Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Evaluating a simulation-based wildfire burn probability map for the conterminous US

Amanda R. Carlson https://orcid.org/0000-0002-0450-2636 A * , Todd J. Hawbaker A , Lucas S. Bair B , Chad M. Hoffman C , James R. Meldrum https://orcid.org/0000-0001-5250-3759 D , L. Scott Baggett E and Paul F. Steblein F
+ Author Affiliations
- Author Affiliations

A US Geological Survey, Geosciences and Environmental Change Science Center, Lakewood, CO 80225, USA.

B US Geological Survey, Southwest Biological Science Center, Flagstaff, AZ 86001, USA.

C Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO 80523, USA.

D US Geological Survey, Fort Collins Science Center, Fort Collins, CO 80525, USA.

E US Department of Agriculture Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80526, USA.

F US Geological Survey, Ecosystems Mission Area, Reston, VA 20192, USA.

* Correspondence to: arcarlson@usgs.gov

International Journal of Wildland Fire 34, WF23196 https://doi.org/10.1071/WF23196
Submitted: 9 December 2023  Accepted: 3 September 2024  Published: 2 January 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments.

Aims

Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim.

Methods

We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We evaluated the distribution of burned areas across BP values, and compared burned area distributions among fire size classes.

Key results

Across all pyromes, mean BP was moderately correlated with observed burned area. An average of 71% of burned area occurred in higher-BP classes, vs 79% expected. BP underpredicted burned area in the Mountain West, especially for extremely large fires.

Conclusions

The FSim BP map was useful for estimating subsequent wildfire hazard, but may have underestimated burned areas where input data did not reflect recent climate change, vegetation change or human ignition patterns.

Implications

Our evaluations indicate that caution is needed when relying on simulation-based BP maps to inform management decisions. Our results also highlight potential opportunities to improve model estimates.

Keywords: burn probability, climate change, fire simulation models, FSim, model evaluation, pyromes, risk assessment, wildfire hazard, wildland fire.

References

Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology 33(1), 121-131.
| Crossref | Google Scholar |

Abatzoglou JT, Battisti DS, Williams AP, et al. (2021) Projected increases in western US forest fire despite growing fuel constraints. Communications Earth & Environment 2(1), 227.
| Crossref | Google Scholar |

Adams MA (2013) Mega-fires, tipping points and ecosystem services: Managing forests and woodlands in an uncertain future. The Mega-Fire Reality 294, 250-261.
| Crossref | Google Scholar |

Ager AA, Day MA, Alcasena FJ, et al. (2021) Predicting Paradise: Modeling future wildfire disasters in the western US. Science of The Total Environment 784, 147057.
| Crossref | Google Scholar | PubMed |

Aitchison J (1986) ‘The statistical analysis of compositional data’, 1st edn. Monographs on Statistics and Applied Probability (Springer: Dordrecht, Netherlands)

Aitchison J (1992) On criteria for measures of compositional difference. Mathematical Geology 24(4), 365-379.
| Crossref | Google Scholar |

Alcasena FJ, Salis M, Ager AA, et al. (2015) Assessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area. Environmental Management 55(5), 1200-1216.
| Crossref | Google Scholar | PubMed |

Alcasena F, Ager A, Le Page Y, et al. (2021) Assessing wildfire exposure to communities and protected areas in Portugal. Fire 4(4), 82.
| Crossref | Google Scholar |

Alexander ME, Cruz MG (2013) Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environmental Modelling & Software 41, 65-71.
| Crossref | Google Scholar |

Alizadeh MR, Abatzoglou JT, Luce CH, et al. (2021) Warming enabled upslope advance in western US forest fires. Proceedings of the National Academy of Sciences 118(22), e2009717118.
| Crossref | Google Scholar | PubMed |

Anderegg WRL, Hicke JA, Fisher RA, et al. (2015) Tree mortality from drought, insects, and their interactions in a changing climate. New Phytologist 208(3), 674-683.
| Crossref | Google Scholar | PubMed |

