Abstract:

Parameterization is a critical step in modelling ecosystem dynamics. However, assigning parameter values can be a technical challenge for structurally complex natural 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 contrasting 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 average 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 deviations (< 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 Shennongjia 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.

Key words: Biome-BGC, Community traits, Forest Ecosystems, Model parameterization