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

Characterising ignition precursors associated with high levels of deployment of wildland fire personnel

Alison C. Cullen A * , Brian R. Goldgeier A B , Erin Belval C and John T. Abatzoglou D
+ Author Affiliations
- Author Affiliations

A Evans School of Public Policy and Governance, University of Washington, Seattle, WA 98195-3055, USA.

B WA Department of Ecology, Lacey, WA 98503, USA.

C USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80526, USA.

D School of Engineering, University of California Merced, Merced, CA 95343, USA.

* Correspondence to: alison@uw.edu

International Journal of Wildland Fire 33, WF23182 https://doi.org/10.1071/WF23182
Submitted: 14 November 2023  Accepted: 5 July 2024  Published: 29 July 2024

© 2024 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

As fire seasons in the Western US intensify and lengthen, fire managers have been grappling with increases in simultaneous, significant incidents that compete for response resources and strain capacity of the current system.

Aims

To address this challenge, we explore a key research question: what precursors are associated with ignitions that evolve into incidents requiring high levels of response personnel?

Methods

We develop statistical models linking human, fire weather and fuels related factors with cumulative and peak personnel deployed.

Key results

Our analysis generates statistically significant models for personnel deployment based on precursors observable at the time and place of ignition.

Conclusions

We find that significant precursors for fire suppression resource deployment are location, fire weather, canopy cover, Wildland–Urban Interface category, and history of past fire. These results align partially with, but are distinct from, results of earlier research modelling expenditures related to suppression which include precursors such as total burned area which become observable only after an incident.

Implications

Understanding factors associated with both the natural system and the human system of decision-making that accompany high deployment fires supports holistic risk management given increasing simultaneity of ignitions and competition for resources for both fuel treatment and wildfire response.

Keywords: Firefighters, Linear regression, Simultaneous wildfire, Suppression personnel competition, Wildfire management, Wildfire response personnel deployment, Wildfire suppression resource.

References

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

Abatzoglou JT, Williams AP (2016) Impact of anthropogenic climate change on wildfire across western US Forests. Proceedings of the National Academy of Sciences 113(42), 11770-11775.
| Crossref | Google Scholar | PubMed |

Abatzoglou JT, Juang CS, Williams AP, Kolden CA, Westerling AL (2021) Increasing synchronous fire danger in forests of the western United States. Geophysical Research Letters 48, e2020GL091377.
| Crossref | Google Scholar |

Anderson HE (1982) ‘Aids to Determining Fuel Models For Estimating Fire Behavior.’ (US Department of Agriculture) Available at https://www.fs.usda.gov/rm/pubs_int/int_gtr122.pdf

Balch JK, Bradley BA, Abatzoglou JT, Nagy RC, Fusco EJ, Mahood 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 |

Bayham J, Yoder JK (2020) Resource allocation under fire. Land Economics 96, 92-110.
| Crossref | Google Scholar |

Bayham J, Belval EJ, Thompson MP, Dunn C, Stonesifer CS, Calkin DE (2020) Weather, risk, and resource orders on large wildland fires in the western US. Forests 11, 169.
| Crossref | Google Scholar |

Belval EJ, O’Connor CD, Thompson MP, Hand MS (2019) The role of previous fires in the management and expenditures of subsequent large wildfires. Fire 2(4), 57.
| Crossref | Google Scholar |

Belval EJ, Short KC, Stonesifer CS, Calkin DE (2022) A historical perspective to inform Strategic Planning for 2020 End-of-Year Wildland Fire Response Efforts. Fire 5(2), 35.
| Crossref | Google Scholar |

Carlson AR, Helmers DP, Hawbaker TJ, Mockrin MH, Radeloff VC (2022) Wildland-urban interface maps for the conterminous U.S. based on 125 million building locations: U.S. Geological Survey data release. 10.5066/P94BT6Q7

Calkin DE, Gebert KM, Jones JG, Neilson RP (2005) Forest Service large fire area burned and suppression expenditure trends, 1970–2002. Journal of Forestry 103(4), 179-183.
| Crossref | Google Scholar |

Cullen AC, Axe T, Podschwit H (2021) High-severity wildfire potential – associating meteorology, climate, resource demand and wildfire activity with preparedness levels (PLs). International Journal of Wildland Fire 30, 30-41.
| Crossref | Google Scholar |

