DOI: 10.5061/dryad.gmsbcc2s2
title: Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests

description: The global burned area declined by nearly one-quarter between 1998 and
 2015. Drylands contain a large proportion of these global fires but there
 are important differences within the drylands, e.g., savannas and tropical
 dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have
 reduced the burned area, while the fire in the TDF is one of the most
 critical factors impacting biodiversity and carbon emissions. Moreover,
 under climate change scenarios TDF is expected to increase its current
 extent and raise the risk of fires. Despite regional and global scale
 effects, and the influence of this ecosystem on the global carbon cycle,
 little effort has been dedicated to studying the influence of climate
 (seasonality and extreme events) and socioeconomic conditions of fire
 regimen in TDF. Here we use the Global Fire Emissions Database and,
 climate and socioeconomic metrics to better understand long-term factors
 explaining the variation in burned area and biomass in TDF at the
 Pantropical scale. On average, fires affected 1.4% of the total TDF’ area
 (60,208 km2) and burned 24.4% (259.6 Tg) of the global burned biomass
 annually at Pantropical scales. Climate modulators largely influence local
 and regional fire regimes. Inter-annual variation in fire regime is shaped
 by El Niño and La Niña. During El Niño and the forthcoming year of La
 Niña, there is an increment in extension (35.2 and 10.3%) and carbon
 emissions (42.9 and 10.6%). Socioeconomic indicators such as land
 management and population were modulators of the size of both, burned area
 and carbon emissions. Moreover, fires may reduce the capability to reach
 the target of “half protected species” in the globe, i.e., high-severity
 fires are recorded in ecoregions classified as nature could reach half
 protected. These observations may contribute to improving fire management.
description: We used the Global Fire
 Emissions Database, Version 4.1 (GFED4s) (Randerson et al.,
 2018). GFED4s
 includes the monthly and daily fire burned above-ground biomass from 1997
 to 2020, for all fire sizes including "small fires”. Burned area
 covers the period 1997-2016. The information has a spatial resolution of
 0.25 degrees. We included small fires to fully capture fire dynamics
 across the TDF in the Pantropic. To
 ensure that we dominantly evaluated the TDF ecosystem, we crossed the
 GFED4s database to the realms defined by Dinerstein et al. (2017), similar
 to others (Zubkova et al., 2019). The realms are redefined and updated from their
 previous version of the terrestrial ecoregions of the world (Olson et al.,
 2001). For this study, we
 included the Tropical & Subtropical Dry Broadleaf Forests and the
 TDF in Mato Grosso, Brazil (Biudes et al., 2022), and
 excluded the woody
 savannas from our analysis, such as the savannas of Africa, Australia, and
 South America (Lehmann et al., 2011; Moncrieff et al., 2016). A total of
 52 different ecoregions
 were included for six Pantropical regions: (i) Caribbean Islands, Central America, and Mexico
 (CEAM); (ii) Northern Hemisphere South America (including Colombia and
 Venezuela) (NHSA); (iii) Southern Hemisphere South America (including
 Bolivia, Brazil, Ecuador, and Peru) (SHSA); (iv) Southern Hemisphere
 Africa (including Angola, Madagascar, and Zambia) (SAHF); (v) Southeast
 Asia (including Cambodia, India, Laos, Myanmar, Sri Lanka, Thailand, and
 Vietnam) (SEAS); and (vi) Equatorial Asia (EQAS).
 To identify the main drivers
 of fires, we focused on characterizing the historical socio-ecological
 conditions (i.e climatic, biophysical, and socioeconomic drivers) in TDF
 regions for each grid-cell at 0.25° resolution. For this analysis, we used
 the most updated and improved biophysical and socio-economic data at the
 finest spatial resolution. All the selected databases have been used,
 either for modeling climate change or have been updated based on the most
 recent climatic modeling. Therefore, our results would be comparable to
 more recent and future studies. Also, all the spatial information has
 global coverage. Overall the spatial resolutions range from 90-m up to
 10-km, and are interpolated to the common GFED4s grid-cell of 0.25
 degrees. Climatic variables such as temperature,
 precipitation, and wind speed have been recognized as key drivers of
 moisture availability and fire propagation (Archibald et al., 2009).
