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Article

Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

1
Department of Geology & Geophysics, University of Wyoming, Laramie, WY 82071, USA
2
Texas A & M Forest Service, College Station, TX 77845, USA
3
Advanced Research Computing Center, University of Wyoming Laramie, WY 82071, USA
4
Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82071, USA
*
Author to whom correspondence should be addressed.
Received: 29 October 2020 / Revised: 4 December 2020 / Accepted: 8 December 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Wildfire Hazard and Risk Assessment)
The scope of wildfires over the previous decade has brought these natural hazards to the forefront of risk management. Wildfires threaten human health, safety, and property, and there is a need for comprehensive and readily usable wildfire simulation platforms that can be applied effectively by wildfire experts to help preserve physical infrastructure, biodiversity, and landscape integrity. Evaluating such platforms is important, particularly in determining the platforms’ reliability in forecasting the spatiotemporal trajectories of wildfire events. This study evaluated the predictive performance of a wildfire simulation platform that implements a Monte Carlo-based wildfire model called WyoFire. WyoFire was used to predict the growth of 10 wildfires that occurred in Wyoming, USA, in 2017 and 2019. The predictive quality of this model was determined by comparing disagreement and agreement areas between the observed and simulated wildfire boundaries. Overestimation–underestimation was greatest in grassland fires (>32) and lowest in mixed-forest, woodland, and shrub-steppe fires (<−2.5). Spatial and statistical analyses of observed and predicted fire perimeters were conducted to measure the accuracy of the predicated outputs. The results indicate that simulations of wildfires that occurred in shrubland- and grassland-dominated environments had the tendency to over-predict, while simulations of fires that took place within forested and woodland-dominated environments displayed the tendency to under-predict. View Full-Text
Keywords: wildfire; predictive modeling; fire spread model; Monte Carlo; spatial modeling; area difference index; statistics; precision; recall; principal components analysis wildfire; predictive modeling; fire spread model; Monte Carlo; spatial modeling; area difference index; statistics; precision; recall; principal components analysis
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MDPI and ACS Style

Ott, C.W.; Adhikari, B.; Alexander, S.P.; Hodza, P.; Xu, C.; Minckley, T.A. Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model. Fire 2020, 3, 71. https://0-doi-org.brum.beds.ac.uk/10.3390/fire3040071

AMA Style

Ott CW, Adhikari B, Alexander SP, Hodza P, Xu C, Minckley TA. Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model. Fire. 2020; 3(4):71. https://0-doi-org.brum.beds.ac.uk/10.3390/fire3040071

Chicago/Turabian Style

Ott, Cory W., Bishrant Adhikari, Simon P. Alexander, Paddington Hodza, Chen Xu, and Thomas A. Minckley 2020. "Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model" Fire 3, no. 4: 71. https://0-doi-org.brum.beds.ac.uk/10.3390/fire3040071

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