the carbon dynamics of subtropical mixed coniferous and broadleaved forests in Fengyang Mountain, Zhejiang Province and their response to climate change. 【Method】The Biome-BGC model was used to simulate the net primary productivity (NPP), gross primary productivity (GPP), and net ecosystem productivity (NEP) in Fengyang Mountain from 1979 to 2018, to investigate the relationships between climate factors and NPP at different time scales. Pearson correlation analysis and quadratic function fitting were performed between climate factors and NPP at different temporal scales to explore the relationship and response patterns between NPP and major climate factors, and finally, different climate scenarios were applied to predict the carbon cycling trends in Fengyang Mountain in the next 100 years. 【Result】The average values of GPP, NPP and NEP of mixed coniferous and broadleaved forests in Fengyang Mountain for 40 years were 1 392.94, 451.25 and 16.21 g/(m²·a), respectively. Except for 1984, 2002, 2005, 2008 and 2010, which were carbon sinks and showed that the sensitivity of NPP to temperature change was the highest, and the increase of temperature in summer had a positive effect on the increase of NPP, while the increase of temperature in winter had a negative effect on NPP. To a certain extent, winter rainfall showed a positive effect on NPP, while summer precipitation showed a negative effect on NPP. The gross primary productivity of Fengyang Mountain forests in RCP2.6, RCP4.5 and RCP6.0 scenarios will keep increasing in the 21st century, and by 2100, the GPP of the studied forests in Fengyang Mountain under RCP2.6, RCP4.5 and RCP6.0 scenarios will reach 1 552.73, 1 660.30 and 1 960.41 g/(m²·a), respectively, and get increased 1.38%, 8.41% and 28.00% relative to the GPP in 2018. 【Conclusion】Overall, the forest ecosystem of Fengyang Mountain exhibited carbon sinks under normal conditions, but the cloudy and rainy summer weather in the mountainous area inhibited the increasing effect of temperature on carbon sinks to some extent. The future warming, increased rainfall and higher CO₂ concentration simultaneously will favor the vegetation growth of mixed coniferous forests in Fengyang Mountain.

Key words: mixed coniferous and broadleaved forest, Biome-BGC model, carbon dynamics, climate change, Fengyang Mountain of Zhejiang Province

CLC Number:

S718.5

HUANG Luyao, DU Shanfeng, JI Xiaofang, GUAN Xin, LIU Shenglong, YE Limin, JIANG Jiang. Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(5): 11-20.

Figures/Tables 9

Table 1

Physiological parameter of Biome-BGC model"

生理参数parameter	优化数值 reference value
叶与细根周转比/a^-1 annual leaf and fine root turnover fraction	0.502 4
年活立木周转率/a^-1 annual live wood turnover fraction	0.720 9
年整株植物死亡率/a^-1 annual whole-plant mortality fraction	0.005 0
年植物火灾死亡率/a^-1 annual fire mortality fraction	0.002 9
细根与叶碳分配比 ratio of new fine root C to new leaf C	0.909 8
茎与叶碳分配比 ratio of new stem C to new leaf C	0.787 8
新生木质组织与总木质组织碳分配比 ratio of new live wood C to total wood C	0.191 8
粗根与茎干碳分配比 coarse root C to new stem C ratio		0.329 2
当前生长部分比例 rate of current growth		0.411 4
叶片碳氮比 leaf C/N		42.0
落叶碳氮比 litter leaf C/N		49.479
细根碳氮比 fine root C/N		41.981
活木质组织碳氮比 live wood C/N		50.086
死木质组织碳氮比 dead wood C/N		295.56
凋落物不稳定比例 leaf litter labile proportion		0.32
凋落物纤维素比例 leaf litter cellulose proportion		0.44
凋落物木质素比例leaf litter lignin proportion		0.24
细根不稳定比例fine root labile proportion		0.30
细根纤维素比例fine root cellulose proportion		0.45
细根木质素比例fine root lignin proportion		0.25
死木纤维比例dead wood cellulose proportion		0.76
死木木质素比例dead wood lignin proportion		0.24
冠层截留系数 intercept coefficient of canopy		0.041
冠层消光系数 extinction coefficient of canopy		0.712
比叶面积/(m²·kg^-1) specific leaf area		11.98
阴阳叶比叶面积比ratio of shaded SLA to sunlit SLA		2.08
Rubisco酶中叶氮质量分数 fraction of leaf N in Rubisco		0.06
最大气孔导度/(m·s^-1) maximum stomatal conductance		0.005
表层电导/(m·s^-1) cuticular conductance		0.000 01
边界层导度/(m·s^-1) boundary layer conductance		0.01
叶水势传导上限/MPa leaf water potential to start of conductance reduction ratio		-0.6
叶水势传导下限/MPa leaf water potential to complete conductance reduction ratio		-3.9
水汽压差限制传导上限/Pa vapor pressure deficit to start of conductance reduction ratio		1 800.0
水汽压差限制传导下限/Pa vapor pressure deficit to complete conductance reduction ratio		4 100.0

