DOI: 10.5061/dryad.2jm63xswg
title: Day of burning dataset: Biogeography of daily wildfire progression

description: Introduction Climate change is predicted to increase the frequency of
 extreme single-day fire spread events, with major ecological and social
 implications. In contrast with well-documented spatio-temporal patterns of
 wildfire ignitions and perimeters, daily progression remains poorly
 understood across continental spatial scales, particularly for extreme
 single-day events (“blow ups”). Here, we characterize daily wildfire
 spread across North America, including occurrence of extreme single-day
 events, duration and seasonality of fire and extremes, and ecoregional
 climatic niches of fire in terms of Actual Evapotranspiration (AET) and
 Climatic Water Deficit (CWD) annual climate normals. Methods Remotely
 sensed daily progression of 9,636 wildfires ≥400 ha was used to
 characterize ecoregional patterns of fire growth, extreme single-day
 events, duration, and seasonality. To explore occurrence, extent, and
 impacts of single-day extremes among ecoregions, we considered
 complementary ecoregional and continental extreme thresholds (Ecoregional
 or Continental Mean Daily Area Burned + 2SD). Ecoregional spread rates
 were regressed against AET and CWD to explore climatic influence on
 spread. Results We found three-fold differences in mean daily area burned
 among 10 North American ecoregions, ranging from 260 ha day-1 in the
 Marine West Coast Forests to 751 ha day-1 in Mediterranean California.
 Ecoregional single-day extreme thresholds ranged from 3829 ha day-1 to
 16626 ha day-1, relative to a continental threshold of 7173 ha day-1. The
 ~3% of single-day events classified as extreme cumulatively account for
 16-55% of ecoregional total area burned. There were four-fold differences
 in mean fire duration, ranging from 2.7 days in the Great Plains to 10.5
 days in the Northwestern Forested Mountains. CWD had a weak positive
 relationship with ecoregional spread, and there was no pattern for AET.
 Discussion Regions with shorter fire durations had greater daily area
 burned, suggesting a paradigm of fast-growing short-duration fires in some
 regions and slow-growing long-duration fires elsewhere. Although climatic
 conditions can set the stage for ignition and influence vegetation and
 fuels, finer-scale mechanisms likely drive variation in daily spread.
 Daily fire progression offers valuable insights into the regional and
 seasonal distributions of extreme single-day spread events, and how these
 events shape net fire effects.
description: <strong><em>Daily Fire Progression and Identification
 of Extreme Events</em></strong> We measured
 daily area burned (ha d<sup>-1</sup>) for individual wildfires
 by interpolating spatially continuous daily progression maps following
 methods developed by Parks (2014; Fig. S1). This technique interpolates
 VIIRS and MODIS hotspot detections to map the most likely day of burning
 at 30-m resolution within final wildfire perimeters obtained from national
 repositories. Previous studies have successfully utilized this technique
 to study various aspects of fire activity, including daily area burned
 (Hart and Preston 2020), spread (Holsinger et al. 2016, Wang et al. 2017),
 and refugia (Meigs et al. 2020, Downing et al. 2021). We constrained all
 daily progression interpolations to final fire perimeters obtained from
 the Monitoring Trends in Burn Severity (MTBS, USA; USDA Forest Service and
 USGS (2023)) and National Burned Area Composite (NBAC, Canada; Hall et al.
 (2020)) national repositories. Centroids of final fire perimeters were
 used to identify within which ecoregion individual fires occurred. Fires
 &lt; 400ha were removed from the NBAC dataset to be consistent with
 the MTBS dataset, and because the interpolation method is inappropriate
 for smaller fires due to the coarse spatial resolution of MODIS and VIIRS
 pixels. Similarly, fires with &lt; 10 hotspot detections were excluded
 from our analyses due to reduced accuracies in daily progression maps
 interpolated from few detections. All detections that occurred between
 midnight and 0600 h local time were assigned to the previous day under the
 assumption that those locations likely burned during the previous day with
 lingering heat signatures. In total, we interpolated daily fire
 progression for 9,636 fires and 66,318 unique daily fire spread events,
 which ranged in size from 25 to 224,565 ha. Daily spread events followed a
 skewed log-normal distribution, so we performed all statistical analyses
 and calculations on log<sub>10</sub> transformed values and
 back-transformed to native values for presentation. One-way analysis of
 variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) were
 used to compare daily areas burned, fire duration, and fire season among
 ecoregions. All geospatial data processing and statistical analyses were
 completed in R 4.2.2 (R Core Team 2022). Next, we
 developed two complementary definitions of extreme single-day fire spread
 thresholds to explore how occurrence, extent, and impacts of single day
 blow-ups vary among ecoregions. Specifically, following a distributional
 definition inspired by Coop at al. (2022), we identified the top 2SD of
 events across northern North America as “continental extreme spread
 events”, as well as by ecoregion, or “ecoregional extreme spread
 events”: Similarly, we calculated continental and
