Seasonality of vegetation fires as modified by human action: observing the deviation from eco‐climatic fire regimes

Abstract

Aim In any region affected, fires exhibit a strong seasonal cycle driven by the dynamic of fuel moisture and ignition sources throughout the year. In this paper we investigate the global patterns of fire seasonality, which we relate to climatic, anthropogenic, land-cover and land-use variables.Location Global, with detailed analyses from single 1°¥ 1°grid cells. MethodsWe use a fire risk index, the Chandler burning index (CBI), as an indicator of the 'natural' , eco-climatic fire seasonality, across all types of ecosystems. A simple metric, the middle of the fire season, is computed from both gridded CBI data and satellite-derived fire detections. We then interpret the difference between the eco-climatic and observed metrics as an indicator of the human footprint on fire seasonality. ResultsDeforestation, shifting cultivation, cropland production or tropical savanna fires are associated with specific timings due to land-use practices, sometimes largely decoupled from the CBI dynamics. Detailed time series from relevant locations provide comprehensive information about these practices and how they are adapted to eco-climatic conditions. Main conclusionsWe find a great influence of anthropogenic activities on global patterns of fire seasonality. The specificity of the main fire practices and their easy identification from global observation is a potential tool to support land-use monitoring efforts. Our results should also prove valuable in the development of a methodological approach for improving the representation of anthropogenic fire practices in dynamic global vegetation models.

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

“…11) that is consistent with southern African biomass burning. Peak anthropogenic biomass burning does not coincide with the height of the dry season, but does coincide with the middle of the burning season over the northern parts of Angola, the Democratic Republic of Congo, and Zambia, where savannah fires are often lit in July/August well before the peak of the dry season (Le Page et al, 2010). This picture is substantiated by individual fire counts based on geostationary satellite observations, which also shows peak fire activity around July/August, with peak burning of about 6 Tg per day in July (Roberts et al, 2009).…”

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

“…In order to represent realistic vegetation-fire interactions, vegetation models need to satisfactorily reproduce observed patterns and dynamics of fuel moisture and vegetation state variables. Consequently, it is necessary to test and improve global vegetation-fire models against multiple observational datasets that cover various aspects of vegetation-fire interactions: for example, satellite datasets on land cover, FAPAR, VOD, biomass (Avitabile et al, 2016;Saatchi et al, 2011;Thurner et al, 2014), and estimates of litter fuels (Pettinari and Chuvieco, 2016) may be useful to constrain vegetation dynamics, biomass allocation, and fuel loads; datasets on surface soil moisture, VOD, and evapotranspiration (Tramontana et al, 2016) may be useful to test hydrological schemes and to constrain fuel moisture; and datasets on burned area, fire size (Hantson et al, 2015b), fire radiative power, fuel consumption (Andela et al, 2016;van Leeuwen et al, 2014), or separations between natural and agricultural fires (Korontzi et al, 2006;Le Page et al, 2010;Magi et al, 2012) may be useful for constraining fire behaviour. Such datasets are currently under-exploited in the development of global vegetation-fire models because (1) they were still missing at the time of model development (Thonicke et al, 2001), (2) there is only little experience in applying formal modeldata integration approaches within global fire modelling, or (3) no appropriate model components or observation operators exist that link for example modelled fuel moisture with satellite-derived surface soil moisture or modelled biomass compartments with VOD.…”

Habitat distribution modeling reveals vegetation flammability and land use as drivers of wildfire in SW Patagonia

“…In contrast, most fine-scale studies, by being limited to a small range of fuel types and socioeconomic conditions (e.g., Kennedy and McKenzie 2010), have unknown applicability across a broader range of biome types or in different socioeconomic contexts. While coarse-resolution analyses of fire activity based on satellite-derived burned area maps are providing important insights into the relative importance of biophysical factors driving fire, there is a strong consensus that existing studies are limited by: (1) inability to distinguish wildland fires from agricultural burning (LePage et al 2010, but see Magi et al 2012); (2) partial understanding of mechanistic relationships of fire to biophysical predictors due to the 0.58 spatial resolution of most studies (e.g., Krawchuk and Moritz 2011;Pausas and Ribeiro, in press); (3) short records (typically ,8 years) of fire in studies based on satellite imagery that preclude the inclusion of a sufficient number of fires for certain regions or robust estimates of mean fire occurrence in some biophysical settings (Chuvieco et al 2008); and (4) aggregation of socioeconomic data across large areas which obscures mechanisms by which humans actually affect fire regimes (Aldersley et al 2011). Fire data acquired at finer spatial resolution and extending over longer time periods is required for development of a more robust quantitative understanding of the relationships of wildland fire activity to biophysical and anthropogenic factors.…”