# What controls fire size in the South American Gran Chaco? Exploring atmospheric, landscape, and anthropogenic drivers

Rodrigo San Martin, Catherine Ottlé, Anna Sorenssön, Pradeebane Vattinada

Ayar, Florent Mouillot, Marielle Malfante

► To cite this version:

Rodrigo San Martin, Catherine Ottlé, Anna Sorenssön, Pradeebane Vattinada Ayar, Florent Mouillot, et al.. What controls fire size in the South American Gran Chaco? Exploring atmospheric, landscape, and anthropogenic drivers. 2025. hal-05237530

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1 What controls fire size in the South American Gran Chaco?

2 Exploring atmospheric, landscape, and anthropogenic drivers.

3 Rodrigo San Martín¹, Catherine Ottlé¹, Anna Sorensön²,³,⁴, Pradeebane Vaittinada Ayar¹, Florent

4 Mouillot⁵, Marielle Malfante⁶

5

6 ¹Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-

7 sur-Yvette, France;

8 ²Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires,

9 Argentina;

10 ³CNRS, CNRS – IRD – CONICET – UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351

11 IFAECI), Argentina;

12 ⁴Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina;

13 ⁵UMR CEFE, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France;

14 ⁶Univ. Grenoble Alpes, CEA, List, Grenoble, France;

15 Correspondence to: Rodrigo San Martin (rodrigo.sanmartin@lsce.ipsl.fr)

16
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**Abstract.** Wildfires are key ecological agents in the Gran Chaco, one of the world's largest dry forest ecosystems, where fire regimes are increasingly shaped by human pressure and climate variability. However, the drivers of fire size variability remain poorly understood. We analysed over 100,000 fire patches (2001–2022) from the FRYv2.0 database to assess environmental controls on fire size and morphology across the Wet, Dry, and Very Dry Chaco. High-resolution fire polygon data were combined with ERA5-Land reanalysis, vegetation and topographic metrics, and anthropogenic layers. Fire sizes were highly skewed: >80% were <5 km², yet large events (Megafires >100 km², Gigafires >1000 km²) dominated burned area (BA). Gigafires were rare but mostly confined to the Dry Chaco, whereas the Wet Chaco had the highest BA, fire frequency, and Megafire count. Fire Weather Index (FWI)–BA correlations reached r = 0.7 in the Wet Chaco but were weaker and spatially fragmented in drier subregions, where fuel continuity and ignition context played larger roles. Lag analyses showed that in drier areas, wet-season biomass buildup (4–6 months prior) increased subsequent fire activity, while in wetter areas short-term dryness (1–3 months prior) was more predictive. During-fire meteorology, especially persistent strong winds, better explained fire morphology than pre-fire conditions. Random Forest models ranked static landscape features (elevation, land-cover evenness, slope, tree cover) highest in size prediction. Our results reveal region-specific fire–environment couplings, clarifying the interplay of meteorological, ecological, and anthropogenic factors, and providing actionable insights for fire risk forecasting and management in the Gran Chaco.
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32 **1 INTRODUCTION**

33 Wildfires shape global ecosystems by influencing vegetation structure, biodiversity, and landscape
34 composition (Bowman et al., 2009; Archibald et al., 2013; Chuvieco et al., 2020). The Gran Chaco,
35 spanning parts of Argentina, Bolivia, Paraguay, and Brazil, is one of the largest remaining dry forest
36 ecosystems, with marked variation in precipitation, vegetation, and human land use (Morello &
37 Adámoli, 1968; Olson et al., 2001; Ginzburg et al., 2005; Torrella & Adámoli, 2005). Fire has long
38 modulated its forest structure and driven transitions between forests, shrublands, and grasslands
39 (Bucher, 1982; Kunst et al., 2003; Vidal-Riveros et al., 2023).

40 In recent decades, Gran Chaco fire regimes have shifted under land-use intensification and climate
41 variability (Gasparri et al., 2008; De Marzo et al., 2021; Baumann et al., 2022; Marengo et al., 2022;
42 Vidal-Riveros et al., 2023; San Martín et al., 2023; San Martín, 2024). These changes often produce
43 larger, more intense fires, especially in areas with non-native grasses or monocultures (D'Antonio &
44 Vitousek, 1992; Bravo et al., 2014; Vidal-Riveros et al., 2023). Natural fire breaks (e.g., water bodies)
45 and traditional management can limit spread (Kunst et al., 2003; Bowman et al., 2011; Archibald et al.,
46 2013; Bravo et al., 2014; Andela et al., 2017, 2019), while landscape heterogeneity further constrains
47 propagation (Bowring et al., 2024), challenging assumptions of uniform anthropogenic effects (Bistinas
48 et al., 2014; Archibald et al., 2018; Kelley et al., 2019). At broader scales, climatic variability—
49 especially rainfall patterns and drought—can outweigh land use in shaping fire size and frequency
50 (Krawchuk et al., 2009; Jolly et al., 2015; Jones et al., 2022).

51 The complexity of fire size drivers in the Gran Chaco is increasingly recognized, yet key mechanisms
52 remain poorly understood (Kelley et al., 2019; Jones et al., 2022; Vidal-Riveros et al., 2023, 2024).
53 Prolonged droughts reduce fuel moisture, increasing flammability and enabling extreme events (Alencar
54 et al., 2015; Naumann et al., 2023). Several major droughts coincided with strong negative El Niño-
55 Southern Oscillation (ENSO) phases, including the record-breaking 2020–2023 La Niña (Doblas-Reyes
56 et al., 2021; De Marzo et al., 2023; Meteorological Organization et al., 2023; Arias et al., 2024).

57 Although recent studies have advanced understanding of Gran Chaco fire regimes, key links between
58 patterns and meteorological or anthropogenic drivers remain unclear. Land cover and socio-
59 environmental factors play a major role: Baumann et al. (2022) found that deforestation pathways vary
60 by actor and context, influencing fire-landscape interactions; San Martín et al. (2023) showed that
61 precipitation-burned area (BA) relationships differ by land cover; and Levers et al. (2024) projected that
62 agribusiness expansion could intensify fire impacts in ecologically and socially sensitive areas.

63 Fire classification efforts also overlook important drivers. Vidal-Riveros et al. (2024) grouped
64 Paraguayan Chaco fire regimes by severity, frequency, and extent, while Naval-Fernández et al. (2025)
65 applied multivariate clustering of landscape attributes to delineate pyroregions in the Argentinian Chaco.
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Both captured spatial variability in fire activity, but neither incorporated meteorological conditions, limiting insights into atmospheric controls on fire behavior and size.

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Research has further addressed post-fire vegetation recovery and cultural dimensions of fire. Saucedo and Kurtz (2025) reported rapid regrowth after the 2022 megafires, followed by climate-constrained stabilization. Sugiyama et al. (2025) highlighted Indigenous fire narratives as valuable sources of local knowledge on ignition, spread, and ecosystem recovery.

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However, no study has yet combined high-resolution meteorological data, fire morphology, and landscape context to assess how fire size responds to both short-term anomalies and long-term environmental patterns in the Gran Chaco.

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Advances in satellite Earth observation now make this integration possible. Global BA products such as FireCCI51 provide consistent daily burned surface estimates at moderate spatial resolutions (Chuvieco et al., 2020). Event-based datasets like FRY (Laurent et al., 2018; Chen, 2025) and the Global Fire Atlas (Andela et al., 2019) reconstruct individual fires from these burned pixels, enabling analysis of attributes such as ignition date, duration, size, and morphology (Moreno et al., 2021; García et al., 2022a; Takacs et al., 2021). In this study, we used FRYv2.0, which integrates the FRYv1.0 pixel aggregation method with FireCCI51 BA mapping (Lizundia-Loiola et al., 2020), and combined it with environmental and climate products to address gaps in understanding BA dynamics and fire size variability in the Gran Chaco.

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Specifically, we aim to answer the following scientific questions:

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(1) What are the primary fire size characteristics and frequency in the Gran Chaco between 2001 and 2022? (2) To what extent do meteorological conditions influence the size and expansion of these fires?

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(3) Beyond weather, what roles do vegetation type, topography, and human activity play in shaping fire size and fire occurrence across the region? (4) Which of these drivers best explains the spatial and temporal variability in fire size across the different Gran Chaco subregions?

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This study adds value by providing a spatially explicit, multiscale analysis of BA and individual fire events, clarifying fire size dynamics across landscapes from wet to arid ecosystems. By quantifying the relative contributions of climate, landscape, and human factors, it advances understanding of fire regimes in one of the world's most dynamic yet understudied deforestation and fire frontiers (Kuemmerle et al., 2017; Baumann et al., 2022; Vidal-Riveros et al., 2023; Levers et al., 2024; San Martín, 2024).

