Modelling the effects of climate change on stand biomass growth of larch plantations

doi:10.12302/j.issn.1000-2006.202211037

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3): 120-128.doi: 10.12302/j.issn.1000-2006.202211037

Previous Articles Next Articles

Modelling the effects of climate change on stand biomass growth of larch plantations

HE Xiao¹(), LEI Xiangdong¹^,^*(), DUAN Guangshuang², FENG Qingrong³, ZHANG Yiru¹, FENG Linyan¹

1. Institute of Forest Resource Information Techniques,Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing 100091, China
2. College of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China
3. Academy of Forest and Grassland Inventory, National Forestry and Grassland Administration, Beijing 100714, China

Received:2022-11-30 Accepted:2022-12-29 Online:2023-05-30 Published:2023-05-25
Contact: LEI Xiangdong E-mail:hexiaonuist@163.com;xdlei@ifrit.ac.cn

Abstract

Abstract:

【Objective】This study aimed to understand the effects of climate change on the stand biomass and growth in larch (Larix spp.) plantations to provide a basis for estimating carbon storage and employing adaptive management decision-making. 【Method】Sample plot data of pure larch plantations from the 6th to 8th National Forest Inventories in northern and northeastern China were used to develop stand biomass growth models based on theoretical growth equations. Taking the tree species as a dummy variable, a climate-sensitive stand biomass growth model was developed with the inclusion of the annual heat-moisture index (AHM). The stand biomass and annual increment under three climate change scenarios, including a constant climate and two greenhouse gas representative concentration paths (RCPs) (RCP 4.5, RCP 8.5), were simulated using the model. The relative differences between the estimated results under two RCP climate scenarios and the current scenario were calculated to quantify the effects of climate change on the stand biomass and annual increment. 【Result】The R² values of the basic stand biomass growth model, model with dummy variables, and model with dummy and climate variables were 0.938 2, 0.947 0, and 0.950 7, respectively. Dummy variables and climatic factors improved the performances of the models. Compared with the current scenario, both increasing and decreasing trends were found for the stand biomass and annual increment of each tree species under RCP 4.5 and RCP 8.5. For stand biomass, the ranges of the mean values of relative differences under RCP 4.5 and RCP 8.5 scenarios were from -3.02% and -4.72 % to 2.69% and 3.41%, respectively. For the annual increment of stand biomass, the ranges of the mean values of relative differences under RCP 4.5 and RCP 8.5 scenarios were from -1.92% and -2.66% to 0.30% and 0.72%, respectively. 【Conclusion】The stand biomass growth model established in this study can be used to simulate the stand biomass growth in pure larch plantations under climate change conditions. There were both increasing and decreasing trends for the stand biomass and annual increments of larch plantations with future climate change in different tree species. The direction and magnitude of the impacts of future climate change on the stand biomass and annual increments differed among tree species. Generally, positive effects on the stand biomass were found for Larix principis-rupprechtii and L. kaempferi, but negative effects were found for other tree species; and negative effects on the stand biomass annual increment were found for all tree species with the exception of L. principis-rupprechtii.

Key words: stand biomass, annual increment, growth model, annual heat-moisture index, climate change

CLC Number:

S757

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.

