Implementation of spatially-varying wind adjustment factor for wildfire simulations. (May 2023)

• Record Type: Journal Article
• Authors: Sutherland, Duncan; Rashid, Mahmood A.; Hilton, James E.; Moinuddin, Khalid A.M.
• Is Part Of: Environmental modelling & software. Volume 163(2023)
• Publication Date: 2023-05
• Subjects: Wildfire -- Wind adjustment factor -- Vegetation height -- Leaf area index -- Fire rate of spread; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105660
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The behaviour and rate of spread of a wildfire is strongly affected by local wind conditions depending on topography and surrounding vegetation. The wind speed within dense vegetation can be substantially lower than the open wind speed above the vegetation. This is commonly accounted for by applying a local correction factor to the local wind speed which can be called the wind adjustment factor (WAF). WAFs are often difficult to calculate and are usually based estimates for a particular vegetation type. Variation in the vegetation may result in spatially varying WAFs. We implement a model for spatially-varying WAFs based on leaf area index (LAI) and vegetation height data, which can be derived from remote sensing data sources. The model is implemented within Spark an operational wildfire prediction framework. Simulations of historical fires using the spatially-varying WAF model generally provided improved predictions compared to recorded fire extents than simulations without a spatially-varying WAF. Highlights: Wind adjustment factor is estimated using data from real tree canopies. The model is implemented in the wildfire spread framework, Spark. Historical fires are used to evaluate predictive improvements of the WAF model.