Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation

Xiaoli Ren, Honglin He, David Joseph Moore, Li Zhang, Min Liu, Fan Li, Guirui Yu, Huimin Wang

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Estimating the exchanges of carbon and water between vegetation and the atmosphere requires process-based ecosystem models; however, uncertainty in model predictions is inevitable due to the uncertainties in model structure, model parameters, and driving variables. This paper proposes a methodological framework for analyzing prediction uncertainty of ecosystem models caused by parameters and applies it in Qianyanzhou subtropical coniferous plantation using the Simplified Photosynthesis and Evapotranspiration model. We selected 20 key parameters from 42 parameters of the model using one-at-a-time sensitivity analysis method and estimated their posterior distributions using Markov Chain Monte Carlo technique. Prediction uncertainty was quantified through Monte Carlo method and partitioned using Sobol' method by decomposing the total variance of model predictions into different components. The uncertainty in predicted net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (RE), evapotranspiration (ET), and transpiration (T), defined as the coefficient of variation, was 61.0%, 20.6%, 12.7%, 14.2%, and 19.9%, respectively. Modeled carbon and water fluxes were highly sensitive to two parameters, maximum net CO2 assimilation rate (Amax) and specific leaf weight (SLWC). They contributed more than two thirds of the uncertainty in predicted NEE, GPP, ET, and T and almost one third of the uncertainty in predicted RE, which should be focused on in further efforts to reduce uncertainty. The results indicated a direction for future model development and data collection. Although there were still limitations in the framework illustrated here, it did provide a paradigm for systematic quantification of ecosystem model prediction uncertainty. Key Points A methodological framework for uncertainty analysis is presented and evaluated The framework is applied to Qianyanzhou subtropical coniferous plantation The results can guide future model development and field measurements

Original languageEnglish (US)
Pages (from-to)1674-1688
Number of pages15
JournalJournal of Geophysical Research: Space Physics
Volume118
Issue number4
DOIs
StatePublished - 2013

Fingerprint

uncertainty analysis
Uncertainty analysis
plantation
Carbon
plantations
Fluxes
uncertainty
ecosystems
Water
carbon
Ecosystems
water
ecosystem
Evapotranspiration
evapotranspiration
prediction
model uncertainty
predictions
respiration
primary production

Keywords

  • ecosystem model
  • Markov Chain Monte Carlo (MCMC)
  • sensitivity analysis
  • Sobol' method
  • uncertainty analysis

ASJC Scopus subject areas

  • Soil Science
  • Forestry
  • Water Science and Technology
  • Palaeontology
  • Atmospheric Science
  • Aquatic Science
  • Ecology

Cite this

Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation. / Ren, Xiaoli; He, Honglin; Moore, David Joseph; Zhang, Li; Liu, Min; Li, Fan; Yu, Guirui; Wang, Huimin.

In: Journal of Geophysical Research: Space Physics, Vol. 118, No. 4, 2013, p. 1674-1688.

Research output: Contribution to journalArticle

Ren, Xiaoli ; He, Honglin ; Moore, David Joseph ; Zhang, Li ; Liu, Min ; Li, Fan ; Yu, Guirui ; Wang, Huimin. / Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation. In: Journal of Geophysical Research: Space Physics. 2013 ; Vol. 118, No. 4. pp. 1674-1688.
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