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96 **2 METHODS**

97 **2.1. Study area**

98

<figure><img src="image_10.png" /><figcaption>**Fig. 1.** The Gran Chaco location in South America (a) and its topography (b) with its different subregions, main rivers, and lakes. Based on Shuttle Radar Topography Mission (SRTM) at 90m (SRTM | Earthdata, 2024) and HydroSHEDS (Lehner et al., 2008).</figcaption></figure>

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The Gran Chaco is an extensive tropical and subtropical region of South America, covering approximately 1,100,000 km² (Fig. 1). It contains the world's largest continuous dry tropical forest and extensive wetland systems (Bucher, 1982; Olson et al., 2001). Terminology varies in the literature (South American Chaco, Gran Chaco, Chaco); here we use Gran Chaco for clarity.

The region is mostly flat (<200 m a.s.l.), with higher terrain in the northeast (to 500 m), Sierras de Córdoba (to 2,900 m), and Andean foothills (~2,000 m). Following Olson et al. (2001), we distinguish a humid eastern Wet Chaco from a drier western Dry Chaco, shaped by west-east gradients in precipitation, vegetation, and hydrology (Bucher, 1982; Ginzburg et al., 2005; Morello and Adámoli, 1968; Torrella and Adámoli, 2005). The Wet Chaco receives up to 1,800 mm/year and supports wetlands and palm savannas, while the Dry Chaco gets 300-800 mm/year and is dominated by drought-adapted forests. To refine this scheme, we follow Baumann et al. (2018) and designate a Very Dry Chaco in the southwest (Mendoza, San Luis, Córdoba, San Juan, La Rioja), characterized by lower biomass, greater aridity, higher elevations, and distinct fire regimes.

The Gran Chaco forms part of the La Plata basin (Musser, 2024). Rivers such as the Pilcomayo, Bermejo, and Salado originate in the Andes, cross the Dry Chaco, and disperse into megafans, streams, and wetlands in the eastern Wet Chaco. This west-east hydrological gradient drives seasonal contrasts: in
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119 dry months, the Dry Chaco faces water scarcity, whereas the Wet Chaco retains permanent wetlands that sustain ecological processes and fauna (Naumann et al., 2023).

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121 The region harbors exceptional biodiversity, with over 3,400 plant species and hundreds of vertebrates,
122 many endemic (Redford et al., 1990; Bucher and Huszar, 1999; Nori et al., 2016).

123

124 **2.2 Datasets**

125 **2.2.1 Fire patches**

126 In this study, we used FRYv2.0, a comprehensive global database dedicated to the functional traits
127 (morphology, fire spread, and timing) of fire patches (FPs), to investigate fire dynamics and their
128 underlying drivers in the Gran Chaco. FRYv2.0 incorporates burned area (BA) data from the FireCCI51
129 dataset as well as from MODIS MCD64A1 in two different versions, with different temporal cut-offs of
130 6, 12, or 24 days, as described in Laurent et al. (2018). It offers medium-resolution FPs covering the
131 period from 2001 to 2022, including metrics for FPs, such as morphological traits (e.g., area, shape
132 index), temporal traits (e.g., burn dates, duration), dynamic traits (e.g., rate of spread, fire radiative
133 power, and burn severity), and land cover.

134 For this work, we selected the FRYv2.0 dataset based on FireCCI51 over the MODIS MCD64A1
135 version, due to the higher spatial resolution of the FireCCI51 input data (250 m compared to 500 m), its
136 suitability for the heterogeneous Chaco landscapes, and its consistency with our previous FireCCI51-
137 based analysis (San Martín et al., 2023), avoiding uncertainties from mixing datasets. The dataset is
138 available at https://osf.io/rjvz5/files/osfstorage (last accessed on 10 June 2025).

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140 **2.2.2 Meteorological Data**

141 To study meteorological and climate time series in the region, we used the ERA5-Land global reanalysis
142 dataset focused on land surface variables, developed by the European Centre for Medium-Range
143 Weather Forecasts (ECMWF) (Muñoz-Sabater et al., 2021). It provides high-resolution data for land-
144 atmosphere interactions, designed to improve the ERA5 dataset by offering finer detail (0.1° instead of
145 0.25° spatial resolution) for variables affecting the land surface.

146 The product is available in the Copernicus Data Store (CDS) in NetCDF at
147 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land (last accessed on 30 May 2024).
148 We downloaded hourly data arrays covering January 2001 through January 2023.

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### 2.2.3 Environmental and Anthropogenic Data

We compiled multiple spatial datasets to represent landscape and human-related drivers of fire activity.

Topography was derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m resolution (https://srtm.csi.cgiar.org, accessed 26 May 2025) and resampled to 0.01° (~1 km).

Slope was calculated from the elevation surface using standard GIS tools.

Land cover (LC) was obtained from the ESA Climate Change Initiative Moderate Resolution Land Cover (ESA CCI MRLC) product (https://cds.climate.copernicus.eu/datasets/satellite-land-cover, accessed 26 May 2025), reclassified into groups relevant to the Gran Chaco (e.g., forests, shrublands, grasslands, seasonally flooded herbaceous vegetation) for 2001–2022.

Human pressure variables included population density from the Gridded Population of the World v4 (CIESIN, 2017; https://www.earthdata.nasa.gov/data/projects/gpw, accessed 26 May 2025) and road density from OpenStreetMap networks (https://www.openstreetmap.org, accessed 26 May 2025) calculated via kernel density estimation.

Livestock density came from the Gridded Livestock of the World v4 (https://dataverse.harvard.edu/dataverse/glw_4, accessed 26 May 2025), resampled to match the analytical resolution.

Soil properties (bulk density, sand content, and organic carbon at 0–5 cm depth) were obtained from SoilGrids250m (Hengl et al., 2017; https://soilgrids.org, accessed 26 May 2026).

### 2.2.3 Climate Oscillations

To account for the influence of large-scale climate variability, we included the Multivariate El Niño–Southern Oscillation (ENSO) Index version 2 (MEI.v2), developed by NOAA's Physical Sciences Laboratory. The MEI.v2 time series was obtained from NOAA PSL at https://psl.noaa.gov/enso/mei/ (last accessed 26 May 2025).

## 2.3 Data processing and analysis methods

### 2.3.1 Fire Weather Index (FWI)

We built an ERA5-Land-based Canadian Fire Weather Index (FWI; Van Wagner, 1987) dataset for the Gran Chaco at 0.1° resolution and daily time steps. We converted hourly accumulated precipitation to hourly rainfall by differencing successive steps and summed totals from 15 UTC (day D-1) to 15 UTC (day D), matching the FWI daily window and corresponding to local noon. We applied this fixed 15 UTC cutoff to avoid inconsistencies from varying national time zones and daylight-saving changes.

We extracted daily meteorological inputs—air temperature, relative humidity, wind speed at local noon, and 24-h precipitation—to compute the six FWI sub-indices: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build-Up Index (BUI), and FWI.
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We performed calculations with an adapted version of the FireDanger Python package (https://github.com/steidani/FireDanger) compatible with xarray and netCDF, including pixel-level day length for DMC and hemisphere-specific drying factors for DC.

We initialized the system on 1 January 1981 using Copernicus ERA5–FWI moisture codes at 0.25° (Vitolo et al., 2020) interpolated to 0.1°. For anomaly analysis, we restricted the time series to 2001–2022 to match satellite-based burned area (BA) records and calculated daily climatologies for all variables and indices using 2001–2020 as the baseline.

### 2.3.2 Fire size classification

To better characterize fire activity across the Chaco, we classified all fire polygons (FPs) from FRYv2.0 into six size categories, ranging from very small fires (<1 km²) to gigafires (>1000 km²), following and adapting the typology proposed by Linley et al. (2022). We used this to assess both the frequency and relative contribution of different fire sizes across regions and seasons.

### 2.3.3 Gridded burned area

To enable a spatio-temporal comparison between fire activity from FRYv2.0 polygons and meteorology, we developed a pipeline to transform the FP-based data into a monthly gridded product at 0.1°, matching the ERA5-Land grid (Fig. 2).

