Estimating national emissions from fires and their uncertainties Prof. Heiko Balzter

1. Estimating national emissions from fires and their uncertaintiesProf. Heiko Balzter Remote Sensing of Fire for National Greenhouse Gas Accounting University of Leicester, UK, 7-8 September 2009

2. Introduction • Greenhouse gas accounting • Remote sensing and GIS • Aims of the training workshop

3. GHG Inventory and Reporting Steps (i) Estimate land use categories. (ii) Conduct key category assessment; using the best methods for your key land categories and most significant C pools and non-CO2 gases. (iii) Ensure that the requirements in terms of emission and removal factors and activity data appropriate to the tier level are being met. (iv) Quantify emissions and removals incl. uncertainties. (v) Fill in the reporting tables using the provided worksheets where appropriate. (vi) Document and archive all information. (vii) Carry out quality control, verification, and expert peer review.

4. FRAMEWORK OF TIER STRUCTURE IN THE GOOD PRACTICE GUIDANCE Tier 1 • Basic method provided in the IPCC Guidelines (Workbook) • Default emission factors provided in the IPCC Guidelines (Workbook and Reference Manual) • Data are usually spatially coarse (nationally or globally available estimates of deforestation rates, agricultural production statistics, and global land cover maps) Tier 2 • Applies emission factors and activity data which are defined by the country. • Can apply stock change methodologies based on country-specific data. • Uses country-specific emission factors/activity data. • Higher resolution activity data are typically used. Tier 3 • Higher order methods: models and inventory measurement systems tailored to address national circumstances, repeated over time, and driven by high-resolution activity data and disaggregated at sub-national to fine grid scales. • Provides greater certainty than lower tiers and has a closer link between biomass and soil dynamics. • Includes GIS analyses integrating several types of monitoring. Can have a climate dependency, and provide source estimates with interannual variability. See BOX 3.1.1

5. Units Units of CO2 emissions/removals and emissions of non-CO2 gases are reported in gigagrams (Gg). To convert tonnes C to Gg CO2, multiply the value by 44/12 and 10-3. To convert unit from kg N2O-N to Gg N2O, multiply the value by 44/28 and 10-6. For the purpose of reporting, which is consistent with the IPCC Guidelines, the signs for removal (uptake) are always (-) and for emissions (+).

6. 3.2.1.4.2.2 Choice of Removals/Emission Factors Tier 1 Estimate quantity of fuel burnt If no local data are available, this can be estimated from Table 3.A.1.13, which tabulates the product of B (the available fuel, or biomass density on the land before combustion) and C (the combustion efficiency). If ‘available fuel densities’ are available the combustion efficiencies in Table 3.A.1.14 may be used. If combustion efficiency is needed, and more specific advice is not available, the IPCC default of 0.5 should be used. When the Equation 3.2.19 is used for the estimation of non- CO2, an emission ratio and a N/C ratio is required. The N/C ratio for the fuel burnt is approximated to be about 0.01 (Crutzen and Andreae, 1990). This is a general default value that applies to leaf litter, but lower values would be appropriate for fuels with greater woody content, if data are available. Emission factors for use with Equations 3.2.19 and 3.2.20 are provided in Tables 3.A.1.15 and 3.A.1.16 respectively. Tiers 2 and 3 Use country-specific data and methods developed through field experiments.

7. GREENHOUSE GAS EMISSIONS FROM BIOMASS BURNING • Biomass burning occurs in many types of land uses causing emissions of CO2, CH4, N2O, CO, and NOx. • Good practice guidance for estimating emissions from biomass burning in: • Forest land remaining Forest land; • Land converted to Forest land; • Land converted to Cropland; and • Land converted to Grassland.

8. FOREST FIRES Note: Fire impact in unmanaged forest lands should not be reported.

10. LAND CONVERSION TO CROPLAND

11. LAND CONVERTED TO GRASSLAND

12. 3.4.2.3 NON-CO2 GREENHOUSE GASES As for all grasslands, sources of CH4 and N2O emissions associated with grassland that have recently undergone a change in land use are likely to be: • Emissions from vegetation fires; • N2O emissions from mineralisation of soil organic matter; • N2O from fertiliser use; • Increase in N2O emissions and reduction in CH4emissions from drainage of organic soils; and • Reduced CH4sink in aerobic soils due to fertiliser use. […] Fire related emissions should be calculated using the methods set out in Section 3.2.1.4, taking account, where data are available to do so, of the fact that the fuel load will often be higher during the transition period if the previous land use was forest.

13. 4.2.4.2.2 QUANTIFYING UNCERTAINTIES • Uncertainties are to be quantified according to methods as described in this report: Chapters 2 and 3 provide the necessary data and methodological advice on estimating uncertainties associated with carbon stock changes and emissions estimation. Chapter 5 (see equations in Section 5.2) shows how to combine these estimates into overall uncertainties. • It is good practice to derive confidence intervals by applying a quantitative method to existing data. Confidence intervals at given confidence levels provide a minimum basis for a simple quantitative estimate of uncertainty. • To remain consistent with GPG2000, uncertainties should be estimated at the 95% confidence limits, using component uncertainties assessed by expert judgement aiming at 95% confidence where quantification is not otherwise possible (see Section 5.2 for guidance on expert judgement).

14. Accuracy assessment of remote sensing data • When remote sensing is employed for classification of land use and detection of land-use change including units of land subject to Article 3.3, the uncertainties could be quantified by verifying classified lands with adequate actual ground truth data or higher resolution imagery (see Sections 5.7.2 and 2.4.4). A confusion matrix as described in Section 2.4.4 can be used to assess accuracy.

16. 5.2.2.1 TIER 1 – SIMPLE PROPAGATION OF ERRORS

17. 5.2.2.2 ESTIMATING UNCERTAINTIES BY CATEGORY USINGMONTE CARLO ANALYSIS (TIER 2) Step 1: Specify uncertainties in the input variables. This includes estimation parameters and LULUCF activity data, their associated means and probability distribution functions (PDFs), and any correlations. The uncertainties can be assessed following the guidance in Section 5.2.3 (Practical Consideration for Quantifying Uncertainties of Input Data) and Section 5.2.4 (Example of Uncertainty Analysis) of this chapter. For guidance on assessment of correlations, see below. Step 2: Set up software package. The emission inventory calculation, the PDFs, and the correlation values should be set up in the Monte Carlo package. The software performs the subsequent steps. In some cases, the inventory agency may decide to set up its own programme to run a Monte Carlo simulation; this can be done using statistical software. See next slide

18. Step 3: Select input values. Input values will normally be the good practice estimates applied in the calculation. This is the start of the iterations. For each input data item, a number is randomly selected from the PDF of that variable. Step 4: Estimate carbon stocks. The variables selected in Step 3 are used to estimate carbon stocks for the base year and the current year (i.e., beginning and end of the inventory period; year t−20 and year t) based on input values. Step 5: Iterate and monitor results. The calculated total from Step 4 is stored, and the process then repeats from step 3. The mean of the totals stored gives an estimate of the carbon stock, and the variability represents uncertainty. Many repetitions are needed for this type of analysis. The number of iterations can be determined in two ways: by setting the number of model runs, a priori, such as 10,000 and allowing the simulation to continue until reaching the set number, or by allowing the mean to reach a relatively stable point before terminating the simulation.