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Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.7 Estimation of uncertainties.

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Presentation on theme: "Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.7 Estimation of uncertainties."— Presentation transcript:

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 also relates to Module 3.3 V1, May 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 (1/2)  This country example shows a 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 (2/2)

4 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Example 2: LULUCF in Finland (1/3)* Table: Inventory uncertainties

5 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Example 2: LULUCF in Finland (2/3)

6 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Example 2: LULUCF in Finland (3/3)  Conversion to/from forest land and related KP activities: estimation of C stock change in all pools is done by: 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 is based on expert judgement.  For emissions from soils under conversions of forest land to cropland and grassland, preliminary estimates are 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 Democratic Republic of Congo (DRC) example (matrix approach) (1/14) (This example also relates to 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 minus harvest minus 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 are 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 (2/14) To From Forest landOther land Forest landForest 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 minus C after Gain-loss: growth minus harvest minus 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 (3/14) 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 (4/14) 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 (5/14) 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 remote sensing).  The underlying assumption is that forests that are sufficiently remote from nonforested 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 (6/14)  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 (7/14) Area transition matrices for a biome: Congo rainforests (ha 000s) 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 (8/14) Deforestation (in 5 yrs) Degraded (in 5 yrs) Sust. mgd. forests Conser- vation 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 - RL27125-249-1252210 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 (9/14) 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 and climate type) be 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 (10/14) Deforestation (in 5 yrs) Degraded (in 5 yrs) Sust. mgd. 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 (11/14) 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 minus 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 deal with the fact that this country used Tier 1 (highly uncertain) EF for a key category?  see next slides

18 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 18 Example 3: REDD+ matrix (12/14) As part of the Kyoto Protocol review process, UNFCCC has approved conservativeness factors linked to specific uncertainty ranges. Essentially, these factors use 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 (13/14) 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 (14/14)  In conclusion, the REDD+ matrix may allow one 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 those countries with limited forest monitoring capacity to join 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 3.1 to 3.3 to proceed with REDD+ assessment and reporting

22 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 22 References  Achard, F., Eva, H. D., Mayaux, P., Stibig, H.-J. and Belward, A., 2004. Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s. Glob. Biogeochem. Cycles 18, GB2008.  Asner, G. P., Knapp, D. E., Broadbent, E. N., Oliveira, P. J. C., Keller, M. and Silva, J. N., 2005. Selective logging in the Brazilian Amazon. Science 310, 480–2.  Bucki, M., D. Cuypers, P. Mayaux, F. Achard, C. Estreguil, and G. Grassi. 2012. “Assessing REDD+ Performance of Countries with Low Monitoring Capacities: The Matrix Approach.” Environmental Research Letters 7 (1) 014031.  Gaston, G., Brown, S., Lorenzini, M. and Singh, K. D., 1998. State and change in carbon pools in the forests of tropical Africa. Glob. Change Biol. 4, 97–114.  Houghton, R. A., 2003. Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850–2000. Tellus, B, 55, 378–90.

23 Module 2.7 Estimation of uncertainties REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 23  Houghton, R. A. and Hackler, J. L., 1999. Emissions of carbon from forestry and land-use change in tropical Asia. Glob. Change Biol. 5, 481–92.  IPCC (Intergovernmental Panel on Climate Change). 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. (Often IPCC GPG.) Geneva, Switzerland: IPCC. http://www.ipcc-nggip.iges.or.jp/public/gp/english/.  IPCC, 2003. 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry, Prepared by the National Greenhouse Gas Inventories Programme, Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Wagner, F. (eds.). Published: IGES, Japan. http://www.ipcc- nggip.iges.or.jp/public/gpglulucf/gpglulucf.html (Often referred to as IPCC GPG)  Statistics Finland. 2013. Greenhouse Gas Emissions in Finland, 1990–2011: National Inventory Report under the UNFCCC and the Kyoto Protocol. Helsinki: Statistics Finland. http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submiss ions/items/7383.php.


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