Figures/Tables 6

Table 1

Table 2

Table 3

Fig.1

Fig.2

Table 4

References 35

[1]	KINDERMANN G, OBERSTEINER M, SOHNGEN B, et al. Global cost estimates of reducing carbon emissions through avoided deforestation[J]. Proc Natl Acad Sci USA, 2008, 105(30):10302-10307.DOI: 10.1073/pnas.0710616105.
[2]	PAN Y D, BIRDSEY R A, FANG J Y, et al. A large and persistent carbon sink in the world’s forests[J]. Science, 2011, 333(6045):988-993.DOI: 10.1126/science.1201609.
[3]	朱光玉, 胡松, 符利勇. 基于哑变量的湖南栎类天然林林分断面积生长模型[J]. 南京林业大学学报(自然科学版), 2018, 42(2):155-162.
	ZHU G Y, HU S, FU L Y. Basal area growth model for oak natural forest in Hunan Province based on dummy variable[J]. J Nanjing For Univ (Nat Sci Ed), 2018, 42(2):155-162.DOI: 10.3969/j.issn.1000-2006.201704059.
[4]	STOKLAND J N. Volume increment and carbon dynamics in boreal forest when extending the rotation length towards biologically old stands[J]. For Ecol Manag, 2021, 488:119017.DOI: 10.1016/j.foreco.2021.119017.
[5]	薛春泉, 徐期瑚, 林丽平, 等. 广东主要乡土阔叶树种单木生物量生长模型[J]. 华南农业大学学报, 2019, 40(2):65-75.
	XUE C Q, XU Q H, LIN L P, et al. Biomass growth models for individual tree of main indigenous broadleaf tree species in Guangdong Province[J]. J South China Agric Univ, 2019, 40(2):65-75.DOI: 10.7671/j.issn.1001-411X.201806031.
[6]	何潇, 曹磊, 徐胜林, 等. 内蒙古大兴安岭林区不同恢复阶段森林生物量特征与影响因素[J]. 北京林业大学学报, 2019, 41(9):50-58.
	HE X, CAO L, XU S L, et al. Forest biomass characteristics and influencing factors in different restoration stages in the Daxing’anling forest region of Inner Mongolia,northern China[J]. J Beijing For Univ, 2019, 41(9):50-58.DOI: 10.13332/j.1000-1522.20190030.
[7]	曹磊, 刘晓彤, 李海奎, 等. 广东省常绿阔叶林生物量生长模型[J]. 林业科学研究, 2020, 33(5):61-67.
	CAO L, LIU X T, LI H K, et al. Biomass growth models for evergreen broad-leaved forests in Guangdong[J]. For Res, 2020, 33(5):61-67.DOI: 10.13275/j.cnki.lykxyj.2020.05.008.
[8]	黄晓强, 信忠保, 赵云杰, 等. 林龄和立地条件对北京山区油松人工林碳储量的影响[J]. 水土保持学报, 2015, 29(6):184-190.
	HUANG X Q, XIN Z B, ZHAO Y J, et al. Effects of stand ages and site conditions on carbon stock of Pinus tabuliformis plantations in Beijing mountainous area[J]. J Soil Water Conserv, 2015, 29(6):184-190.DOI: 10.13870/j.cnki.stbcxb.2015.06.033.
[9]	CLARK J S, BELL D M, HERSH M H, et al. Climate change vulnerability of forest biodiversity:climate and competition tracking of demographic rates[J]. Glob Change Biol, 2011, 17(5):1834-1849.DOI: 10.1111/j.1365-2486.2010.02380.x.
[10]	BONTEMPS J D, BOURIAUD O. Predictive approaches to forest site productivity:recent trends,challenges and future perspectives[J]. Forestry (Lond), 2014, 87(1):109-128.DOI: 10.1093/forestry/cpt034.
[11]	ZHANG X Q, LEI Y C, PANG Y, et al. Tree mortality in response to climate change induced drought across Beijing,China[J]. Clim Change, 2014, 124(1):179-190.DOI: 10.1007/s10584-014-1089-0.
[12]	SHARMA M, SUBEDI N, TER-MIKAELIAN M, et al. Modeling climatic effects on stand height/site index of plantation-grown jack pine and black spruce trees[J]. For Sci, 2015, 61(1):25-34.DOI: 10.5849/forsci.13-190.
[13]	HE X, LEI X D, DONG L H. How large is the difference in large-scale forest biomass estimations based on new climate-modified stand biomass models?[J]. Ecol Indic, 2021, 126:107569.DOI: 10.1016/j.ecolind.2021.107569.
[14]	LINDNER M, FITZGERALD J B, ZIMMERMANN N E, et al. Climate change and European forests:what do we know,what are the uncertainties,and what are the implications for forest management?[J]. J Environ Manag, 2014, 146:69-83.DOI: 10.1016/j.jenvman.2014.07.030.
[15]	MEDLYN B E, DUURSMA R A, ZEPPEL M J B. Forest productivity under climate change:a checklist for evaluating model studies[J]. Wiley Interdiscip Rev Clim Change, 2011, 2(3):332-355.DOI: 10.1002/wcc.108.
[16]	国家林业和草原局. 中国森林资源报告2014-2018[M]. 北京: 中国林业出版社, 2019.