<figure><img src="image_16.png" /><figcaption>**Fig. 2.** Example of a FRY polygon (red line) over the gridded FRY dataset. Each grid cell at 0.1° is assigned the burned area corresponding to the total fraction of the polygon that overlaps it. The values printed over each grid cell correspond to these values.</figcaption></figure>
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206 The temporal assignment of fires to months followed a hybrid strategy: where MODIS-derived hotspot
detection dates (*mindtc_frп* and *maxdtc_frп*) were available in a given FP (typically absent in very small
207 FPs) they were used. Both FireCCI51- and MODIS-based versions of FRYv2.0 include these hotspot
208 date variables when available for the FP. When hotspot dates were missing, we used the FireCCI51-
209 derived burn dates (*minBD* and *maxBD*), which are based on surface reflectance changes and are
210 available for all FPs. For FPs spanning multiple months, we assigned the fire to the month in which it
211 started, unless its duration in a subsequent month exceeded that of the starting month by more than two
212 days.
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214 Each FP was rasterized over the ERA5 grid by intersecting it with individual cells. The intersected area
215 in square kilometers was computed using the WGS84 ellipsoid model. These contributions were
216 aggregated per cell and per assigned month to build a three-dimensional array of monthly BA (lat x lon
217 x time). A similar procedure was implemented for fire counts, using ignition coordinates when available.
218 Each FP's fire ignition coordinate was allocated to the closest cell in the 0.1° grid. The resulting monthly
219 gridded dataset included two variables: BA and counts.
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220

221 **2.3.4 Fire-weather types**

222 We classified fire patches (FPs) into three groups based on associated atmospheric conditions using the
223 K-means clustering algorithm (MacQueen, 1967) in scikit-learn v1.3. This approach follows prior
224 applications in fire studies (Ruffault et al., 2016, 2020; Vidal-Riveros et al., 2024) and aimed to identify
225 distinct fire-weather types and assess their influence on fire size and shape.

226 We retained only FPs between 1 and 100 km² (N = 76,263) to reduce biases from very small or very
227 large events. For each FP, we extracted daily ERA5-Land meteorological data and generated FWI time
228 series from 7 months before ignition to 7 months after. Two feature sets were built: one for pre-fire
229 conditions and one for during-fire conditions.

230 For the *Pre-Fire* set, we used normalized anomalies of 2-m air temperature, 10-m wind speed, relative
231 humidity (RH), drought code (DC), and duff moisture code (DMC) (Ruffault et al., 2020). Pre-fire
232 values were calculated as the 3-day mean from ignition day (D) to D-2 to limit detection-date bias
233 (Lizundia Loiola et al., 2020; Pettinari et al., 2021) while avoiding noise from longer lags.

234 For the *During-Fire* set, we computed the same variables averaged over the fire's duration and added a
235 metric specifically designed to capture the role of strong, persistent winds in shaping fire behavior: the
236 Extreme Wind Directionality Index (*EW_dir_index*). This index measures both how often extreme
237 winds occurred and how steady their direction was.

238 The first component, fraction of extreme-wind days (*EW Frac*), is the proportion of burning days when
239 the daily maximum wind speed exceeded 25 km h⁻¹:

240 (Eq. 1):
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$$241 \qquad EW\_frac = \frac{EW}{N}$$

242 where EW is the number of days with extreme winds and N is the total fire duration (days).

243 High values indicate that strong winds occurred on many burning days.

244 The second component, wind direction steadiness (wind_dir_R), reflects how consistent the wind direction was across the fire's duration (N). Each day's mean wind direction (θ, in radians) is represented as a unit vector, summed across all days, and normalized by the fire duration:

247 (Eq. 2):

$$248 \qquad wind\_dir\_R = \frac{\sqrt{(\sum_{i=1}^{N} \cos \theta_i)^2 + (\sum_{i=1}^{N} \sin \theta_i)^2}}{N}$$

249 Values near 1 mean winds blew in a stable direction throughout the event, while values near 0 mean wind directions shifted substantially from day to day.

251 The EW_dir_index is the product of EW_r and wind_dir_R:

252 (Eq. 3):

$$253 \qquad EW\_dir\_index = EW\_frac \times wind\_dir\_R$$

254 It reaches high values only when strong winds occur on many burning days and blow consistently from the same direction, identifying fires likely driven by sustained, unidirectional wind conditions.

255 All variables were standardized (mean = 0, σ = 1) before clustering. The resulting data matrix (nnn fires × ppp variables) was clustered with k = 3, squared Euclidean distance, k-means++ initialization, 50 random restarts, and a convergence tolerance of 10⁻⁴. We retained three clusters based on a prior hypothesis (wind-driven, drought-driven, and neutral), an elbow in the within-cluster sum-of-squares curve, and a peak in the silhouette coefficient at k = 3.

261 Cluster labels were assigned by interpreting centroid positions in principal component space and examining the temporal evolution of variables (Fig. A1). Robustness was assessed using mean silhouette coefficients and their distribution across clusters. The first two principal components explained more than 60 % of the variance and clearly separated cluster centroids.

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266 **2.3.5 Fire size drivers**

267 To investigate the role of environmental and anthropogenic variables in shaping fire activity, we extracted a diverse set of FP-level predictors encompassing topographic, climatic, anthropogenic, vegetation, and landscape heterogeneity dimensions. These variables, listed in Table 1, were used as inputs in the Random Forest (RF) models to assess their relative importance in explaining fire size and frequency.

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<figure><img src="image_21.png" /><figcaption>Table 1. Polygon-level predictor variables used in the Random Forest models, grouped by variable type.</figcaption></figure>

<table><thead><tr><th>Category</th><th>Variables</th><th>276</th></tr></thead><tbody><tr><td>Topographic</td><td>Mean Slope (%)<br>Mean Elevation (m)</td><td></td></tr><tr><td>Climatic (during fire)</td><td>Precipitation (mm)<br>Maximum Wind Speed (km/h)<br>Extreme Wind and Direction Index (EW_dir_index)<br>Extreme Wind Days Fraction (EW frac)</td><td></td></tr><tr><td>Anthropogenic</td><td>Cattle Density (heads/km²)<br>Road Density (km/km²)<br>Population Density (p/km²)</td><td></td></tr><tr><td>Vegetation productivity</td><td>LAI for previous growing season (MODIS-derived)</td><td></td></tr><tr><td>Land Cover Composition</td><td>Flooded Herbaceous vegetation (%)<br>Tree Cover (%)<br>Shrublands (%)<br>Trees/Shrubs/Herbs Mosaics (%)<br>Natural/Croplands Herbaceous Mosaics (%)</td><td></td></tr><tr><td>Landscape Heterogeneity</td><td>Land Cover Diversity (Shannon Index, H)<br>Land Cover Evenness (Pielou Index, E)</td><td></td></tr></tbody></table>

The Shannon diversity (H) and Pielou's evenness (E) were computed as follows:

(Eq. 4) Shannon Diversity Index (Shannon, 1948):

$$H = - \sum_{i=1}^{m} p_i \log (p_i)$$

Where *m* is the number of land cover classes present in the polygon, *p*<sub>i</sub> is the proportion of land cover type i, and the sum includes all classes with *p*<sub>i</sub> > 0.

(Eq. 5) Pielou's evenness (Pielou, 1966):

$$E = \frac{H}{\log (m)}$$

Where *H* is the Shannon Diversity Index and *m* is the number of land cover classes present in the polygon.

Once all predictor variables were derived, we trained RF models using a set of 17 explanatory variables to analyze the drivers of fire behavior, using the variable *n_cell* from the FRY dataset as the response variable. This variable represents the number of FireCCI51 pixels within each FP and was preferred over polygon-based *area* due to the latter's dependency on latitude, which introduced artificial discontinuities. In contrast, *n_cell* provided a discrete and spatially consistent proxy for BA, improving model stability and interpretability.
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We implemented 12 RF models across five configurations: (i) a global model using all 76,263 polygons (1–100 km²); (ii) three subregion-specific models for the Wet, Dry, and Very Dry Chaco; (iii) two seasonal models based on ignition season (wet vs dry); and (iv) two sets of three cluster-based models (pre-fire and during-fire conditions) derived from the meteorological classification (see Section 2.3.4).
All models were trained using the *ranger* R package (Wright and Ziegler, 2017) with quantile regression forests (Meinshausen, 2006). We used 500 trees, a minimum node size of 5, variance-based importance, and the Poisson split rule, with 4 variables considered at each split. Feature selection included correlation filtering (r > 0.8 threshold) and preliminary importance scores. Each model was trained on 75% of the data and validated on the remaining 25%. We evaluated feature contributions using SHAP (SHapley Additive exPlanations) values.
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## 3 RESULTS

### 3.1 Burned area and ignitions

<figure><img src="image_25.png" /><figcaption>**Fig. 3.** Total annual burned areas (a), ignitions (b), and mean fire durations (c), between 2001 and 2022 in the Wet, Dry, and Very Dry Chaco regions. Extracted from FRYv2.0.</figcaption></figure>

We examined the interannual relationship between total burned area (BA) and the number of fire polygons (FPs) across the Chaco (Fig. 3). Overall, BA and ignition counts show a positive association, though with regional and seasonal variability. In the Wet Chaco, strong correlations were found in both wet and dry seasons ($R^2$ = 0.96 and 0.91), indicating fire extent is largely proportional to ignition frequency (Fig. A2). The Dry Chaco also showed a high wet-season correlation ($R^2$ = 0.87), but a weaker dry-season one ($R^2$ = 0.45), suggesting a greater role of other drivers in the latter. In the Very Dry Chaco, wet-season fires were sparse and weakly correlated with BA ($R$ = 0.11), while a stronger correlation emerged in the dry season ($R^2$ = 0.78). Mean fire duration remained relatively stable over time, implying that interannual variability in BA is primarily linked to ignition frequency and fire size, rather than duration.

