Performance of operational fire spread models in California

Adrián Cardil; Santiago Monedero; Phillip SeLegue; Miguel Ángel Navarrete; Sergio de-Miguel; Scott Purdy; Geoff Marshall; Tim Chavez; Kristen Allison; Raúl Quilez; Macarena Ortega; Carlos A. Silva; Joaquin Ramirez

doi:10.1071/WF22128

RESEARCH ARTICLE (Open Access)

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Performance of operational fire spread models in California

Adrián Cardil

^A ^B ^C ^* , Santiago Monedero ^A , Phillip SeLegue ^D , Miguel Ángel Navarrete ^A , Sergio de-Miguel ^B ^C , Scott Purdy ^A , Geoff Marshall ^D , Tim Chavez ^D , Kristen Allison ^E , Raúl Quilez ^A , Macarena Ortega

^F , Carlos A. Silva ^G and Joaquin Ramirez ^A ^*

+ Author Affiliations

- Author Affiliations

^A Technosylva Inc, La Jolla, CA, USA. Email: smonedero@tecnosylva.com, spurdy@technosylva.com, manavarrete@tecnosylva.com, rquilez@tecnosylva.com

^B Department of Crop and Forest Sciences, University of Lleida, Lleida, Spain. Email: sergio.demiguel@udl.cat

^C Joint Research Unit CTFC - AGROTECNIO - CERCA, Solsona, Spain.

^D CAL FIRE Sacramento Headquarters Intel, Sacrimento, CA, USA. Email: phillip.selegue@fire.ca.gov, geoff.Marshall@fire.ca.gov, tim.chavez@fire.ca.gov

^E USDA Forest Service, Pacific Southwest Region, Operations Southern California. Email: kristen.allison@usda.gov

^F Forest Fire Laboratory, Department of Forest Engineering, University of Córdoba, Campus de Rabanales, 14071, Córdoba, Spain.

^G Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries and Geomatics Sciences, University of Florida, PO Box 110410, Gainesville, FL 32611, USA. Email: c.silva@ufl.edu

^* Correspondence to: acardil@tecnosylva.com, jramirez@technosylva.com

International Journal of Wildland Fire 32(11) 1492-1502 https://doi.org/10.1071/WF22128

Submitted: 1 July 2022 Accepted: 8 June 2023 Published: 7 July 2023

© 2023 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 simulators allow estimating fire spread and behaviour in complex environments, supporting planning and analysis of incidents in real time. However, uncertainty derived from input data quality and model inherent inaccuracies may undermine the utility of such predictions.

Aims

We assessed the performance of fire spread models for initial attack incidents used in California through the analysis of the rate of spread (ROS) of 1853 wildfires.

Methods

We retrieved observed fire growth from the FireGuard (FG) database, ran an automatic simulation with Wildfire Analyst Enterprise and assessed the accuracy of the simulations by comparing observed and predicted ROS with well-known error and bias metrics, analysing the main factors influencing accuracy.

Key results

The model errors and biases were reasonable for simulations performed automatically. We identified environmental variables that may bias ROS predictions, especially in timber areas where some fuel models underestimated ROS.

Conclusions

The fire spread models’ performance for California is in line with studies developed in other regions and the models are accurate enough to be used in real time to assess initial attack fires.

Implications

This work allows users to better understand the performance of fire spread models in operational environments and opens new research lines to further improve the performance of current operational models.

Keywords: fire behaviour, fire simulation modelling, Rothermel, Wildfire Analyst.

References

Albini FA (1976) Estimating wildfire behavior and effects. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)

Albini FA (1979) Spot fire distance from burning trees: a predictive model. Technical Report INT-56. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)

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 |

Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock RA, Fogarty L, Gould J, McCarthy G, Marsden-Smedley JB, Matthews S, Mattingley G, Pearce HG, van Wilgen BW (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. International Journal of Wildland Fire 24, 443-460.
| Crossref | Google Scholar |

Andrews PL (2012) Modeling wind adjustment factor and midflame wind speed for Rothermel’s surface fire spread model. General Technical Report RMRS-GTR-266. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)

Andrews PL (2014) Current status and future needs of the BehavePlus Fire Modeling System. International Journal of Wildland Fire 23, 21-33.
| Crossref | Google Scholar |

Artès T, Cardil A, Cortés A, Margalef T, Molina D, Pelegrín L, Ramírez J (2015) Forest Fire Propagation Prediction Based on Overlapping DDDAS Forecasts. Procedia Computer Science 51, 1623-1632.
| Crossref | Google Scholar |

