Abstract
Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. We developed a model using MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500 m spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050, 0.125, 0.250∘). We estimated fire emissions of 0.68 Pg C yr−1 at 500 m resolution and 0.82 Pg C yr−1 at 0.25∘ resolution; a difference of 24 %. At 0.25∘ resolution, our model results were relatively similar to GFED4, which also runs at 0.25∘ resolution, whereas our 500 m estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 Pg C yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse-resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500 m and 0.25∘ resolution simulations, besides those stemming from representation errors in the calibration process, namely (1) biome misclassification leading to errors in parameterization, (2) errors due to the averaging of input data and the associated reduction in variability, and (3) a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations of up to a factor 4 and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse-resolution models and suggest the need for increased spatial resolution in global fire emission models.
Highlights
Fires exert a key influence on the global climate by the release of trace gases and aerosols into the atmosphere (Andreae and Merlet, 2001; Ciais et al, 2013; Ward et al, 2012)
We discuss the results of the model calibration and validation for AGBw, fuel load (FL), and fuel consumption (FC) at 500 m resolution
We compare this to the results for the 0.25◦ resolution model calibration and relate this to GFED4
Summary
Fires exert a key influence on the global climate by the release of trace gases and aerosols into the atmosphere (Andreae and Merlet, 2001; Ciais et al, 2013; Ward et al, 2012). Fires partly shape, and in the long-term sometimes determine, the vegetation state of landscapes, affecting the storage capacity of carbon (Rabin et al, 2017). About 70 % of global burned area occurs in Africa (Giglio et al, 2018), mostly due to surface fires with relatively low fuel consumption, leading to roughly half of the global fire carbon emissions (van der Werf et al, 2010). The majority of fires in Africa occur in the savannas (Archibald et al, 2009), an ecosystem that is depending on fires and where trees have evolved to tolerate fire (Beerling and Osborne, 2006). African savannas are currently undergoing major shifts in fire activity due to demographic changes and agricultural expan-. R. van der Werf: Modelling biomass burning emissions sion, leading to a decrease in fire occurrence (Andela and van der Werf, 2014)
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