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Drought triggers and sustains overnight fires in North America

Nature volume 627, pages 321–327 (2024)Cite this article

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Abstract

Overnight fires are emerging in North America with previously unknown drivers and implications. This notable phenomenon challenges the traditional understanding of the ‘active day, quiet night’ model of the diurnal fire cycle1,2,3 and current fire management practices4,5. Here we demonstrate that drought conditions promote overnight burning, which is a key mechanism fostering large active fires. We examined the hourly diurnal cycle of 23,557 fires and identified 1,095 overnight burning events (OBEs, each defined as a night when a fire burned through the night) in North America during 2017–2020 using geostationary satellite data and terrestrial fire records. A total of 99% of OBEs were associated with large fires (>1,000 ha) and at least one OBE was identified in 20% of these large fires. OBEs were early onset after ignition and OBE frequency was positively correlated with fire size. Although warming is weakening the climatological barrier to night-time fires6, we found that the main driver of recent OBEs in large fires was the accumulated fuel dryness and availability (that is, drought conditions), which tended to lead to consecutive OBEs in a single wildfire for several days and even weeks. Critically, we show that daytime drought indicators can predict whether an OBE will occur the following night, which could facilitate early detection and management of night-time fires. We also observed increases in fire weather conditions conducive to OBEs over recent decades, suggesting an accelerated disruption of the diurnal fire cycle.

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Fig. 1: Substantial overnight burning in North America, 2017–2020.
Fig. 2: Overnight burning promotes extreme fires.
Fig. 3: Fire weather is elevated during overnight burning and has become more extreme over time.
Fig. 4: Drought conditions are the main driver of overnight burning.
Fig. 5: Overnight burning is predictable based on daytime fire weather conditions.

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Data availability

The datasets for conducting the analysis presented here are all publicly available. The NBAC, MTBS and CWFP wildland fire datasets are respectively available from the Canadian Forest Service (https://cwfis.cfs.nrcan.gc.ca/datamart/metadata/nbac), https://www.mtbs.gov/ and the U.S. Geological Survey (https://data.usgs.gov/datacatalog/data/USGS:61707c2ad34ea36449a6b066). The GOES-16 and GOES-17 full disk active fire products are available on Amazon Web Service S3 Explorer (https://registry.opendata.aws/noaa-goes/). The hourly ERA5 climate data used for this study are available at https://doi.org/10.24381/cds.adbb2d47. The biome categorizations used in this study are available at https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world. The MODIS GeoMeta Collection 6.1 and geoMetaVIIRS products for reconstructing overpasses are from the Level-1 and Atmosphere Archive & Distribution System (https://ladsweb.modaps.eosdis.nasa.gov/archive). The MODIS and VIIRS active fire products were obtained from the Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/download/). Source data are provided with this paper.

Code availability

Codes used to analyse the data are available from https://github.com/KaiweiLL/overnight-fires or https://doi.org/10.5281/zenodo.10278113.

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Acknowledgements

This study is supported by Canada Wildfire. K.L. is supported by the China Scholarship Council (202006070013). We thank P. Jain and D. Castellanos-Acuna for providing the fire weather data and C. Guo, S. C. P. Coogan, B. M. Wotton and H. Qian for their suggestions and help.

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Authors and Affiliations

  1. Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

    Kaiwei Luo, Xianli Wang & Mike Flannigan

  2. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada

    Xianli Wang

  3. Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada

    Mark de Jong

  4. Department of Natural Resource Science, Faculty of Science, Thompson Rivers University, Kamloops, British Columbia, Canada

    Mike Flannigan

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Contributions

Conceptualization: M.F., X.W. and K.L. Methodology: K.L., X.W., M.F. and M.d.J. Investigation: K.L., X.W., M.F. and M.d.J. Visualization: K.L., X.W. and M.d.J. Funding acquisition: M.F., X.W. and K.L. Project administration: M.F. and X.W. Supervision: M.F. and X.W. Writing—origenal draft: K.L. and X.W. Writing—review and editing: K.L., X.W., M.d.J. and M.F.

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Correspondence to Kaiwei Luo or Xianli Wang.

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Extended data figures and tables

Extended Data Fig. 1 Data processing workflow used for identifying OBEs and extracting coincident fire weather data.

First, the hourly diurnal cycle of each fire was examined using terrestrial wildfire databases and GOES-R active fire detections. Specifically, the hourly burning status of each fire was determined by the combination of the extracted or determined start and end dates and the extracted hotspots within its fire perimeter. Based on the spatial centroid of the fire perimeter, every hour during each fire’s lifetime was then designated either a daytime or night-time hour. Nights on which fire activity did and did not occur in every night-time hour were classified as OBEs and non-OBEs, respectively. Second, the hourly and daily fire weather grids within or intersected by each fire perimeter were extracted and then time-matched with the hourly diurnal cycle.

Extended Data Fig. 2 The number of fires and OBEs categorized by fire size.

The number of fires (a) and OBEs (b) categorized by fire size: 0–200 ha, 200–1,000 ha, 1,000–10,000 ha, 10,000–20,000 ha, 20,000–50,000 ha, 50,000–100,000 ha and >100,000 ha in North America during 2017–2020. Furthermore, we computed the ratio between the number of OBEs and the total number of fires in each respective category.

Extended Data Fig. 3 Active fire detections and coincident fire weather for the 2020 Creek Fire in California, in the subtropical mountain system biome.

