首页|Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

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Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natu-ral plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrast-ing forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted aver-age over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3％.In addition,there were only slight devia-tions(＜10％)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shen-nongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Biome-BGCCommunity traitsForest EcosystemsModel parameterization

Minzhe Fang、Changjin Cheng、Nianpeng He、Guoxin Si、Osbert Jianxin Sun

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School of Ecology and Nature Conservation,Beijing Forestry University,Beijing 100083,China

Research Institute of Energy Saving,Environmental Protection,Occupational Safety and Health,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China

Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China

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国家自然科学基金

31870426

2024

10.1007/s11676-023-01650-1

林业研究(英文版)
东北林业大学,中国生态学学会

林业研究(英文版)

CSTPCDEI
影响因子：0.365
ISSN：1007-662X
年,卷(期)：2024.35(1)
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