Ascoli D, Vacchiano G, Motta R, Bovio G (2015) Building Rothermel fire behaviour fuel models by genetic algorithm optimisation. International Journal of Wildland Fire 24, 317-328.
| Crossref | Google Scholar |

Ascoli D, Moris J, Sil Â, Fernandes P (2022) Using the Rothermel package in R to test standard and custom fuel models against global fire behavior data. Environmental Sciences Proceedings 17, 86.
| Crossref | Google Scholar |

Barrows J (1951) Fire behavior in Northern Rocky Mountain forests. Station Paper 29. (USDA Forest Service, Northern Rocky Mountain Forest and Range Experiment Station: Missoula, MT)

Benali A, Sá ACL, Ervilha AR, Trigo RM, Fernandes PM, Pereira JMC (2017) Fire spread predictions: Sweeping uncertainty under the rug. Science of The Total Environment 592, 187-196.
| Crossref | Google Scholar |

Brewer MJ, Clements CB (2020) The 2018 Camp Fire: Meteorological Analysis Using In Situ Observations and Numerical Simulations. Atmosphere 11, 47.
| Crossref | Google Scholar |

California Department of Forestry and Fire Protection (CAL FIRE) (2019) Request for Innovative Ideas (RFI2) Wildfire Management. Available at: https://caleprocure.ca.gov/event/3540/0000012234 [last accessed 3 October 2023]

Cardil A, Molina DM (2015) Factors Causing Victims of Wildland Fires in Spain (1980–2010). Human and Ecological Risk Assessment: An International Journal 21, 67-80.
| Crossref | Google Scholar |

Cardil A, Monedero S, Silva CA, Ramirez J (2019) Adjusting the rate of spread of fire simulations in real time. Ecological Modelling 395, 39-44.
| Crossref | Google Scholar |

Catchpole EA, Catchpole WR, Rothermel RC (1993) Fire Behavior Experiments in Mixed Fuel Complexes. International Journal of Wildland Fire 3, 45-57.
| Crossref | Google Scholar |

Cheney NP, Gould JS (1995) Fire Growth in Grassland Fuels. International Journal of Wildland Fire 5, 237-247.
| Crossref | Google Scholar |

Cruz MG, Alexander ME (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software 47, 16-28.
| Crossref | Google Scholar |

Cruz MG, Alexander ME, Sullivan AL, Gould JS, Kilinc M (2018) Assessing improvements in models used to operationally predict wildland fire rate of spread. Environmental Modelling & Software 105, 54-63.
| Crossref | Google Scholar |

Finney MA (1998) FARSITE, fire area simulator – model development and evaluation. Research Paper RMRS-RP-4 Revised. (USDA Forest Service, Rocky Mountain Research Station: Ogden, UT)

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 ‘Fuels Management – How to Measure Success: Conference Proceedings’, 28–30 March 2006, Portland, OR (Eds PL Andrews, BW Butler) pp. 213–220. (USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS‐P 41: Fort Collins, CO)

Forthofer J, Shannon K, Butler B (2009) Simulating diurnally driven slope winds with WindNinja. In ‘Proceedings of 8th Symposium on Fire and Forest Meteorological Society; 2009 October 13‐15; Kalispell, MT’.

Jin S, Homer C, Yang L, Danielson P, Dewitz J, Li C, Zhu Z, Xian G, Howard D (2019) Overall Methodology Design for the United States National Land Cover Database 2016 Products. Remote Sensing 11, 2971.
| Crossref | Google Scholar |

Li S, Banerjee T (2021) Spatial and temporal pattern of wildfires in California from 2000 to 2019. Scientific Reports 11, 8779.
| Crossref | Google Scholar |

Liang J, Calkin DE, Gebert KM, Venn TJ, Silverstein RP (2008) Factors influencing large wildland fire suppression expenditures. International Journal of Wildland Fire 17, 650-659.
| Crossref | Google Scholar |

Minsavage-Davis CD, Davies GM (2022) Evaluating the Performance of Fire Rate of Spread Models in Northern European Calluna vulgaris Heathlands. Fire 5, 46.
| Crossref | Google Scholar |

Molina-Terrén DM, Xanthopoulos G, Diakakis M, Ribeiro L, Caballero D, Delogu GM, Viegas DX, Silva CA, Cardil A (2019) Analysis of forest fire fatalities in southern Europe: Spain, Portugal, Greece and Sardinia (Italy). International Journal of Wildland Fire 28, 85-98.
| Crossref | Google Scholar |

Monedero S, Ramirez J, Cardil A (2019) Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction. Ecological Modelling 392, 103-107.
| Crossref | Google Scholar |