Balch JK, Bradley BA, Abatzoglou JT, et al. (2017) Human-started wildfires expand the fire niche across the United States. Proceedings of the National Academy of Sciences 114(11), 2946-2951.
| Crossref | Google Scholar | PubMed |

Barbero R, Abatzoglou JT, Kolden CA, et al. (2015) Multi-scalar influence of weather and climate on very large fires in the eastern United States. International Journal of Climatology 35(8), 2180-2186.
| Crossref | Google Scholar |

Bar Massada A, Syphard AD, Hawbaker TJ, et al. (2011) Effects of ignition location models on the burn patterns of simulated wildfires. Environmental Modelling & Software 26(5), 583-592.
| Crossref | Google Scholar |

Bar Massada A, Syphard AD, Stewart SI, et al. (2013) Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA. International Journal of Wildland Fire 22(2), 174-183.
| Crossref | Google Scholar |

Bayham J, Yoder JK, Champ PA, et al. (2022) The economics of wildfire in the United States. Annual Review of Resource Economics 14(1), 379-401.
| Crossref | Google Scholar |

Beverly JL, McLoughlin N (2019) Burn probability simulation and subsequent wildland fire activity in Alberta, Canada – Implications for risk assessment and strategic planning. Forest Ecology and Management 451, 117490.
| Crossref | Google Scholar |

Boer MM, Resco de Dios V, Bradstock RA (2020) Unprecedented burn area of Australian mega forest fires. Nature Climate Change 10(3), 171-172.
| Crossref | Google Scholar |

Bühlmann P (1997) Sieve bootstrap for time series. Bernoulli 3(2), 123-148.
| Crossref | Google Scholar |

Calkin DE, Cohen JD, Finney MA, et al. (2014) How risk management can prevent future wildfire disasters in the wildland–urban interface. Proceedings of the National Academy of Sciences 111(2), 746-751.
| Crossref | Google Scholar | PubMed |

Carmel Y, Paz S, Jahashan F, et al. (2009) Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management 257(1), 370-377.
| Crossref | Google Scholar |

Chen B, Jin Y (2022) Spatial patterns and drivers for wildfire ignitions in California. Environmental Research Letters 17(5), 055004.
| Crossref | Google Scholar |

Davis KT, Dobrowski SZ, Higuera PE, et al. (2019) Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proceedings of the National Academy of Sciences 116(13), 6193-6198.
| Crossref | Google Scholar | PubMed |

Dillon GK, Scott JH, Jaffe MR, et al. (2023) ‘Spatial datasets of probabilistic wildfire risk components for the United States (270m)’, 3rd edn’. (USDA Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2016-0034-3

Eidenshink J, Schwind B, Brewer K, et al. (2007) A project for Monitoring Trends in Burn Severity. Fire Ecology 3(1), 3-21.
| Crossref | Google Scholar |

Finney MA (1998) Fire Area Simulator – Model development and evaluation. Research Report RMRS-RP-4. p. 47. (USDA Forest Service, Rocky Mountain Research Station: Missoula, MT)

Finney MA (2002) Fire growth using minimum travel time methods. Canadian Journal of Forest Research 32, 1420-1424.
| Crossref | Google Scholar |

Finney MA (2006) An overview of FlamMap fire modeling capabilities. In ‘Conference Proceedings. Fuels Management-How to Measure Success’. (US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO)

Finney MA, McHugh CW, Grenfell IC, et al. (2011) A simulation of probabilistic wildfire risk components for the continental United States. Stochastic Environmental Research and Risk Assessment 25(7), 973-1000.
| Crossref | Google Scholar |

Fusco EJ, Finn JT, Balch JK, et al. (2019) Invasive grasses increase fire occurrence and frequency across US ecoregions. Proceedings of the National Academy of Sciences 116(47), 23594-23599.
| Crossref | Google Scholar | PubMed |

Haas JR, Calkin DE, Thompson MP (2013) A national approach for integrating wildfire simulation modeling into wildland–urban interface risk assessments within the United States. Landscape and Urban Planning 119, 44-53.
| Crossref | Google Scholar |

Halofsky JE, Peterson DL, Harvey BJ (2020) Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecology 16(1), 4.
| Crossref | Google Scholar |

Hawbaker TJ, Challis BL, Carlson AR, et al. (2023) The Wildfire Hazard and Risk Assessment Inventory: US Geological Survey Data Release. 10.5066/P9EZVDUZ.

