IGSNRR OpenIR
Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion
Ren, Xiaoli1; He, Honglin1,2; Zhang, Li1,2; Li, Fan3; Liu, Min4; Yu, Guirui1,2; Zhang, Junhui5
2018-07-10
Source PublicationECOLOGICAL MODELLING
ISSN0304-3800
Volume379Pages:39-53
Corresponding AuthorHe, Honglin(hehl@igsnrr.ac.cn)
AbstractProcess-based ecosystem models are increasingly used to estimate the carbon and water exchanges between ecosystems and the atmosphere. These models inevitably suffer from deficiencies and uncertainties, which should be thoroughly examined to better understand the processes governing the ecosystem dynamics. In this paper, we systematically explored the uncertainties in model predictions of Changbaishan (CBS) broad-leaved Korean pine mixed forest using the Simplified PhotosyNthesis and Evapo-Transpiration (SIPNET) model and eddy flux and meteorological data from 2004 to 2009. We first screened out 21 key parameters from 42 model parameters using Morris global sensitivity analysis method, and then estimated their probability distributions through Markov Chain Monte Carlo technique. Two optimization set-ups, i.e. using observed net ecosystem exchange of CO2 (NEE) only and using observed NEE and evapotranspiration (ET) simultaneously, were conducted to detect the different constraints of different observations on model parameters. Four parameters were well constrained using observed NEE only, including photosynthesis and respiration related parameters. While seven parameters were well constrained using measured NEE and ET simultaneously, four of which were water related parameters. Obviously, more information can be derived from the simultaneous optimization, since there was additional process information in water flux observation. The modeled ET of the NEE and ET optimization set-up had a much better fit to measured values than the NEE only optimization set-up (R-2 = 0.70 vs. R-2 = 0.30), although the modeled NEE from the two set-ups had a good fit to the observations (R-2 = 0.85 vs. R-2 = 0.83). This implied that assimilating carbon and water fluxes simultaneously can improve the parameterization and overall performance of the model. Then, we quantified the uncertainties in model predictions using Monte Carlo simulation, and trace them to specific parameter and parameter interactions through Sobol' variance decomposition method. The uncertainties of five outputs of interest in CBS site, NEE, gross primary productivity (GPP), ecosystem respiration (RE), ET and transpiration (T), were 50.82%, 22.35%, 21.25%, 9.98% and 19.54%, respectively. The uncertainty in predicted NEE was much larger since NEE is a small difference between two large fluxes, i.e. GPP and RE. The maximum net CO2 assimilation rate (A(max)) and carbon content of leaves (SLW) were classified as highly sensitive parameters for all outputs of interest in CBS site, contributing more than 70% of the uncertainties in all outputs except NEE. The importance of these two parameters holds for one subtropical evergreen coniferous plantation and one subtropical evergreen broad-leaved forest, too. Therefore, these two parameters and their underlying processes should be a focus of future model research, plant trait data collection and field measurement, at least for the sites in this study. This can help connect the model simulation research and field data collection, making them mutually informative.
KeywordSensitivity analysis Uncertainty analysis Morris global sensitivity analysis method Markov Chain Monte Carlo (MCMC) Sobol' variance decomposition method
DOI10.1016/j.ecolmodel.2018.03.013
WOS KeywordSUB-ALPINE FOREST ; SENSITIVITY-ANALYSIS ; DATA ASSIMILATION ; DIFFUSE-RADIATION ; CYCLE FEEDBACKS ; HIGH-ELEVATION ; CO2 FLUX ; CLIMATE ; EXCHANGE ; RESPIRATION
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016YFC0500204] ; National Natural Science Foundation of China[31700417]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000433645000004
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54773
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHe, Honglin
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
3.China Natl Offshore Oil Corp Res Inst, Beijing 100028, Peoples R China
4.East China Normal Univ, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200062, Peoples R China
5.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Ren, Xiaoli,He, Honglin,Zhang, Li,et al. Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion[J]. ECOLOGICAL MODELLING,2018,379:39-53.
APA Ren, Xiaoli.,He, Honglin.,Zhang, Li.,Li, Fan.,Liu, Min.,...&Zhang, Junhui.(2018).Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion.ECOLOGICAL MODELLING,379,39-53.
MLA Ren, Xiaoli,et al."Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion".ECOLOGICAL MODELLING 379(2018):39-53.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ren, Xiaoli]'s Articles
[He, Honglin]'s Articles
[Zhang, Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ren, Xiaoli]'s Articles
[He, Honglin]'s Articles
[Zhang, Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ren, Xiaoli]'s Articles
[He, Honglin]'s Articles
[Zhang, Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.