Faostat 2013

[Octavis Uberstine]

Specificfire emission categories are significant in terms of their overall regional impacts. For instance, in Africa, prescribed burning of savanna represents 25 % of total agricultural emissions (Tubiello et al. 2014). In South-east Asia, fires in drained tropical peatland, often linked to palm oil cultivation, can lead to significant emissions in specific years, with impacts seen at large regional and even global scales (Hayasaka et al. 2014).

Fire emissions linked to human-induced land-use and land use change activities, such as prescribed burning of savanna, deforestation and peatland degradation, need to be reported by countries to the UN Framework Convention on Climate Change (UNFCCC) (Stocker et al. 2013). Reporting obligations include compilation and submission of national GHG inventories within both National Communications and Biennial Update Reports. The Guidelines for National Greenhouse Gas Inventories, developed by the Intergovernmental Panel on Climate Change (IPCC 2006) allow countries to estimate and report data at various tiers of complexity, from simpler (Tier 1) to more complex (Tiers 2 and 3) approaches.

The simplest Tier 1 approach consists in the multiplication of burned area by default, static parameters describing the biomass available for combustion per hectare, and by GHG emission factors, differentiated by broad geographical, climatic or vegetation characteristics. Tier 2 approaches use the same method, but employ country specific, rather than default parameters. Tier 3 approaches allow for the use of more complex models to derive the necessary parameters, including simulation of biophysical variables at fine temporal and geospatial scales.

Most published estimation methods for quantifying GHG emissions from biomass fires are Tier 3 approaches (e.g., Stroppiana et al. 2010). Experts in developing countries however, including those tasked with implementing national GHG inventories, often face technical and institutional constraints that may limit access, generation and maintenance of data using the complex estimation methods linked to these higher tiers. Understanding differences in and availability of emission data estimated at differing levels of complexity is therefore important in order to plan and implement sustainable national GHG inventory systems.

While both databases rely on remote-sensing information on burned area by land cover class, GFED3 is a Tier 3 approach, employing a dynamic vegetation model to determine fuel biomass consumption values at fine temporal and spatial scales. By contrast, FAOSTAT applies a Tier 1 approach using default vegetation parameters from the 2006 IPCC Guidelines (Tubiello et al. 2014). The aim of this work is to conduct a first robust assessment of differences and similarities between the two databases. While the results of such a comparison are of general scientific interest, given the above-discussed importance of biomass fires emissions at global and regional scales, they also provide specific, valuable information to national GHG inventory compilers, especially in developing countries, towards assessing the costs and the benefits of moving from simple approaches, of easy applicability and sustainability, to more sophisticated modelling tools requiring complex data processing and management.

Emissions from peatland fires are also computed in both GFED3 and FAOSTAT, in an attempt to quantify the additional burning of the peat soil component underlying a given burned area and land cover type. The association of a given burned area to peat fire is made independently from the MODIS MCD12Q1 land cover information, as peat soil is not a land cover type. To this end, GFED3 uses a map of ecosystem types (Olson et al. 2001), as a proxy to associate burned areas to peat fires, but limited to Brunei, Indonesia and Malaysia. FAOSTAT applies instead the Harmonized World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISSCAS/JRC 2012), a robust proxy for characterizing peat soils globally (IPCC 2006).

Estimates of fuel biomass consumption values represented the main difference between GFED and FAOSTAT. GFED employs a revised version of the Carnegie-Ames-Stanford-Approach (CASA) dynamic vegetation growth model (Potter et al. 1993) to estimate available biomass amounts as a function of climate and soil parameters, at each pixel and over time for the period of interest. Fuel biomass consumption values and their associated GHG emissions are estimated in monthly intervals and were then aggregated to yearly values. By contrast, FAOSTAT utilizes IPCC default annual fuel biomass consumption values from IPCC (2006), differentiated by different vegetation, soil and climate types (see Table 1), but constant over time (Tubiello et al. 2014). Peatland coefficients were taken from the IPCC Wetlands Supplement (IPCC 2013).

