Methane gas emissions from savanna fires: what analysis of local burning regimes in a working West African landscape tell us

Savanna fires contribute significantly to greenhouse gas emissions. While it is recognized that these fires play a critical role in the global methane cycle, there are too few accurate estimates of emissions from West Africa, the continent's most active fire region. Most estimates of methane emissions contain high levels of uncertainty as they are based on generalizations of diverse landscapes that are burned by complex fire regimes. To improve estimates we used an approach grounded in the burning practices of people who set fires to working landscapes. We collected and analyzed smoke samples for 36 experimental fires using a canister method for the early dry season (EDS) and mid-dry season (MDS). We also collected data for savanna type, grass type, biomass composition and amount consumed, scorch height, speed of fire front, fire type, and ambient air conditions for two sites in Mali. We report values for fire intensity, combustion completeness, patchiness, modified combustion efficiency (MCE), emission factor (EF) and methane emission density. Our study found that mean methane EFs ranged from 3.83 g kg−1 in the EDS to 3.18 g kg−1 in the MDS, but the small sample did not provide enough power for this effect to be significant. We found head fires had nearly double the CH4 EF of backfires (5.12 g kg−1 to 2.74), a significant difference. Byram's fire intensity was a significant driver of CH4 EF but with weak effect. Methane emission density increased marginally from 0.839 g m−2 in the EDS to 0.875 g m−2 in the MDS, a difference that was not significant. Head fires, however, had much higher emission densities than backfires – 1.203 vs. 0.708 g m−2 – respectively, a significant difference. We suggest the reason for the higher methane emissions from head fires, which have higher intensity, is the longer flame lengths that burn green leaves on trees, releasing methane. We conclude that policies aimed at shifting the burning regime earlier to reduce methane emissions will not have the desired effects, especially if fire type is not considered. Future research should consider the state and amount of leafy biomass combusted in savanna fires.

Curah Hujan, Anomali Sea Surface Temperature (SST) dan Kebakaran Hutan Sabana di Waingapu

Waingapu is one of the areas in Nusa Tenggara Timur Province which often suffered from savanna fires. Savanna fires prevention can be done by utilizing hotspot data for analysis using Geographic Information System (GIS). The climate is one of factors influence the occurrence of savanna fires in Waingapu. The purpose of this study was to analyze the relationship between precipitation, SST anomalies, and the occurrences of savanna fires in Waingapu. This research was conducted on February April 2021 at the Forest and Land Fires Laboratory, Department of Silviculture, Faculty of Forestry and Environment, IPB University. The data used are MODIS and VIIRS hotspot data, daily precipitation data and SST 3.4 anomaly data. The results showed that precipitation was inversely related to hotspots with a negative correlation value. SST anomaly is inversely related to precipitation a negative correlation value. While the SST anomaly with hotspots is directly proportional with a positive correlation value. Keywords: climate, hotspot, Geographic Information System (GIS), Waingapu

Methane gas emissions from savanna fires: What analysis of local burning regimes in a working West African landscape tell us

Savanna fires contribute significantly to greenhouse gas emissions. While it is recognized that these fires play an important role in the global methane cycle, there are too few accurate estimates of emissions from West Africa, the continent's most active fire region. Most estimates of methane emissions contain high levels of uncertainty because they are based on generalizations of diverse landscapes that are burned by complex fire regimes. To improve estimates we used an approach grounded in the burning practices of people who set fires to working landscapes. We conducted 97 experimental fires collecting data for savanna type, grass type, biomass composition and amount consumed, scorch height, speed of fire front, fire type and ambient air conditions for two sites in Mali. We collected smoke samples for 36 fires using a canister method. We report values for fire intensity, combustion completeness, patchiness, modified combustion efficiency (MCE) and emission factor (EF). Our study finds that methane EFs ranged from 3.71 g/kg in the early dry season (EDS) to 2.86 in the mid-dry season (MDS). We found head fires had nearly double the CH4 EF of backfires (4.89 g/kg to 2.92). Fires during the MDS have the lowest intensity values and the lowest methane emissions 0.981 g/m2 compared with 1.030 g/m2 for EDS and 1.102 g/m2 for the late dry season (LDS). We conclude that policies aimed at shifting the burning regime earlier to reduce methane emissions will not have the desired effects, especially if fire type is not considered. We recommend using the adjusted mean value of 0.862 g/m2—based on the carbon content for West African grasses—for calculating emissions for West African savannas.

Standing dead trees contribute significantly to carbon budgets in Australian savannas

Previous estimates of greenhouse gas emissions from Australian savanna fires have incorporated on-ground dead wood but ignored standing dead trees. However, research from eucalypt woodlands in southern Queensland has shown that the two pools of dead wood burn at similar rates. New field data from semiarid savannas across northern Australia confirmed that standing dead trees comprise about four times the mass of on-ground dead wood. Further, the proportion of total woody biomass comprising dead wood increases with decreasing fire frequency and a decreasing proportion of late dry season (August to December) fires. This gives scope for increasing the carbon stock in the dead wood pool with a reduced fire frequency. Following a previously published approach to quantify total dead wood loads in savannas, new and previously collected data on tree stand structures were used across the whole savanna zone to quantify dead wood loads in equilibrium with historic fire regimes. New parameters are presented for calculating dead wood dynamics including dead trees in Australia’s savannas.

Biomass burning aerosol over the Amazon: analysis of aircraft, surface and satellite observations using a global aerosol model

Vegetation fires emit large quantities of aerosol into the atmosphere, impacting regional air quality and climate. Previous work has used comparisons of simulated and observed aerosol optical depth (AOD) in regions heavily impacted by fires to suggest that emissions of aerosol particles from fires may be underestimated by a factor of 2–5. Here we use surface, aircraft and satellite observations made over the Amazon during September 2012, along with a global aerosol model to improve understanding of aerosol emissions from vegetation fires. We apply three different satellite-derived fire emission datasets (FINN, GFED, GFAS) in the model. Daily mean aerosol emissions in these datasets vary by up to a factor of 3.7 over the Amazon during this period, highlighting the considerable uncertainty in emissions. We find variable agreement between the model and observed aerosol mass concentrations. The model reproduces observed aerosol concentrations over deforestation fires well in the western Amazon during dry season conditions with FINN or GFED emissions and during dry–wet transition season conditions with GFAS emissions. In contrast, the model underestimates aerosol concentrations over savanna fires in the Cerrado environment east of the Amazon Basin with all three fire emission datasets. The model generally underestimates AOD compared to satellite and ground stations, even when the model reproduces the observed vertical profile of aerosol mass concentration. We suggest it is likely caused by uncertainties in the calculation of AOD, which are as large as ∼90 %, with the largest sensitivities due to uncertainties in water uptake and relative humidity. Overall, we do not find evidence that particulate emissions from fires are systematically underestimated in the Amazon region and we caution against using comparison with AOD to constrain particulate emissions from fires.