DOI: 10.1002/2016MS000687
Scopus记录号: 2-s2.0-85014123041
论文题名: Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion
作者: Du Z ; , Zhou X ; , Shao J ; , Yu G ; , Wang H ; , Zhai D ; , Xia J ; , Luo Y
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 1 起始页码: 548
结束页码: 565
语种: 英语
英文关键词: Ecology
; Ecosystems
; Forecasting
; Nitrogen
; Probability density function
; Uncertainty analysis
; Coupling modeling
; Data assimilation
; Relative information
; Shannon information
; Uncertainty
; Digital storage
; Bayesian analysis
; biomass
; carbon sequestration
; coupling
; data assimilation
; data inversion
; data set
; ecosystem modeling
; index method
; nitrogen
; probability density function
; quantitative analysis
; terrestrial ecosystem
; uncertainty analysis
英文摘要: Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO-C-C (carbon (C)-only model), TECO-CN-C (TECO-CN coupled model with only C measurements as assimilating data), and TECO-CN-CN (TECO-CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long-term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long-term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process-induced disequilibrium in the initial value of C pools. However, in the long-term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C-N coupling in ecosystem models and highlight critical observational needs for future studies. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75805
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
There are no files associated with this item.
作者单位: Tiantong National Field Observation Station for Forest Ecosystem, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, ECNU-UH Joint Translational Science and Technology Research Institute, East China Normal University, Shanghai, China; Center for Global Change and Ecological Forecasting, East China Normal University, Shanghai, China; Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing, China; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, United States; Center for Earth System Science, Tsinghua University, Beijing, China
Recommended Citation:
Du Z,, Zhou X,, Shao J,et al. Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(1)