Table 1

Table 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 37

[1]	沈永平, 王国亚. IPCC第一工作组第五次评估报告对全球气候变化认知的最新科学要点[J]. 冰川冻土, 2013, 35(5):1068-1076.
	SHEN Y P, WANG G Y. Key findings and assessment results of IPCC WGI fifth assessment report[J]. J Glaciol Geocryol, 2013, 35(5):1068-1076.DOI: 10.7522/j.issn.1000-0240.2013.0120.
[2]	CANADELL J G, MOONEY H A, BALDOCCHI D D, et al. Commentary:carbon metabolism of the terrestrial biosphere:a multitechnique approach for improved understanding[J]. Ecosystems, 2000, 3(2):115-130.DOI: 10.1007/s100210000014.
[3]	李媛. 陆地植被净初级生产力估算及影响因素研究现状[J]. 宁夏大学学报(自然科学版), 2018, 39(4):362-366.
	LI Y. Research status of net primary productivity estimation of terrestrial vegetation and its influencing factors[J]. J Ningxia Univ (Nat Sci Ed), 2018, 39(4):362-366.DOI: 10.3969/j.issn.0253-2328.2018.04.014.
[4]	刘国华, 傅伯杰, 方精云. 中国森林碳动态及其对全球碳平衡的贡献[J]. 生态学报, 2000, 20(5):733-740.
	LIU G H, FU B J, FANG J Y. Carbon dynamics of Chinese forests and its contribution to global carbon balance[J]. Acta Ecol Sin, 2000, 20(5):733-740.DOI: 10.3321/j.issn:1000-0933.2000.05.004.
[5]	韩其飞, 罗格平, 李超凡, 等. 基于Biome-BGC模型的天山北坡森林生态系统碳动态模拟[J]. 干旱区研究, 2014, 31(3):375-382.
	HAN Q F, LUO G P, LI C F, et al. Simulation of carbon trend in forest ecosystem in northern slope of the Tianshan Mountains based on Biome-BGC model[J]. Arid Zone Res, 2014, 31(3):375-382.DOI: 10.13866/j.azr.2014.03.025.
[6]	FIELD C B, BEHRENFELD M J, RANDERSON J T, et al. Primary production of the biosphere:integrating terrestrial and oceanic components[J]. Science, 1998, 281(5374):237-240.DOI: 10.1126/science.281.5374.237.
[7]	黄国贤. 基于CBM-CFS3模型的江西省森林生态系统碳动态模拟[D]. 南昌: 江西农业大学, 2016.
	HUANG G X. Carbon dynamics of forest ecosystems in Jiangxi:CBM-CFS3 model simulation[D]. Nanchang: Jiangxi Agricultural University, 2016.
[8]	温永斌, 韩海荣, 程小琴, 等. 基于Biome-BGC模型的千烟洲森林水分利用效率研究[J]. 北京林业大学学报, 2019, 41(4):69-77.
	WEN Y B, HAN H R, CHENG X Q, et al. Forest water use efficiency in Qianyanzhou based on Biome-BGC model,Jiangxi Province of eastern China[J]. J Beijing For Univ, 2019, 41(4):69-77.DOI: 10.13332/j.1000-1522.20190001.
[9]	曾攀儒, 张福平, 冯起, 等. 祁连山地区不同植被生态系统固碳价值量估算及时空演变分析[J]. 冰川冻土, 2019, 41(6):1348-1358.
	ZENG P R, ZHANG F P, FENG Q, et al. Estimation of the carbon sequestration value and spatial and temporal evolution of different vegetation ecosystems in Qilian Mountains[J]. J Glaciol Geocryol, 2019, 41(6):1348-1358.DOI: 10.7522/j.issn.1000-0240.2019.0047.
[10]	李传华, 韩海燕, 范也平, 等. 基于Biome-BGC模型的青藏高原五道梁地区NPP变化及情景模拟[J]. 地理科学, 2019, 39(8):1330-1339.
	LI C H, HAN H Y, FAN Y P, et al. NPP change and scenario simulation in Wudaoliang area of the Tibetan Plateau based on Biome-BGC model[J]. Sci Geogr Sin, 2019, 39(8):1330-1339.DOI: 10.13249/j.cnki.sgs.2019.08.015.
[11]	李旭华, 于大炮, 代力民, 等. 长白山阔叶红松林生产力随林分发育的变化[J]. 应用生态学报, 2020, 31(3):706-716.
	LI X H, YU D P, DAI L M, et al. Changes of productivity with stand development in broadleaf-Korean pine forest in Changbai Mountain,China[J]. Chin J Appl Ecol, 2020, 31(3):706-716.DOI: 10.13287/j.1001-9332.202003.018.
[12]	CHEN Y R, XIAO W F. Estimation of forest NPP and carbon sequestration in the Three Gorges Reservoir Area,using the Biome-BGC model[J]. Forests, 2019, 10(2):149.DOI: 10.3390/f10020149.
[13]	范敏锐, 余新晓, 张振明, 等. 北京山区油松林净初级生产力对气候变化情景的响应[J]. 东北林业大学学报, 2010, 38(11):46-48.
	FAN M R, YU X X, ZHANG Z M, et al. Net primary productivity of a Pinus tabulaeformis forest in Beijing mountainous area in response to different climate change scenarios[J]. J Northeast For Univ, 2010, 38(11):46-48.DOI: 10.13759/j.cnki.dlxb.2010.11.026.
[14]	张文海, 吕锡芝, 余新晓, 等. 气候和CO₂变化对北京山区油松林NPP的影响[J]. 广东农业科学, 2012, 39(6):4-7.
	ZHANG W H, LV X Z, YU X X, et al. Impact of climate and CO₂ change on net primary productivity of Pinus tabulaeformis forest in Beijing mountain area[J]. Guangdong Agric Sci, 2012, 39(6):4-7.DOI: 10.16768/j.issn.1004-874x.2012.06.064.
[15]	纪小芳, 龚元, 郑翔, 等. 凤阳山森林生态系统碳交换及其物候特征[J]. 地球环境学报, 2020, 11(4):376-389.
	JI X F, GONG Y, ZHENG X, et al. Local-scale carbon exchange and phenological characteristics of forest ecosystem in Fengyang Mountain of Zhejiang,China using tower-based eddy covariance technique[J]. J Earth Environ, 2020, 11(4):376-389.DOI: 10.7515/JEE192052.
[16]	孟苗婧, 郭晓平, 张金池, 等. 海拔变化对凤阳山针阔混交林地土壤微生物群落的影响[J]. 生态学报, 2018, 38(19):7057-7065.
	MENG M J, GUO X P, ZHANG J C, et al. Effects of altitude on soil microbial community in Fengyang Mountain coniferous and broad-leaved forest[J]. Acta Ecol Sin, 2018, 38(19):7057-7065.DOI: 10.5846/stxb201708211503.
[17]	孟苗婧, 张金池, 郭晓平, 等. 海拔变化对黄山松阔叶混交林土壤有机碳组分的影响[J]. 南京林业大学学报(自然科学版), 2018, 42(6):106-112.
	MENG M J, ZHANG J C, GUO X P, et al. Effects of altitude change on soil organic carbon fractions in Pinus taiwanensis and broad-leaved mixed forest[J]. J Nanjing For Univ (Nat Sci Ed), 2018, 42(6):106-112.DOI: 10.3969/j.issn.1000-2006.201712031.