Cullen AC, Prichard SJ, Abatzoglou JT, Dolk A, Kessenich L, Bloem S, Bukovsky MS, Humphrey R, McGinnis S, Skinner H, Mearns LO (2023) Growing convergence research: coproducing climate projections to inform proactive decisions for managing simultaneous wildfire risk. Risk Analysis 43, 2262-2279.
| Crossref | Google Scholar | PubMed |

ERA5 (2023) ECMWF Reanalysis v5. Available at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 [accessed January 2023]

Gebert KM, Calkin DE, Yoder J (2007) Estimating suppression expenditures for individual large wildland fires. Western Journal of Applied Forestry 22, 188-196.
| Crossref | Google Scholar |

Gude PH, Jones K, Rasker R, Greenwood MC (2013) Evidence for the effect of homes on wildfire suppression costs. International Journal of Wildland Fire 22, 537-548.
| Crossref | Google Scholar |

Hand MS, Gebert KM, Liang J, Calkin DE, Thompson MP, Zhou M (2014) ‘Economics of Wildfire Management: The Development and Application of Suppression Expenditure Models.’ (Springer Briefs in Fire: New York, NY, USA)

Hand MS, Thompson MP, Calkin DE (2016) Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data. Journal of Forest Economics 22, 80-102.
| Crossref | Google Scholar |

Hand M, Katuwal H, Calkin DE, Thompson MP (2017) The influence of incident management teams on the deployment of wildfire suppression resources. International Journal of Wildland Fire 26, 615-629.
| Crossref | Google Scholar |

Hartig F (2022) _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models_. R package version 0.4.6. Available at https://CRAN.R-project.org/package=DHARMa

Haugo RD, Kellogg BS, Cansler CA, Kolden CA, Kemp KB, Robertson JC, Metlen KL, Vaillant NM, Restaino CM (2019) The missing fire: quantifying human exclusion of wildfire in Pacific Northwest Forests, USA. Ecosphere 10(4), e02702.
| Crossref | Google Scholar |

Hersbach H, Bell B, Berrisford P, Hirahara S, Horanyi A, Munoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146(730), 1999-2049.
| Crossref | Google Scholar |

Ives AR (2015) For testing the significance of regression coefficients, go ahead and log‐transform count data. Methods in Ecology and Evolution 6(7), 828-835.
| Crossref | Google Scholar |

Jolly WM, Freeborn PH, Page WG, Butler BW (2019) Severe fire danger index: a forecastable metric to inform firefighter and community wildfire risk management. Fire 2(3), 47.
| Crossref | Google Scholar |

Knief U, Forstmeier W (2021) Violating the normality assumption may be the lesser of two evils. Behavior Research Methods 53(6), 2576-2590.
| Crossref | Google Scholar | PubMed |

Landfire (2023a) 13 Anderson Fire Behavior Fuel Models Layer, LANDFIRE 2.0.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture. Available at https://www.landfire.gov/fbfm13.php [accessed 24 January 2023]

Landfire (2023b) Canopy Cover Layer, LANDFIRE 2.0.0, U.S. Department of the Interior, Geological Survey, and U.S. Department of Agriculture. Available at https://www.landfire.gov/cc.php [accessed 22 March 2023]

McGinnis S, Kessenich L, Mearns L, Cullen A, Podschwit H, Bukovsky M (2023) Future regional increases in simultaneous large western USA wildfires. International Journal of Wildland Fire 32(9), 1304-1314.
| Crossref | Google Scholar |

O’Hara RB, Kotze DJ (2010) Do not log-transform count data: do not log-transform count data. Methods in Ecology and Evolution 1(2), 118-122.
| Crossref | Google Scholar |

Podschwit H, Cullen AC (2020) Patterns and trends in simultaneous wildfire activity in the Continental United States from 1984-2015. International Journal of Wildland Fire 29(12), 1057-1071.
| Crossref | Google Scholar |

Podschwit HR, Larkin NK, Steel EA, Cullen A, Alvarado E (2019) Multi-model forecasts of very-large fire occurences during the end of the 21st century. Climate 6(4), 100.
| Crossref | Google Scholar |

Pourmohamad Y, Abatzoglou J, Belval E, Fleishman E, Short K, Reeves MC, Nauslar N, Higuera PE, Henderson E, Ball S, AghaKouchak A, Prestemon JP, Olszewski J, Sadegh M (2024) Physical, social, and biological attributes for improved understanding and prediction of wildfires FPA-FOD-attributes dataset. Earth System Science Data 16(6), 3045-3060.
| Crossref | Google Scholar |

