Uncertainty of C Emissions Estimates

in Mato Grosso, Brazilian Amazon:

implications for REDD Projects

Carlos Souza Jr.1, Marcio Sales1,

Douglas Morton2, Bronson Griscom3

2 31

Measurement, Reporting and Verification in Latin American REDD+ Projects

A CIFOR Workshop, March 8-9, 2012 – Petrópolis, RJ, Brazil

2

0

5000

10000

15000

20000

25000

30000

35000

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Are

a (

km

2/y

ea

r)Annual Deforestation Rate - INPE

Acre

Amazonas

Amapá

Maranhão

Mato Grosso

Pará

Rondônia

Roraima

Tocantins

Brazilian Amazon

MRV Case of Study of Mato Grosso, Brazil

Study 1: Morton et al. (2011). Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 1) Source Data Uncertainties. Carbon Balance & Management, 6:18.

Study 2: Sales et al. (in prep.) Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 2) Modeling Carbon Emissions Uncertainty.

Study 3: Souza Jr. et al. (in prep) Long-term deforestation and forestation degradation C Emissions in MatoGrosso.

3

Mato Grosso State

• Area: 903.357 km2

• Amazon Biome: 47%

• Predominant land uses:

mechanized agriculture,

ranching and logging

• Advanced in REDD preparation

Measuring Forest Area and C Stocks Changes

http://www.gofc-gold.uni-jena.de/redd/

Measuring Gross Carbon Emissions

⋅+

⋅= ∑∑

==

n

j

m

i

lossemgr jdgrjdgrilossi CACAC11

_ )()()()(

Deforestation DegradationGross carbon

emissions

Aloss = Area of deforestation (ha)

Closs = Carbon emission from deforestation (t/ha) for forest types i … m

Adgr = Area affected by degradation (ha)

Cdgr = Carbon emission from degradation (t/ha) for degrad. types j … n

Deforestation and Forest Degradation

7

Selectively logged forest Deforested area for plantation

Forest degradation is a type of land modification, which

means that the originalstructure and composition is

temporarily or permanently changed, but it is not replaced

by other type of land cover type (Lambin, 1999).

Deforestation replaces the original forest cover by other

land cover type

Sinop-MT, Brazil

Forest Change Processes

8

Souza Jr. (in review)

Souza Jr. et al., (2009)

Sources of Deforestation Information for MT

Morton et al., (2011), CBM.

Spatial Disagreement of

Deforestation Maps

Spatial differences between PRODES-Digital and SEMA

Source; Morton et al. (2011), CBM

Dynamic of Forest

Degradation

1998

Logged and Burned

a

Logged

Logged

Old

Logged

Old Logged and

Burned

Old Logged and

Burned

Logged and Burned

c d

e f

b

• Degrataion signal changes fast.

• There is a synergism of forest degradation processes that can reduces more C stocks of degraded forests.

• Reccurrent forest degratation is expected and creates even more loss of C stocks.

• Annual monitoring is required to keep track of forest degrataion process.

Souza Jr. et al. (2005; 2009)

Classification 2002

R: NDFI02, G: NDFI03

B: NDFI03 Classificaiton 2003

Forest Change Detection

Old Deforestation

New Deforestation

Non-forest

Forest Degradation

Deforestation

LoggingOld Logging

LoggingDeforestation

Logging

Forest loss

Regrowth

Non Change

Forest Change Detection Results

13

14

25 Yars of Forest Change in Mato Grosso

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 20090

2000

4000

6000

8000

10000

12000

Are

a (

Km

2)

Deforestation

Forest degradation

Annual Forest Change

Source: Souza Jr. et (in prep.)

• Total biomass varies from 39 to 93 PgC (1015gC = billions of

tons of C).

• Maps have high spatial disagreement.

Adaptado de Houghton et al, 2001

Forest Biomass Maps

Modified from Houghton et al, 2001

Recent Forest Biomass Maps for the

Brazilian Amazon

Malhi et al. (2006)

Saatchi et al. (2007)

Sales et al. (2007), Ecol. Modelling

Sales (2010), UCSB M.Sc. Thesis

Stochastic Simulation of Forest Biomass

Difference in Forest Biomass Maps in Mato Grosso

18Morton et al., (2011), CBM.

Carbon Emission Simulator (CES)

• CES was used to compute estimates of carbon fluxes and model sources data uncertainties.

• Model-based uncertainties were estimated on the variability of emissions factors found in the literature.

• Source-data uncertainties were calculated based on the combination forest biomass and deforestation data products.

– Run 100 Monte Carlo simulations of the historical carbon releases .

Sales et al. (in prep.)

Emission Factors and Model Parameters of the Carbon

Emissions Simulator (CES).

20

CES model parametersVariable

nameValue Range References

Carbon Fraction CF 0.47 - 0.5 IPCC, 2006

Nogueira et al. 2008

Malhi et al. 2006

Forest Timber Fraction FTF 0.03 - 0.08 of AGLB Feldspauch et al. 2005 , Figueira et al. 2008

Asner et al. 2005, Ramankutty et al. 2007

Sawmill Losses SL 0.4-0.6 IMAZON 2003,

Winjum et al. 1998

Wood Products WP (1-SL) * FTF

Combustion

Completeness of 1st

Deforestation Fire

CC 0.4 – 0.65 Fearnside et al. 1993, Kauffman et al. 1995

Guild et al. 1998, Araújo et al. 1999

Carvalho Jr. et al. 2001, Morton et al. 2008

van der Werf et al. 2009, Righi et al. 2009

Elemental Fraction

(charcoal)

EF 0.03-0.06 Fearnside et al. 1993, Righi et al. 2009

Wood debris WD (remaining balance)

Heterotrophic

Respiration

k 0.05 – 0.124 Brown 1997, Houghton et al. 2000, van der Werf et al. 2004

Pyle et al. 2008

Simulations of C Emissions for Mato Grosso, Brasil

Morton et al., (2011); Sales et al. (in prep.)

Figure 1. Annual deforestation carbon emissions (Tg C) for combinations of

deforestation and biomass data. For CES model results, dashed lines indicate model-

based uncertainty of ±1 standard deviation of the mean annual deforestation

emissions from Monte Carlo simulations.

a) Tier 1/Approach 2 b) Tier 2.a/Approach 3 c) Tier 2.m/Approach 3,

Summary of C Emissions by IPCC Tier/Approaches

De

fore

sta

tio

n E

mis

sio

ns

(Tg

C)

Morton et al., (2011); Sales et al. (in prep.)

Final Remarks

• Forest biomass remains the major source of uncertainty in C emissions;

• Deforestation is the most important emissions source;

• Degradation from selective logging is not a large net source of C emissions relative to deforestation;

• Secondary forest dynamics are poorly known;

• Emissions from understory fires are potentially large, but could not be quantified based on available data sources.

23

Final Remarks

• Baseline and targets for REDD Projects should

be defined based on C Emissions.

• Forest are change baseline and high

uncertainties could limit climate benefits from

mitigation actions

24

Final Remarks• Apply a continuous process to improve

estimates of forest carbon emissions for

REDD:

– analyze available data,

– estimate emissions

– quantify uncertainties

– build baseline

– plan for new data collection and analysis to reduce

uncertainties.

– Reconstruct baseline and propose new targets

25

Aknowledgement

• TNC, Washington DC

• Gordon & Betty Moore Foundation

• Fundo Vale

• Skoll Foundation

• Climate Land Use Alliance

26