Climatic Change

, Volume 103, Issue 1–2, pp 55–67 | Cite as

Toward Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry

Article

Abstract

The Greenhouse Gas Inventory for the United Kingdom currently uses a simple carbon-flow model, CFLOW, to calculate the emissions and removals associated with forest planting since 1920. Here, we aim to determine whether a more complex process-based model, the BASic FORest (BASFOR) simulator, could be used instead of CFLOW. The use of a more complex approach allows spatial heterogeneity in soils and weather to be accounted for, but places extra demands on uncertainty quantification. We show how Bayesian methods can be used to address this problem.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Broadmeadow MSJ, Ray D, Samuel CJA (2005) Climate change and the future for broadleaved tree species in Britain. Forestry 78:145–161CrossRefGoogle Scholar
  2. Bun R, Hamal K, Gusti M et al (2010) Spatial GHG inventory on regional level: accounting for uncertainty. Clim Change. doi: 10.1007/s10584-010-9907-5 Google Scholar
  3. Dewar RC, Cannell MGR (1992) Carbon sequestration in the trees, products and soils of forest plantations—an analysis using UK examples. Tree Physiol 11:49–71Google Scholar
  4. Galloway JN (1985) The deposition of sulphur and nitrogen from the remote atmosphere. In: Galloway JN, Charlson RJ, Andrew MO et al (eds) Biogeochemical cycling of sulphur and nitrogen in the remote atmosphere. NATO ASI Series D. Reidel, DordrechtGoogle Scholar
  5. Global Soil Data Task (2000) Global gridded surfaces of selected soil characteristics (IGBP-DIS). International Geosphere–Biosphere programme—Data and information services. Available at http://www.daac.ornl.gov
  6. Hulme M, Jenkins GJ (1998) Climate change scenarios for the United Kingdom: scientific report. KCIP Technical report no 1, Climatic Research Unit, Norwich, 80 pp. Available at http://www.cru.uea.ac.uk/link/ukcip/ukcip_report.html
  7. Joos F, Bruno M, Fink R et al (1996) An efficient and accurate representation of complex oceanic and biospheric models of anthropogenic carbon uptake. Tellus 48B:397–416Google Scholar
  8. Levy PE, Wendler R, van Oijen M et al (2004) The effects of nitrogen enrichment on the carbon sink in coniferous forests: uncertainty and sensitivity analyses of three ecosystem models. Water Air Soil Pollut Focus 4:67–74CrossRefGoogle Scholar
  9. Monni S, Peltoniemi M, Palosuo T et al (2007) Uncertainty of forest carbon stock changes—implications to the total uncertainty of GHG inventory of Finland. Clim Change 81:391–413CrossRefGoogle Scholar
  10. Patenaude G, Milne R, van Oijen M et al (2008) Integrating remote sensing datasets into ecological modelling: a Bayesian approach. Int J Remote Sens 29:1295–1315CrossRefGoogle Scholar
  11. Peltoniemi M, Palosuo T, Monni S et al (2006) Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation. For Ecol Manag 232:75–85CrossRefGoogle Scholar
  12. Penman J, Gytarsky M, Hiraishi T et al (eds) (2003) Good practice guidance for land use, land-use change and forestry, IGES/IPCC. Available at http://www.ipcc-nggip.iges.or.jp
  13. Robert CP, Casella G (1999) Monte Carlo statistical methods. Springer, New York, pp xxi + 507Google Scholar
  14. Thomson AM, van Oijen M (eds) (2007) Inventory and projections of UK emissions by sources and removals by sinks due to land use, land use change and forestry. Centre for Ecology and Hydrology/DEFRA, London, 197 ppGoogle Scholar
  15. van Oijen M, Jandl R (2004) Nitrogen fluxes in two Norway spruce stands in Austria: an analysis by means of process-based modelling. Austrian J For Sci 12:167–182Google Scholar
  16. van Oijen M, Rougier J, Smith R (2005) Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol 25:915–927Google Scholar
  17. van Oijen M, Ågren GI, Chertov O et al (2008) Application of process-based models to explain and predict changes in European forest growth. In: Kahle HP, Karjalainen T, Schuck A et al (eds) Causes and consequences of forest growth trends in Europe. Brill, Leiden, pp 67–80Google Scholar
  18. van Oijen M, Dauzat J, Harmand J-M et al (2010) Coffee agroforestry systems in Central America: II. Development of a simple process-based model and preliminary results. Agroforest Syst. doi: 10.1007/s10457-010-9291-1 Google Scholar
  19. Winiwarter W, Muik B (2010) Statistical dependences in input data of national GHG emission inventories: effects on the overall GHG uncertainty and related policy issues. Clim Change. doi: 10.1007/s10584-010-9921-7 Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Centre for Ecology and Hydrology (CEH-Edinburgh)PenicuikUK

Personalised recommendations