Abstract
Aim of the study:Forest fuel classification and characterization is a critical factor in wildfire management. The main purpose of this study was to develop custom fuel models for accurately mapping wildfire spread compared to standard models.Area of study: The study was conducted at a replanted forest dominated by coniferous species, in the Arabdagh region,GolestanProvince, northernIran.Material and methods: Six custom fuel models were developed to characterize the main vegetation types in the study area. Fuel samples were collected from 49 randomly selected plots. In each plot, the fuel load of 1-hr, 10-hr, 100-hr, 1000-hr, live herbs, live woody plants, surface area volume ratio, and fuel depth were estimated using the Fuel Load (FL) sampling method along three transects. Canopy fuel load was calculated for each fuel model. The performance of the custom fuel models versus standard fuel models on wildfire behavior simulations was compared using the FlamMap MTT simulator.Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy. Compared to the observed fire, simulation results did not give realistic results to the crown fire. The simulation using standard fuel models did not result in crown fire, while the simulation using custom fuel models showed a moderate rate of crown fire with a Kappa coefficient of 0.54.Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management.Keywords: custom fuel model; FlamMap; replantation; vegetation type; wildfire behavior.
Highlights
Millions of hectares of forests are burned annually, affecting all major biomes in terms of ecosystem structure, biogeochemical cycles, and atmospheric composition (Andela et al, 2019)
Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy
Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management
Summary
Millions of hectares of forests are burned annually, affecting all major biomes in terms of ecosystem structure, biogeochemical cycles, and atmospheric composition (Andela et al, 2019). Predicting the spread and behavior of wildfires is essential for a better understanding of fire processes and to inform fire and land management decision-makers (Taylor et al, 2013). The use of advanced modeling tools to predict fire spread and behavior is widely recognized as an effective method to support wildland management (Keeley et al, 2004). Fire prediction tools provide land and forest managers the opportunity to better evaluate appropriate fuel management methods for wildfire risk mitigation (Salis et al, 2018) or potential fire control practices for more efficient and cost-effective wildfire management (Jahdi et al, 2014)
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