Global estimates of emissions from fires, Part 1: Emission estimates from fires in the Tropics and Subtropics, Guido R. van der Werf 1, James T. Randerson 2, G. James Collatz 3, Louis Giglio 4 1 USDA-FAS, NASA Goddard Space Flight Center. 2 Divisions of Geological and Planetary Sciences and Engineering and Applied Science, California Institute of Technology. 3 Biospheric Sciences branch, NASA Goddard Space Flight Center. 4 Science Systems and Applications Inc., NASA Goddard Space Flight Center Introduction Global carbon emissions from fires are difficult to quantify and have the potential to influence interannual variability and long-term trends in atmospheric CO 2 concentrations. We used 4 years of Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) satellite data and a modified biogeochemical model to assess spatial and temporal variability of carbon emissions from tropical fires. The TRMM satellite data extended between 38ºN and 38ºS and covered the period from 1998 to A relationship between TRMM fire counts and burned area was derived using estimates of burned area from MODIS imagery in Africa and Australia, and the average burned area per fire count was found to be related to tree cover density. We modified the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model to account for both direct combustion losses and the decomposition from fire-induced mortality, using both TRMM and Sea-viewing Wide Field of view Sensor (SeaWiFS) satellite data as model drivers. Methods 1. from TRMM fire counts to burned area: We have used multiple MODIS tiles (~10º×10º) in Australia and Africa (see Giglio et al. poster) to asses burned area from a change in vegetation index and developed a relationship between fire counts and burned area, using percentage treecover maps (DeFries et al., 1999) Fig 2 Flowchart of CASA with fire influences. A = area burned, E = combustion factor, M = fire induced mortality) Table 1 Combustion factors and mortality rates for living biomass (woody and herbaceous) and litter Fig 1 Relationship between the burned area per fire counts and tree cover density. Possible explanations for this trend: - fires in ‘open’ areas burn quickly: smaller TRMM detection probability - fires in ‘open’ areas tend to burn less patchy: higher MODIS detection probability To calculate burned areas, we have used a linear relationship, depending on the amount of treecover, between fire counts and burned area for 0-40% treecover and an independent relationship in wooded areas (> 40% treecover, scenario 1). To investigate the influence of possibly missed burned areas in forested areas (due to the lack of change in Vegetation Index in case of patchy fires or ground fires) on emissions we have used another relationship which was independent of percentage treecover at 35% instead of 40% treecover, so the burned area per firecount in wooded areas is boosted (scenario 2). Finally, we scaled our total burned area for Africa to the mean value from a study by Barbosa et al., (1999) since their burned area estimate for southern Africa was close to a study based on different satellite data by Scholes et al. (1996). This factor (1.5) was applied globally (scenario 3). 2. from burned area to carbon loss: Fuel properties (biomass and detritus) were calculated using the CASA biogeochemical model as the balance between: Input:NPP = FPAR × PAR × LUE (temperature, moisture) Losses:Respiration f(turnover times, temperature, moisture) Herbivory f(foliage NPP) Fire f(burned area, mortality, combustion factor) Fuelwood combustion (population density × tradition fuel demand) The model ran on a monthly time step with a 1º×1º resolution After Shea et al. (1996) and Hoffa et al. (1999) Results and discussion Fig 6 CO 2 emissions, in g CO 2 / m 2 / yr (using emission factors by Andreae and Merlet, 2001). Table 5 A comparison of burned area and emissions from different studies Summary We have embedded fire processes in a biogeochemical model, using both TRMM and MODIS satellite data to estimate burned area. Other input parameters (combustion factors and mortality rates) were derived from literature. Total calculated emissions, averaged over were calculated at 2.1 Pg C/yr plus another 0.4 Pg C/yr from fuel wood burning. Interannual variability was calculated at 0.6 Pg C/yr, with 1998 being the highest, and 2000 and 2001 the lowest fire years. When comparing to other studies (i.e. Scholes et al. (1996), Barbosa et al (1999)) our burned areas seem to be conservative but emission are relatively high because CASA predicts higher average fuel loads and because we included the mortality of woody biomass. Fuel loads in frequently burning areas were equal to estimates used in Barbosa et al. (1999) and our biomass estimates are comparable to values measured (Olson et al. 1983). References Andreae and Merlet, GBC, 2001 Barbosa et al., GBC, 1999 DeFries et al., JGR, 1999 Grégoire et al., IJRS, in press Hoffa et al., JGR, 1999 Scholes et al., JGR, 1996 Shea et al., JGR, 1996 Fig 3 Annual percentage of the area that burned (using scenario 1) Table 2 Sensitivity of fuel content and combustion on different burned areas Fire competes with herbivory and respiration to consume the carbon that was fixed during photosynthesis. In the graph below we plotted the fluxes from combustion, indirect loss (which is the respiration from fire induced mortality), herbivory and fuel wood collection for a moisture gradient. Fires were most important in ecosystems with intermediate levels of moisture, Fig 4 Carbon fluxes from combustion, indirect loss (respiration from fire induced mortality), herbivory, and fuel wood along a moisture gradient. We have used the same moisture gradient to define different vegetation types; tropical forest was defined where tree cover density exceeded 70% and MAP exceeded 1500 mm. All other biomes were defined based on MAP. Below are the mean values for precipitation and tree cover density given, together with biomass and litter properties. Also the woody vegetation mortality (related to percentage tree cover, see Table 1), combustion factor on ecosystem level (dependent on the relative distribution of different types of fuel, see Table 1), and the satellite derived fire return times are given. Table 3 Vegetation type characteristics Finally, we give the calculated average CO2 emissions and a comparison between different studies for estimated burned area and emissions for southern Africa. Our estimated burned area is relatively low (almost equal to Barbosa’s lower scenario) but our emission estimates are higher because we include the mortality of woody vegetation and because our average fuel loads were higher. 1 Standard deviation in parentheses 2 Combustion factor on ecosystem level. Calculated using tissue dependent combustion factor from Table 1 With the TRMM dataset we were able to calculate emissions for 4 consecutive years; Below are the values given for different regions. The ENSO year 1998 showed the highest emissions, especially the forested areas of central and south America and southeast Asia showed greater emission in 1998 than in other years. Emission from Australia in 1998 were about half of other years. Interannual variability in Africa seems to be smaller than in other regions The distribution of emissions sources over regions is given below. About half of the emissions originated in Africa, almost evenly distribute over the two hemispheres. Fires in wooded areas (tropical forest and moist woodland) were most important in South America and Southeast Asia. Although areas burned in Australia were huge, the total emissions are relatively small because of lower fuel loads. Fig 5 Interannual variability in emissions for different regions indirect loss is greatest in more forested areas where the availability and mortality of living woody biomass is higher. Living woody biomass consists mostly of stems with a low combustion factor so there is relatively more carbon left after a fire to respire. Table 4 Emission sources for different regions (Tg/yr)