NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data

Barr, Alan; Ricciuto, D. M.; Schaefer, K.; Richardson, A D; Agarwal, Deb; Thornton, P.E.; Davis, K. J.; Cook, R.B.; Hollinger, D. Y.; Ingen, Catharine van; Amiro, B. D.; Andrews, A. E.; Arain, M. Altaf; Baldocchi, D. D.; Black, T. A.; Bolstad, Paul V.; Desai, Ajinkya; Dragoni, D.; Flanagan, Lawrence B.; Gu, L.; Katul, Gabriel G.; Law, B. E.; Lafleur, Peter M.; Margolis, Hank A.; Matamala, Roser; Meyers, Tilden P.; McCaughey, J. H.; Monson, Russell K.; Munger, J. W.; Oechel, W. C.; Oren, Ram; Roulet, Nigel T.; Torn, M. S.; Verma, S. B.

doi:10.3334/ornldaac/1178

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data available for the calibration of model parameters consist of the Harvard Forest's daily NEE values processed for the NACP site synthesis study (Barr et al, 2013) based on flux data measured at the site (Urbanski et al, 2007). Hill et al (2012) estimated that daily NEE estimates follow a normal distribution.…”

Section: Calibration Datamentioning

confidence: 99%

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

Safta¹,

Ricciuto²,

Sargsyan³

et al. 2014

Preprint

View full text Add to dashboard Cite

Abstract. In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model. A Global Sensitivity Analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for Quantities of Interest obtained with the Data Assimilation Linked Ecosystem Carbon (DALEC) model. We then employ a Bayesian approach to calibrate the parameters of DALEC using net ecosystem exchange observations at the Harvard Forest site. The calibration exercise is guided by GSA and by Fisher information matrix results that quantify the amount of information carried by the experimental data about specific model parameters. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks. These checks show a better performance for the non-steady state model during the growing season compared to the one employing steady state assumptions. Overall, this study leads to a 40% improvement in the predictive skill of DALEC and highlights the importance of considering correlations in the model parameters as informed by the data.

show abstract

Section: Calibration Datamentioning

confidence: 99%

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

Safta¹,

Ricciuto²,

Sargsyan³

et al. 2014

Preprint

View full text Add to dashboard Cite

show abstract

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

et al. 2016

Self Cite

View full text Add to dashboard Cite

Croplands are highly productive ecosystems that contribute to land-atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization Author's personal copy for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.Keywords Carbon and energy fluxes Á Cropland ecosystems Á Land-atmosphere exchange Á Modeldata comparison Á Cropland carbon and energy exchange

show abstract

NACP Site: Tower Meteorology, Flux Observations with Uncertainty, and Ancillary Data

Cited by 2 publications

References 0 publications

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the Data Assimilation Linked Ecosystem Carbon model

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Contact Info

Product

Resources

About