|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Conditional inversion to estimate parameters from eddy-flux observations显示文摘Aims Data assimilation is a useful tool to extract information from large datasets of the net ecosystem exchange(NEE)of CO_(2) obtained by eddy-flux measurements.However,the number of parameters in ecosystem models that can be constrained by eddy-flux data is limited by conventional inverse analysis that estimates parameter values based on one-time inversion.This study aimed to improve data assimilation to increase the number of constrained parameters.Methods In this study,we developed conditional Bayesian inversion to maximize the number of parameters to be constrained by NEE data in several steps.In each step,we conducted a Bayesian inversion to constrain parameters.The maximum likelihood estimates of the constrained parameters were then used as prior to fix parameter values in the next step of inversion.The conditional inversion was repeated until there were no more parameters that could be further constrained.We applied the conditional inversion to hourly NEE data from Harvard Forest with a physiologically based ecosystem model.Important Findings Results showed that the conventional inversion method constrained 6 of 16 parameters in the model while the conditional inversion method constrained 13 parameters after six steps.The cost function that indicates mismatch between the modeled and observed data decreased with each step of conditional Bayesian inversion.The Bayesian information criterion also decreased,suggesting reduced information loss with each step of conditional Bayesian inversion.A wavelet analysis reflected that model performance under conditional Bayesian inversion was better than that under conventional inversion at multiple time scales,except for seasonal and half-yearly scales.In addition,our analysis also demonstrated that parameter convergence in a subsequent step of the conditional inversion depended on correlations with the parameters constrained in a previous step.Overall,the conditional Bayesian inversion substantially increased the number of parameters to be constrained by NEE data and can be a powerful tool to be used in data assimilation in ecology. | Xiaowen Wu Yiqi Luo Ensheng Weng Luther White Yong Ma Xuhui Zhou | 2009 | Journal of Plant Ecology2009，2，2： | 3 |
| 2 | Inverse analysis of coupled carbon-nitrogen cycles against multiple datasets at ambient and elevated CO_(2)显示文摘Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)processes.However,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well quantified.Methods Here,we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from Duke Forests under ambient and elevated carbon dioxide(CO_(2)).Important FindingsOur results indicated that 8 of the 14 target parameters,such as C:N ratios in most ecosystem compartments,plant N uptake and external N input,were well constrained by available datasets whereas the others,such as N allocation coefficients,N loss and the initial value of mineral N pool were poorly constrained.Our analysis showed that elevated CO_(2)led to the increases in C:N ratios in foliage,fine roots and litter.Moreover,elevated CO_(2)stimulated plant N uptake and increased ecosystem N capital in Duke Forests by 25.2 and 8.5%,respectively.In addition,elevated CO_(2)resulted in the decrease of C exit rates(i.e.increases in C residence times)in foliage,woody biomass,structural litter and passive soil organic matter,but the increase of C exit rate in fine roots.Our results demonstrated that CO_(2)enrichment substantially altered key parameters in determining terrestrial C and N interactions,which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change. | Zheng Shi Yuanhe Yang Xuhui Zhou Ensheng Weng Adrien C.Finzi Yiqi Luo | 2016 | Journal of Plant Ecology2016，9，3： | 2 |
| 3 | Uncertainty analysis of forest carbon sink forecast with varying measurement errors:a data assimilation approach显示文摘Aims Accurate forecast of ecosystem states is critical for improving natural resourcemanagement and climate change mitigation.Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting.However,influences ofmeasurement errors on parameter estimation and forecasted state changes have not been carefully examined.This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model,the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystemmodel.The data were the observations of foliage biomass,wood biomass,fine root biomass,microbial biomass,litter fall,litter,soil carbon and soil respiration,collected at the Duke Forest free-air CO_(2)enrichment facilities from 1996 to 2005.Three levels ofmeasurement errorswere assigned to these data sets by halving and doubling their original standard deviations.Important Findings Results showed that only less than half of the 30 parameters could be constrained,though the observations were extensive and themodelwas relatively simple.Highermeasurement errors led to higher uncertainties in parameters estimates and forecasted carbon(C)pool sizes.The longterm predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools.Assimilated data contributed less information for the pools with long residence times in long-term forecasts.These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system.Improving the estimation of parameters of slowturnover C pools is the key to better forecast long-term ecosystem C dynamics. | Ensheng Weng Yiqi Luo Chao Gao Ram Oren | 2011 | Journal of Plant Ecology2011，4，3： | 2 |
| 4 | Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models 显示文摘 | LUO YIQI WENG ENSHENG WU XIAOWEN | 2009 | Ecological Applications2009，19，3： | 1 |
| 5 | Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems显示文摘 | Knapp Alan K Beier Claus Briske David D Classen Aimée T Luo Yiqi Reichstein Markus Smith Melinda D Smith Stanley D Bell Jesse E Fay Philip A Heisler Jana L Leavitt Steven W Sherry Rebecca Smith Benjamin Weng Ensheng | 2008 | Bioscience2008，，9： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员