Development and evaluation of generalized fuel models for predicting fire behaviour in northern European heathlands

Résumé fourni par la source

Northern European heathlands and moorlands dominated by Calluna vulgaris are internationally recognized for their conservation importance while also supporting traditional, low-intensity agriculture and game hunting. Managed burning plays an important role in maintaining these ecosystems but climate and land-use changes, including planned or unplanned transitions to forest and woodland, are now resulting in concerns about increasing wildfire frequency, intensity and severity. In combination with rapidly-changing regulations surrounding managed burning, this has highlighted the need to understand current and potential future fuel structures to effectively model fire behaviour and develop evidence-based regulations surrounding managed burning. We developed standardized heathland fuel descriptions and modeled associated fire behaviour for heathlands in the UK (England, Scotland) and Norway. Utilizing existing fuel and biomass data, we used cluster analysis to identify five distinct fuel models and assessed how they were represented across C. vulgaris life-stages, geographic locations and EUNIS habitat-types. We validated their independence by examining predicted fire rates of spread based across three representative fire weather scenarios. Fire rates of spread differed between C. vulgaris life stages, regardless of EUNIS community or country. Mature stage and taller building stage fuels produced the highest fire rates of spread and early, shorter building and pioneer stage fuels produced the lowest. Moss and litter fuel loads proved to be important determinants of fire rate of spread in a high-risk fire weather scenario. An understanding of links between fuel types and potential fire behaviour can be used to inform management and policy decisions. To aid in this, we used classification tree analysis to link fuel types to easily-observable characteristics. This will facilitate pairing the fuel models with fire behaviour prediction software to make evidence-based assessments of management fire safety and wildfire risk.

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