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Key sources of uncertainty in forest carbon inventories Raisa Mäkipää with Mikko Peltoniemi, Suvi Monni, Taru Palosuo, Aleksi Lehtonen & Ilkka Savolainen.

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Presentation on theme: "Key sources of uncertainty in forest carbon inventories Raisa Mäkipää with Mikko Peltoniemi, Suvi Monni, Taru Palosuo, Aleksi Lehtonen & Ilkka Savolainen."— Presentation transcript:

1 Key sources of uncertainty in forest carbon inventories Raisa Mäkipää with Mikko Peltoniemi, Suvi Monni, Taru Palosuo, Aleksi Lehtonen & Ilkka Savolainen EU Workshop on Uncertainties in Greenhouse Gas Inventories 5-6 September 2005 Helsinki, Finland

2 GHG reporting under the UNFCCC and Kyoto Protocol (KP) IPCC guidance for GHG reporting –1996 revised guidelines –2003 GPG for LULUCF sector (other sectors already in 2000) –2006 under expert review (from Sept 12) Introduction

3 KP ->Need to improve inventories Completeness: all pools to be included Consistency: time-series 1990-present Transparency: default values, reporting Accuracy: uncertainty analysis help to priroritise efforts to improve inventories; uncertainty to be reduced as far as practicable

4 Inventories Land-use change –methods (sampling based NFI, remote sensing, land- use statistics) –categories (definitions vs. monitroing system) –challenges e.g. initial C stocks and time of transition Forest remaining forest, major C stock and sink of LULUCF sector

5 Biomass carbon inventories Default method: Growth – Drain Stock change method: Stock t+1 – Stock t

6 Uncertainty analysis Guidance by IPCC GPG Chapter 5.2 Identifying and quantifying uncertainties 1.Error propagation equations 2.Monte Carlo Analysis

7 Error propagation equations Uncertainty of a product of several quantities where: U total : the percentage uncertainty in the product of the quantities (the 95% confidence interval divided by the total and expressed as a percentage). Note that this uncertainty is twice the relative standard error (in %), a commonly used statistical estimate of relative uncertainty. U i :the percentage uncertainties associated with each of the quantities. (Equation 5.2.2, IPCC GPG 2004 )

8 Uncertainty of biomass stock estimates can be one value where: U v : uncertainty of the volume U d : uncertainty of the wood density U BEF : uncertainty of BEF

9 Relative standard error (r stock ) and percentage uncertainty of biomass stock of spruce for Svealand (%) Measure used in NFIs Used by IPCC GPG

10 Uncertainty of a sum of several quatities where: U E :percentage uncertainty of the sum U i :percentage uncertainty associated with source/sink i E i :emission/removal estimate for source/sink I (Equation 5.2.1, IPCC GPG 2004)

11 Uncertainty of stock change: how stock estimates apply on sink assessment Could give for uncertainty of the change in biomass stock (example with illustrative values)

12 Soil GHG inventory Peatlands based on flux measurements –area * emission factor Upland forest soils –change in C stock

13 Methods to assess change in soil C stock 1.Repeated measurements 2.Statistical models on soil C as a function of stand and tree parameters 3.Dynamic soil model integrated to NFI data on forest resources Differences in uncertainty assessment??

14 Uncertainty analysis IPCC GPG Ch 5.2 Identifying and quantifying uncertainties 1.Error propagation equations 2.Monte Carlo Analysis

15 Aggregated or averaged input data on –growing stock, area (forest land, no peat), growth indices, harvests, temperature, natural mortality Annual estimates of growing stock interpolated from the estimates at calculation period ends (GS start, GS end ) using growth indices and drain estimates Integrated with dynamic soil C model An inventory based carbon model combining a dynamic soil component

16 Methods to estimate uncertainties and key factors: Approach 2 - Monte Carlo X= 1 P1P1 1 P2P2 1 Result X = Any operator P i,j = Any parameter, input or variable in the system

17 Laskennan kulku Model for litter of understory vegetation Inventory: stand volume BEFs Errors of living biomasses by component Errors of biomass turnover rates Errors in the amounts of litter for three different litter types (input to the soil model) Inventory: Area Drain statistics Error of drain biomass (harvest residues) Errors of source data and models The result distributions for the amount of soil carbon, changes in carbon, soil respiration Errors related to the parameters in the soil model stem, branches, roots, etc.. Underst. litter production Extractives Cellulose Fine woody Coarse woody Lignin-like Humus 1 Humus 2 CO 2

18 Carbon stocks in 1990 (Tg) VegetationSoilForest total CV ~ 2.5% CV ~ 47% CV ~ 32%

19 Carbon sinks in 1990 (Tg) VegetationSoilForest total CV ~ 8% CV ~ 43% CV ~ 21%

20 Uncertainty of C sink

21 Key factors of uncertainty: vegetation sink and stock

22 The key factors of uncertainty: soil sink and stock Combined effect in the 1st run

23 CO2 emissions and removals, error bar is 95% CI

24 Summary Soil C sink can be estimated with a dynamic soil C model; input derived from biomass data provided by NFI Complete inventories incl. all pools needed LULUCF contribute notably to overall uncertainty of the GHG inventory Error propagation equations are OK for uncertainty analysis of carbon sink of biomass Soil carbon model -> MonteCarlo analysis Soil model parameters determine most of the uncertainty of forest/soil stocks. Variables that are related changes contribute to the uncertainty of forest sinks

25 Discussion: Reliability of results (1/2) Sources of uncertainty not covered in the study: –model structure: possible wrong or missing components or processes? –classification/applicability of submodels to this case –forest inventories report average GS for a period, not for a single year –The use of average climatic conditions for the soil model instead of detailed data

26 Discussion: Reliability of results (2/2) Subjectivity of uncertainty estimates –Expert judgment needed for parameters missing uncertainty estimates –What is included in the reported uncertainty estimates varies

27 Discussion Estimation of annual sinks introduces extra input variables into the system Information on variability or correlation of model parameters lacks although the information could be available for input data –For some model parameters (eg. turnover rates) potential variability should be treated as uncertainty because there is no data The method is more applicable for the estimation of long term sinks

28 Thank you!


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