Fuel models for forest soil cover plant

Autorzy

• Łukasz Tyburski Forest Research Institute, Laboratory of Forest Fire Protection
  Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, Poland
  phone: +48 22 7150430, e-mail: l.tyburski@ibles.waw.pl
• Damian Czubak Forest Research Institute, Laboratory of Forest Fire Protection
  Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, Poland
• Ryszard Szczygieł Forest Research Institute, Laboratory of Forest Fire Protection
  Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, Poland
• Mirosław Kwiatkowski Forest Research Institute, Laboratory of Forest Fire Protection
  Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, Poland

Abstract

This study presents the development and application of forest fuel models to predict fire behaviour in different forest habitats, focusing on biomass distribution, moisture variability and the calorific value of soil cover components. On analysing forest fire data from 2007 to 2013, we identified high-risk habitats, especially in younger stands and in stands dominated by Scots pine. The variability of moisture in the different soil cover components, including litter and deadwood, was statistically analysed; it revealed significant dependencies on litter moisture and rain-free intervals. These dependencies allowed the creation of equations to accurately predict fuel load and moisture content based on stand age, habitat type and environmental conditions.
Using the calorific value measurements with an average calorific value of 18,920 kJ/kg for dry soil cover materials, fuel models were developed to estimate the potential intensity of fires. The models categorise the biomass into decayed, dead and live components and take into account key variables such as bulk density and moisture content that influence fire spread. Data-driven categorisation enables fire risk assessments at the compartment level, which increases accuracy compared to more comprehensive district-level assessments.
Applications of fuel models include mapping the spatial distribution of biomass by fuel type, creating fire hazard maps and optimising firefighting infrastructure. These models support planning and operations by enabling forest managers to visualise high-risk areas and predict fire behaviour based on real-time meteorological data. The models are a critical step towards a more effective firefighting system that provides fine-grained, actionable insights into fire risk at the local level.

Keywords

• fuel classification
• soil cover
• forest habitat type
• forest fires