[18]	赵友朋, 孟苗婧, 张金池, 等. 不同林地类型土壤团聚体稳定性与铁铝氧化物的关系[J]. 水土保持通报, 2018, 38(4):75-81,86.
	ZHAO Y P, MENG M J, ZHANG J C, et al. Relationship between soil aggregate stability and different forms of Fe and Al oxides in different forest types[J]. Bull Soil Water Conserv, 2018, 38(4):75-81,86.DOI: 10.13961/j.cnki.stbctb.2018.04.012.
[19]	赵友朋, 孟苗婧, 张金池, 等. 凤阳山主要林分类型土壤团聚体及其稳定性研究[J]. 南京林业大学学报(自然科学版), 2018, 42(5):84-90.
	ZHAO Y P, MENG M J, ZHANG J C, et al. Study on the composition and stability of soil aggregates of the main forest stands in Fengyang Mountain,Zhejiang Province[J]. J Nanjing For Univ (Nat Sci Ed), 2018, 42(5):84-90.DOI: 10.3969/j.issn.1000-2006.201801013.
[20]	张洋. 浙江凤阳山不同林分土壤有机碳矿化研究[D]. 南京: 南京林业大学, 2015.
	ZHANG Y. Study on soil organic carbon mineralization in different forests of Fengyang Mountain[D]. Nanjing: Nanjing Forestry University, 2015.
[21]	田月亮. 凤阳山主要林分类型结构特征及其改土效应[D]. 南京: 南京林业大学, 2012.
	TIAN Y L. The structural properties of main forest stand and their effects of soil improvement in Fengyang Mountain[D]. Nanjing: Nanjing Forestry University, 2012.
[22]	THORNTON P E, RUNNING S W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature,humidity,and precipitation[J]. Agric For Meteor, 1999,93(4):211-228.DOI: 10.1016/s0168-1923(98)00126-9.
[23]	THORNTON P E, HASENAUER H, WHITE M A. Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation:an application over complex terrain in Austria[J]. Agric For Meteor, 2000,104(4):255-271.DOI: 10.1016/s0168-1923(00)00170-2.
[24]	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[J]. Agric For Meteor, 2002,113(1/2/3/4):185-222.DOI: 10.1016/s0168-1923(02)00108-9.
[25]	朱再春, 刘永稳, 刘祯, 等. CMIP5模式对未来升温情景下全球陆地生态系统净初级生产力变化的预估[J]. 气候变化研究进展, 2018, 14(1):31-39.
	ZHU Z C, LIU Y W, LIU Z, et al. Projection of changes in terrestrial ecosystem net primary productivity under future global warming scenarios based on CMIP5 models[J]. Clim Change Res, 2018, 14(1):31-39.DOI: 10.12006/j.issn.1673-1719.2017.042.
[26]	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[J]. Earth Interact, 2000, 4(3):1-85.DOI: 10.1175/1087-3562(2000)004<0003:pasaot>2.0.co;2.
[27]	WATSON T A, DOHERTY J E, CHRISTENSEN S. Parameter and predictive outcomes of model simplification[J]. Water Resour Res, 2013, 49(7):3952-3977.DOI: 10.1002/wrcr.20145.
[28]	董艳辉, 李国敏, 郭永海, 等. 应用并行PEST算法优化地下水模型参数[J]. 工程地质学报, 2010, 18(1):140-144.
	DONG Y H, LI G M, GUO Y H, et al. Optimization of model parameters for groundwater flow using parallelized pest method[J]. J Eng Geol, 2010, 18(1):140-144.DOI: 10.3969/j.issn.1004-9665.2010.01.021.
