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An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils

Author

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  • Ogle, Stephen M.
  • Breidt, F. Jay
  • Easter, Mark
  • Williams, Steve
  • Paustian, Keith

Abstract

Simulation modelling is used to estimate C sequestration associated with agricultural management for purposes of greenhouse gas mitigation. Models are not completely accurate or precise estimators of C pools, however, due to insufficient knowledge and imperfect conceptualizations about ecosystem processes, leading to uncertainty in the results. It can be difficult to quantify the uncertainty using traditional error propagation techniques, such as Monte Carlo Analyses, because of the structural complexity of simulation models. Empirically based methods provide an alternative to the error propagation techniques, and our objective was to apply this alternative approach. Specifically, we developed a linear mixed-effect model to quantify both bias and variance in modeled soil C stocks that were estimated using the Century ecosystem simulation model. The statistical analysis was based on measurements from 47 agricultural experiments.

Suggested Citation

  • Ogle, Stephen M. & Breidt, F. Jay & Easter, Mark & Williams, Steve & Paustian, Keith, 2007. "An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils," Ecological Modelling, Elsevier, vol. 205(3), pages 453-463.
    • Handle: RePEc:eee:ecomod:v:205:y:2007:i:3:p:453-463
    DOI: 10.1016/j.ecolmodel.2007.03.007
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    Citations

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    Cited by:

    1. Wang, Gangsheng & Chen, Shulin, 2013. "Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty," Ecological Modelling, Elsevier, vol. 253(C), pages 97-106.
    2. Monte, Luigi, 2009. "Multi-model approach and evaluation of the uncertainty of model results. Rationale and applications to predict the behaviour of contaminants in the abiotic components of the fresh water environment," Ecological Modelling, Elsevier, vol. 220(12), pages 1469-1480.
    3. Shuang Gao & Patrick L. Gurian & Paul R. Adler & Sabrina Spatari & Ram Gurung & Saurajyoti Kar & Stephen M. Ogle & William J. Parton & Stephen J. Grosso, 2018. "Framework for improved confidence in modeled nitrous oxide estimates for biofuel regulatory standards," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(8), pages 1281-1301, December.
    4. Hoyoung Kwon & Carmen M Ugarte & Stephen M Ogle & Stephen A Williams & Michelle M Wander, 2017. "Use of inverse modeling to evaluate CENTURY-predictions for soil carbon sequestration in US rain-fed corn production systems," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-18, February.

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