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<figure><img src="image_26.png" /><figcaption>331 3.2 Fire size distribution and regional differences</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Fig. 4. (a) and (b) Land-cover distribution in the Gran Chaco based on ESA-CCI MRLC for 2001 and 2022, respectively. (c) Forest transition classes between 2001 and 2022, showing forest loss (forest to non-forest), forest gain (non-forest to forest), and stable forest. Forests include all tree cover classes; non-forest pixels appear in grey. (d) Spatial distribution of fire events (2001–2022) categorized by fire size using FRYv2.0 data. Fire-size classes range from Very Small (< 1 km²) to Gigafires (> 1000 km²). Fires polygons overlapping the Chaco boundary are retained.</figcaption></figure>

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**Fig. 4** shows the LC distribution of the Gran Chaco in 2001 and 2022 (panels a and b), the spatial pattern of forest transitions between 2001 and 2022 (panel c), and all fire events recorded during 2001–2022 categorized by fire size (panel d). The Wet Chaco is dominated by seasonally flooded herbaceous vegetation, forest mosaics, productive grasslands, and croplands, and it exhibits the highest fire frequency. In contrast, the Dry and Very Dry Chaco regions show increasing proportions of shrublands, fragmented forests, and agricultural frontiers.

Fire size distribution is strongly right-skewed across all subregions: over 80 % of events fall within the Very Small (< 1 km²) and Small (1–5 km²) categories (Table A1; Fig. A3). Larger fires, although less frequent, account for a disproportionate share of total burned area. While Very Small to Large (10–100 km²) fires are widespread, Megafires (100–1000 km²) are most common in the Wet Chaco, likely due to continuous fuel beds in grasslands and wetlands. These large fires often occur in areas dominated by seasonally flooded herbaceous vegetation, which can generate high flammability during dry periods. Gigafires (> 1000 km²), although rare, are almost exclusively observed in the Dry and Very Chaco.

Forest loss is widespread across the Chaco in all three countries, with extensive deforestation frontiers in both Argentina and Paraguay. However, the association between fires and these frontiers differs regionally. In Argentina, deforestation zones often coincide with clusters of small and medium fires, whereas in Paraguay and Bolivia fire activity is less evident along recent forest loss edges. In all regions, most large fires occurred in non-forest areas. Shrublands were excluded from the forest class definition, which here only includes tree-cover categories.
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<figure><img src="image_32.png" /><figcaption>Fig. 5. Cumulative burned area (2001–2022) by fire-size class across the Wet, Dry, and Very Dry Chaco subregions.</figcaption></figure>

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According to Fig. 5, the Wet Chaco registers the highest total burned area, nearly double that of the Dry and Very Dry regions. In this subregion, Large fires contribute ~40% of annual BA, and Small fires ~20% (Fig. A4). Despite their modest size, small fires contribute substantially to BA in the Wet Chaco due to their high frequency between 2001 and 2022 (>36,000). Extreme years such as 2003 and 2020 were marked by widespread outbreaks.

In the Dry Chaco, fire frequency is lower, but large fires play a more prominent role. Large fires account for about 25% of the annual burned area, and Gigafires can dominate totals in some years. For example, in 2019, just three Gigafires in the Dry Chaco burned approximately 10,000 km², which corresponds to the region's mean annual BA and represented more than 50% of the total for that year.

The Very Dry Chaco, while recording the lowest overall BA, exhibits abrupt interannual peaks driven by isolated Megafires and Gigafires, pointing to a more stochastic fire regime.

Between 2020 and 2022, the Wet Chaco experienced an unprecedented number of Megafires and Gigafires, both in terms of event counts and their contribution to total BA. These patterns align with the extreme fire-weather anomalies described in **Section 3.3**.
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378 **3.3 Fire-weather relationship**

379

<figure><img src="image_35.png" /><figcaption>Fig. 6. Seasonality of burned area (BA, km²) in the Wet, Dry, and Very Dry Chaco. The black curve is the 2001–2022 monthly mean and the grey band shows the interquartile range (25–75%). Colored curves overlay monthly BA for 2020 (orange circles), 2021 (blue crosses), and 2022 (green squares), highlighting differences from the climatological envelope. Y-axis limits differ by panel.</figcaption></figure>

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**Fig. 6** presents the monthly BA climatology (2001–2022) with 2020–2022 overlaid for the Wet, Dry, and Very Dry Chaco. In the Wet Chaco, BA in 2020 is above average for most months, with a secondary pulse in March–April (late wet season) preceding pronounced peaks in August–September (winter/dry season). In contrast, anomalies in 2021–2022 are concentrated in the summer/wet season (December–March), reaching levels similar to the typical late-winter/early-spring maximum, while post-winter months in 2022 remain mostly below average. In the Dry Chaco, 2020 stands out as extreme, particularly in July and September, whereas 2021 records an exceptional August at or above historical maxima and 2022 stays near or below the mean. In the Very Dry Chaco, positive anomalies are dominated by 2020, with a sharp October maximum; 2021 shows only minor increases, and 2022 remains subdued. Overall, 2020 shows widespread positive anomalies lasting several months across all subregions. In contrast, 2021 and 2022 generally feature shorter peaks, often concentrated in summer, although 2021 also records exceptional winter fires in the Dry Chaco. Activity during the canonical late-winter fire season is otherwise limited, particularly in 2022.

Spatial patterns of fire-weather coupling are explored in **Fig. 7**, which shows the per-pixel Pearson correlation between monthly Fire Weather Index (FWI) anomalies and BA during wet and dry seasons. Significant positive correlations (p < 0.05) are concentrated in the Wet Chaco, where coefficients reach up to 0.7 during the wet season. In contrast, the Dry and Very Dry Chaco show weaker and more spatially scattered relationships, partly due to lower fire frequency.
<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /><figcaption>**Fig. 7.** Spatial distribution of pixel-wise Pearson correlation coefficients between monthly Fire Weather Index (FWI) anomalies and monthly burned area (BA) for the period 2001–2022: (a) Wet Season and (b) Dry Season. The color bar indicates the strength and direction of the correlation (from negative in blue to positive in red). Inset statistics summarize the distribution of coefficients (Min, Mean, Max). Pixels marked with small black circles represent non-significant correlations (p-value > 0.05), while unmarked pixels indicate significant correlations (p-value < 0.05). Only pixels with more than 3 time steps with burned area >0 were kept to avoid biased correlations related to very few or no fires.</figcaption></figure>

To further explore the spatial sensitivity of fire activity to fire weather, **Fig. 8** compares per-pixel correlations between monthly FWI anomalies and two metrics: fire counts (ignitions) and BA. Each dot represents a 0.1° grid cell, and quadrants classify response types. In the Wet Chaco, 93% of cells fall in Q1, where both metrics show positive correlations with FWI, with moderate mean values (0.17 ± 0.12 for ignitions, 0.19 ± 0.13 for BA) and strong inter-metric correlation (r = 0.76). The Dry and Very Dry Chaco show more heterogeneous patterns, with Q1 proportions of 59% and 61%, and weaker mean correlations (~0.04–0.06). Still, inter-metric spatial correlations remain high (r = 0.81 and r = 0.72), indicating that regions more sensitive to fire weather in terms of ignitions also tend to be more sensitive in terms of fire extent.

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<figure><img src="image_39.png" /><figcaption>**Fig. 8.** Each panel shows a scatterplot of per-pixel Pearson correlation coefficients between the Fire Weather Index (FWI) and two fire activity metrics—ignition frequency (x-axis) and burned area (y-axis)—over the period 2001–2022. The panels correspond to the Wet, Dry, and Very Dry Chaco subregions, and each dot represents a 0.1° × 0.1° grid cell. Quadrants are defined by the sign of each correlation coefficient to classify spatial patterns of fire-weather association: Q1 (top-right) includes pixels with positive correlations for both ignitions and burned area; Q3 (bottom-left) includes negative correlations for both; Q2 and Q4 represent divergent cases. For each subregion, quadrant counts, percentages, and summary statistics (mean ± standard deviation of each correlation axis and Pearson r between them) are annotated.</figcaption></figure>

Finally, the temporal co-evolution of annual BA and FWI anomalies is illustrated in the appendix (Figs. A5–A6). Several years, especially in the Wet Chaco, show strong spatial correspondence between extensive fire activity and positive FWI anomalies (e.g. 2012, 2020–2022). However, other years (e.g. 2003) reveal extensive BA without matching FWI extremes, underscoring that weather is not the sole driver of interannual variability.

## 3.4 Temporal dynamics of fire-environment interactions

To explore how conditions evolve before and after fire events, we analyzed both regional time series and lagged correlations between BA anomalies and three key drivers: FWI, rainfall, and vegetation greenness (EVI), over the period 2001–2022.

The time series analysis (Fig. A07) reveals a coherent pattern in all subregions. Typically, positive rainfall anomalies (which automatically decrease FWI) are followed by increased EVI, indicating vegetation growth and fuel accumulation. When this is then followed by elevated FWI values (due to negative rain and humidity anomalies, extreme heat and/or strong winds), peaks in BA are frequently observed. This pattern supports the interpretation of a fire-favoring sequence: moisture enables biomass build-up, which is later dried and made flammable under high fire-weather conditions, culminating in fire activity. This cycle is particularly evident in major fire years such as 2020 and 2022, especially in the Wet Chaco, where the alignment between environmental anomalies and BA peaks is striking. In the Dry and Very Dry Chaco, the sequence is also well defined, although slightly more variable probably due to limited fuel accumulation.

The influence of large-scale climate variability, particularly the El Niño–Southern Oscillation (ENSO), is also reflected in the fire-environment dynamics. During La Niña phases (negative ENSO), we observe reduced rainfall and elevated FWI values, often coinciding with increased BA. Conversely, El Niño
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episodes (positive ENSO) are associated with wetter conditions, lower fire-weather pressure, and reduced fire activity (Fig. A7 and Fig. A8).

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<figure><img src="image_42.png" /><figcaption>**Fig. 9.** Lagged correlations between monthly anomalies of FWI, rainfall, and EVI with burned area in the Chaco. Each heatmap shows the Pearson correlation coefficient between the anomaly of a given variable (FWI, rainfall, or EVI) at different time lags and the burned area anomaly, for each Chaco subregion. Negative lags indicate the variable leads burned area; positive lags indicate it follows. Correlations are computed from pixel-based, region-averaged monthly time series for 2001–2022.</figcaption></figure>

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**Fig. 9** shows lagged Pearson correlations between monthly anomalies of BA and FWI, rainfall, and EVI for the three Chaco subregions. Positive correlations between BA and FWI at lags 0 to +1 months, indicate that peak fire activity coincides with high fire-weather conditions. Rainfall and EVI display negative correlations with BA at short negative lags (–1 to –3 months), consistent with dry, senescent vegetation promoting flammability. At longer negative lags (–5 to –6 months), especially in the Dry and Very Dry Chaco, both variables correlate positively with BA, suggesting that wetter, greener periods months earlier promote fuel build-up. In the Wet Chaco, lag correlations are weaker and less structured, likely due to consistently moist conditions that buffer fire–environment coupling.
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3.5 Fire-weather types

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<figure><img src="image_45.png" /><figcaption>Fig. 10. Spatial distribution and frequency of pre- and during-fire meteorological clusters across the Gran Chaco (2001–2022). Panels (a) and (b) show the geographic location of fire patches classified into three Fire-Weather Types (FWTs)—Neutral (blue), Drought-Driven (orange), and Wind-Driven (green)—for the pre-fire and during-fire periods, respectively, overlaid on Chaco sub-region boundaries. Some patches overlap through the years and may partially or totally cover each other. Panels (c) and (d) display the total number of patches assigned to each FWT for pre-fire and during-fire clustering methods, respectively.</figcaption></figure>

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**Fig. 10** shows the spatial distribution and frequency of three Fire-Weather Types (FWTs)—Neutral, Drought-Driven, and Wind-Driven—for the pre-fire and during-fire periods. Using k-means clustering with *k* = 3, each FP was assigned an FWT twice: first based on conditions in the 0–3 days before ignition (*Pre-Fire*) and then based on mean conditions during the active burning period (*During-Fire*).

Neutral FWTs dominate both clusterings, but their share decreases from 50.9 % to 45.3 % overall, while Drought-Driven rises from 26.6 % to 30.8 % and Wind-Driven from 22.4 % to 23.9 % (Fig. 10c–d and Fig. A9). In the Wet Chaco, Neutral drops from 49 % to 42 % with a marked increase in Drought-Driven; in the Dry Chaco, both non-neutral types grow moderately; in the Very Dry Chaco, Wind-
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496 Driven increases sharply (15 % → 26 %), especially in the south where complex topography may

497 strongly influence fire-atmosphere dynamics (see Section 2.1).

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<figure><img src="image_47.png" /><figcaption>Fig. 11. Mean daily anomalies of temperature (Temp.), relative humidity (RH), 10-meter wind speed, Drought Code (DC), and Duff Moisture Code (DMC) from 20 days before to 20 days after fire ignition, averaged over fire polygons assigned to the Neutral, Drought-Driven, and Wind-Driven clusters for Pre-Fire (left) and During-Fire (right) clustering approaches.</figcaption></figure>

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<figure><img src="image_48.png" /><figcaption>Fig. 12. Clusters mean morphology profiles for (a) Pre-Fire and (b) During-Fire clusterings. Each axis represents a standardised morphology variable (z-score), and each colored polygon shows the mean profile for one cluster. The radial extent indicates the relative value of each variable within the dataset.</figcaption></figure>

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509 Fig. 11 shows mean daily anomalies from 20 days before to 20 days after ignition for each FWT. Wind-

510 Driven fires present a sharp rise in wind speed and temperature in the days around ignition, coupled with
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a drop in RH, creating highly flammable conditions. Drought-Driven fires exhibit a long build-up of dryness before ignition, with persistently high DC and DMC values and low RH, indicating extended fuel curing. Neutral fires occur under conditions close to climatology, with only small fluctuations in all variables.

Morphology across *Pre-Fire* FWTs is broadly similar (Fig. 12, A10–A11), with comparable FP area, shape index, core-area index, eccentricity, and perimeter-to-area ratio. In contrast, *During-Fire* FWTs display clear differences: Wind-Driven fires tend to be larger, more elongated, and more cohesive (higher core-area index, lower perimeter-to-area ratio) than Drought-Driven fires, consistent with directional spread under sustained winds.

Overall, *Pre-Fire* FWTs capture the atmospheric context leading to ignition, whereas *During-Fire* FWTs better reflect the conditions that shape the eventual size and geometry of the burned area. Other factors such as fuel continuity, topography, and human interventions likely modulate these outcomes.

## 3.6 Fire size drivers

To identify drivers of fire size and shape beyond meteorological conditions, we trained Random Forest (RF) models using 17 landscape and environmental predictors for all FPs between 1 km² and 100 km² (see Section 2.3.5).

<figure><img src="image_51.png" /><figcaption>**Fig. 13.** SHAP summary plot for the Random Forest model predicting fire polygon size (n_cell) using all fire patches in the FRY dataset with areas between 1 km² and 100 km², with 17 explanatory features extracted for each polygon. The left panel shows the mean absolute SHAP</figcaption></figure>

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value for each feature, ranking them by overall importance. The right panel displays the distribution of SHAP values for each feature across

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<caption>**Fig. 14.** SHAP feature importance ranks across all trained Random Forest models used to predict fire polygon size (n_cell) based on 17 explanatory variables. Colored dots at the end of bars shows the rank of a variable's importance (1 = most important, 17 = least important) for a given model.</caption>

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In the global RF model (Fig. 13), static topographic and vegetation structure variables dominated: mean elevation had the highest mean SHAP value (31.3), followed by land-cover (LC) evenness (21.0), tree cover (19.3) and mean slope (15.2). These four variables consistently ranked in the top positions across all twelve cluster-specific and global models (Fig. 14). Land-cover composition metrics such as cropland or flooded herbaceous cover showed moderate contributions, while meteorological and social variables (e.g. maximum wind speed, precipitation, population or cattle density) were surprisingly of lower importance.

