An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment

• Published in: Environmental modelling & software : with environment data news 2025-01, Vol.183, p.106254, Article 106254
• Main Authors: Perello, Nicolò, Trucchia, Andrea, D’Andrea, Mirko, Degli Esposti, Silvia, Fiorucci, Paolo, Gollini, Andrea, Negro, Dario
• Format: Article
• Language: English
• Subjects: algorithms; calibration; computer software; Dead fuel moisture content; fire hazard; Fire weather; forest fires; fuel moisture index; fuels; fuels (fire ecology); Optimization; Particle swarm optimization; risk management; weather; Wildfire danger; Wildfires; wildland fire management
• ISSN: 1364-8152
• DOI: 10.1016/j.envsoft.2024.106254

Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-h fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management. [Display omitted] •Fuel moisture content is a key factor in wildfire risk management and fire modeling.•Fuel moisture models are mainly defined on daily basis and they neglect fuel types.•The proposed model with fuel-specific parameters captures hourly fuel moisture dynamics.•The calibration framework used for 10-h fuel sticks can be adapted to other fuel types.•The model advances wildfire danger assessment with hourly fuel-aware moisture dynamics.