Higuera PE, Abatzoglou JT (2021) Record-setting climate enabled the extraordinary 2020 fire season in the western United States. Global Change Biology 27(1), 1-2.
| Crossref | Google Scholar | PubMed |

Iglesias V, Balch JK, Travis WR (2022) US fires became larger, more frequent, and more widespread in the 2000s. Science Advances 8(11), eabc0020.
| Crossref | Google Scholar | PubMed |

McDermott A (2024) Fire in the desert. Proceedings of the National Academy of Sciences 121(12), e2402794121.
| Crossref | Google Scholar |

McFarlane BL, McGee TK, Faulkner H (2011) Complexity of homeowner wildfire risk mitigation: an integration of hazard theories. International Journal of Wildland Fire 20(8), 921-931.
| Crossref | Google Scholar |

Mell WE, Manzello SL, Maranghides A, et al. (2010) The wildland–urban interface fire problem – current approaches and research needs. International Journal of Wildland Fire 19(2), 238-251.
| Crossref | Google Scholar |

Menser P (2020) ‘One year after historic Sheep Fire, Idaho Site contractors, agencies eye coming fire season.’ (Idaho National Laboratory) Available at https://inl.gov/community-outreach/after-sheep-fire/

Mueller SE, Thode AE, Margolis EQ, et al. (2020) Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. Forest Ecology and Management 460, 117861.
| Crossref | Google Scholar |

Murray AT, Baik J, Figueroa VE, et al. (2023) Developing effective wildfire risk mitigation plans for the wildland–urban interface. International Journal of Applied Earth Observation and Geoinformation 124, 103531.
| Crossref | Google Scholar |

Nowacki GJ, Abrams MD (2008) The demise of fire and ‘mesophication’ of forests in the eastern United States. BioScience 58(2), 123-138.
| Crossref | Google Scholar |

Oliveira S, Rocha J, Sá A (2021) Wildfire risk modeling. Current Opinion in Environmental Science & Health 23, 100274.
| Crossref | Google Scholar |

Omernik JM (1987) Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77(1), 118-125.
| Crossref | Google Scholar |

Parisien MA, Kafka VG, Hirsch KG, et al. (2005) Mapping wildfire susceptibility with the Burn-P3 simulation model. Information report NOR-X-405. p. 36. (Canadian Forest Service Northern Forestry Centre: Edmonton, Alberta)

Parisien MA, Ager AA, Barros AM, et al. (2020) Commentary on the article ‘Burn probability simulation and subsequent wildland fire activity in Alberta, Canada – Implications for risk assessment and strategic planning’ by J.L. Beverly and N. McLoughlin. Forest Ecology and Management 460, 117698.
| Crossref | Google Scholar |

Paz S, Carmel Y, Jahshan F, et al. (2011) Post-fire analysis of pre-fire mapping of fire-risk: a recent case study from Mt Carmel (Israel). Forest Ecology and Management 262(7), 1184-1188.
| Crossref | Google Scholar |

Poulos H (2015) Fire in the Northeast: learning from the past, planning for the future. Journal of Sustainable Forestry 34(1–2), 6-29.
| Crossref | Google Scholar |

Prestemon JP, Pye JM, Butry DT, et al. (2002) Understanding broadscale wildfire risks in a human-dominated landscape. Forest Science 48(4), 685-693.
| Crossref | Google Scholar |