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<figure><img src="image_57.png" /><figcaption>Fig. 15. SHAP dependence plots for all 17 explanatory variables used to predict fire polygon size (n_cell) with the Random Forest model trained on all fire patches between 1 km² and 100 km². Each panel shows the SHAP value (y-axis) across the range of a given feature (x-axis), illustrating the marginal effect of that feature on the model's output. Dots are colored by fire size (number of burned pixels), with darker tones indicating larger fires.</figcaption></figure>

SHAP dependence plots (Fig. 15) revealed clear non-linear responses. Elevation had a steep positive effect up to ~70 m, plateauing thereafter, suggesting that slightly elevated terrain favors larger fires, while low-lying areas may be constrained by water bodies or vegetation type. Slope effects were similar: flat to gently undulating terrain (≤10 %) supported larger fires, while steeper slopes curtailed spread. Lower LC evenness (i.e. more homogeneous fuels) and sparse tree cover were associated with larger predicted sizes, reflecting the role of fuel continuity and open vegetation in promoting spread; conversely, heterogeneous landscapes and dense tree cover dampened fire growth.

Most other predictors showed weak or flat SHAP responses. Only maximum wind speed displayed a consistent positive association with fire size among the dynamic variables, indicating a secondary but detectable influence compared with dominant topographic and structural gradients.

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566 **4 DISCUSSION**

567 Building on event-level fire polygons (FPs), we examine how meteorology, landscape structure, and
568 human pressures shape fire size and morphology across the Wet, Dry, and Very Dry Chaco.

569 **4.1 Fire regime and extreme events**

570 FP data reveal a strongly skewed size distribution: many small fires (<5 km²) and a few very large events
571 that dominate burned area (BA), consistent with global patterns (Archibald et al., 2009; García et al.,
572 2022b; Haas et al., 2022; Hantson et al., 2015, 2017). Megafires (>100 km²) are most frequent in the
573 Wet Chaco, where continuous herbaceous fuels in savannas and seasonally flooded vegetation support
574 spread. Gigafires (>1000 km²), although rare, occur almost exclusively in the drier subregions, often in
575 remote areas with limited suppression access, higher shrub biomass, and lower humidity. In extreme
576 years such as 2019–2022, a handful of these events contributed a substantial share of total BA in their
577 respective regions.

578 These size patterns indicate that both fuel configuration and atmospheric conditions influence the
579 potential for very large fires. We therefore examined how short-term fire weather relates to BA across
580 subregions. Fire weather–BA coupling shows marked spatial variability: in the Wet Chaco, high FWI is
581 consistently associated with large BA, confirming moisture limitation and strong sensitivity to
582 atmospheric conditions, in line with earlier BA-based analyses (San Martín et al., 2023). In the Dry and
583 Very Dry Chaco, correlations are weaker and more heterogeneous, indicating partial decoupling
584 between short-term fire weather and final size, mediated by fuel continuity and antecedent conditions.
585 Lagged relationships clarify this contrast: in drier areas, positive rainfall and vegetation productivity 4–
586 6 months before fire are followed by higher BA once fuels cure, supporting the fire–productivity
587 hypothesis (Pausas and Bradstock, 2007). In wetter areas, where fuels are rarely limiting, short dry spells
588 immediately prior to fire are more predictive of activity, consistent with a moisture-limited regime
589 within varying-constraint frameworks across resource gradients (Krawchuk and Moritz, 2011).

590

591 **4.2 Fire-weather types across the Chaco region**

592 To assess how daily fire weather influences fire size, we built on the framework of Hernandez et al. 593 (2015) and Ruffault et al. (2016, 2020), who classified Mediterranean wildfires into Fire-Weather Types 594 (FWTs) based on pre-fire meteorological anomalies (heat, drought, wind) and found that Hot-Drought 595 and Wind-Driven types were strongly linked to large events. Applying a similar pre-fire clustering in 596 the Gran Chaco (Neutral, Drought-Driven, Wind-Driven) captured ignition contexts but explained little 597 variation in final size or shape.

598 In contrast, clustering based on during-fire variables (maximum wind speed, total precipitation, drought
599 indices, and the Extreme Wind Directionality Index developed in this study) clearly separated groups
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600 with significant differences in size and morphology. Dry, windy days during the fire, favored rapid and
601 large expansion.

602 Our findings contrast with Ruffault et al. (2016, 2020) and Belhadj-Kheder et al. (2020), who found pre-
603 fire or near-ignition anomalies predictive in Mediterranean and North African settings, respectively,
604 with the latter highlighting anomaly duration in low-suppression contexts. This stronger size–weather
605 link for during-fire meteorology likely reflects Chaco-specific traits such as flat terrain, continuous fuels,
606 and permissive fire conditions (Bucher, 1982; Vidal-Riveros et al., 2023), which make wind and
607 humidity more decisive than pre-fire anomalies. In the Mediterranean, fragmented fuels, complex
608 topography, and strong suppression (Ruffault and Mouillot, 2015, 2017), translate into ignition-day
609 extremes mattering more. Similar modulation by suppression capacity occurs in western U.S. forests
610 (Higuera et al., 2015).

611 Our clustering extends fire-weather typologies to a tropical dry forest context and complements recent
612 Gran Chaco regime classifications (Vidal-Riveros et al., 2024; Naval-Fernández et al., 2025) that
613 omitted meteorological variables, highlighting the key role of fire-active weather in shaping fire
614 morphology.

615 Separately, our results also showed that La Niña phases, characterised by precipitation deficits in the
616 Gran Chaco, coincided with elevated FWI, higher BA, and a greater likelihood of large fire events. This
617 pattern was particularly evident during the extreme fire seasons of 2019–2022, illustrating how
618 interannual climate variability modulates fire size potential at regional scales.

619

620 **4.3 Landscape pattern influence on fire types**

621 Beyond meteorological effects, anthropogenic and structural landscape factors strongly modulated fire
622 size. Random Forest (RF) models consistently identified elevation as the most important predictor across
623 all subregions and seasons, followed by land-cover evenness, tree cover, and slope (Fig. 14). While
624 elevation is not a direct control on combustion, it reflects broad ecological gradients in vegetation
625 composition, fuel moisture regimes, and land-use history that shape the conditions under which fires
626 develop. In the Chaco, these gradients often translate into water presence and seasonal flooding in
627 lowlands, which can limit spread, and stronger, more persistent winds in higher terrain, which can
628 enhance it.

629 Vegetation composition exerted a strong influence on size outcomes. Areas dominated by herbaceous
630 or shrub cover, often linked to past or ongoing land-use change, were more prone to large fires, whereas
631 higher tree cover was associated with smaller fires. This pattern aligns with global evidence that
632 increasing tree cover generally reduces burned area (Bistinas et al., 2014; Haas et al., 2022), although
633 exceptions occur where certain forest types, such as introduced pine plantations, have higher
634 flammability than native broadleaf evergreen forests (Barros and Pereira, 2014; Paritsis et al., 2018;
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635 Vidal-Riveros et al., 2023). Differences in live fuel moisture between growth forms (Yebra et al., 2019)

636 further explain the greater spread potential in shrub- and grass-dominated systems.

637 Landscape heterogeneity, expressed as lower land-cover evenness (i.e., more homogeneous fuels), was

638 also linked to larger fires, reflecting the role of continuous fuel beds in enabling propagation.

639 Conversely, heterogeneous mosaics with high evenness disrupted spread, acting as natural firebreaks

640 (Povak et al., 2018). Together, these results show that while fire-active weather is an important

641 determinant of spread (Section 4.2), the physical and vegetative structure of the landscape sets the upper

642 limits for how large fires can become.

643

644 **4.4 Fire shape as an indicator of fire weather**

645 Building on the fire-weather clustering (Section 4.2) and landscape controls (Section 4.3), we examined

646 whether fire morphology can reveal the influence of landscape or climatic drivers of spread, taking

647 advantage of the detailed FP-level shape and size metrics provided by FRYv2.0 (Laurent et al., 2018;

648 Chen, 2025). We hypothesized that elongation and perimeter complexity would be enhanced by strong,

649 steady winds, whereas complex topography or fragmented fuels would produce more irregular shapes.

