REDD Methodological Module. Estimation of uncertainty for REDD project activities

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1 1 REDD Methodological Module Estimation of uncertainty for REDD project activities Version 1.0 April 009 I. SCOPE, APPLICABILITY AND PARAMETERS Scope This module allows for estimating uncertainty in the estimation of emissions and removals in REDD project activities. Applicability The module is applicable for estimating the uncertainty of estimates of all emissions and removals of CO -e generated from REDD project activities. Where an uncertainty value is not known or cannot be simply calculated if a project can justify that it is using an indisputably conservative number then an uncertainty of 0% may be used for this component. Guidance on uncertainty a precision target of a 90% confidence interval equal to 10% of the recorded value should be targeted. This is especially important in terms of project planning for measurement of carbon stocks where sufficient measurement plots should be included to achieve this precision level across the measured stocks. Required conditions Levels of uncertainty should be known for all aspects of baseline and project. Uncertainty will generally be known as 90% confidence interval as a percentage of the mean. Where uncertainty is not known it should be demonstrated that the value used is indisputably conservative. Parameters This module provides procedures to determine the following parameter: Parameter SI Unit Description C REDD_ERROR % Total uncertainty for REDD project activity 1

2 II. PROCEDURE Estimated carbon emissions and removals arising from AFOLU activities have uncertainties associated with the measures/estimates of: area or other activity data, carbon stocks, biomass growth rates, expansion factors, and other coefficients. It is assumed that the uncertainties associated with the estimates of the various input data are available, either as default values given in IPCC Guidelines (006), IPCC GPG-LULUCF (003), expert judgment 1, or estimates based of sound statistical sampling. Alternatively, (indisputably) conservative estimates can also be used instead of uncertainties, provided that they are based on verifiable literature sources or expert judgment. In this case the uncertainty is assumed to be zero. However, this module provides a procedure to combine uncertainty information and conservative estimates resulting in an overall project uncertainty. Planning to Diminish Uncertainty It is important that the process of project planning consider uncertainty. Procedures including stratification (see Module X-STR), and the allocation of sufficient measurement plots can help ensure that low uncertainty results and ultimately full crediting can result. It is good practice to apply this module at an early stage to identify the data sources with the highest uncertainty to allow the opportunity to conduct further work to diminish uncertainty. Part 1 Uncertainty in Baseline Estimates Step 1: Assess uncertainty in projection of baseline rate of deforestation or degradation Where rates are based on actual deforestation plans (as under instances of planned deforestation) assume: Uncertainty RATE = 0 In all other scenarios an error propagation should be conducted on the uncertainty in area of deforestation in each time period based on a statistical sampling-based ground truthing or analysis of a series of very high resolution images (e.g. aerial photographs). Uncertainty RATE = ( U * E ) + ( U * E ) ( U * E ) t1 t1 E t1 t + E t t E tn tn tn Uncertainty RATE Percentage uncertainty in the rate of deforestation for areas through time; % (1) 1 Justification should be supplied for all values derived from expert judgement

3 3 U R E R t Percentage error rate in the interpretation of new areas of deforestation in remotely sensed imagery for image captured at time t; % Area of deforestation as recorded in remotely sensed imagery for image captured at time t; ha 1,, 3, t* years in which analyzed remote sensing imagery was collected NumberIncorrect t U R = TotalNumber U R NumberIncorrect t TotalNumber t t t Percentage error rate in the interpretation of deforestation in remotely sensed imagery for image captured at time t; % Number of ground truthing points incorrectly classified at time t Total number of ground truthing points at time t 1,, 3, t* years () Where deforestation is projected using regression equations of past deforestation rate versus an independent variable such as time, the uncertainty introduced by this analysis must be incorporated. Take the mean squared error (MSE) of the regression and apply a Monte Carlo statistical analysis to include this source of error into Uncertainty RATE. Step : Assess uncertainty of project emissions and removals Uncertainty should be expressed as the 90% confidence interval as a percentage of the mean. Uncertainty SS = ( U * E ) + ( U * E ) ( U * E ) E SS + E SS SS E (3) Uncertainty SS U SS Percentage uncertainty in the combined carbon stocks and greenhouse gas sources in the baseline case; % Percentage uncertainty (expressed as 90% confidence interval as a percentage of the mean where appropriate) for carbon stocks and greenhouse gas sources in the baseline case (1, n represent different carbon pools and/or GHG sources); % 3