 ecoregional “large” event<sub> </sub>thresholds (e.g., mean +
 1SD) to identify single-day events that are substantially larger than
 common spread days, but not as massive as extreme spread days. By
 presenting both tiers of continental and ecoregion-specific spread events,
 we aim to provide 1) a common continental definition of extremes that
 facilitates intuitive comparison of occurrence, extent, and impacts among
 ecoregions and 2) regionally contextualized estimates that may more
 accurately characterize realized differences in extreme fire activity
 among ecoregions. <strong><em>Duration of
 Fire Spread, Duration of Extreme Activity, Fire Season, and Interannual
 Trends in Extremes’ Occurrence </em></strong>
 Day of burning information from daily fire progression maps
 provides insight towards several temporal aspects of fire activity. First,
 we calculated each fire’s duration as the difference between its maximum
 and minimum day of burning, plus one day so that the final day of burning
 was effectively counted (e.g., a fire that began and ended on the same day
 would have a calculated duration of 1 day). To determine how fire duration
 is related to average and maximum daily spread, we regressed fires’
 duration against their average and maximum daily areas burned. We included
 a model term for ecoregion to allow different intercepts and trends among
 regions (e.g., linear regression: Average or Maximum Daily Area Burned ~
 Duration*Ecoregion). Second, we characterized duration of single-day
 extreme fire activity by counting consecutively occurring days of
 ecoregional and continental extremes within each fire. Third, we examined
 differences in ecoregional fire seasons by modeling day-of-year of burning
 among ecoregions (e.g., ANOVA: day-of-year of burning ~ Ecoregion) and by
 plotting ecoregional frequency histograms of common, large, and extreme
 events’ occurrences across a year. Finally, we used Sen’s slopes
 calculated with the “trend” package (Pohlert 2023) to analyze temporal
 trends in ecoregional annual counts of both continental and
 ecoregion-specific large and extreme single-day spread events between 2002
 and 2021. <strong><em>Daily Fire Spread and
 the Climate Space of Fire </em></strong> We
 characterized ecoregions’ average climatic conditions in terms of AET and
 CWD by sampling TerraClimate 2002-2021 annual climate normal reference
 climatology rasters within the cumulative area burned by all fires that
 occurred within each ecoregion. These climate normal values were
 summarized as the ecoregional average AET or CWD ± 1SD. To explore
 broad-scale patterns between daily fire spread and these key climatic
 gradients, we regressed ecoregional average daily spread rates as well as
 large and extreme fire spread thresholds against ecoregional burned areas’
 annual average AET and CWD conditions (e.g., linear model; common, large,
 or extreme daily fire spread threshold ~ Ecoregion*[AET or
 CWD]).
description: # Day of burning dataset: Biogeography of daily wildfire progression
 [https://doi.org/10.5061/dryad.2jm63xswg](https://doi.org/10.5061/dryad.2jm63xswg) Daily fire progression dataset of daily areas burned. ## Description of the data and file structure Variable Descriptions: Ecoregion_10: US EPA level 1 ecoregion fire occurred in. combined.ID: concatenation of MTBS or NBAC fire.id and fire.year (year of fire's occurrence) fire.year: year of fire's occurrence fire.id: MTBS or NBAC event ID of final fire perimeter DOB: day of burning as day of year, numeric (1-366) pixel.count: n 30x30m pixels per DOB region area.ha: DOB region area in hectares Within.Ecoregion.Areal.Ex.Threshold: area burned threshold for classifying days as extreme events; defined as ecoregional mean + 2 SD extreme.day: binary variable, FALSE = daily area burned did not exceed Within.Ecoregion.Areal.Ex.Threshold, TRUE = daily area burned exceeded Within.Ecoregion.Areal.Ex.Threshold Country: Country fire occurred in; USA for fires from MTBS, Canada for fires from NBAC Perim.Source: MTBS or NBAC n.Hotspots: n MODIS or VIIRS hotspots used for daily fire progression interpolation ig.date: Date of ignition from MTBS or NBAC final perimeters area.m2: DOB region area in m2 area.m2.cum: Cumulative area burned by a given fire on a given day of burning Eq.Radius.m: Radius of a circle with area equal to area.m2.cum (circularized cumulative area burned) Radial.Growth.m.d: Daily radius increment. For day 1, previous day's radius was assumed to be 0m. days.burned: total days burned per fire; same for all rows of a given fire. Chrono.DOB: chronological day of fire progression; date of ignition is day = 1. DOB_Month: numeric month to which a day of burning belongs aet_raw: actual evapotranspiration sampled from TerraClimate, in mm/month def_raw: climate water deficit sampled from TerraClimate, in mm/month aet_annual_Z: actual evapotranspiration sampled from TerraClimate, Z scored relative to pixel-wise conditions observed across the year of burning. def_annual_Z: climate water deficit sampled from TerraClimate, Z scored relative to pixel-wise conditions observed across the year of burning. ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Data was derived from the following sources: * MTBS: [https://www.mtbs.gov/](https://www.mtbs.gov/) * NBAC: [https://cwfis.cfs.nrcan.gc.ca/datamart?wbdisable=true](https://cwfis.cfs.nrcan.gc.ca/datamart?wbdisable=true) * MODIS/VIIRS: [https://firms.modaps.eosdis.nasa.gov/usfs/](https://firms.modaps.eosdis.nasa.gov/usfs/)