LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Zhou, Xuhui; Du, Zhenggang; Nie, Yuanyuan; He, Yanghui; Yu, Guirui; Wang, Huimin (2015)
Publisher: Tellus B
Journal: Tellus B
Languages: English
Types: Article
Subjects: Meteorology. Climatology, QC851-999, net ecosystem exchange, Bayesian inversion, Markov chain Monte Carlo, complementarity, data assimilation, Complementarity; net ecosystem exchange; biometric data; Bayesian inversion; data assimilation; MCMC, biometric data
To improve models for accurate projections, data assimilation, an emerging statistical approach to combine models with data, have recently been developed to probe initial conditions, parameters, data content, response functions and model uncertainties. Quantifying how many information contents are contained in different data streams is essential to predict future states of ecosystems and the climate. This study uses a data assimilation approach to examine the information contents contained in flux- and biometric-based data to constrain parameters in a terrestrial carbon (C) model, which includes canopy photosynthesis and vegetation–soil C transfer submodels. Three assimilation experiments were constructed with either net ecosystem exchange (NEE) data only or biometric data only [including foliage and woody biomass, litterfall, soil organic C (SOC) and soil respiration], or both NEE and biometric data to constrain model parameters by a probabilistic inversion application. The results showed that NEE data mainly constrained parameters associated with gross primary production (GPP) and ecosystem respiration (RE) but were almost invalid for C transfer coefficients, while biometric data were more effective in constraining C transfer coefficients than other parameters. NEE and biometric data constrained about 26% (6) and 30% (7) of a total of 23 parameters, respectively, but their combined application constrained about 61% (14) of all parameters. The complementarity of NEE and biometric data was obvious in constraining most of parameters. The poor constraint by only NEE or biometric data was probably attributable to either the lack of long-term C dynamic data or errors from measurements. Overall, our results suggest that flux- and biometric-based data, containing different processes in ecosystem C dynamics, have different capacities to constrain parameters related to photosynthesis and C transfer coefficients, respectively. Multiple data sources could also reduce uncertainties in parameter estimation if these data sources contain complementary information.Keywords: complementarity, net ecosystem exchange, biometric data, Bayesian inversion, data assimilation, Markov chain Monte Carlo(Published: 16 March 2015)Citation: Tellus B 2015, 67, 24102, http://dx.doi.org/10.3402/tellusb.v67.24102
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baldocchi, D. D., Hincks, B. B. and Meyers, T. P. 1988. Measuring biosphere atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology. 69, 1331 1340.
    • Barrett, D. J. 2002. Steady state turnover time of carbon in the Australian terrestrial biosphere. Global Biogeochem. Cy. 16, 55 51.
    • Braswell, B. H., Sacks, W. J., Linder, E. and Schimel, D. S. 2005. Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations. Global Change Biol. 11, 335 355.
    • Chang, M. 2012. Forest Hydrology: an Introduction to Water and Forests. CRC Press, Boca Raton, p. 203.
    • Davidson, E. A., Janssens, I. A. and Luo, Y. 2006. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biol. 12, 154 164.
    • Dowd, M. and Meyer, R. 2003. A Bayesian approach to the ecosystem inverse problem. Ecol. Model. 168, 39 55.
    • Farquhar, G., von Caemmerer, S. and Berry, J. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta. 149, 78 90.
    • Franks, S. W., Beven, K. J. and Gash, J. H. 1999. Multi-objective conditioning of a simple SVAT model. Hydrol. Earth Syst. Sci. 3, 477 488.
    • Gelfand, A. E. and Smith, A. F. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398 409.
    • Hastings, W. K. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 57, 97 109.
    • Hoff, J. H. 1899. Lectures on Theoretical and Physical Chemistry. Edward Arnold Publishers, Oxford, pp. 230 235.
    • Hollinger, D. and Richardson, A. 2005. Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiol. 25, 873 885.
    • Huang, M., Ji, J., Li, K., Liu, Y., Yang, F. and co-authors. 2007. The ecosystem carbon accumulation after conversion of grasslands to pine plantations in subtropical red soil of South China. Tellus B. 59, 439 448.
    • IPCC. 2007. Contribution of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Climate Change 2007: The Physical Science Basis (eds. S. Solomom, D. Qin, M. Manning, Z. Chen, M. Marquis and co-editors). Cambridge University Press, Cambridge, UK, pp. 337 383.
    • Keenan, T., Davidson, E., Munger, J. and Richardson, A. 2013. Rate my data: quantifying the value of ecological data for models of terrestrial carbon cycle. Ecol. Appl. 23, 273 268.
    • Leuning, R. 1995. A critical appraisal of a combined stomatal photosynthesis model for C3 plants. Plant. Cell Environ. 18, 339 355.
    • Li, X. R., Liu, Q. J., Cai, Z. and Ma, Z. 2006a. Leaf area index measurement of Pinus elliotii plantation. Acta Ecol. Sin. 26(12), 4099 4105.
    • Li, X. R., Liu, Q. J., Chen, Y., Hu, L. and Yang, F. 2006b. Aboveground biomass of three conifers in Qianyanzhou plantation. Chin. J. Appl. Ecol. 17(8), 1382 1388.
    • Luo, Y., Ogle, K., Tucker, C., Fei, S., Gao, C. and co-authors. 2011. Ecological forecasting and data assimilation in a datarich era. Ecol. Appl. 21, 1429 1442.
    • Luo, Y. and Reynolds, J. F. 1999. Validity of extrapolating field CO2 experiments to predict carbon sequestration in natural ecosystems. Ecology. 80, 1568 1583.