National Guard Association (2021) Senators Push to Extend FireGuard Program. Available at https://www.ngaus.org/about-ngaus/newsroom/senators-push-extend-fireguard-program [verified 4 October 2022]

Nelson Jr RM (2000) Prediction of diurnal change in 10-h fuel stick moisture content. Canadian Journal of Forest Research 30, 1071-1087.
| Crossref | Google Scholar |

Pausas JG, Keeley JE (2009) A burning story: the role of fire in the history of life. BioScience 59, 593-601.
| Crossref | Google Scholar |

Perrakis DDB, Lanoville RA, Taylor SW, Hicks D (2014) Modeling Wildfire Spread in Mountain Pine Beetle-Affected Forest Stands, British Columbia, Canada. Fire Ecology 10, 10-35.
| Crossref | Google Scholar |

Pimont F, Parsons R, Rigolot E, de Coligny F, Dupuy J-L, Dreyfus P, Linn RR (2016) Modeling fuels and fire effects in 3D: Model description and applications. Environmental Modelling & Software 80, 225-244.
| Crossref | Google Scholar |

Pyne S, Andrews P, Laven R (1998) ‘Introduction to wildland fire.’ (Wiley: New York)

Ramírez J, Monedero S, Buckley D (2011) New approaches in fire simulations analysis with Wildfire Analyst. In ‘The 5th International Wildland Fire Conference Sun City, South Africa 9–13 May 2011’.

Ramirez J, Monedero S, Silva CA, Cardil A (2019) Stochastic decision trigger modelling to assess the probability of wildland fire impact. Science of The Total Environment 694, 133505.
| Crossref | Google Scholar |

Rothermel R (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)

Rothermel R (1991) Predicting behavior and size of crown fires in the Northern Rocky Mountains. Research Paper INT-438. (USDA Forest Service, Intermountain Research Station: Ogden, UT)

Rothermel R, Rinehart G (1983) Field procedures for verification and adjustment of fire behavior predictions. General Technical Report INT-142. 25 pp. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)

Sandberg DV, Riccardi CL, Schaaf MD (2007) Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds. Canadian Journal of Forest Research 37, 2438-2455.
| Crossref | Google Scholar |

Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS‐GTR‐153. (U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO)

Stocks BJ, Martell DL (2016) Forest fire management expenditures in Canada 1970–2013. The Forestry Chronicle 92, 298-306.
| Crossref | Google Scholar |

Stratton R (2006) Guidance on spatial wildland fire analysis: models, tools, and techniques. General Technical Report RMRS-GTR-183. 15 pp. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)

Sugihara N, Barbour M (2006) Fire and California vegetation. In ‘Fire in California’s Ecosystems’. (Eds N Sugihara, J van Wagtendonk, J Fites-Kaufman, K Shaffer, A Thode) (University of California Press: Berkeley, CA)

Sullivan AL, Matthews S (2013) Determining landscape fine fuel moisture content of the Kilmore East ‘Black Saturday’ wildfire using spatially extended point-based models. Environmental Modelling & Software 40, 98-108.
| Crossref | Google Scholar |

US Department of Defense (2021) Defense Department Imagery Information Aids Wildland Firefighters. Available at https://www.defense.gov/News/Releases/Release/Article/2764368/defense-department-imagery-information-aids-wildland-firefighters/ [verified 4 October 2022]

Vacchiano G, Ascoli D (2015) An Implementation of the Rothermel Fire Spread Model in the R Programming Language. Fire Technology 51, 523-535.
| Crossref | Google Scholar |

Van Wagner CE (1977) Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7, 23-34.
| Crossref | Google Scholar |

van Wagtendonk JW, Fites-Kaufman JA, Safford HD, North MP, Collins BM (2018) Sierra Nevada bioregion. In ‘Fire in California ecosystems’, 2nd edn. (Eds JW van Wagtendonk, NG Sugihara, SL Stephens, AE Thode, KE Shaffer, JA Fites-Kaufman) pp. 249–278. (University of California Press: Berkeley)

Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmospheric Chemistry and Physics 16, 5229-5241.
| Crossref | Google Scholar |

WFAS (2022) Wildland Fire Assessment System (WFAS) database. Available at https://www.wfas.net/index.php/national-fuel-moisture-database-moisture-drought-103 [accessed on January 2022]

Zhao Q, Shi Y, Liu Q, Fränti P (2015) A grid-growing clustering algorithm for geo-spatial data. Pattern Recognition Letters 53, 77-84.
| Crossref | Google Scholar |