A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Abstract

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within processoriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and processoriented global fire models, we introduce a new flexible datadriven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Recent global and regional trends in burned area and their compensating environmental controls

“…We simulated burned area using an empirical fire model based on the SOFIA (Satellite Observations for FIre Activity) approach, which estimates monthly burned area from observed time series of land cover, vegetation, climate variables and human population density (Forkel et al 2017).…”

“…Indeed, analyses of the FireMIP simulations suggest that, while they represent the climate controls on burned area reasonably well, they underestimate the sensitivity to previousseason plant productivity and have over-simplistic representations of the influence of human activities on burned area (Forkel et al 2019). Empirical fire models (Moritz et al 2012, Bistinas et al 2014, Forkel et al 2017 provide an alternative and arguably better approach to reproduce observed fire dynamics and to quantify the relative importance of climate, vegetation and human factors on temporal changes in burned area.…”

Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

“…Biomass burning is one of the key processes affecting vegetation productivity, land cover, soil erosion, hydrological cycles and atmospheric emissions Forkel et al, 2017;Gaveau et al, 2014;van der Werf et al, 2017). It has social implications as well, impacting people's lives and properties (Roos et al, 2016), particularly in developed countries where urban areas are intermixed with forests (Bowman et al, 2017).…”

“…Fire is affected by climate, as burnings are associated with high to extreme weather conditions (particularly droughts and heatwaves) (Forkel et al, 2017). However, fire affects climate too, due to its impacts on carbon budgets and greenhouse gas emissions (van der Werf et al, 2017).…”

“…In conjunction with other human and physical variables, BA 20 datasets are required to understand factors controlling fire activity (Forkel et al, 2017) and particularly those affecting changes in fire regimes Andela et al, 2017), with an increasing concern on fire patch identification derived from higher resolution pixel-level information (Nogueira et al, 2017). Atmospheric emission models require precise information on spatio-temporal patterns of fire occurrence, as well as combustion characteristics (van der Werf et al, 2017;Knorr et al, 2016).…”