An emergy (spelled with an “m”) evaluation of Qianyanzhou ecological station was performed in order to study its progress during 7 years' development, using changes of emergy inputs and outputs Emergy indices ...An emergy (spelled with an “m”) evaluation of Qianyanzhou ecological station was performed in order to study its progress during 7 years' development, using changes of emergy inputs and outputs Emergy indices of Qianyanzhou were evaluated and compared with those from other countries The comparison showed that Qianyanzhou may be developing optimum use of its natural resources展开更多
We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes ...We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m-2·a-1), 34.33% (79 g C·m-2·a-1) and 5.4% (92 g C·m-2·a-1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m-2·a-1), 2.1% (5.7 g C·m-2·a-1), and 0.26% (4.3 g C·m-2·a-1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%-8%, 7%-22% and 2%-4% respectively at annual timescale.展开更多
文摘An emergy (spelled with an “m”) evaluation of Qianyanzhou ecological station was performed in order to study its progress during 7 years' development, using changes of emergy inputs and outputs Emergy indices of Qianyanzhou were evaluated and compared with those from other countries The comparison showed that Qianyanzhou may be developing optimum use of its natural resources
基金Supported by National Natural Science Foundation of China (Grant No. 30570347)Innovative Research International Partnership Project of the Chinese Academy of Sciences (Grant No. CXTD-Z2005-1)National Basic Research Program of China (Grant No. 2002CB412502)
文摘We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a dou-ble-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization meth-ods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m-2·a-1), 34.33% (79 g C·m-2·a-1) and 5.4% (92 g C·m-2·a-1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m-2·a-1), 2.1% (5.7 g C·m-2·a-1), and 0.26% (4.3 g C·m-2·a-1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%-8%, 7%-22% and 2%-4% respectively at annual timescale.
