Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives.
No abstract
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&#215;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>
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