[17]	ZANG H, LEI X D, MA W, et al. Spatial heterogeneity of climate change effects on dominant height of larch plantations in northern and northeastern China[J]. Forests, 2016, 7(12):151.DOI: 10.3390/f7070151.
[18]	LEI X D, YU L, HONG L X. Climate-sensitive integrated stand growth model (CS-ISGM) of Changbai larch (Larix olgensis) plantations[J]. For Ecol Manag, 2016, 376:265-275.DOI: 10.1016/j.foreco.2016.06.024.
[19]	ZENG W S, DUO H R, LEI X D, et al. Individual tree biomass equations and growth models sensitive to climate variables for Larix spp.in China[J]. Eur J Forest Res, 2017, 136(2):233-249.DOI: 10.1007/s10342-017-1024-9.
[20]	REINEKE L H. Perfecting a stand-density index for even-aged forests[J]. Journal of Agricultural Research, 1933, 46(7): 627-638. DOI: 10.1111/j.1744-7348.1933.tb07434.x.
[21]	ZANG H, LEI X D, ZENG W S. Height-diameter equations for larch plantations in northern and northeastern China:a comparison of the mixed-effects,quantile regression and generalized additive models[J]. Forestry, 2016, 89(4):434-445.DOI: 10.1093/forestry/cpw022.
[22]	国家林业局. 立木生物量模型及碳计量参数落叶松:LY/T 2654-2016[S]. 北京: 中国标准出版社, 2017.
[23]	陈传国, 朱俊凤. 东北主要林木生物量手册[M]. 北京: 中国林业出版社,1989.
	CHEN C G, ZHU J F. Handbook of biomass of main trees in northeast China[M]. Beijing: China Forestry Publishing House,1989.
[24]	WANG C K. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests[J]. For Ecol Manag, 2006, 222(1/2/3):9-16.DOI: 10.1016/j.foreco.2005.10.074.
[25]	WANG T L, WANG G Y, INNES J L, et al. ClimateAP:an application for dynamic local downscaling of historical and future climate data in Asia Pacific[J]. Front Agr Sci Eng, 2017, 4(4):448.DOI: 10.15302/j-fase-2017172.
[26]	唐守正. 广西大青山马尾松全林整体生长模型及其应用[J]. 林业科学研究, 1991, 4(S): 8-13.
	TANG S Z. Integrated stand growth model and its application of masson pine in Guangxi Daqingshan[J]. Forest Research, 1994, 4(supplement): 8-13.
[27]	THAPA R, BURKHART H E. Modeling stand-level mortality of loblolly pine (Pinus taeda L.) using stand,climate,and soil variables[J]. For Sci, 2015, 61(5):834-846.DOI: 10.5849/forsci.14-125.
[28]	USOLTSEV V A, MERGANICOVÁ K, KONÔPKA B, et al. Fir (Abies spp.) stand biomass additive model for Eurasia sensitive to winter temperature and annual precipitation[J]. Central Eur For J, 2019, 65(3/4):166-179.DOI: 10.2478/forj-2019-0017.
[29]	BENNETT A C, PENMAN T D, ARNDT S K, et al. Climate more important than soils for predicting forest biomass at the continental scale[J]. Ecography, 2020, 43(11):1692-1705.DOI: 10.1111/ecog.05180.
[30]	LATTA G, TEMESGEN H, ADAMS D, et al. Analysis of potential impacts of climate change on forests of the United States Pacific Northwest[J]. For Ecol Manag, 2010, 259(4):720-729.DOI: 10.1016/j.foreco.2009.09.003.
[31]	TYLIANAKIS J M, DIDHAM R K, BASCOMPTE J, et al. Global change and species interactions in terrestrial ecosystems[J]. Ecol Lett, 2008, 11(12):1351-1363.DOI: 10.1111/j.1461-0248.2008.01250.x.
[32]	KHAN D, MUNEER M A, NISA Z U, et al. Effect of climatic factors on stem biomass and carbon stock of Larix gmelinii and Betula platyphylla in Daxing’anling Mountain of Inner Mongolia,China[J]. Adv Meteorol, 2019, 2019:1-10.DOI: 10.1155/2019/5692574.
[33]	GAO Z G, WANG Q Y, HU Z D, et al. Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China[J]. Int J Biomath, 2019, 12(7):1950053.DOI: 10.1142/s1793524519500530.
[34]	AGUIRRE A, DEL RÍO M, RUIZ-PEINADO R, et al. Stand-level biomass models for predicting C stock for the main Spanish pine species[J]. For Ecosyst, 2021, 8(1):29.DOI: 10.1186/s40663-021-00308-w.
[35]	XIE Y L, WANG H Y, LEI X D. Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PGmix model[J]. J Environ Manag, 2020, 261:110249.DOI: 10.1016/j.jenvman.2020.110249.

