气候变化领域集成服务门户(CCPortal): Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model

CCPortal

DOI	10.3390/f15030552
	Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model
	Sun, Jiaqian; Mao, Fangjie; Du, Huaqiang; Li, Xuejian; Xu, Cenheng; Zheng, Zhaodong; Teng, Xianfeng; Ye, Fengfeng; Yang, Ningxin; Huang, Zihao
发表日期	2024
EISSN	1999-4907
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.
英文关键词	net ecosystem productivity; BIOME-BGC model; global sensitivity analysis; subtropical China; forest ecosystem
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001191765500001
来源期刊	FORESTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302959
作者单位	Zhejiang A&F University; Zhejiang A&F University; Zhejiang A&F University
推荐引用方式 GB/T 7714	Sun, Jiaqian,Mao, Fangjie,Du, Huaqiang,et al. Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model[J],2024,15(3).
APA	Sun, Jiaqian.,Mao, Fangjie.,Du, Huaqiang.,Li, Xuejian.,Xu, Cenheng.,...&Huang, Zihao.(2024).Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model.FORESTS,15(3).
MLA	Sun, Jiaqian,et al."Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model".FORESTS 15.3(2024).