Prescribed burning as a mechanism to mitigate emissions of extreme fire events: a case study from the Brazilian Cerrado

The risk of fire occurrence and the frequency of extreme fire events have been increasing globally due to climate change. As a result, greenhouse gas (GHG) emissions are higher worldwide, including in the Brazilian Cerrado. Cerrado is a fire-prone Biome in central Brazil, where fire is essential for maintaining the Biome diversity and integrity. Cerrado presents distinct rainy and dry seasons. In the dry season, the accumulated biomass available for burning becomes highly flammable and fire can rapidly spread from grasslands and savannas to forests. In fire-prone ecosystems globally, prescribed burning prevents intense and frequent wildfires in the drier months by intentionally applying fire under controlled conditions at the end of the rainy season and/or the beginning of the dry season. In Cerrado, prescribed burning is applied in the early dry season (EDS; April-June) to avoid severe wildfires in the late dry season (LDS; August-October), but so far there have been no documented estimates of the effect of prescribed burning on carbon emissions. In this study, we evaluate the potential of prescribed burning to mitigate emissions from extreme fire events in the Brazilian Cerrado region. We modelled fire emissions in Cerrado with JULES-INFERNO over a 30-year period (1990-2019), using the ISIMIP3a simulations. We adjust JULES-INFERNO to represent Cerrado, and then simulate prescribed burning by setting an additional ignition to C4 grass during EDS. We analyse years with large burned areas, including El Niño years, to represent years with intense fire events. Over the 30 years, prescribed burning resulted in reduced fire emissions in the LDS, especially in years when there was high burned area. This indicates that prescribed burning can be used in the Cerrado to reduce the impacts of uncontrolled fires in the drier months. We also observe that the effectiveness of prescribed burning in reducing emissions depends on the C4 grass recovery rate. Further investigation is needed to better understand the model’s performance, including analysis of modelled parameters such as the C4 grass post-fire recovery.