In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation of the net carbon flux. An optimization scheme is proposed to estimate two key pa...In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation of the net carbon flux. An optimization scheme is proposed to estimate two key parameters （V2max and Q10） by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate V25max and Q10 of the boreal ecosystem productivity simulator （BEPS） to improve its NEP simulation in the boreal North American region. Then, in situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations from BEPS with initial and optimized parameters. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results implicate that it is possible to optimize ecosystem model parameters by different sensitivities of V25max and Q10 during growing and non-growing seasons through atmospheric inversion or data assimilation techniques.展开更多
基金supported by the National Basic Research Program of China(2010CB950703)the National Natural Science Foundation of China(41571338)
文摘In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation of the net carbon flux. An optimization scheme is proposed to estimate two key parameters （V2max and Q10） by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate V25max and Q10 of the boreal ecosystem productivity simulator （BEPS） to improve its NEP simulation in the boreal North American region. Then, in situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations from BEPS with initial and optimized parameters. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results implicate that it is possible to optimize ecosystem model parameters by different sensitivities of V25max and Q10 during growing and non-growing seasons through atmospheric inversion or data assimilation techniques.