4 4 E SS Carbon stock or GHG sources (e.g. trees, down dead wood, soil organic carbon, emission from fertilizer addition, emission from biomass burning etc.) (1, n represent different carbon pools and/or GHG sources) in the baseline case; t CO -e Step 3: Estimate total uncertainty in baseline scenario BSL RATE SS Uncertaint y = Uncertainty + Uncertainty (4) Uncertainty BSL Total uncertainty in baseline scenario; % Uncertainty RATE Percentage uncertainty in the rate of deforestation for areas through time; % Uncertainty SS Percentage uncertainty in the combined carbon stocks and greenhouse gas sources in the baseline case; % Part Uncertainty in the With-Project Scenario Area of deforestation or degradation in the with-project scenario should be tracked directly with little to no uncertainty. Uncertainty SS = ( U * E ) + ( U * E ) ( U * E ) E SS + E SS SS E (5) Uncertainty P Total uncertainty in the with-project scenario; % U SS E SS Percentage uncertainty (expressed as 90% confidence interval as a percentage of the mean where appropriate) for carbon stocks, greenhouse gas sources and leakage emissions in the with-project case (1, n represent different carbon pools and/or GHG sources); % Carbon stock, GHG sources or leakage emission type (e.g. trees, down dead wood, soil organic carbon, emission from fertilizer addition, 4

5 5 emission from biomass burning, emission from leakage due to activity shifting etc.) (1, n represent different carbon pools and/or GHG sources) in the with-project case; t CO -e Part 3 Total Error in REDD Project Activity C REDD _ ERROR = Uncertainty BSL + Uncertainty P (6) C REDD_ERROR Total uncertainty for REDD project activity; % Uncertaint y BSL Total uncertainty in baseline scenario; % Uncertainty P Total uncertainty in the with-project scenario; % Part 4 Implications for Project Accounting If C REDD_ERROR 10% of C REDD, t then no deduction should result for uncertainty If C REDD_ERROR > 10% of C REDD, t then the modified value for C REDD, t to account for uncertainty should be: = 100 C REDD _ ERROR 100 *C REDD,t (7) C REDD, t Net anthropogenic greenhouse emission reductions at time t; t CO -e C REDD_ERROR Total uncertainty for REDD project activity; % III. DATA AND PARAMETERS MONITORED Data / parameter: E RATE Data unit: ha Used in equations: 1 Area of forest as recorded in remotely sensed imagery Analysis of remotely sensed imagery (or ground data for degradation) 5

6 6 Data / parameter: E BSL SS Data unit: t CO -e Used in equations: 3 Carbon stock or GHG sources (e.g. trees, down dead wood, soil organic carbon, emission from fertilizer addition, emission from biomass burning etc.) in the baseline case The terms denoting significant carbon stocks, GHG sources or leakage emissions from baseline modules (BL-DFW, BL-PL, BL-UP) used to calculate net emission reductions. Data / parameter: E SS Data unit: t CO -e Used in equations: 5 Carbon stock or GHG sources (e.g. trees, down dead wood, soil organic carbon, emission from fertilizer addition, emission from biomass burning etc.) in the with-project case The terms denoting significant carbon stocks, GHG sources or leakage emissions used in calculating net emission reductions from the following relevant modules: CP-A, CP-B, CP-D, CP-L, CP-S, CP-W, LK-AS LK-ASU, LK-DFW, LK-ME, E-BB, E-FFC, E-NA. Data / parameter: NumberIncorrect t Data unit: dimensionless Used in equations: 6

7 7 Number of ground truthing points incorrectly classified for remotely sensed image captured at time t Ground truthing of analyzed satellite imagery with through field data points or analysis of high resolution imagery (e.g. aerial photography) Data / parameter: TotalNumber t Data unit: Used in equations: dimensionless Total number of ground truthing points for remotely sensed image captured at time t Ground truthing of analyzed satellite imagery with through field data points or analysis of high resolution imagery (e.g. aerial photography) Data / parameter: U SS Data unit: % Used in equations: 3 Percentage uncertainty (expressed as 90% confidence interval as a percentage of the mean where appropriate) for carbon stocks and greenhouse gas sources in the baseline case (1, n represent different carbon pools and/or GHG sources) Calculations arising from field measurement data Data / parameter: U SS Data unit: % 7

8 8 Used in equations: 5 Percentage uncertainty (expressed as 90% confidence interval as a percentage of the mean where appropriate) for carbon stocks and greenhouse gas sources in the with-project case (1, n represent different carbon pools and/or GHG sources) Calculations arising from field measurement data V. TERMS ORIGINATING IN OTHER MODULES Data / parameter: C REDD, t Data unit: t CO -e Used in equations: 1 Net anthropogenic greenhouse emission reductions at time t; t CO -e Module parameter originates in: REDD-MF 8