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Uncertainty analysis of modeled carbon fluxes for a broad-leaved Korean pine mixed forest using a process-based ecosystem model
- Zhang, Li, Yu, Guirui, Gu, Fengxue, He, Honglin, Zhang, Leiming, Han, Shijie
- Journal of forest research 2012 v.17 no.3 pp. 268-282
- Pinus koraiensis, carbon, carbon dioxide, ecosystem respiration, ecosystems, environmental factors, forests, global change, meteorological data, models, net ecosystem exchange, primary productivity, regression analysis, relative humidity, uncertainty analysis
- The uncertainty in the predicted values of a process-based terrestrial ecosystem model is as important as the predicted values themselves. However, few studies integrate uncertainty analysis into their modeling of carbon dynamics. In this paper, we conducted a local sensitivity analysis of the model parameters of a process-based ecosystem model at the Chaibaishan broad-leaved Korean pine mixed forest site in 2003–2005. Sixteen parameters were found to affect the annual net ecosystem exchange of CO2 (NEE) in each of the three years. We combined a Monte Carlo uncertainty analysis with a standardized multiple regression method to distinguish the contributions of the parameters and the initial variables to the output variance. Our results showed that the uncertainties in the modeled annual gross primary production and ecosystem respiration were 5–8% of their mean values, while the uncertainty in the annual NEE was up to 23–37% of the mean value in 2003–2005. Five parameters yielded about 92% of the uncertainty in the modeled annual net ecosystem exchange. Finally, we analyzed the sensitivity of the meteorological data and compared two types of meteorological data and their effects on the estimation of carbon fluxes. Overestimating the relative humidity at a spatial resolution of 10 km × 10 km had a larger effect on the annual gross primary production, ecosystem respiration, and net ecosystem exchange than underestimating precipitation. More attention should be paid to the accurate estimation of sensitive model parameters, driving meteorological data, and the responses of ecosystem processes to environmental variables in the context of global change.