1. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.7 Estimation of uncertainties Module developers: Giacomo Grassi, Joint Research Centre Suvi Monni, Benviroc Frédéric Achard, Joint Research Centre Andreas Langner, Joint Research Centre Martin Herold, Wageningen University Country examples: 1.Biomass Burning 2.LULUCF in Finland 3.Appling the conservativeness approach to the DRC example (matrix approach) – this example is in common with Module 3.3 V1, April 2015 Source: IPCC GPG LULUCF Creative Commons License

2. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 Example 1: Biomass burning (1)  This country example shows combination of uncertainties for non-CO 2 emissions from biomass burning for an Annex I Party  Note that no uncertainty is assumed for GWP values  The table below shows the data used in the calculations ValueUncertaintyGWP value Area burned1.16 kha±10% CH 4 EF43 Mg CH 4 /kha±70%21 N 2 O EF0.3 Mg N 2 O/kha±70%310

3. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Example 1: Biomass burning (2)

4. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Example 2: LULUCF in Finland (1)*

5. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Example 2: LULUCF in Finland (2)

6. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Example 2: LULUCF in Finland (3)  Conversion to/from forest land and related KP activities: estimation of C stock change in all pools consist in AD x EF  Uncertainty of AD due to sampling was estimated from NFI ● Because of small land areas involved a high sampling error is reported: e.g. U% for deforestation is 30%  U% in the increment of living biomass and in the mineral and organic soil emission factors are based on expert judgement  For emissions from soils under conversions of Forest land to Cropland and Grassland preliminary estimates: 60 - 150%

7. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 Example 3: Appling the conservativeness approach to the DRC example (matrix approach) (1) (This example is in common with Exercise 4 and Module 3.3) IPCC basics to estimate forest C stock changes  Emissions = activity data (AD) x emission factor (EF)  Six land uses: Forest land, Cropland, Grassland, Wetlands, Settlements, Other lands  Methods to estimate C stock changes: GAIN-LOSS: growth - harvest – other losses (all tiers) STOCK CHANGE: difference of C stock over time (only tiers 2-3) IPCC would require tier-2/3 methods for EF in "Key Categories" (likely including deforestation and degradation in most cases), but most developing countries not ready yet for tier 2/3

8. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 Example 3: REDD+ matrix (2) to From Forest LandOther Land Forest Land Forest Degradation Forest conservation Sustainable Management of Forests Enhancement of carbon stocks Deforestation Other Land Enhancement of carbon stocks (Afforestation/ Reforestation) How would REDD+ activities fit into IPCC land uses? Stock change method: C before – C after Gain-loss: growth – harvest – other losses IPCC (very uncertain) FAOSTAT: very difficult to get the right data! Difficult to get data Overall, unlikely to estimate C stock changes from degradation with tier 1

9. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Example 3: REDD+ matrix (3) Don’t forget degradation! Estimates of carbon emissions from degradation (expressed as an additional percentage to the emissions from deforestation) Study Area Additional emissions due to forest degradation Reference Humid tropics+6%Achard et al 2004 Brazilian Amazon, Peruvian region +25-47%Asner et al 2005 Tropical regions+29%Houghton 2003 South East Asia+25-42% Houghton and Hackler 1999 Tropical Africa+132%Gaston et al 1998

10. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 Example 3: REDD+ matrix (4) to from Forest land Other Land “Intact (natural) Forest” “ non-intact Forest ” Forest land “Intact (natural) Forest” Forest conservation Forest Degradation Deforestation “ non-intact Forest ” Enhancement of C stocks (forest restoration) Sustainable Management of Forests Deforestation Other Land- Enhancement of C stocks (A/R) Modified IPCC land transition matrix (REDD+ matrix) Stock change method: C before – C after Gain-loss: growth – harvest – other losses

11. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Example 3: REDD+ matrix (5) How to identify “non-intact forests” ?  Among different possible approaches, “forest edges” may be used as a simple and pragmatic proxy to identify non-intact areas (“boundary forests”), or at least may be a first step to be complemented by other more accurate approaches (i.e. high-resolution RS)  The underlying assumption is that forests that are sufficiently remote from non forested areas (i.e. at a certain distance from roads, navigable waters, crops, grasslands, mines, etc.) are protected against significant anthropogenic degradation

12. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 12 Example 3: REDD+ matrix (6)  Input: Binary forest maps using the methodology of FAO Remote Sensing Survey  Intensified sampling 60x60 m²  Treatment: Morphological Spatial Pattern Analysis (MSPA)  Biome specific: Rainforest in Congo Basin (Edge size=500m)  Could as well be called « exposed », « potentially degraded », « managed » or simply « other » forests Example of identification of “boundary forests”

13. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 13 Example 3: REDD+ matrix (7) Area transition matrices for a biome (Congo rainforests), 000 ha a) 2000-2005b) 2005-2010 NFL 2005 BFL 2005 OL 2005 Total 2000 NFL 2010 BFL 2010 OL 2010 total 2005 NFL 2000 78,4248282679,278 NFL 2005 76,95014076678,424 BFL 2000 -24,74731625,063 BFL 2005 -24,97659925,575 OL 2000 0 -123,839 OL 2005 0 - 124,182124,181 Total 2005 78,42425,575124,181228,180 Total 2010 76,95026,383124,847228,180 Case study in DRC NFL = Natural Forest land; BFL = Boundary Forest; OL = Other Land

14. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 14 Example 3: REDD+ matrix (8) Deforestation (in 5 yrs) Degrada t (in 5 yrs) Sust. Mgmt. Forests Conservati on Total NFL to OL EFL to OL NFL to EFL EFL to EFL NFL to NFL Area (10 3 ha) Historical 2000-20052631682824,74778,424228,180 Ref. Level 2005-2010 +100% = 52 +100% = 632 +100% = 1,656 2494376,716228,180 Actual 2005-2010665991,40724,97676,950228,180 Difference Actual - RL 27125-249-1252210 Area based hypothetical Reference level NFL = Natural Forest land; BFL = Boundary Forest; OL = Other Land

15. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 15 Example 3: REDD+ matrix (9) Estimating C stock changes for REDD activities  Once the transition matrix for AD is done, each AD will need to be multiplied by the relevant EF, to get C stock change for each REDD+ activity For “natural forest" tier 1 EF are available from IPCC For “boundary forests" data may be taken from the literature (or a crude assumption of half of C stock of NFL may be considered)  Uncertainties values need to be associated with each EF The proposed approach requires that the same tier-1 EF (stratified by forest & climate type) is used in both Reference Level (RL) and in the “accounting period”. This means that the errors of EF in the RL and accounting period are fully "correlated“.

16. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 16 Example 3: REDD+ matrix (10) Deforestation (in 5 yrs) Degradat (in 5 yrs) Sust. Mgmt. Forests Conservation Total NFL to OL EFL to OL NFL to EFL EFL to EFL NFL to NFL Area (10 3 ha) Difference Actual - RL15-33-249-1252210 C losses (-), tC/ha (a) -150-73-78 C increment (+), tC/ha/yr (...)(…) Cumulated credits(+) or debits (-) in 2010, MtC (b) -2,32,419,3 (…) 19.4 NFL = Natural/Intact Forest land; BFL = Boundary Forest; OL = Other Land (a) Assuming these values of biomass C stocks: NFL: 155 tC/ha (IPCC 2006); EFL: NFL/2 (or 50% degradation on average in exposed forests); OL: 5 tC/ha (b) Calculated as the difference in area (Actual minus RL) x the C stock change

17. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 17 Example 3: REDD+ matrix (11) NFL to OLBFL to OLNFL to BFL Tier-1 C stock change (tC/ha)1507378 Uncertainty % (95%CI)5280125 NFLBFLOL Tier-1 C stocks (tC/ha)155785 Uncertainty % (95%CI)507550 Assume that estimates for (“accounting period” – RL) obtained with adequate methods for AD but NOT for EF (tier 1) When the uncertainties above are combined, total uncertainty of the emission reduction (19,4 Mt C) becomes >100% (95%CI) Taking uncertainties into account How to consider that this country used tier 1 (highly uncertain) EF for a key category?

18. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 18 Example 3: REDD+ matrix (12) As part of the Kyoto Protocol review process, UNFCCC has approved conservativeness factors linked to specific uncertainty ranges. Essentially, these factors consider the 50% confidence interval

19. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 19 Example 3: REDD+ matrix (13) 95% confidence interval 50% confidence interval In this example, by discounting the emissions reduction by about 30% (following the approach of KP review), the risk of overestimating the reduction of emissions is significantly reduced Lower bound of 50% CI (≈14MtC)

20. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 20 Example 3: REDD+ matrix (14)  In conclusion, the REDD+ matrix may allow to estimate C stock change from Deforestation/Degradation based on IPCC tier 1  The application of a conservative discount to address the high uncertainty of tier-1 based estimates increases the credibility of any possible claim of result-based payment  The simplicity and cost-effectiveness of this approach may allow: Broadening the participation to REDD+, allowing to join also those countries with limited forest monitoring capacity Increasing the credibility of emission reductions estimated with tier-1, while maintaining strong incentives for further increasing the accuracy of the estimates, i.e. to move to higher tiers

21. Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 21 Recommended modules as follow up  Module 2.8 to learn more about the role of evolving technologies for monitoring of forest area changes and changes in forest carbon stocks  Modules 3. to proceed with REDD+ assessment and reporting