Upload
fay-cameron
View
218
Download
1
Tags:
Embed Size (px)
Citation preview
Module 2.7 Estimation of uncertaintiesREDD+ 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
Module 2.7 Estimation of uncertaintiesREDD+ 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-CO2 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.
Value Uncertainty GWP value
Area burned 1.16 kha ±10%
CH4 EF 43 Mg CH4/kha ±70% 21
N2O EF 0.3 Mg N2O/kha ±70% 310
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
3
Example 1: Biomass burning (2/2)
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
4
Example 2: LULUCF in Finland (1/3)*
For its GHG inventory, . T.
* Synthesis from the National Inventory Report of Finland (Statistics Finland
2013).
Table: Inventory uncertainties
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
5
Example 2: LULUCF in Finland (2/3)
For forest land remaining forest land, uncertainty analysis is done by pools: living biomass, mineral, and organic soils. Both sampling uncertainty of national forest inventory (NFI) and model uncertainty (used for reporting) are considered.
gain-loss method is applied, which requires data regarding increment and losses:
• Uncertainty (U%) includes sampling of volume increment (4-13%), BCEF (0.5-2.5%),
Module 2.7 Estimation of uncertaintiesREDD+ 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%.
Module 2.7 Estimation of uncertaintiesREDD+ 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.
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
8
Example 3: REDD+ matrix (2/14)
ToFrom
Forest land Other land
Forest land Forest degradation Forest conservation
Sustainable management of forestsEnhancement 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
Module 2.7 Estimation of uncertaintiesREDD+ 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 areaAdditional 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
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
10
Example 3: REDD+ matrix (4/14)
To
From
Forest land
Other landIntact (natural) forest
Non–intactforest
Forest land
Intact (natural)
forestForest conservation
Forest degradation
Deforestation
Non–intact forest
Enhancement ofC stocks
(forest restoration)
Sustainable management of
forestsDeforestation
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
Module 2.7 Estimation of uncertaintiesREDD+ 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.
Module 2.7 Estimation of uncertaintiesREDD+ 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
Module 2.7 Estimation of uncertaintiesREDD+ 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. 2000–2005 b. 2005–2010
NFL 2005
BFL 2005 OL 2005 Total
2000 NFL 2010
BFL 2010 OL 2010 total
2005
NFL 2000 78,424 828 26 79,278 NFL
2005 76,950 1407 66 78,424
BFL 2000 - 24,747 316 25,063 BFL
2005 - 24,976 599 25,575
OL 2000 0 - 123,839 123,839 OL 2005 0 - 124,182 124,181
Total2005 78,424 25,575 124,181 228,180 Total
2010 76,950 26,383 124,847 228,180
Case study in DRC
NFL = natural forest land; BFL = boundary forest; OL = other land.
Module 2.7 Estimation of uncertaintiesREDD+ 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
(103 ha)
Historical 2000-2005 26 316 828 24,747 78,424 228,180
Ref. level 2005-2010 +100%
=52
+100%=
632
+100% =1,656
24943 76,716 228,180
Actual 2005-2010 66 599 1,407 24,976 76,950 228,180
Difference actual - RL
27 125 -249 -125 221 0
Area-based hypothetical reference level
NFL = natural forest land; BFL = boundary forest; OL = other land
Module 2.7 Estimation of uncertaintiesREDD+ 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.
Module 2.7 Estimation of uncertaintiesREDD+ 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.
forestsConservation
Total
NFL to OL
EFL to OL
NFL to EFL
EFL to EFL NFL to NFL
Area (103 ha)
Difference actual - RL 15 -33 -249 -125 221 0
C losses (-), tC/ha (a) -150 -73 -78
C increment (+), tC/ha/yr (...) (…)
Cumulated credits(+) or debits (-) in 2010, MtC (b) -2,3 2,4 19,3 (…) (…) 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.
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
17
Example 3: REDD+ matrix (11/14)
NFL to OL BFL to OL NFL to BFLTier-1 C stock change (tC/ha) 150 73 78
Uncertainty % (95%CI) 52 80 125
NFL BFL OLTier-1 C stocks (tC/ha) 155 78 5Uncertainty % (95%CI) 50 75 50
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
Module 2.7 Estimation of uncertaintiesREDD+ 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.
Module 2.7 Estimation of uncertaintiesREDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
19
0
10
20
30
40
50
Red
uced
em
issi
ons
(M tC
)
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)
Module 2.7 Estimation of uncertaintiesREDD+ 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
Module 2.7 Estimation of uncertaintiesREDD+ 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
Module 2.7 Estimation of uncertaintiesREDD+ 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.
Module 2.7 Estimation of uncertaintiesREDD+ 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_submissio
ns/items/7383.php.