650 In the Gran Chaco, fires occurring under strong, persistent winds displayed significantly larger

651 perimeters and greater elongation, supporting our hypothesis and highlighting morphology as a signature

652 of wind-driven fire types.

653 To our knowledge, the hypothesis that fire elongation and perimeter complexity can serve as indicators

654 of prevailing wind influence on fire spread has rarely been tested directly, making this a novel

655 contribution of our study. Barros et al. (2012, 2013) showed that watershed orientation influenced fire

656 spread in California, and Mansuy et al. (2014) reported similar effects in Canadian boreal forests, but

657 neither explicitly linked shape to dominant wind direction. We propose that the combined analysis of

658 shape and size offers a valuable benchmark for process-based fire models, which often rely on simplified

659 ellipsoidal spread assumptions (Hantson et al., 2016), and could help train emerging machine-learning

660 approaches for global fire hazard prediction (Li et al., 2023; Liu et al., 2025; Zhang et al., 2023).

661

662 **4.5 Deforestation and Prescribed Burning**

663 Anthropogenic influences on the Gran Chaco fire regime include the advancing agricultural frontier,

664 characterized by rapid land-use change and deforestation (Arriaga Velasco-Aceves et al., 2021; Boletta

665 et al., 2006), and the widespread use of fire as a management tool. Prescribed burning typically occurs

666 in late winter and early spring, before the wet season (San Martín et al., 2023), and is generally limited

667 to periods with lower wind speed and limited drought, following decision-support guidelines for ignition

668 (Hsu et al., 2025). However, forecasts are uncertain, and fire-prone conditions can quickly develop after
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669 ignition, allowing burns to escape their intended boundaries. Such escaped prescribed fires, although 670 often managed to limit societal impacts, remain a recurrent hazard (Black et al., 2020; Li et al., 2025).

671 FRYv2.0 and other global burned-area products cannot distinguish between wildfires and prescribed 672 burns, restricting our ability to assess their occurrence in the region. Although Hsu et al. (2025) compiled 673 a global prescribed fire dataset, the Gran Chaco is not covered. Many spring fires are likely prescribed 674 burns, but systematic monitoring is lacking. Similarly, we could not isolate deforestation fires, which in 675 the region tend to occur mostly within three years after forest clearing (San Martín et al., 2023). High- 676 resolution burned-area products combined with tree-cover data could help identify such events, as 677 demonstrated for Africa (Khairoun et al., 2024).

678 Improved detection of prescribed and deforestation fires would enable better risk assessment of escaped 679 burns and could promote greater societal acceptance of prescribed fire as part of integrated fire 680 management for hazard mitigation (Oliveras Menor et al., 2025).

681

682 **4.6 Limitations**

683 Direct human influences, such as ignition sources, suppression actions, and fire management practices, 684 could not be explicitly included in this study due to limited data availability. Their effects are likely 685 reflected indirectly through variables such as vegetation structure, road density, population density, and 686 land cover, but their absence restricts our ability to fully capture anthropogenic modulation of fire size.

687 The ERA5-Land reanalysis at 0.1° (~9 km) resolution, although considered high for a global 688 meteorological dataset, remains too coarse to fully represent local-scale wind variability, solar radiation 689 heterogeneity, and terrain-induced thermal gradients that can influence fire spread. Advances in 690 downscaling techniques for wind (Dujardin and Lehning, 2022), solar radiation (Druel et al., 2025), and 691 temperature (Kusch and Davy, 2022) may improve the spatial realism of these variables in future fire 692 regime analyses, especially in complex landscapes. However, these approaches were not applied here.

693 More fundamentally, the absence of dynamic coupling between fire behaviour and atmospheric 694 processes remains a key constraint, as fire-atmosphere feedbacks are not represented in our predictors.

695 The FRYv2.0 fire dataset is based on the global 250 m FireCCI51 product, which can both overestimate 696 and underestimate fire size. Overestimation may occur when partially burned pixels are classified as 697 fully burned, particularly along fire edges or within heterogeneous scars (Pettinari et al., 2021).

698 Underestimation arises from omission errors, which are common for small, low-intensity, or fragmented 699 fires that fall below the detection threshold, or in areas affected by cloud cover, dense smoke, or mixed 700 land cover (Lizundia-Loiola et al., 2022).

701 Other FireCCI51-specific limitations should also be acknowledged. BA is likely underestimated during 702 the early period of the dataset (2001 to mid-2002) when only Terra MODIS data were available. Ignition 703 dates may contain biases depending on satellite detection quality and meteorological conditions 704 (Lizundia-Loiola et al., 2020). Furthermore, the aggregation of pixels into FPs depends on temporal
<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

705 thresholds used to group neighbouring pixels within the same event (Moreno et al., 2021; Oom et al.,

706 2016).

707 Future developments in fine-resolution burned-area products (e.g. 20 m), such as FireCCISFD20, have

708 already demonstrated substantial improvements in Africa, detecting 80–120 % more burned area

709 (Chuvieco et al., 2022). Delivering similar products at continental or global scale, as long requested by

710 the fire science community (Mouillot et al., 2014), will be critical to reduce both overestimation from

711 coarse-pixel classification and underestimation from omission errors, and to improve the accuracy of

712 fire size and distribution assessments.

713
<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /></figure>

714 **5 CONCLUSIONS**

715 This study advances understanding of fire regimes across the Wet, Dry, and Very Dry Chaco through a
716 spatially explicit analysis of fire events from 2001–2022. We document strong regional contrasts in fire
717 size, seasonality, and drivers, shaped by interactions between fuels, weather, and land use.

718 Fire sizes were highly skewed: over 80% of detected fires were <5 km², yet large events dominated
719 burned area (BA). Megafires (>100 km²) occurred in all subregions, with the Wet Chaco recording the
720 most. Gigafires (>1000 km²) were rare but concentrated in the Dry Chaco, where some single events
721 exceeded 50% of annual BA. The Wet Chaco burned most extensively (~2× the Dry Chaco), with the
722 highest fire frequency and ignition density, reflecting greater biomass productivity and continuous fuels.

723 The Fire Weather Index (FWI) showed its strongest, most coherent relationship with BA in the Wet
724 Chaco (r up to 0.7), while drier subregions displayed weaker, more heterogeneous patterns, indicating
725 additional controls. The 2020–2022 drought produced unprecedented fire activity, though large
726 outbreaks also occurred without extreme FWI, underscoring the role of ignition patterns and fuel
727 availability. In the Wet Chaco, 93% of pixels had positive FWI–fire correlations, compared to ~60% in
728 the Dry and Very Dry Chaco.

729 Lag analyses revealed dual mechanisms: in drier areas, wet-season biomass buildup (4–6 months prior)
730 preceded high fire activity, while in wetter areas, short-term pre-fire dryness was more predictive. La
731 Niña phases amplified fire potential via reduced rainfall and elevated FWI.

732 During-fire clustering of fire-weather types (FWTs) identified wind intensity and directionality as
733 stronger predictors of fire morphology than other pre-fire conditions. Persistent winds produced larger,
734 elongated, and cohesive burns, highlighting morphology as an indicator of wind-driven dynamics.

735 Random Forest models ranked mean elevation, land cover evenness, tree cover, and slope highest in
736 size prediction. Larger fires occurred in flat, low-elevation areas with low tree cover; steeper slopes and
737 higher forest cover limited spread.

738 In the Dry and Very Dry Chaco, part of the BA comes from one-time deforestation fires occurring after
739 clearing, generally small to moderate in size. Extreme megafires and gigafires instead resulted from rare
740 alignments of continuous fuels and exceptional weather, especially persistent winds and prolonged
741 dryness, which exceeded suppression capacity. This distinction is critical for separating land-use-related
742 burns from large climatic extremes in risk assessments.

743 By combining medium-resolution fire patch data, reanalysis-based weather metrics, machine learning,
744 and landscape analysis, we identify key biophysical, climatic, and anthropogenic determinants of fire
745 size and shape. These findings inform fire risk forecasting and management under ongoing land-use
746 intensification and climate variability, and highlight the potential of morphology and during-fire wind
747 metrics to benchmark and improve process-based global fire models.