 Some studies used weather
 conditions as drivers that modulate variations in ignition efficiency,
 fire spread rate, and fire size (Andela et al., 2017; van der Werf et al.,
 2017; Jiang et al., 2020), while others included eco-climatic zones (Chuvieco et
 al., 2008). Contrasting to previous studies, we used the
 “near current climate” (1970-2000) data from WorldClim version 2.1
 released in 2020’ (Fick and Hijmans, 2017) with a spatial resolution of
 1-km. We used the 19 current bioclimatic variables (Bio1 to Bio 19), mean
 solar radiation, water vapor pressure, and wind speed. All the bioclimatic
 variables are derived from the monthly temperature and rainfall values.
 The bioclimatic variables represent annual trends (e.g., mean annual
 temperature, annual precipitation), seasonality (e.g., annual range in
 temperature and precipitation), and extreme or limiting environmental
 factors (e.g., temperature of the coldest and warmest month, and
 precipitation of the wet and dry quarters) (Fick and Hijmans, 2017).
 Complementarily, from Trabucco and Zomer (2019) we used potential
 evapotranspiration, and Priestley-Taylor alpha coefficient (Lhomme, 1997).
 Precipitation and temperature alone have shown to be inadequate to measure
 the hydrological condition (Quan et al., 2013), we evaluated water
 deficiency in the TDF based on two different aridity indexes. The first
 index considers annual precipitation and temperature (Lang index). The
 second index further includes reference evapotranspiration (Trabucco and
 Zomer, 2019). Lang aridity index is the ratio of annual precipitation to
 the mean annual temperature (mm per °C) (Lang, 1920). This index suggests
 that the rise in temperature increases water deficiency and makes the air
 drier if not sufficiently recharged by precipitation and/or underground
 water (Quan et al., 2013). Trabucco and Zomers aridity index shows
 moisture availability/deficit for potential growth of reference vegetation
 excluding the impact of soil mediating water runoff events.
 Fuel availability relates to biomass favoring burning. NDVI has
 been used as a proxy for the conditions of vegetation, particularly for
 fuel availability (Jiang et al., 2020). However, we propose to use
 above-ground biomass (AGB) in live plants as a fuel load proxy, similar to
 others (Corona-Núñez et al., 2020; Tang et al., 2021). The AGB dataset
 refers to the epoch of the years 2000’s with a spatial resolution of 1-km
 (Avitabile et al., 2016). The AGB was transformed to above-ground carbon
 stocks (AGC) assuming a mean C concentration of 47.2% (Corona-Núñez et
 al., 2018). To estimate the potential biomass losses from fires we assumed
 that the total fuel load refers to the AGB.
 Socioeconomic factors have been shown to be important drivers of
 deforestation, forest degradation, and modulators of fire frequency and
 size. In this study, we evaluated population, richness, land
 accessibility, and land management as drivers of fires (Chuvieco et al.,
 2008; Archibald et al., 2009; Andela et al., 2017; Zubkova et al., 2019).
 We included population density (GPWv4) and gross domestic product (G-Econ
 v4) with a resolution of 30 arc seconds (CIESIN, 2018). We evaluated land
 accessibility by two means. Firstly, we used altitude. Altitude has been a
 significant driver to understand forest degradation and deforestation in
 the tropics (Mendoza-Ponce et al., 2018; Corona-Núñez et al., 2021).
 Altitude comes from a digital terrain model with a spatial resolution of
 90-m from the SRTM (Shuttle Radar Topography Mission - V.2.1, NASA).
 Secondly, we used road density at a resolution of 5 arc-minutes (Meijer et
 al., 2018). Roads have been shown to be important drivers to explain
 burned areas (Archibald and Roy, 2009; Zubkova et al., 2019) and TDF
 degradation (Corona-Núñez et al., 2021). To integrate land-management
 practices we included the proportion of croplands, irrigated agriculture
 (Ramankutty et al., 2010a), and pastures (Ramankutty et al., 2010b) within
 5 arc-minutes grid-cell, a similar approach undertaken by others (Chuvieco
 et al., 2008; Archibald et al., 2009; Andela et al., 2017; Zubkova et al.,
 2019). The global croplands, irrigated agriculture, and pastures data set
 represent the proportion of land areas used in the year 2000. This data
 was estimated from MODIS and SPOT sensors combined with inventory data
 (Ramankutty et al., 2008).