No abstract

Landscape fires are substantial sources of (greenhouse) gases and aerosols. Fires in savanna landscapes represent more than half of global fire carbon emissions. Quantifying emissions from fires relies on accurate burned area, fuel load and burning efficiency data. Of these, fuel load remains the source of the largest uncertainty. In this study, we used high spatial resolution images from an Unmanned Aircraft System (UAS) mounted multispectral camera, in combination with meteorological data from the ERA-5 land dataset, to model instantaneous pre-fire above-ground biomass. We constrained our model with ground measurements taken in two locations in savanna-dominated regions in Southern Africa, one low-rainfall region (660 mm year−1) in the North-West District (Ngamiland), Botswana, and one high-rainfall region (940 mm year−1) in Niassa Province (northern Mozambique). We found that for fine surface fuel classes (live grass and dead plant litter), the model was able to reproduce measured Above-Ground Biomass (AGB) (R2 of 0.91 and 0.77 for live grass and total fine fuel, respectively) across both low and high rainfall areas. The model was less successful in representing other classes, e.g., woody debris, but in the regions considered, these are less relevant to biomass burning and make smaller contributions to total AGB.

Abstract. Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements are challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2, CO, CH4, organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98 % of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2, EC, and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was the other way round for C3 emissions (13C-enriched) and C4 emissions (13C-depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel fire based on the δ13C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa Special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provided a range of C3:C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3-to-C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3:C4 ratio in the field or other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3-vs.-C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges.

<p>Tropical savannas and grasslands are the most frequently burned biome in the world, and fire constitutes an important part of the ecosystem. In this ecosystem it can have both rejuvenating and destructive effects, depending on several factors including fuel conditions, weather conditions, and time of year. For centuries humanity has used fire in these landscapes for hunting, land clearance, agriculture, and most recently carbon offsetting. Land managers in locations with a monsoonal climate and frequent fire regimes such as tropical savannas use prescribed burning as a management tool in the ‘early dry season’ (EDS) shortly after the last rains of the year. Fires at this time tend to be cooler, restricted to surface level and less severe, meaning they can be controlled more easily and tend to go out at night without external input. Commonly a specific, fixed date is used to indicate when this window of safe burning has expired, set based on experience of the local or regional authority. In this work, we have defined a method of determining when this window expires on the basis of active fire hotspot data from the twin MODIS instruments from 2001 through to 2021. By using the relationship between day and night-time active fire detections, we set a flexible date for the transition between the early and late dry seasons in fire-prone savannas globally in the five major tropical savanna regions - Northern & Southern hemisphere South America (NHSA & SHSA), Northern & Southern hemisphere Africa (NHAF & SHAF), and Australia (AUST). The variability across each region was high (lowest mean standard deviation annually was 24 days in NHAF and highest was 56 in AUST). The fraction of area burned in the late dry season ranged from 15% (SHSA) to as high as 85% (AUST) on average, with many parts of Africa and Australia especially showing a significant skew towards the late dry season. This suggests potential for implementation of prescribed burning programmes to increase the amount of desirable fire in the global savanna ecosystems.</p>

Abstract. Landscape fires, predominantly in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the elements in the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst others, the type of fuel consumed, the moisture content of that fuel and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF-variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global models. We collected over 4500 EF bag measurements of CO2, CO, CH4 and N2O using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with known locations and dates listed in the literature. Based on the locations, dates and time of the fires we retrieved a variety of fuel-, weather- and fire severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate dynamic EFs for CO2, CO, CH4 and N2O and calculated the spatiotemporal impact on BB emissions over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO2, CO and CH4 were tree cover density, fuel moisture content and the grass to litter ratio. The grass to litter ratio and the nitrogen to carbon ratio were important indicators for N2O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error in EF estimates by 60–85 % compared to static biome averages. Using dynamic EFs, global savanna emission estimates for 2002–2016 were 1.8 % higher for CO while CH4 and N2O emissions were respectively 5 % and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH4 and N2O EFs in mesic regions and lower ones in xeric regions. Seasonal drying resulted in a decrease of the EFs of these species with the fire season progressing, with a stronger trend in open savannas than woodlands. Contrary to the minor impact on annual savanna average emissions, the model predicts localized reductions in EFs of CO, CH4 and N2O exceeding 60 % under seasonal conditions.

<p>Roughly half of global fire emissions originate from savannas, and emission factors (EF) are used to quantify the amount of trace gases and aerosols emitted per unit dry matter burned. It is well known that these EFs vary substantially even within a single biome but so far quantifying their dynamics has been hampered by a lack of EF measurements. Therefore, global emission inventories currently use a static averaged EF for the entire savanna biome. To increase the spatiotemporal coverage of EF measurements, we collected over 4500 EF bag measurements of CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O using an unmanned aerial system (UAS) and measured fuel parameters and fire severity proxies during 129 individual landscape fires. These measurements spanned various widespread savanna ecosystems in Africa, South America and Australia, with early and late dry season campaigns. We trained random forest (RF) regressors to estimate daily dynamic EFs for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O at 500×500-meter resolution based on satellite and reanalysis data. The RF models reduced the difference between measured and modelled EFs by 60-85% compared to static biome averages. The introduction of EF dynamics resulted in a spatial redistribution of CO, CH<sub>4</sub> and N<sub>2</sub>O emissions compared to the Global Fire Emissions Database version 4 (GFED4s) with higher emissions in higher rainfall savanna regions. While the impact from using dynamic EFs on the global annual emission estimates from savannas was relatively modest (+2% CO, -5% CH<sub>4</sub> and -18% N<sub>2</sub>O), the impact on local EFs may exceed 60% under dry seasonal conditions.</p>

Abstract. Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements is challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g., trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2, CO, CH4, organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98 % of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2, EC and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was opposite for C3 emissions (13C-enriched) and C4 emissions (13C-depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel-fire based on the δ13C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provide a range of C3 : C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3 to C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3 : C4 ratio in the field, or that other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3 vs C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges.