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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

Abstract

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.

Keywords

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

Notes

Acknowledgments

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)

References

  1. Agarwal DK, Silander JA, Gelfand AE, Dewar RE, Mickelson JG (2005) Tropical deforestation in Madagascar: analysis using hierarchical spatially explicit, Bayesian regression models. Ecol Model 185:105–131CrossRefGoogle Scholar
  2. Bachu S (2008) CO2 storage in geological media: role, means, status and barriers to deployment. Progr Energy Combust Sci 34:254–273CrossRefGoogle Scholar
  3. Benson SM (2007) Monitoring geological storage of carbon dioxide, carbon capture and geologic sequestration. In: Wilson EJ, Gerard D (eds) Integrating technology, monitoring and regulation. Blackwell, Ames, pp 73–100Google Scholar
  4. Benson SM, Gasperikova E, Hoversten GM (2004) Monitoring protocols and life-cycle costs for geologic storage of carbon dioxide. In: Proceedings of the 7th international conference on greenhouse gas control technologies (GHGT-7), Vancouver, 5–9 Sept 2004Google Scholar
  5. Berliner LM (2000) Hierarchical Bayesian modeling in the environmental sciences, Allgemeines Statistisches Archiv. J German Stat 84:141–153Google Scholar
  6. Borsuk ME, Higdon D, Stow CA et al (2001) A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecol Model 143:165–181CrossRefGoogle Scholar
  7. Buchmann N (2000) Biotic and abiotic factors controlling soil respiration rates in Picea abies stands. Soil Biol Biochem 32:1625–1635CrossRefGoogle Scholar
  8. Cable JM, Ogle K, Tyler AP, Pavao-Zuckerman MA, Huxman TE (2009) Woody plant encroachment impacts on soil carbon and microbial processes: results from a hierarchical Bayesian analysis of soil incubation data. Plant Soil 320:153–157CrossRefGoogle Scholar
  9. Carlyle JC, Than UB (1988) Abiotic control of soil respiration beneath an eighteen-year-old pinus radiate stand in south-eastern Australia. J Ecol 76:654–662CrossRefGoogle Scholar
  10. Colosimo BM, Del Castillo E (eds) (2007) Bayesian process monitoring, control and optimization. Chapman & Hall/CRC Press, London/Boca RatonGoogle Scholar
  11. Cortis A, Oldenburg CM, Benson SM (2008) The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites. Int J Greenh Gas Control 2:640–652CrossRefGoogle Scholar
  12. Davidson EA, Belk E, Boone RD (1998) Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest. Glob Change Biol 4:217–227CrossRefGoogle Scholar
  13. Davidson EA, Verchotl LV, Cattânio JH, Ackerman IL, Carvalho JEM (2000) Effects of soil water content on soil respiration in forests and cattle pastures of eastern Amazonia. Biogeochemistry 48:53–69CrossRefGoogle Scholar
  14. Davidson EA, Janssens IA, Luo Y (2006) On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Glob Change Biol 12:154–164CrossRefGoogle Scholar
  15. de Coninck H, Stephens JC, Metz B (2009) Global learning on carbon capture and storage: a call for strong international cooperation on CCS demonstration. Energy Policy 37:2161–2165CrossRefGoogle Scholar
  16. Downes BJ, Barmuta LA, Fairweather PG, Faith DP, Keough MJ, Lake PS, Mapstone BD, Quinn GP (2002) Monitoring ecological impacts: concepts and practice in flowing waters. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  17. Fang C, Moncrieff JB (2001) The dependence of soil CO2 efflux on temperature. Soil Biol Biochem 33:155–165CrossRefGoogle Scholar
  18. Gelman A, Hill J (2006) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New YorkCrossRefGoogle Scholar
  19. Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian data analysis, 2nd edn. Chapman & Hall/CRC Press, Boca RatonGoogle Scholar
  20. Goyal A, Small MJ, von Stackelberg K, Burmistrov D, Jones N (2005) Estimation of fugitive lead emission rates from secondary lead facilities using hierarchical Bayesian models. Environ Sci Technol 39:4929–4937CrossRefGoogle Scholar
  21. Hanson PJ, Edwards NT, Garten CT, Andrew JA (2000) Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48:115–146CrossRefGoogle Scholar
  22. Healy RW, Striegl RG, Russell TF, Hutchinson GL, Livingston GP (1996) Numerical evaluation of Static-chamber measurements of soil–atmosphere gas exchange: identification of physical processes. Soil Sci Soc Am J 60:740–747CrossRefGoogle Scholar
  23. Hepple RP, Benson SM (2005) Geologic storage of carbon dioxide as a climate change mitigation strategy: performance requirements and the implications of surface seepage. Environ Geol 47:576–585CrossRefGoogle Scholar
  24. Hibbard KA, Law BE, Reichstein M (2005) An analysis of soil respiration across northern hemisphere temperate ecosystems. Biogeochemistry 73:29–70CrossRefGoogle Scholar
  25. Jassal RS, Black TA, Drewitt GB, Novak MD, Gaumont-Guay D, Nesic Z (2004) A model of the production and transport of CO2 in soil: predicting soil CO2 concentrations and CO2 efflux from a forest floor. Agric Forest Meteorol 124:219–236CrossRefGoogle Scholar