[29]	HE J, YANG K, TANG W J, et al. The first high-resolution meteorological forcing dataset for land process studies over China[J]. Sci Data, 2020, 7(1):25.DOI: 10.1038/s41597-020-0369-y.
[30]	《第三次气候变化国家评估报告》编写委员会. 第三次气候变化国家评估报告[M]. 北京: 科学出版社, 2015: 213-231.
	Committee for The Preparation of The Third National Assessment Report on Climate Change. Third national assessment report on climate change[M]. Beijing: Science Press, 2015: 213-231.
[31]	何学兆, 周涛, 贾根锁, 等. 光合有效辐射总量及其散射辐射比例变化对森林GPP影响的模拟[J]. 自然资源学报, 2011, 26(4):619-634.
	HE X Z, ZHOU T, JIA G S, et al. Modeled effects of changes in the amount and diffuse fraction of PAR on forest GPP[J]. J Nat Resour, 2011, 26(4):619-634.
[32]	ZHANG Y L, CHENG G D, LI X, et al. Coupling of a simultaneous heat and water model with a distributed hydrological model and evaluation of the combined model in a cold region watershed[J]. Hydrol Process, 2013, 27(25):3762-3776.DOI: 10.1002/hyp.9514.
[33]	张越, 刘康, 张红娟, 等. 基于Biome-BGC模型的秦岭北坡太白红杉林碳源/汇动态和趋势研究[J]. 热带亚热带植物学报, 2019, 27(3):235-249.
	ZHANG Y, LIU K, ZHANG H J, et al. Carbon source/sink dynamics and trend of Larix chinensis in northern slope of Qinling Mountains based on Biome-BGC model[J]. J Trop Subtrop Bot, 2019, 27(3):235-249.DOI: 10.11926/jtsb.4008.
[34]	张凤英, 张增信, 田佳西, 等. 长江流域森林NPP模拟及其对气候变化的响应[J]. 南京林业大学学报(自然科学版), 2021, 45(1):175-181.
	ZHANG F Y, ZHANG Z X, TIAN J X, et al. Forest NPP simulation in the Yangtze River basin and its response to climate change[J]. J Nanjing For Univ (Nat Sci Ed), 2021, 45(1):175-181.DOI: 10.12302/j.issn.1000-2006.201907039.
[35]	LIU H Y, ZHANG M Y, LIN Z S. Relative importance of climate changes at different time scales on net primary productivity:a case study of the Karst area of northwest Guangxi,China[J]. Environ Monit Assess, 2017, 189(11):539.DOI: 10.1007/s10661-017-6251-5.
[36]	李亮, 何晓军, 胡理乐, 等. 1958—2008年太白山太白红杉林碳循环模拟[J]. 生态学报, 2013, 33(9):2845-2855.
	LI L, HE X J, HU L L, et al. Simulation of the carbon cycle of Larix chinensis forest during 1958 and 2008 at Taibai Mountain,China[J]. Acta Ecol Sin, 2013, 33(9):2845-2855.
[37]	侯英雨, 柳钦火, 延昊, 等. 我国陆地植被净初级生产力变化规律及其对气候的响应[J]. 应用生态学报, 2007, 18(7):1546-1553.
	HOU Y Y, LIU Q H, YAN H, et al. Variation trends of China terrestrial vegetation net primary productivity and its responses to climate factors in 1982-2000[J]. Chin J Appl Ecol, 2007, 18(7):1546-1553.

