文献类型：期刊文献

英文题名：Sensitivity analysis of Biome-BGCMuSo for gross and net primary productivity of typical forests in China

作者：Ren, Hongge[1,2] Zhang, Li[1] Yan, Min[1] Tian, Xin[3] Zheng, Xingbo[4,5]

第一作者：Ren, Hongge

通信作者：Yan, M[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Chinese Acad Sci, Inst Appl Ecol, Key Lab Forest Ecol & Management, Shenyang 110016, Peoples R China;[5]Chinese Acad Sci, Res Stn Changbai Mt Forest Ecosyst, Antu 133613, Jilin, Peoples R China

年份：2022

卷号：9

外文期刊名：FOREST ECOSYSTEMS

收录：;Scopus(收录号：2-s2.0-85124958342);WOS:【SCI-EXPANDED(收录号:WOS:000783919400012)】；

基金：This study was funded by the National Natural Science Foundation of China (grant number 41871279 and 41901364).

语种：英文

外文关键词：Sensitivity analysis; Biome-BGCMuSo; Productivity; Regression analysis; EFAST

摘要：Background: Process-based models are widely used to simulate forest productivity, but complex parameterization and calibration challenge the application and development of these models. Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation. However, parameters are not dependent on each other, and the results of sensitivity analysis usually vary due to different forest types and regions. Hence, global and representative sensitivity analysis would provide reliable information for simple calibration. Methods: To determine the contributions of input parameters to gross primary productivity (GPP) and net primary productivity (NPP), regression analysis and extended Fourier amplitude sensitivity testing (EFAST) were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX. Results: Generally, GPP and NPP were highly sensitive to C:N-leaf (C:N of leaves), W-int (canopy water interception coefficient), k (canopy light extinction coefficient), FLNR (fraction of leaf N in Rubisco), MRpern (coefficient of linear relationship between tissue N and maintenance respiration), VPDf (vapor pressure deficit complete conductance reduction), and SLA1 (canopy average specific leaf area in phenological phase 1) at all observation sites. Various sensitive parameters occurred at four observation sites within different climate zones. GPP and NPP were particularly sensitive to FLNR, SLA1 and W-int, and C:N-leaf in temperate, alpine and subtropical zones, respectively. Conclusions: The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient. We found that parameter calibration should be performed according to plant functional type (PFT), and more attention needs to be paid to the differences in climate and environment. These findings contribute to determining the target parameters in field experiments and model calibration.