  26. Jassal RS, Black TA, Novak MD, Gaumont-Guay D, Nesic Z (2008) Effect of soil water stress on soil respiration and its temperature sensitivity in an 18-year-old temperate Douglas-fir stand. Glob Change Biol 14:1305–1318CrossRefGoogle Scholar
  27. Klusman RW (2003) Evaluation of leakage potential from a carbon dioxide EOR/sequestration project. Energy Convers Manage 44:1921–1940CrossRefGoogle Scholar
  28. Leuning R, Etheridge D, Luhar A, Dunse B (2008) Atmospheric monitoring and verification technologies for CO2 geosequestration. Int J Greenh Gas Control 2(3):401–414CrossRefGoogle Scholar
  29. Lewicki JL, Oldenburg CM, Dobeck L, Spangler L (2007) Surface CO2 leakage during two shallow subsurface CO2 releases. Geophys Res Lett 34:L24402CrossRefGoogle Scholar
  30. Lloyd J, Taylor JA (1994) On the temperature dependence of soil respiration. Funct Ecol 8:315–323CrossRefGoogle Scholar
  31. Lockwood JR, Schervish MJ, Gurian P, Small MJ (2001) Characterization of arsenic occurrence in US drinking water treatment facility source waters. J Am Stat Assoc 96:1184–1193CrossRefGoogle Scholar
  32. Lockwood JR, Schervish MJ, Gurian P, Small MJ (2004) Analysis of contaminant co-occurrence in community water systems. J Am Stat Assoc 99:45–56CrossRefGoogle Scholar
  33. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:325–337CrossRefGoogle Scholar
  34. Moncrieff JB, Fang C (1999) A model for soil CO2 production and transport 2: application to a Florida Pinus elliottii plantation. Agric Forest Meteorol 95:237–256CrossRefGoogle Scholar
  35. O’Connell AM (1990) Microbial decomposition (respiration) of litter in eucalypt forests of South-Western Australia: an empirical model based on laboratory incubations. Soil Biol Biochem 22:153–160CrossRefGoogle Scholar
  36. Oldenburg CM, Lewicki JL, Hepple RP (2003) Near-surface monitoring strategies for geologic carbon dioxide storage verification. Lawrence Berkeley National Laboratory, LBNL-54089Google Scholar
  37. Patrick LD, Ogle K, Tissue DT (2009) A hierarchical Bayesian approach for estimation of photosynthetic parameters of C3 plants. Plant Cell Environ 32:1695–1709CrossRefGoogle Scholar
  38. Pumpanena J, Ilvesniemib H, Kulmala L, Siivola E, Laakso H, Kolari P, Helenelund C, Laakso M, Uusimaa M, Hari P (2008) Respiration in boreal forest soil as determined from carbon dioxide concentration profile. Soil Sci Soc Am J 72:1187–1196CrossRefGoogle Scholar
  39. Raich JW, Schlesinger WH (1992) The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44:81–99CrossRefGoogle Scholar
  40. Reichstein M, Rey A, Freibauer A et al (2003) Modelling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Global Biogeochem Cycle 17(4):15.1–15.15CrossRefGoogle Scholar
  41. Reth S, Reichstein M, Falge E (2005) The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux—a modified model. Plant Soil 268:21–33CrossRefGoogle Scholar
  42. Richardson AD, Braswell BH, Hollinger DY, Prabir Burman P, Davidson EA, Evans RS, Flanagan LB, Munger JW, Savage K, Urbanski SP, Wofsy SC (2006) Comparing simple respiration models for eddy flux and dynamic chamber data. Agric Forest Meteorol 141:219–234CrossRefGoogle Scholar
  43. Smith KA, Ball T, Conen F, Dobbie KE, Massheder J, Rey A (2003) Exchange of greenhouse gases between soil and atmosphere: interactions of soil physical factors and biological processes. Eur J Soil Sci 54:779–791CrossRefGoogle Scholar
  44. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit. J Roy Stat Soc B 64:583–639CrossRefGoogle Scholar
  45. Strazisar BR, Wells AW, Diehl JR, Hammack RW, Veloski GA (2009) Near-surface monitoring for the ZERT shallow CO2 injection project. Int J Greenh Gas Control 3:736–744CrossRefGoogle Scholar
  46. Tsiamyrtzis P, Hawkins DM (2005) A Bayesian scheme to detect changes in the mean of a short run process. Technometrics 47:446–456CrossRefGoogle Scholar
  47. US Environmental Protection Agency (2009) Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities—Unified Guidance, EPA 530-R-09-007, March. http://www.epa.gov/epawaste/hazard/correctiveaction/resources/guidance/sitechar/gwstats/index.htm
  48. van Alphen K, Hekkert MP, Turkenburg WC (2009) Comparing the development and deployment of carbon capture and storage technologies in Norway, the Netherlands, Australia, Canada and the United States—an innovation system perspective. Energy Proced 1:4591–4599CrossRefGoogle Scholar
  49. Wang W, Feng J, Oikawa T (2009) Contribution of root and microbial respiration to soil CO2 efflux and their environmental controls in a humid temperate grassland of Japan. Pedosphere 19(1):31–39CrossRefGoogle Scholar
  50. Wikle CK (2003) Hierarchical models in environmental science. Int Stat Rev 71:181–199CrossRefGoogle Scholar
  51. Wilson EJ, Friedmann SJ, Pollack MF (2007) Research for deployment: incorporating risk, regulation, and liability for carbon capture and sequestration. Environ Sci Technol 41:5945–5952CrossRefGoogle Scholar

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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