748
<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

749 **6 APPENDIX A**

750

<figure><img src="image_72.png" /><figcaption>Fig. A1: Principal Component Analysis (PCA) biplot of pre-fire meteorological anomalies used for K-means clustering, showing the distribution of fire patches across the first two principal components (PC1 and PC2), which explain 49.1% and 23.6% of the total variance, respectively. The three clusters are color-coded and numbered as follows: Cluster 1 (blue) corresponds to Neutral conditions, Cluster 2 (orange) to Drought-Driven conditions (with high DC and DMC anomalies), and Cluster 3 (pink) to Wind-Driven conditions (characterized by elevated wind speed and temperature anomalies). Arrows represent the contribution of the original variables to the PCA axes. This ordination was used to guide the semantic naming of clusters.</figcaption></figure>

751

752

753

754

755

756

757

758

<figure><img src="image_73.png" /><figcaption>Fig. A2: Scatter plots and linear regressions between total annual BA and total annual ignitions between 2001 and 2022 in the Wet, Dry and Very Dry Chaco, divided into wet season fires (blue circles) and dry season fires (red crosses).</figcaption></figure>

759

760

761

762
<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /><figcaption>Fig. A3: Total counts of fire polygons separated by size category between 2001 and 2022 in the Wet, Dry, and Very Dry Chaco.</figcaption></figure>

<table><caption>Table A1. Number of fires detected by FRYv2.0 between 2001 and 2022 classified by fire size. WS: wet season; DS: dry season.</caption><thead><tr><th rowspan="2">Region</th><th colspan="2">Very Small<br>(0-1 km²)</th><th colspan="2">Small<br>(1-5 km²)</th><th colspan="2">Medium<br>(5-10 km²)</th><th colspan="2">Large<br>(10-100 km²)</th><th colspan="2">Megafire<br>(100-1000 km²)</th><th colspan="2">Gigafire<br>(&gt; 1000 km²)</th><th rowspan="2">Total</th></tr><tr><th>WS</th><th>DS</th><th>WS</th><th>DS</th><th>WS</th><th>DS</th><th>WS</th><th>DS</th><th>WS</th><th>DS</th><th>WS</th><th>DS</th></tr></thead><tbody><tr><td rowspan="2">Wet</td><td>8414</td><td>6322</td><td>17,018</td><td>18,992</td><td>4340</td><td>5667</td><td>3264</td><td>4534</td><td>91</td><td>163</td><td>2</td><td>0</td><td>68,807</td></tr><tr><td>14,736</td><td></td><td>36,010</td><td></td><td>10,007</td><td></td><td>7,798</td><td></td><td>254</td><td></td><td>2</td><td></td><td></td></tr><tr><td rowspan="2">Dry</td><td>3526</td><td>5332</td><td>5754</td><td>10,302</td><td>1201</td><td>2485</td><td>841</td><td>1991</td><td>24</td><td>94</td><td>0</td><td>15</td><td>31,565</td></tr><tr><td>8,858</td><td></td><td>16,056</td><td></td><td>3,686</td><td></td><td>2,832</td><td></td><td>118</td><td></td><td>15</td><td></td><td></td></tr><tr><td rowspan="2">Very Dry</td><td>334</td><td>300</td><td>708</td><td>691</td><td>187</td><td>203</td><td>200</td><td>238</td><td>13</td><td>29</td><td>0</td><td>1</td><td>2,904</td></tr><tr><td>634</td><td></td><td>1,399</td><td></td><td>390</td><td></td><td>438</td><td></td><td>42</td><td></td><td>1</td><td></td><td></td></tr><tr><td>Total</td><td>24,228</td><td></td><td>53,465</td><td></td><td>14,083</td><td></td><td>11,068</td><td></td><td>414</td><td></td><td>18</td><td></td><td>103,276</td></tr></tbody></table>

763

764

765

766

767

768
<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /><figcaption>Fig. A4: Annual percentage distribution of burned areas across different size categories between 2001 and 2022 in the Wet, Dry, and Very Dry Chaco.</figcaption></figure>

769
770
771
772

<figure><img src="image_78.png" /><figcaption>Fig. A5: Annual burned area maps of the Chaco region between 2001 and 2022. Burned areas extracted from FRYv2.0.</figcaption></figure>

773
774
<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /><figcaption>Fig. A6: Annual mean Fire Weather Index (FWI) anomalies with respect to the period 2001–2020, averaged for the Chaco region for each year between 2001 and 2022. FWI built from ERA5-Land.</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>Fig. A7: Monthly anomalies of rainfall, vegetation (EVI), fuel dryness (FWI), and burned area in the Chaco subregions. Panels show 3-month running means of region-averaged anomalies for each variable, calculated from gridded (pixel-based) data and averaged over the Wet, Dry, and Very Dry Chaco subregions. Shaded backgrounds in the burned area panel indicate ENSO phases (red for El Niño, blue for La Niña), calculated with the Multivariate ENSO index (MEI).</figcaption></figure>

775

776

777

778

779

780

781

782

783
<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

784

<figure><img src="image_84.png" /><figcaption>Fig. A8: The maps display the monthly anomalies (with 2001–2021 as the baseline) for the Chaco region for each year within the period. Additionally, each map counts with the Multivariate ENSO Index (MEI) showing the presence of an El Niño (EN; red) or La Niña (LN; blue) when during five consecutive three-month periods, MEI values are above +0.5 or below -0.5, respectively. Otherwise, the months are in a neutral (N) phase. The Niño/Niña events are classified by intensity based on the absolute MEI values. W: Weak (≥ 0.5); M: Moderate (≥ 1); S: Strong (≥ 1.5); VS: Very Strong (≥ 2).</figcaption></figure>

785

786

787

788

789

790

791

792
<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

<figure><img src="image_87.png" /><figcaption>**Fig. A9:** Regional distribution of fire-weather types (FWTs) across the three Chaco subregions based on the Pre-Fire clustering (top row) and the During-Fire clustering (bottom row). Pie charts represent the proportion of fire patches assigned to each cluster—Drought-Driven (orange), Wind-Driven (green), and Neutral (blue)—based on pre-fire (0–3 days before ignition) and during-fire meteorological conditions.</figcaption></figure>

793

794

795

796

<figure><img src="image_88.png" /><figcaption>**Fig. A10:** Distribution of morphology variables by cluster (quartile-dot plots). For each morphology variable, the interquartile range (IQR; thick horizontal bar) and median (dot) are shown for each cluster, separately for Pre-Fire and During-Fire clusterings (first and second rows, respectively). This visualizes the spread and central tendency of each variable within clusters, highlighting differences in fire patch morphology between cluster types and fire periods.</figcaption></figure>

797

798

799

800

801

802

803

<figure><img src="image_89.png" /><figcaption>**Fig. A11:** Each heatmap shows the mean z-score (standardised value) of key fire patch morphology variables for each cluster, separately for Pre-Fire (left) and During-Fire (right) cluster assignments. Rows correspond to clusters (Neutral, Drought-Driven, Wind-Driven), and columns to morphology variables. The color scale indicates the relative position of each cluster's mean within the overall distribution, highlighting differences in fire patch shape and size between clusters and fire periods.</figcaption></figure>

804

805

806

807

808
<figure><img src="image_90.png" /></figure>

809 **7 AUTHOR CONTRIBUTION**

810

811 RSM collected and processed the data, analyzed the results, and drafted the manuscript. CO and AS

812 conceived the idea and led the project. PVA contributed to data analysis, specifically by performing

813 Random Forest modeling. All co-authors discussed the results, provided critical feedback, and reviewed

814 the manuscript.

815

816 **8 COMPETING INTERESTS**

817 The authors declare that they have no conflict of interest.

818

819 **9 ACKNOWLEDGEMENTS**

820 The authors thank all the researchers and institutions involved in providing open-access datasets,

821 including ESA CCI, ERA5-Land, and the Copernicus Climate Data Store (CDS). We acknowledge the

822 computational infrastructure and support provided by the Laboratoire des Sciences du Climat et de

823 l'Environnement (LSCE/IPSL). We also express our gratitude to Dr. Sandra Bravo for her important

824 collaboration and contributions to our understanding of the fire regime in the region, as well as

825 colleagues from CONICET for their valuable insights into Chaco ecology. The authors also

826 acknowledge the use of AI-based tools to assist with text editing, code debugging, and figure scripting

827 throughout the preparation of the manuscript.

828

829 **9 FINANCIAL SUPPORT**

830 This research was partially funded by the European Space Agency through the Climate Change Initiative

831 programme, under contract numbers ESA/No. 4000126564 (Land_Cover_cci) and ESA ESRIN/No.

832 4000125259/18/I-NB. A. Sörensson acknowledges support from the Agencia Nacional de Promoción

833 Científica y Tecnológica (ANPCyT, Argentina) via project PICT 2018-02511, and from the Consejo

834 Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) through grant PIP

835 11220200102141CO. R. San Martin received doctoral funding from the Environmental Science

836 Doctoral School of Île-de-France (DS 129).

837
<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

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