Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach

doi:10.3390/f11040369

	Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach
	Hu, Longwei 1; He, Honglin 2,3,4; Shen, Yan 1; Ren, Xiaoli 2,3; Yan, Shao-kui 5; Xiang, Wenhua 1,6; Ge, Rong 2,3,7; Niu, Zhongen 2,3,7; Xu, Qian 2,3,7; Zhu, Xiaobo 2,3,8,9
刊名	FORESTS
	2020-04-01
卷号	11 期号:4 页码:16
关键词	process-based terrestrial ecosystem model model-data fusion multi-source data carbon cycle plantation
DOI	10.3390/f11040369
通讯作者	He, Honglin(hehl@igsnrr.ac.cn)
英文摘要	Process-based terrestrial ecosystem models are increasingly being used to predict carbon (C) cycling in forest ecosystems. Given the complexity of ecosystems, these models inevitably have certain deficiencies, and thus the model parameters and simulations can be highly uncertain. Through long-term direct observation of ecosystems, numerous different types of data have accumulated, providing valuable opportunities to determine which sources of data can most effectively reduce the uncertainty of simulation results, and thereby improve simulation accuracy. In this study, based on a long-term series of observations (biometric and flux data) of a subtropical Chinese fir plantation ecosystem, we use a model-data fusion framework to evaluate the effects of different constrained data on the parameter estimation and uncertainty of related variables, and systematically evaluate the uncertainty of parameters. We found that plant C pool observational data contributed to significant reductions in the uncertainty of parameter estimates and simulation, as these data provide information on C pool size. However, none of the data effectively constrained the foliage C pool, indicating that this pool should be a target for future observational activities. The assimilation of soil organic C observations was found to be important for reducing the uncertainty or bias in soil C pools. The key findings of this study are that the assimilation of multiple time scales and types of data stream are critical for model constraint and that the most accurate simulation results are obtained when all available biometric and flux data are used as constraints. Accordingly, our results highlight the importance of using multi-source data when seeking to constrain process-based terrestrial ecosystem models.
资助项目	Strategic Priority Research Program of Chinese Academy of Science[XDA19020301] ; National Natural Science Foundation of China[41571424]
WOS关键词	ENSEMBLE KALMAN FILTER ; MULTIPLE DATA STREAMS ; LAND-SURFACE MODEL ; DATA-ASSIMILATION ; FOREST ECOSYSTEMS ; ESTIMATING PARAMETERS ; WATER FLUXES ; SEQUESTRATION ; PROJECTIONS ; INVERSION
WOS研究方向	Forestry
语种	英语
出版者	MDPI
WOS记录号	WOS:000534632500007
资助机构	Strategic Priority Research Program of Chinese Academy of Science ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159522]
专题	中国科学院地理科学与资源研究所
通讯作者	He, Honglin
作者单位	1.Cent South Univ Forestry & Technol, Changsha 410004, Peoples R China 2.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Natl Ecosyst Sci Data Ctr, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Huitong Expt Stn Forest Ecol, State Key Lab Forest & Soil Ecol, Inst Appl Ecol, Shenyang 110164, Peoples R China 6.Natl Engn Lab Appl Technol Forestry & Ecol South, Changsha 410004, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.Minist Educ, Chongqing Jinfo Mt Field Sci Observat & Res Stn K, Chongqing 400715, Peoples R China 9.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China
推荐引用方式 GB/T 7714	Hu, Longwei,He, Honglin,Shen, Yan,et al. Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach[J]. FORESTS,2020,11(4):16.
APA	Hu, Longwei.,He, Honglin.,Shen, Yan.,Ren, Xiaoli.,Yan, Shao-kui.,...&Zhu, Xiaobo.(2020).Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach.FORESTS,11(4),16.
MLA	Hu, Longwei,et al."Modeling the Carbon Cycle of a Subtropical Chinese Fir Plantation Using a Multi-Source Data Fusion Approach".FORESTS 11.4(2020):16.