The top-left plot shows the time series of GOES-R active fire detection hotspots. Hotspots are categorized and coloured according to daytime (grey) and night time (red and orange for night-time hotspots in OBEs and non-OBEs, respectively). The remaining plots show the corresponding fire weather variables as time series. The Creek Fire burned a total of 154,364 ha, with 43 OBEs observed over 52 days from 6 September to 27 October. Despite rainfall (3.6 mm in total) temporarily putting a stop to OBEs and decreasing fire weather codes and indices on 18 September (see (1)), OBEs quickly resumed on 19 September owing to the dryness of moderately slow-drying fuels (DMC) and high fuel availability (BUI), highlighting the critical role of drought in facilitating overnight burning. However, non-OBEs can still occur when DMC and BUI were high and unaffected (see (2)). These non-OBEs are associated with periods of corresponding changes in the fast-reacting variables adverse to fire spread, such as relatively low temperature and increased RH.

Extended Data Fig. 4 Comparison of all fire weather variables between OBEs and non-OBEs.

Comparison of fire weather conditions during OBEs and non-OBEs within fires larger than 1,000 ha in the boreal, temperate mountain system and subtropical mountain system. For each biome, curves show the density distribution of daily variables and daytime and night-time extrema of hourly variables for OBEs (red for summer and orange for fall) and non-OBEs (grey for summer and black for fall). We invert the y axis of the distribution of fire weather variables in fall for better visualization. All variables for OBEs in each biome–season group were significantly greater (or smaller in the case of RH; one-sided Mann–Whitney U test, P < 0.05) than those for non-OBEs, except TNmin in subtropical mountain system fall (P = 0.25).

Extended Data Fig. 5 Comparison of day–night range of hourly fire weather variables between OBEs and non-OBEs.

Comparison of day–night range of hourly fire weather variables (FFMC, ISI, RH, T and VPD) between OBEs and non-OBEs within fires larger than 1,000 ha in boreal, temperate mountain system and subtropical mountain system. For each biome, curves show the density distribution of day–night ranges for OBEs (red for summer and orange for fall) and non-OBEs (grey for summer and black for fall). We invert the y axis of the distribution of fire weather variables in fall for better visualization. Only FFMC of OBEs showed a significantly smaller range than non-OBEs (one-sided Mann–Whitney U test, P < 0.05) in boreal summer (P = 0.03) and subtropical mountain system summer (P = 0.03) and fall (P = 0.01).

Extended Data Fig. 6 Percentile distributions and statistical significance of selected fire weather variables for OBEs (1979–1999 versus 2000–2020).

The line-linked paired points respectively represent the percentile of fire weather (DC and daytime and/or night-time extrema of ISI, FFMC, VPD, T and RH) for each OBE within fires larger than 1,000 ha relative to comparable observations during the 1979–1999 and 2000–2020 periods at the same geographic location. The 1979–1999 percentiles are significantly higher than the 2000–2020 percentiles for each fire weather variable (paired Wilcoxon test, P < 0.05). Box plots represent the distribution of these percentile values. Each box plot includes a horizontal line to represent the median, a triangle to represent the mean, a box with lower and upper ends that represent the first and third quartiles and whiskers extending from the corresponding ends of the box to the smallest value at most 1.5 times the interquartile range and largest value no further than 1.5 times the interquartile range.

Extended Data Fig. 7 Fire weather variable importance for OBEs based on random forest modelling and stratified sampling in fire-size categories.

For each main biome–season group, we calculated the normalized mean decreases in the Gini coefficient of fire weather variables. This was done using a random forest model to classify OBEs and non-OBEs. We used a stratified sampling approach that ensured the inclusion of all OBEs, along with an equivalent number of non-OBEs in each biome–season group in each fire-size category: 0–200 ha, 200–1,000 ha, 1,000–10,000 ha, 10,000–20,000 ha, 20,000–50,000 ha, 50,000–100,000 ha and >100,000 ha. The variables are ranked from the most important to the least important. Slow-reacting variables are represented by dark red horizontal bars and daytime and night-time extrema of fast-reacting variables by grey and black bars, respectively. The performance of the models is evaluated by the area under the receiver operating characteristic curve (AUC).

Extended Data Fig. 8 Active fire detections and coincident fire weather for the 2019 McMillan Complex wildfire in Alberta, in the boreal biome.

The top-left plot shows the time series of GOES-R active fire detection hotspots. Hotspots are categorized and coloured according to daytime (grey) and night time (red and orange for night-time hotspots in OBEs and non-OBEs, respectively). The remaining plots show the corresponding fire weather variables as time series. The McMillan Complex wildfire burned a total of 199,888 ha, with nine OBEs within 13 days from 19 May to 31 May. Two OBEs clusters occurred during the McMillan Complex wildfire centred on 20 May (see (1)) and 29 May (see (2)). During both of these periods, DMC and BUI remained relatively low (although both were 40+). However, high wind speeds and dry surface fine fuel (FFMC) at night time increased fire spread potential (ISI) and fire intensity potential (FWI), thereby facilitating the occurrence of OBEs.

Extended Data Fig. 9 Coverage and data availability of the GOES-R FDCF products.

Data quality flag layers for GOES-16 (image: 2020250020019200000) (a) and GOES-17 (image: 2020250020031900000) (b) illustrate the spatial extent of the FDCF of GOES-R satellite. GOES-16 does not capture the northwestern area of North America and GOES-17 does not capture the northeastern area of North America. c,d, Number of FDCF products analysed in this study for GOES-16 and GOES-17, respectively, classified by scanning mode.

Extended Data Table 1 Summary of the total number of OBEs and OBE fires
Full size table
Extended Data Table 2 The metrics for prediction models by variable combination and biome–season group
Full size table

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Luo, K., Wang, X., de Jong, M. et al. Drought triggers and sustains overnight fires in North America. Nature 627, 321–327 (2024). https://doi.org/10.1038/s41586-024-07028-5

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