    • Luo, Y., Sherry, R., Zhou, X. and Wan, S. 2009. Terrestrial carbon-cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest. GCB Bioenergy. 1, 62 74.
    • Luo, Y., White, L. W., Canadell, J. G., DeLucia, E. H., Ellsworth, D. S. and co-authors. 2003. Sustainability of terrestrial carbon sequestration: a case study in Duke Forest with inversion approach. Global Biogeochem. Cy. 17, 1021.
    • Luo, Y., Wu, L., Andrews, J. A., White, L., Matamala, R. and coauthors. 2001. Elevated CO2 differentiates ecosystem carbon processes: deconvolution analysis of Duke Forest FACE data. Ecol. Monogr. 71, 357 376.
    • Ma, Z., Liu, Q., Xu, W., Li, X. and Liu, Y. 2007. Carbon storage of artificial forest in Qianyanzhou, Jiangxi Province. Scientia Silvae Sinicae. 43, 1 7.
    • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. 1953. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087 1092.
    • Mo, X. and Beven, K. 2004. Multi-objective parameter conditioning of a three-source wheat canopy model. Agr. Forest Meteorol. 122, 39 63.
    • Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C. and co-authors. 2006. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences. 3, 571 583.
    • Raupach, M., Rayner, P., Barrett, D., DeFries, R., Heimann, M. and co-authors. 2005. Model data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications. Global Change Biol. 11, 378 397.
    • Richardson, A. D. and Hollinger, D. Y. 2005. Statistical modeling of ecosystem respiration using eddy covariance data: maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models. Agr. Forest Meteorol. 131, 191 208.
    • Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J., Dail, D. B. and co-authors. 2010. Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints. Oecologia. 164, 25 40.
    • Schlesinger, W. H. and Lichter, J. 2001. Limited carbon storage in soil and litter of experimental forest plots under increased atmospheric CO2. Nature. 411, 466 469.
    • Sellers, P., Berry, J., Collatz, G., Field, C. and Hall, F. 1992. Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ. 42, 187 216.
    • Seneviratne, S. I., Lu¨ thi, D., Litschi, M. and Scha¨ r, C. 2006. Land atmosphere coupling and climate change in Europe. Nature. 443, 205 209.
    • Shao, J., Zhou, X., He, H., Yu, G., Wang, H. and co-authors. 2014. Partitioning climatic and biotic effects on interannual variability of ecosystem carbon exchange in three ecosystems. Ecosystems. 17, 1186 1201.
    • Shen, W. 2006. Carbon Budgets of Coniferous Plantations in Qianyanzhou Experimental Station, Jiangxi China. PhD Thesis. Beijing Forestry University, Beijing (In Chinese).
    • Song, X. 2007. The Seasonal Variations and Controlling Mechanisms of Ecosystem Water Use Efficiency for Subtropical Plantation Conifer in Qianyanzhou. PhD Thesis. Graduate University of Chinese Academy of Sciences, Beijing, China.
    • Valentini, R., Matteucci, G., Dolman, A., Schulze, E.-D., Rebmann, C. and co-authors. 2000. Respiration as the main determinant of carbon balance in European forests. Nature. 404, 861 865.
    • Van Wijk, M., Dekker, S., Bouten, W., Bosveld, F., Kohsiek, W. and co-authors. 2000. Modeling daily gas exchange of a Douglas-fir forest: comparison of three stomatal conductance models with and without a soil water stress function. Tree Physiol. 20, 115 122.
    • Wang, Y.-P., Trudinger, C. M. and Enting, I. G. 2009. A review of applications of model data fusion to studies of terrestrial carbon fluxes at different scales. Agr. Forest Meteorol. 149, 1829 1842.
    • Wen, X.-F., Wang, H.-M., Wang, J.-L., Yu, G.-R. and Sun, X.-M. 2010. Ecosystem carbon exchanges of a subtropical evergreen coniferous plantation subjected to seasonal drought, 2003 2007. Biogeosciences. 7, 357 369.
    • White, L., White, F., Luo, Y. and Xu, T. 2006. Estimation of parameters in carbon sequestration models from net ecosystem exchange data. Appl. Math. Comput. 181, 864 879.
    • Williams, M., Schwarz, P. A., Law, B. E., Irvine, J. and Kurpius, M. R. 2005. An improved analysis of forest carbon dynamics using data assimilation. Global Change Biol. 11, 89 105.
    • Wu, X., Luo, Y., Weng, E., White, L., Ma, Y. and co-authors. 2009. Conditional inversion to estimate parameters from eddyflux observations. J. Plant Ecol. 2, 55 68.
    • Xu, T., White, L., Hui, D. and Luo, Y. 2006. Probabilistic inversion of a terrestrial ecosystem model: analysis of uncertainty in parameter estimation and model prediction. Global Biogeochem. Cy. 20, GB2007.
    • Zhang, L., Luo, Y., Yu, G. and Zhang, L. 2010. Estimated carbon residence times in three forest ecosystems of eastern China: applications of probabilistic inversion, J. Geophys. Res. 115, G01010.
    • Zhang, D., Sun, X., Zhou, G., Yan, J., Wang, Y. and co-authors. 2006. Seasonal dynamics of soil CO2 effluxes with responses to environmental factors in lower subtropical forests of China. Sci. China Ser. D. 49, 139 149.
    • Zhou, X., Luo, Y., Gao, C., Verburg, P. S., Arnone, J. A. and coauthors. 2010. Concurrent and lagged impacts of an anomalously warm year on autotrophic and heterotrophic components of soil respiration: a deconvolution analysis. New Phytol. 187, 184 198.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article