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

Ren, Xiaoyong; He, Honglin; Moore, David J. P.; Zhang, Li; Liu, Min; Li, Fan; Yu, Guirui; Wang, Huimin

doi:10.1002/2013jg002402

Cited by 16 publications

(10 citation statements)

References 67 publications

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“…Whereas the uncertainty of land surface model parameters has been subject to intensive investigations (e.g., Ren et al, 2013;Xiao et al, 2014), and several works are dedicated to reducing this uncertainty, for example by parameter estimation with data assimilation methods (e.g., Safta et al, 2015), the other sources of uncertainty are less studied. Earlier work has concluded that parameter uncertainty alone cannot explain observed deviations between measured and simulated NEE (e.g., Pridhoko et al, 2008;Wang et al, 2011), and the remaining deviations are often attributed to model structural errors.…”

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confidence: 99%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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confidence: 99%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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“…However, E0, Rref and LUEmax led to the greatest uncertainties in the NEP, followed by LSTopt and a. LSTmin and LSTmax led to low uncertainties in the NEP prediction. In addition to the parameters, forcing variables and model structure also resulted in uncertainties in the model outputs [40]. Observation instruments contain stochastic error.…”

Section: Uncertainties Resulted From Model Parametersmentioning

confidence: 99%

An Algorithm for Gross Primary Production (GPP) and Net Ecosystem Production (NEP) Estimations in the Midstream of the Heihe River Basin, China

Wang

Guodong

et al. 2015

Remote Sensing

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An accurate estimation of carbon fluxes is very important in carbon cycle studies. A remote sensing based gross primary production (GPP) and net ecosystem production (NEP) algorithm, RS-CFLUX, was presented in this work. The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of carbon fluxes at both stations, using photosynthetically active radiation (PAR), land surface temperature (LST), normalized difference water index (NDWI) and enhanced vegetation index (EVI) as input. RS-CFLUX model were sensitive to maximum light use efficiency, respiration at reference temperature, activation energy parameter of respiration.

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“…1, and more detailed information of the parameter estimation procedure was described by Braswell et al (2005) and Ren et al (2013).…”

Section: Parameterization Of Sipnetmentioning

confidence: 99%

The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems

Liu

Ren

et al. 2015

Ecological Modelling

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a b s t r a c tThe ability of terrestrial biogeochemical models in predicting land-atmospheric carbon and water exchanges is largely hampered by the insufficient characterization of model parameters. The direct observations of carbon/water fluxes and the associated environmental variables from eddy covariance (EC) flux towers provide a notable opportunity to examine the underlying processes controlling carbon and water exchanges between terrestrial ecosystems and the atmosphere. In this study, we applied the Metropolis simulated annealing technique to conduct parameter optimization analyses of a processbased biogeochemical model, simplified PnET (SIPNET), using a variety of constraining variables from EC observations and leaf area index (LAI) from MODIS at three ChinaFLUX forest sites: a temperate mixed forest (CBS), a subtropical evergreen coniferous plantation (QYZ) and a subtropical evergreen broadleaved forest (DHS). Our analyses focused on (1) identifying the key model parameters influencing the simulation of carbon and water fluxes with SIPNET; (2) evaluating how different combinations of constraining variables influence parameter estimations and associated uncertainties; and (3) assessing the model performance with the optimized parameterization in predicting carbon and water fluxes in the three forest ecosystems. Our sensitivity analysis indicated that, among three different forest ecosystems, the prediction of carbon and water fluxes was mostly affected by photosynthesis-related parameters. The performances of the model simulations depended on different parameterization schemes, especially the combinations of constraining variables. The parameterization scheme using both net ecosystem exchange (NEE) and evapotranspiration (ET) as constraining variables performed best with most well-constrained parameters. When LAI was added to the optimization, the number of well-constrained model parameters was increased. In addition, we found that the model cannot be well-parameterized with only growingseason observations, especially for those forest ecosystems with distinct seasonal variation. With the optimized parameterization scheme using both NEE and ET observations all year round, the SIPNET were able to simulate the seasonal and inter-annual variations of carbon and water exchanges in three forest ecosystems.

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

Cited by 16 publications

References 67 publications

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

An Algorithm for Gross Primary Production (GPP) and Net Ecosystem Production (NEP) Estimations in the Midstream of the Heihe River Basin, China

The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems

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