Radeloff VC, Helmers DP, Kramer HA, et al. (2018) Rapid growth of the US wildland–urban interface raises wildfire risk. Proceedings of the National Academy of Sciences 115(13), 3314-3319.
| Crossref | Google Scholar | PubMed |

Raymond CA, McGuire LA, Youberg AM, et al. (2020) Thresholds for post-wildfire debris flows: insights from the Pinal Fire, Arizona, USA. Earth Surface Processes and Landforms 45(6), 1349-1360.
| Crossref | Google Scholar |

Reilly MJ, Zuspan A, Halofsky JS, et al. (2022) Cascadia Burning: the historic, but not historically unprecedented, 2020 wildfires in the Pacific Northwest, USA. Ecosphere 13(6), e4070.
| Crossref | Google Scholar |

Rodrigues M, Alcasena F, Gelabert P, et al. (2020) Geospatial modeling of containment probability for escaped wildfires in a Mediterranean region. Risk Analysis 40(9), 1762-1779.
| Crossref | Google Scholar | PubMed |

Rodrigues M, Cunill Camprubí À, Balaguer-Romano R, et al. (2023) Drivers and implications of the extreme 2022 wildfire season in southwest Europe. Science of The Total Environment 859, 160320.
| Crossref | Google Scholar | PubMed |

Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18(3), 235-249.
| Crossref | Google Scholar |

Rosen Z, Henery G, Slater KD, et al. (2022) A culture of fire: identifying community risk perceptions surrounding prescribed burning in the Flint Hills, Kansas. Journal of Applied Communications 106(4), Art 6.
| Crossref | Google Scholar |

Schoennagel T, Balch JK, Brenkert-Smith H, et al. (2017) Adapt to more wildfire in western North American forests as climate changes. Proceedings of the National Academy of Sciences 114(18), 4582-4590.
| Crossref | Google Scholar | PubMed |

Schug F, Bar-Massada A, Carlson AR, et al. (2023) The global wildland–urban interface. Nature 621(7977), 94-99.
| Crossref | Google Scholar | PubMed |

Scott JH, Thompson MP, Calkin DE (2013) A wildfire risk assessment framework for land and resource management. General Technical Report GTR-315. p. 83. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO) Available at https://www.fs.usda.gov/rm/pubs/rmrs_gtr315.pdf

Senande-Rivera M, Insua-Costa D, Miguez-Macho G (2022) Spatial and temporal expansion of global wildland fire activity in response to climate change. Nature Communications 13(1), 1208.
| Crossref | Google Scholar | PubMed |

Short KC (2017) ‘Spatial wildfire occurrence data for the United States, 1992-2015’, 4th edn. [FPA_FOD_20170508]. (USDA Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2013-0009.4

Short KC, Grenfell IC, Riley KL, et al. (2020a) Pyromes of the conterminous United States. (USDA Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2020-0020

Short KC, Finney MA, Scott JH, et al. (2020b) Spatial dataset of probabilistic wildfire risk components for the conterminous United States (270 m), 2nd edn. (USDA Forest Service Research Data Archive: Fort Collins, CO) 10.2737/RDS-2016-0034

Sibold JS, Veblen TT (2006) Relationships of subalpine forest fires in the Colorado Front Range with interannual and multidecadal-scale climatic variation. Journal of Biogeography 33(5), 833-842.
| Crossref | Google Scholar |

Thompson MP, Bowden P, Brough A, et al. (2016) Application of wildfire risk assessment results to wildfire response planning in the southern Sierra Nevada, California, USA. Forests 7(3), 64.
| Crossref | Google Scholar |

US Department of Agriculture Forest Service, Pyrologix and Headwaters Economics (2020) Wildfire risk to communities. Available at wildfirerisk.org

Vardoulakis S, Marks G, Abramson MJ (2020) Lessons Learned from the Australian bushfires: climate change, air pollution, and public health. JAMA Internal Medicine 180(5), 635-636.
| Crossref | Google Scholar | PubMed |