CarboInvent: Methods for quantifying forest carbon budgets B. Schlamadinger & W. Galinski 3. USDA Symposium on GHGs in Agric. and Forestry Baltimore, March,

Forests in the Kyoto Protocol Afforestation, Reforestation, Deforestation Forest Management IPCC Good Practice Guidance LULUCF A) Detect lands subject to these activities B) Estimate C stock changes and GHG emissions on these lands Projects (JI, CDM)

CarboInvent Multi-source inventory methods for quantifying carbon stocks and stock changes in European Forests 14 participants, 10 countries November October 2005 Joanneum Research Austria (coordination) EFIFinland JRCEuropean Community University College DublinIreland METLAFinland Inst Forest Ecosystem ResCzech Republic PIKGermany Hung. Forest Res InstHungary Ghent UnivBelgium Fed Forest Res InstAustria Swed Univ of Agr SciSweden Univ of HamburgGermany CREAFSpain Univ of PaduaItaly

Objectives Identify / develop / test methods for improved estimates of C stock changes for UNFCCC and KP reporting  establish database of BEFs and biomass equations for major EU forest types  develop methods for soil C assessment to be combined with forest inventories over large spatial scales  develop multi-source (RS, soils, forest inventory) methods for assessing C stock changes including their regional distribution and uncertainties  apply in test sites and suggest upscaling methods to national level

Biomass expansion factors at stand level Database: in preparation BEF

Results – biomass estimation

Carbon stocks in Swedish forest soils and its relation to site factors Brussels, February Erlandsson, M., Olsson, M., Van Ranst, E and Lundin, L.

Test country Sweden: Purpose

 Increase in soil C from north to south

Remote sensing applications Stratification K Nearest Neighbours (kNN) Method Interpolation / extrapolation of forest inventories with time Mapping aerial extent of severe damages Monitoring Afforestation / Reforestation / Deforestation (Direct estimation of biomass carbon stocks)

Classification of Remote Sensing Imagery Example: Forest Area

Disturbances (feed into both bottom-up and top-down approaches) Windthrow Ground view

Disturbances (feed into both bottom-up and top-down approaches) RS view

Errors of source data and models Errors of living biomasses by component Errors of biomass turnover rates Errors in the amounts of litter for three different litter types (input to soil model) Errors related to the parameters in the soil model Inventory data Dry wood density Biomass allocation Carbon content Drain from EFISCEN Errors of drain biomass (harvest residues) Result distributions for the amount of soil carbon, changes in carbon, soil respiration Result distribution for biomass carbon Results – Top-down approach Uncertainty analysis

Uncertainty of 1990 biomass carbon stock Uncertainty of biomass carbon sink Uncertainty of carbon stock and stock change estimates for Finland. CV%=2.12 CV%=25.1 Vilén, T, Peltoniemi, M. & Meyer, J. Comparable uncertainty estimates of stocks and long-term sinks of biomass and soil carbon in an inventory based method combining a soil model for some European countries. Manuscript in preparation. Results – Top-down approach 640 MtC 720 MtC 50 MtC150 MtC

“Bottom-up” Integration Plot data Soil data BEF (field and default) Carbon estimates (without soil) per plot Remote Sensing Carbon Budget

Test sites

Joint Implementation projects

Results – CDM: Keep it simple!

Linking temporary credits to emissions trading CDM projects result in temporary credits Not exchangeable with other emissions allowances Separate the liability from credit Combine with the later part of credit stream from “energy projects” Objective: facilitate linking of sinks offset projects with EU Emissions Trading System

Workshop, May 2-4 Graz / Austria Land-use Related Choices under the Kyoto Protocol Obligations, Options and Methodologies for Defining “Forest” and for Selecting Activities under Kyoto Protocol Article 3.4