DOI: 10.5281/zenodo.16593501
title: Distinct drivers of extreme and non-extreme fires in the Brazilian Amazon

description: The Amazon forests are pivotal in regulating the global terrestrial carbon cycle (1, 2) and providing habitats for a rich diversity of species (3). These forests are facing a growing threat from intense fire disturbances, especially in areas where forests are fragmented and converted to other types of vegetation (4, 5). Fire can alter vegetation structure and function (6, 7) and affect the regional and global energy budget by changing surface albedo (8) and emitting aerosols and greenhouse gases (9), posing a significant threat to the valuable ecosystem services of tropical forests. In recent years, extreme fires have occurred more frequently and caused more severe damage to tropical forests than non-extreme fires; however, it remains unclear whether the drivers behind extreme and non-extreme fires differ (10, 11). Understanding the drivers of fire disturbance while distinguishing between fire severity levels in the Brazilian Amazon region is therefore of great importance to understanding and protecting the world’s largest tropical forest from fire disturbances. 

Lightning-ignited wildfires and anthropogenic fires for farming expansion are two important sources of fire events in the Brazilian Amazon (12, 13). According to satellite observations, there is a significant variation in both the size and intensity of these events (14). Previous studies have identified climatic and anthropogenic drivers of fires in the Brazilian Amazon, including fire foci (15), El Niño-Southern Oscillation events (16-20), logging (21-23), and expansion of roads (24), stockbreeding and agriculture (12, 13, 25). One experiment showed how a fire can evolve into an extreme one under suitable conditions (26), but the main drivers of extreme and non-extreme fires in the Brazilian Amazon remain unclear. Understanding these drivers is crucial for preventing non-extreme fires from escalating and causing severe damage to forests, and for providing guidance to policymakers to prevent and reduce fires in the face of global warming.

Here, extreme fire is defined as 0.5°×0.5° grid cells with annual burned area exceeding the 90th percentile of the entire time series, while non-extreme fire includes 0.5°×0.5° grid cells with annual burned area greater than 0 but below the 90th percentile. In this study, we use machine learning models to build empirical relationships between climate, anthropogenic activities (represented by land use and land use change), and burned area in the Brazilian Amazon (see the workflow in Fig. S1). The spatial extent of the study region applied here is the Brazilian Amazon biome excluding the Cerrado (Methods). We first apply a random forest classification algorithm to categorize grid cells (0.5°×0.5°) into two types representing extreme fire and non-extreme fire, based on 4 different satellite-based burned area datasets covering the Brazilian Amazon during 1985~2020 (FireCCI51 (27), MCD64CMQ (28), MapBiomas Fire (29), and GABAM (30)). 

We then use random forest quantile regression models to establish non-linear relationships between burned area, climate and anthropogenic variables in grid cells with extreme fires and non-extreme fires, respectively (Methods). The combined classification and regression models follow a previous study for reconstructing historical burned area (31). These random forest models are applied to predict future burned area in two different climate and land use change scenarios under Shared Socio-economic Pathways (SSPs) (32) and Representative Concentration Pathways (RCPs) (33). These are pairings of SSP1 & RCP2.6, SSP5 & RCP8.5. SSP1 & RCP2.6 represents a low-warming climate and more sustainable land use changes, whereas SSP5 & RCP8.5 represents a high-warming climate and less sustainable land use changes.

 

This dataset is currently for manuscript submission to the scientific journal 'Nature Communications'