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

Climatic Change

Volumn 103, Issue 1-2, 2010, Pages 55-67

  van Oijen, Marcel ^a   Thomson, Amanda ^a  

^a CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; BAYESIAN METHODS; COMPLEX PROCESSES; FLOW MODEL; FOREST PLANTING; FORESTRY MODEL; GREENHOUSE GAS INVENTORY; LAND-USE CHANGE; SPATIAL HETEROGENEITY; UNCERTAINTY QUANTIFICATIONS; UNITED KINGDOM;

BAYESIAN NETWORKS; GAS EMISSIONS; GLOBAL WARMING; GREENHOUSE GASES; LAND USE; TIMBER;

FORESTRY;

BAYESIAN ANALYSIS; CARBON FLUX; EMISSION INVENTORY; FORESTRY MODELING; GREENHOUSE GAS; HETEROGENEITY; LAND USE CHANGE; PLANTATION FORESTRY;

EMISSION; FORESTRY; FORESTS; GREENHOUSE GASES; LAND USE; PROBLEM SOLVING; UNITED KINGDOM;

UNITED KINGDOM;

EID: 77958490513     PISSN: 01650009     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10584-010-9917-3     Document Type: Article

References (19)

1. Broadmeadow MSJ, Ray D, Samuel CJA (2005) Climate change and the future for broadleaved tree species in Britain. Forestry 78: 145-161.
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.
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: 49-71.
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, Dordrecht.
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: 397-416.
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: 67-74.
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: 391-413.
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: 1295-1315.
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: 75-85.
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 + 507.
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 pp.
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: 167-182.
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: 915-927.
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 67-80.
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.
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.