Radeloff VC, Helmers DP, Kramer HA, Mockrin MH, Alexandre PM, Bar-Massada A, Butsic V, Hawbaker TJ, Martinuzzi S, Syphard AD, Stewart SI (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 |

Riley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA (2013) The relationship of large fire occurrence with drought and fire danger indices in the Western USA, 1984–2008: the role of temporal scale. International Journal of Wildland Fire 22(7), 894-909.
| Crossref | Google Scholar |

ROSS/IROC (Resource Ordering and Status System/Interagency Resource Ordering Capability) (2022) Lockheed Martin Enterprise Solutions & Services. Available at https://famit.nwcg.gov/applications/ROSS [accessed 1 July 2022]

Short KC (2022) Spatial Wildfire Occurrence Data for the United States 1992-2020 [FPA_FOD_2022_1014 6th edition, Fort Collins, Colorado Forest Service Research Data Archive]. 10.2737/RDS-2013-0009.6

Shuman JK, Balch JK, Barnes RT, Higuera PE, Roos CI, Schwilk DW, Stavros EN, Banerjee T, Bela MM, Bendix J, Bertolino S, Bililign S, Bladon KD, Brando P, Breidenthal RE, Buma B, Calhoun D, Carvalho LMV, Cattau ME, Cawley KM, Chandra S, Chipman ML, Cobian-Iñiguez J, Conlisk E, Coop JD, Cullen A, Davis KT, Dayalu A, De Sales F, Dolman M, Ellsworth LM, Franklin S, Guiterman CH, Hamilton M, Hanan EJ, Hansen WD, Hantson S, Harvey BJ, Holz A, Huang T, Hurteau MD, Ilangakoon NT, Jennings M, Jones C, Klimaszewski-Patterson A, Kobziar LN, Kominoski J, Kosovic B, Krawchuk MA, Laris P, Leonard J, Loria-Salazar SM, Lucash M, Mahmoud H, Margolis E, Maxwell T, McCarty JL, McWethy DB, Meyer RS, Miesel JR, Moser WK, Nagy RC, Niyogi D, Palmer HM, Pellegrini A, Poulter B, Robertson K, Rocha AV, Sadegh M, Santos F, Scordo F, Sexton JO, Sharma AS, Smith AMS, Soja AJ, Still C, Swetnam T, Syphard AD, Tingley MW, Tohidi A, Trugman AT, Turetsky M, Varner JM, Wang Y, Whitman T, Yelenik S, Zhang X (2022) Reimagine fire science for the Anthropocene. PNAS Nexus 1(3), 115.
| Crossref | Google Scholar | PubMed |

St. Denis LA, Short KC, McConnell K, Cook MC, Mietkiewicz NP, Buckland M, Balch JK (2023) All-hazards dataset mined from the US National Incident Management System 1999–2020. Scientific Data 10, 112.
| Crossref | Google Scholar | PubMed |

St-Pierre AP, Shikon V, Schneider DC (2018) Count data in biology—data transformation or model reformation? Ecology and Evolution 8(6), 3077-3085.
| Crossref | Google Scholar | PubMed |

Thompson MP, Belval EJ, Bayham J, Calkin DE, Stonesifer CS, Flores D (2023) Wildfire response: a system on the brink. Journal of Forestry 121(2), 121-124.
| Crossref | Google Scholar |

USDA (United States Department of Agriculture) (2023) On fire: The report of the Wildland Fire Mitigation and Management Commission. Available at https://www.preventionweb.net/publication/fire-report-wildland-fire-mitigation-and-management-commission

Van Wagner CE (1987) Development and structure of the Canadian forest fire weather index system. Forestry Technical Report. (Canadian Forest Service: Ottawa, Canada)

Wei Y, Thompson MP, Belval EJ, Calkin DE, Bayham J (2020) Understand daily fire suppression resource ordering and assignment patterns by unsupervised learning. Machine Learning and Knowledge Extraction 3(1), 14-33.
| Crossref | Google Scholar |

Welty JL, Jeffries MI (2021) Combined wildland fire datasets for the United States and certain territories, 1800s-Present: U.S. Geological Survey data release. 10.5066/P9ZXGFY3

Yoder J, Gebert K (2012) An econometric model for ex ante prediction of wildfire suppression costs. Journal of Forest Economics 18, 76-89.
| Crossref | Google Scholar |