基于Biome-BGC模型的南方型杨树人工林碳通量模拟

doi:10.13287/j.1001-9332.202608.009

摘要/Abstract

摘要： 为实现对南方型杨树人工林碳通量的精准模拟,本研究基于涡度通量塔观测数据,采用PEST参数优化方法对Biome-BGC模型的生理生态参数进行校准,模拟了该人工林生态系统逐日总初级生产力(GPP)和生态系统呼吸(Re),并识别了敏感性参数与主要气象驱动因子。结果表明:参数优化后,模型对GPP和Re的模拟精度显著提高,决定系数(R²)分别达0.74和0.63,较优化前提升27.6%和31.3%,平均绝对误差(MAE)分别降低15.6%和7.5%,均方根误差(RMSE)分别降低16.9%和5.9%。模型模拟的5年间平均GPP和Re分别为1.71 和1.55 kg C·m^-2·a^-1。敏感性分析表明,叶片碳氮比和冠层消光系数为影响碳通量模拟的高敏感参数,二磷酸核酮糖羧化酶中叶氮占比和凋落物碳氮比为中敏感参数。通径分析显示,在日尺度上,气温和短波辐射通量密度是驱动GPP增加的主要气象因子,而气温、降水量和短波辐射通量密度则显著促进Re增加,其中气温的正向驱动效应最强。综上,PEST参数优化可显著提高Biome-BGC模型在南方型杨树人工林碳通量模拟中的效果,影响模拟精度的关键敏感参数为叶片碳氮比和冠层消光系数,主导GPP和Re变化的关键气象因子为气温。

关键词: 生态系统碳通量, 过程模型, 参数优化, 敏感性分析, 通径分析

Abstract: To accurately simulate carbon fluxes of southern poplar plantations, we applied the PEST parameter optimization method to calibrate the ecophysiological parameters in the Biome-BGC model based on eddy covariance flux tower. We simulated the daily gross primary productivity (GPP) and ecosystem respiration (Re) of the plantation, and identified the sensitive parameters and main meteorological drivers. The results showed that the simulation accuracy of the model for GPP and Re was significantly improved after parameter optimization. The coefficients of determination (R²) for GPP and Re reached 0.74 and 0.63, respectively, which were 27.6% and 31.3% higher than those before optimization. The mean absolute error (MAE) for GPP and Re decreased by 15.6% and 7.5%, and the root mean square error (RMSE) for GPP and Re decreased by 16.9% and 5.9%, respectively. The average GPP and Re simulated by the model over five years were 1.71 and 1.55 kg C·m^-2·a^-1, respectively. Sensitivity analysis showed that leaf carbon-nitrogen ratio and the canopy light extinction coefficient strongly affected carbon flux simulation, and the fraction of leaf nitrogen in Rubisco and the litter carbon-nitrogen ratio were moderately sensitive parameters. Path analysis showed that air temperature and shortwave radiation flux density were the main meteorological factors driving the increase in GPP on the daily scale. Air temperature, precipitation and shortwave radiation flux density significantly promoted the increase in Re, with the effect of air temperature being the strongest. In summary, the PEST parameter calibration substantially improved Biome-BGC performance for carbon flux simulation in southern poplar plantations. The key sensitive parameters affecting the simulation accuracy were leaf carbon-nitrogen ratio and canopy light extinction coefficient. The key meteorological factor that dominated GPP and Re changes was air temperature.

Key words: ecosystem carbon flux, process-based model, parameter optimization, sensitivity analysis, path analysis

李湘玉, 苏梦琳, 闫珂, 王维枫. 基于Biome-BGC模型的南方型杨树人工林碳通量模拟[J]. 应用生态学报, 2026, 37(8): 2684-2692.

LI Xiangyu, SU Menglin, YAN Ke, WANG Weifeng. Carbon flux simulation of southern poplar plantation based on Biome-BGC model[J]. Chinese Journal of Applied Ecology, 2026, 37(8): 2684-2692.

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