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CARBON CYCLE MODELING OF A BROAD-LEAVED KOREAN PINE FOREST IN CHANGBAI MOUNTAIN OF CHINA USING THE MODEL-DATA FUSION APPROACH
Received date: 2009-03-02
Accepted date: 2009-05-15
Online published: 2021-04-29
Aims Our objective was to use multiple terrestrial carbon observations to improve existing terrestrial ecosystem models.
Methods We conducted a Bayesian probabilistic inversion to estimate the key parameter (i.e., carbon residence time) of a terrestrial ecosystem model (TECO) by using biometric and eddy covariance flux data measured at a temperate broad-leaved Korean pine forest in Changbai Mountain (CBS) of China from 2003 to 2005. We then estimated carbon stocks, carbon fluxes and uncertainties with posterior estimates of parameters. Biometric measurements consisted of foliage biomass, fine root biomass, woody biomass, litterfall, soil organic matter (SOM) and soil respiration.
Important findings Residence times of carbon for most pools can be constrained by eddy covariance flux and biometric measurements, except for the passive soil organic matter pool. Estimated residence times of C ranged from 2 to 6 months for litter and microbial biomass pools, 1 to 2 years for foliage and fine root biomass, 8 to 16 years for slow SOM pool and 77-109 and 409-1 879 years for woody biomass and passive SOM pools, respectively. Model results showed that the prediction uncertainties of carbon stocks and accumulated carbon fluxes increased with time. When air temperature increased 10% and 20%, annual gross primary productivity (GPP) increased 6.5% and 9.9%, but annual net ecosystem productivity (NEP) changed with soil temperature. If soil temperature is constant, annual NEP increased 11.4%-21.9% and 17.6%-33.1%, while if soil temperature increased 10% and 20%, annual NEP decreased to a level that was lower than that under ambient temperature. Given the same climate condition and seasonal variation for leaf area index during 2003-2005, annual NEP and soil respiration in 2020 would be 163±12 and 721±14 g C·m-2·a-1. Markov Chain Monte Carlo method is an effective way to estimate model parameters and to evaluate model prediction uncertainties. However, more studies are needed on a) estimation of residence time of C for passive soil organic matter, b) uncertainty analysis of input data and model structure and c) model-data fusion methods so as to improve the prediction accuracy of terrestrial ecosystem models.
ZHANG Li, YU Gui-Rui, LUO Yiqi, HE Hong-Lin, ZHANG Lei-Ming . CARBON CYCLE MODELING OF A BROAD-LEAVED KOREAN PINE FOREST IN CHANGBAI MOUNTAIN OF CHINA USING THE MODEL-DATA FUSION APPROACH[J]. Chinese Journal of Plant Ecology, 2009 , 33(6) : 1044 -1055 . DOI: 10.3773/j.issn.1005-264x.2009.06.004
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