In order to match the IPCC classes with the UMD land cover classes used by GFED3-BA, and thus assign appropriate IPCC fuel biomass consumption values to each pixel, we determined the agro-ecological zones and ecosystem types associated to specific IPCC fuel biomass consumption values, based on IPCC references, and then assigned these values using geo-referenced maps. Specifically, IPCC fuel biomass consumption values for forest subcategories (i.e., boreal, temperate, tropical forest) were assigned to burned forest area pixels by using the FAO Global Ecological Zones map (FAO 2012). For pixels characterized by other vegetation types (e.g., savanna, woody savanna, grassland), tropical and non-tropical IPCC parameter values were assigned using the JRC-IPCC Climate Zones map (JRC 2010).

GFED3 emissions are computed by classifying fires into six land cover classes: i) Deforestation; ii) Forest; iii) Woodland; iv) Savanna and Grassland; v) Peatland; and vi) Agriculture. While the underlying, GFED-BA burned area data follow the six land cover UMD classes, as discussed, the GFED emissions data are partitioned using UMD classes and ancillary datasets, such as the MOD44 MODIS continuous vegetation field product and the already mentioned ecosystems map. By contrast, FAOSTAT emissions classes are solely based on the UMD land cover classification, and aggregated in line with IPCC guidelines and UNFCCC reporting requirements. FAOSTAT emissions thus include the following six classes: i) Humid Tropical Forest; ii) Other Forest; iii) All Savanna (subdivided in savanna, woody savanna, closed and open shrublands, and grasslands); iv) Peatland; and v) Agriculture.

For the purpose of this work, it was necessary to re-aggregate FAOSTAT emission classes into those used by GFED. The re-aggregation performed was rather straightforward (Table 2) and allowed for direct comparison between the two emissions datasets. The overall mapping of UMD-based FAOSTAT into the GFED emission classes deforestation and peat fires nonetheless required application of common sense and expert judgement, corroborated by repeated testing. This is because in GFED, fire emissions are associated to deforestation and peat fires based on more than land cover classification. Specifically, in GFED the deforestation fire emission class is determined by using additional MODIS fire products, and the peat fires emission class is determined by using additional information on soil water status, obtained via complex hydrological modelling. In the end, FAOSTAT emission estimates from fire in humid tropical forests were used to compare to the GFED deforestation class, and FAOSTAT emissions estimates from peatlands were used to compare to the GFED peat fires class.

The comparison between GFED and FAOSTAT emissions data was limited to CH4, N2O and CO2 gases, as these are those included in national GHG inventories. We did not compare emissions for the agricultural fires class, since FAOSTAT does not use GFED-BA products to estimate them, but rather the simpler IPCC Tier 1 approach, based on total harvested area by crop from national statistics. Comparisons over the remaining five classes were furthermore performed using CO2eq units rather than considering the three single gases CO2, CH4 and N2O, since trends in each component gas are fully correlated to their equivalent total. This is because the emission factor for each gas is a constant multiplied by the same underlying biomass combustion value. Finally, a downscaling factor of 10 was applied to all GFED3 burned area data, in order to reconcile data from the GFED online database with those published in van der Werf et al. (2010) (L. Giglio, personal communication).

The reader is advised that while we discuss herein results based on the GFED3-BA products in order to maintain coherence with published GFED emissions data and with the data used in the IPCC AR5, the FAOSTAT GHG emission estimates currently distributed by FAO (Tubiello et al. 2015) are already based on the new GFED4-BA dataset (Giglio et al. 2013).

While the above mean emission values by class were found to be not statistically different, differences in CVs between GFED and FAOSTAT were apparent for several emission classes (Table 3). This is related to the inherent higher interannual variability of biomass used in GFED compared to the static values used in FAOSTAT. This mechanism may explain differences in CV found for forest (34 and 16 %, respectively), woodland (17 and 11 %, respectively) and deforestation (42 and 22 %, respectively), although we did not have access to the underlying biomass data to verify quantitatively this conclusion. For peat fire emission estimates CVs were 189 and 132 %, respectively.

This work investigates differences between GHG emissions from biomass fires, comparing the FAOSTAT estimates, obtained using a default Tier 1 approach according to the IPCC definition, with the GFED v.3 estimates, obtained through a more complex Tier 3 approach. Results show that the simple Tier 1 approach used in the FAOSTAT Emissions database provides estimates that are not statistically different, at global level, from the Tier 3 GFED estimates. The data estimated with the two methods were shown to have close correspondence with respect to all the land cover classes considered.

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