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

情景 scenario	气温升高 (每10 a)/℃ increased air temperature value	降水量增加 (每10 a)/% increased precipitation value	CO₂浓度 (至2100年)/ (μmol·mol^-1) CO₂ concentration
RCP2.6	0.08	0.6	421
RCP4.5	0.26	1.1	538
RCP6.0	0.61	1.6	670

Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 37

Related Articles 15

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[1]	HE Xu, MIAO Zimei, TIAN Jiaxi, YANG Liu, ZHANG Zengxin, ZHU Bin. Temperature, precipitation and runoff prediction in the Yangtze River basin based on CMIP 6 multi-model [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(2): 1-8.
[2]	SHI Song, LI Wen, ZHAI Yucen, LIN Xiaopeng, DING Yishu. Spatiotemporal changes of vegetation NDVI and those reasons in northeast China Tiger and Leopard National Park [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(4): 31-41.
[3]	GAI Junpeng, CHEN Dongsheng, JIA Weiwei, WANG Zheng. Developing height growth model of Larix kaempferi based on genetic and climate effects [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(4): 51-60.
[4]	HE Xiao, LEI Xiangdong, DUAN Guangshuang, FENG Qingrong, ZHANG Yiru, FENG Linyan. Modelling the effects of climate change on stand biomass growth of larch plantations [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(3): 120-128.
[5]	ZOU Xiaoming, WANG Guobing, GE Zhiwei, XIE Youchao, RUAN Honghua, WU Xiaoqiao, YANG Yan. Mechanisms and methods for augmenting carbon sink in forestry [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(6): 167-176.
[6]	WU Fan, ZHU Peihuang, JI Kongshu. Responses of masson pine(Pinus massoniana) distribution patterns to future climate change [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(2): 196-204.
[7]	MIAO Jing, WANG Yong, WANG Lu, XU Xiaogang. Prediction of potential geographical distribution pattern change for Castanopsis sclerophylla on MaxEnt [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(3): 193-198.
[8]	ZHANG Fengying, ZHANG Zengxin, TIAN Jiaxi, HUANG Richao, KONG Rui, ZHU Bin, ZHU Min, WANG Yiming, CHEN Xi. Forest NPP simulation in the Yangtze River Basin and its response to climate change [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(1): 175-181.
[9]	ZHANG Heng, ZHANG Qiuliang, YUE Yang, SONG Ximing, DAI Haiyan, YI Bole. The impact of climate change on forest and grassland fires and future trends in Hulunbuir City, Inner Mongolia [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(5): 222-230.
[10]	HUANG Honglan, ZHONG Wogu, YI Deping, CAI Junhuo, ZHANG Lu. Predicting the impact of future climate change on the distribution patterns of Toona ciliata var. pubescens in China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 163-170.
[11]	CHANG Juan, ZHANG Zengxin, TIAN Jiaxi, CHEN Xi, CHEN Yizhao. Spatio‑temporal characteristics of grassland water use efficiency and its response to climate change in northwest China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 119-125.
[12]	CHEN Yuheng, LÜ Yiwei, YIN Xiaojie. Predicting habitat suitability of 12 coniferous forest tree species in southwest China based on climate change [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(6): 113-120.
[13]	JIANG Yifan,LI Mingyang,LIU Yanan,LIU Fei. Impact of climate change on suitable habitats of Pinus massoniana in Hunan Province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(04): 94-100.
[14]	YAN Boqian, LIN Wanzhong, LIU Qijing, YU Jian^{2 }. Response of radial growth of Larix chinensis* to climatic factors in Aoshan Mountain of the Qinling Mountains, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 42(06): 61-67.
[15]	CHEN Jiaxin,YANG Hongqiang. Advances and frontiers in global forest and harvested wood products carbon science [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 42(04): 1-8.