Related Articles 15

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[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]	QIU Siyu, CAO Yuanshuai, SUN Yujun, PAN Lei. Age-independent dominant height growth model for Chinese fir plantation [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(5): 121-127.
[14]	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.
[15]	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.

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

树种 tree species	观测数 number of observations	平均林龄/a stand mean age	平方平均胸径/cm quadratic mean diameter	平均树高/m mean stand height	林分密度指数/ (株·hm^-2) stand density index	林分蓄积/ (m³·hm^-2) stand volume	林分生物量/ (t·hm^-2) stand biomass
华北落叶松 Larix principis-rupprechtii	314	26±9	11.9±4.0	9.0±2.8	487±298	65.71±51.40	55.60±43.87
日本落叶松 L. kaempferi	94	22±8	14.3±4.5	13.5±4.5	511±237	102.82±60.79	84.22±47.79
兴安落叶松 L. gmelinii	291	25±8	11.8±3.9	10.8±3.5	428±239	60.19±40.77	58.73±38.28
长白落叶松 L. olgensis	456	26±10	12.7±4.1	12.0±4.3	467±219	87.63±52.09	69.90±40.19

模型model	R²	σ(SE)/ (t·hm^-2)
基础生长模型(模型4) basic growth model (Eq. 4)	0.938 2	10.49
含哑变量的生长模型(模型5) growth model with dummy variables (Eq. 5)	0.947 0	9.72
含气候和哑变量的生长模型(模型6) growth model with climatic and dummy variables (Eq. 6)	0.950 7	9.38

参数 parameter	平均值 mean	标准差 SD
a₁	135.596 5	0.572 9
a₁₂	-23.166 7	0.171 6
a₂	0.231 8	0.001 7
a₂₁	0.075 6	0.000 8
b₁	0.011 2	0.000 1
b₂	3.171 8	0.008 3
c	0.321 1	0.000 8
c₁	0.035 4	0.000 2
c₂	-0.037 3	0.000 2
c₃	-0.006 1	0.000 1
评价指标 fitting statistics R²= 0.949 2,σ(SE)=9.52 t/hm²

树种 tree species	RCP 4.5			RCP 8.5
树种 tree species	年湿热指数相对差值 relative difference for annual heat- moisture index	林分生物量相对差值 relative difference for stand biomass	连年生长量相对差值 relative difference for annual increment	年湿热指数相对差值 relative difference for annual heat- moisture index	林分生物量相对差值 relative difference for stand biomass	连年生长量相对差值 relative difference for annual increment
华北落叶松 L. principis-rupprechtii	-4.07±17.48	2.69±8.25	0.30±8.56	-4.62±14.54	3.41±6.38	0.72±6.82
日本落叶松 L. kaempferi	-2.40±19.73	0.05±3.19	-0.12±3.14	0.72±17.01	-0.50±2.69	-0.26±2.66
兴安落叶松 L. gmelinii	5.51±12.34	-3.02±3.83	-1.92±3.99	9.49±10.78	-4.72±3.61	-2.66±3.84
长白落叶松 L. olgensis	1.37±19.19	-1.27±3.93	-1.46±4.03	5.32±16.96	-2.62±3.63	-1.69±3.89

Modelling the effects of climate change on stand biomass growth of larch plantations

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 35

Related Articles 15

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer