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Iufro uncertainty mexicov5_cw
1. Forest Carbon Stock Change
Uncertainty Estimation in Mexico
Oswaldo Carrillo, Jorge Morfín, Craig Wayson, Gustavo Rodríguez, Luis Rangel,
Miguel Muñoz, Marcela Olguín and Lucio Santos
October 2014
2. • What is the Monitoring Reporting and Verification (MRV) system in Mexico
• General process of carbon stock change estimation in Mexico and its uncertainty under a MRV
system
• Importance of uncertainty estimation in a national GHG emissions report
• Limitations in developing countries to estimate GHG emissions and its uncertainty
• General methodologies of carbon stock change estimation in Mexico
• Evolution of carbon emission estimation in Mexico and changes in trends
• What are the political implications?
• So we need to be sure that this carbon stock change Is real for FL-FL
• For FL-FL, how was forest carbon stock change estimated and as well as its uncertainty?
• The National Forest Inventory data shows that Mexican forest sector is a huge sink and, their
uncertainties of estimations are moderate
• Next steps
3. What is the MRV system in México
MRV is part of strategies and policies for reducing emissions from
deforestation and forest degradation, as well as the role of
conservation, sustainable management of forests, and enhancement of
forest carbon stocks (REDD+).
A comprehensive national forest MRV system should consider 3 major
components for measuring, monitoring and reporting anthropogenic
GHG emissions by sources and removals by sinks in the forest sector:
• a Satellite Land Monitoring System to assess activity data (AD), forest area and
forest area changes.
• a National Forest Inventory to assess carbon stocks and changes in carbon
stocks (i.e. emission factors - EF);
• a National Greenhouse Gas (GHG) Inventory to estimate and report
anthropogenic emissions by sources and removals by sinks;
4. The MRV system in México
Some characteristics of an MRV system to support REDD+
implementation:
• Multiscale: designed to measure, monitor, and report
forest resources at a national and subnational scale.
• Should rely on both remote sensing and ground based
forest inventory approaches.
• The MRV system must be transparent :
• well documented robust methodologies
• consistent
• accurate (reducing uncertainties at least for key
categories, and incorporating Quality Control and Quality
Assessment in all steps).
• MRV system should provide timely and appropriate
feedback to policymakers on the effectiveness of
REDD+ strategies.
5. General process of carbon stock change estimation in Mexico and their
uncertainties under a MRV system
One of the main objectives of Mexican MRV system is the update of GHG report in the LULUCF category
7. ¿What are the limitations in developing countries to estimate GHG
emissions and their uncertainties?
It is not a
common
practice
not enough
information
not
representative
at national
levelchanges are
made in the
methodologies
no standard
statistical
methods
no
permanent
staff to
estimate
GHG
emissions
decision makers
don´t have the
technical
background
8. General methodologies of carbon stocks changes estimation in Mexico -
inputs
Stratified, systematic random sample
Grid of km | 5x5 | 10x10 | 20x20 |
Sampling: 2004-2007
Re sampling: 2009 - 2013
UMP = 26, 220
Sitos = 81, 665
INEGI Land cover map Series lV (2007)
INEGI Land cover map Series V (2012)
Land cover maps developed by the Statistics
and Geography National Institute (INEGI)
9. General methodologies of carbon stock changes estimation in Mexico -
categories
FL-OU
FL-FL
FL-FLd
FLd-FL
OU-FL
Forest Land (FL) converted to Other Uses (OU)
Forest Land remaining Forest Land
Forest Land remaining Forest Land but degraded (FLd)
Degraded Forest Land changing to no degraded
Forest Land
Other Uses converted to Forest Land
11. -180000
-160000
-140000
-120000
-100000
-80000
-60000
-40000
-20000
0
1980 1990 2000 2010 2020
Including FL-FL
Including FL-FL
0
10000
20000
30000
40000
50000
1985 1990 1995 2000 2005 2010 2015
Not including FL-FL
Not including FL-FL
• We found that FL-FL seems to be a large sink.
• When de total removals of LF remain LF is included in the LULUCF total emission, the emission trend at national level
change and the LULUCF sector was estimated to be a sink.
• Its means, that LF-LF are a huge sink and is able to soak up all the emission coming from FL-OU and FL-FLd
Evolution of carbon emission estimation in Mexico and changes in trends
12. What are the political implications?
• The forest sector of Mexico seems to be a sink, and can be a strategic sector to reduce the total carbon emissions of
Mexico
• For this reason, the Mexican government is interested in having a robust statistical support for these estimations and
their uncertainties.
• If these results are well supported and their uncertainties are moderate, the total sink of FL-FL is able to soak up an
important part of the total carbon emissions of Mexico!
-40,000,000 -30,000,000 -20,000,000 -10,000,000 - 10,000,000
FL-FL
FL-FLd
FLd-FL
OU-FL
FL-OU
FL-FL FL-FLd FLd-FL OU-FL FL-OU
Emissions -39,733,337 494,260 -1,733,354 -799,587 3,067,576
Emissions
13. So we need to be sure that this carbon stock change Is real for FL-FL
• Is accurate and precise this estimation?
• We need a precise and accurate estimation
as the IPCC guidelines suggested
• Following the sampling design of the NFI, ratio estimator is a precise and accurate estimator:
6
4
75
8
3
87
2
9
53
5
9
31
Reference: IPCC 2006
R =
𝑖=1
𝑛
y𝑖
𝑖=1
𝑛
𝑎𝑖
Where:
R =Ratio estimator at strata level
y𝑖 =Total carbon at sub-plot level (o UMS) 𝑖
𝑎𝑖 = Sub-plot sampled area (o UMS) 𝑖 (400m2)
𝑛 = Number of sub − plots
𝑅 =
5 + 6 + 4 + 7 + 7 + 8 + 3 + 8 + 3 + 2 + 9 + 5 + 1 + 5 + 9 + 3
0.04 × 16
𝑅 =
85
0.64
= 132.8
14. We need to be sure that this carbon stock change in FL-FL Is real
• What is the level of uncertainty of this estimation?
• Uncertainty estimation of EF at strata level
• Error propagation IPCC methods (2006)
• Analytical method 𝑈𝑡𝑜𝑡𝑎𝑙 =
𝑈1∗𝐴1
2+ 𝑈2∗𝐴2
2+⋯+ 𝑈 𝑛∗𝐴 𝑛
2
𝐴1+𝐴2+⋯+𝐴 𝑛
• where:
Utotal : total uncertainty.
Ui : Uncertainty of factor i, con i=1…n
𝑛: Number of factors
Ai : Uncertain quantities i.
• Monte Carlo simulation
𝑈 =
1
2 𝐼𝐶
𝑅
=
1.96 𝑉 𝑅
𝑅
× 100
𝑉 𝑅 =
1
𝑛 𝑛 − 1 𝑎2
𝑖=1
𝑛
𝑦𝑖
2
− 2 𝑅
𝑖=1
𝑛
𝑦𝑖 𝑎𝑖 + 𝑅2
𝑖=1
𝑛
𝑎𝑖
2
Reference: IPCC 2006
15. For FL-FL, how was forest carbon stock change estimated and as well as
its uncertainty?
“FL remaining as FL”
• GHG in FL-FL needs the estimates of
carbon stock changes.
• Methods
Gain and
loss
Stock change
Is a closer
approach to TIER2
This approach is better
suited to the data
available in Mexico
Is convenient when there
is National Forest
Inventory
Reference: IPCC 2006
16. We estimate the carbon stock change at sub-plot level
C1=0.8
C5=1.2
C2=1
C3=2.5
C4=1.5
C1=0.9
C5=1.5
C2=1.2
C3=2.5
C4=1.8
C3=2.7
∆𝐶1 = 8.1 − 7.0= 0.1
t1
t2
𝐶1 = 0.8 + 1 + 2.5 + 1.5 + 1.2
𝐶1 =7
𝐶1 = 0.9 + 1.2 + 2.7 + 1.8 + 1.5
𝐶1 = 8.1
For FL-FL, how was forest carbon stock change estimated and as well as
its uncertainty?
17. t1 t2
t2-t1
BPE
BE
P
BPE
BE
P
PPBPE
BE
BPE-BE
BE-P
𝐶𝐴 𝑡1
𝐶𝐴 𝑡2
∆𝐶𝐴 = 𝐶 𝐴 𝑡2
− 𝐶𝐴 𝑡1
∆𝐶𝑖
∆𝑆1 = 𝑆1 𝑡2 − 𝑆1 𝑡1
∆𝑆2 = 𝑆2 𝑡2 − 𝑆2 𝑡1
∆𝑆3 = 𝑆3 𝑡2 − 𝑆3 𝑡1
∆𝑆4 = 𝑆4 𝑡2 − 𝑆4 𝑡1
For FL-FL, how was forest carbon stock change estimated and as well as
its uncertainty?
19. The National Forest Inventory data shows that Mexican forest sector is a
huge sink and, their uncertainties of estimations are moderate
Mexico can move in
the emission ranking
Forest carbon sector is important
to reduce emissions of Mexico
FL-FL subcategori of
Mexico is a sink
This estimations can be
used by maker decisions
What does It
mean for
Mexico?
• Mexico could move from 14th to
36th position in the emission
countries ranking.
Mean 04
• Forest sector is very important
to offset the total carbon
emission of Mexico.
Mean
03
• Carbon stock change in FL-FL
is weakly negative.
• Mexican needs to improve the
management and
conservation of the forest
sector.
Mean 02
• The estimations are accurate.
• The levels of uncertainty are
moderate for key strata (18-
63%).
Mean 01
20. Next steps
We need to try reduce the uncertainty and make more accurate our estimations of carbon stock change
in the forest sector at national and subnational levels
• We need to increase our knowledge from other uncertainty sources
• We need to improve methods of measurement and estimation methodologies of NFI to reduce uncertainty
Reference: Chave, 2012
