Environmental Earth Sciences

, Volume 64, Issue 3, pp 787–798 | Cite as

Bayesian hierarchical models for soil CO2 flux and leak detection at geologic sequestration sites

  • Ya-Mei YangEmail author
  • Mitchell J. Small
  • Brian Junker
  • Grant S. Bromhal
  • Brian Strazisar
  • Arthur Wells
Original Article


Proper characterizations of background soil CO2 respiration rates are critical for interpreting CO2 leakage monitoring results at geologic sequestration sites. In this paper, a method is developed for determining temperature-dependent critical values of soil CO2 flux for preliminary leak detection inference. The method is illustrated using surface CO2 flux measurements obtained from the AmeriFlux network fit with alternative models for the soil CO2 flux versus soil temperature relationship. The models are fit first to determine pooled parameter estimates across the sites, then using a Bayesian hierarchical method to obtain both global and site-specific parameter estimates. Model comparisons are made using the deviance information criterion (DIC), which considers both goodness of fit and model complexity. The hierarchical models consistently outperform the corresponding pooled models, demonstrating the need for site-specific data and estimates when determining relationships for background soil respiration. A hierarchical model that relates the square root of the CO2 flux to a quadratic function of soil temperature is found to provide the best fit for the AmeriFlux sites among the models tested. This model also yields effective prediction intervals, consistent with the upper envelope of the flux data across the modeled sites and temperature ranges. Calculation of upper prediction intervals using the proposed method can provide a basis for setting critical values in CO2 leak detection monitoring at sequestration sites.


Bayesian hierarchical model Geologic carbon sequestration Soil respiration CO2 flux CO2 leakage Statistical leak detection 



This research was conducted as part of the Carnegie Mellon–West Virginia University project, Statistical Methods for Integrating Near-Surface CO2 Migration Modeling with Monitoring Network Analysis, supported by the U.S. Department of Energy, National Energy Technology Laboratory (NETL), through the DOE University Based Environmental Science Division Support program for Monitoring, Measurement, and Verification (MMV) Statistics, Subtask TSK.41817.606.04.03. Donald Gray, Egemen Ogretim, and Gavin Liu at West Virginia University provided useful suggestions in the development of this paper. We would also like to thank the AmeriFlux scientists for the soil respiration data.

Supplementary material

12665_2011_903_MOESM1_ESM.pdf (225 kb)
Supplementary material 1 (PDF 225 kb)


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Ya-Mei Yang
    • 1
    Email author
  • Mitchell J. Small
    • 1
    • 2
  • Brian Junker
    • 3
  • Grant S. Bromhal
    • 4
  • Brian Strazisar
    • 5
  • Arthur Wells
    • 5
  1. 1.Department of Civil and Environmental EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Engineering and Public PolicyCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  4. 4.National Energy Technology Laboratory, Morgantown, West VirginiaMorgantownUSA
  5. 5.National Energy Technology Laboratory, Pittsburgh, PennsylvaniaPittsburghUSA

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