The capacity to predict fire dynamics in fuel beds comprised of live and dead fuel components is constrained by our limited understanding of the effects of live fuels on fire propagation. A field-based experimental burning program was conducted to specifically address the effect of the degree of curing, the proportion of dead fuels in the fuel bed, on fire propagation in grasslands. Experimental fires were conducted at two sites characterised by structurally distinct fuels with curing levels varying between 20% and 100%. Fire sustainability experiments showed that fire propagation can occur down to curing levels as low as 20%. Rate of fire spread varied between 41.7 and 102 m min–1 in fully cured fuels and between 2.8 and 43.5 m min–1 in partially cured grasslands. The degree of curing was found to be the best variable describing the damping effect of live fuels in a natural, senescing grassland. Live fuel moisture content by itself was not found to be related to the damping effect of live fuels on the rate of fire spread. Existing models for the effect of grass curing on fire behaviour presently used in Australia were found to under-predict the rate of forward fire spread in partially cured grasslands. A new curing relationship for southern Australian grasslands derived from the study results is proposed.

In Australia, the Grassland Fire Danger Index is determined by several inputs including an essential component, the degree of grassland curing, defined as the proportion of senescent material. In the state of Victoria (south-eastern Australia), techniques used for curing assessment have included the use of ground-based observations and the use of satellite imagery. Both techniques alone have inherent limitations. An improved technique has been developed for estimating the degree of curing that entails the use of satellite observations adjusted by observations from the ground. First, a satellite model was developed, named MapVictoria, based on historical satellite and ground-based observations. Second, with use of the new (MapVictoria) satellite model, an integrated model was developed, named the Victorian Improved Satellite Curing Algorithm, combining near-real-time satellite data with weekly observations of curing from the ground. This integrated model was deployed in operations supporting accurate fire danger calculations for grasslands in Victoria in 2013.

Grass senescence, or grassland curing, is a dynamic process in which grass fuels transition from a live to dead state and, in turn, influence fire dynamics. In the present study we examined the process of curing with specific consideration of changes in fuel structure that will affect potential fire behaviour. Our sampling protocol expanded the fuel component groups from two (live and dead) to four (green, senescing, new dead and old dead fuel). We found that all these components had significant fuel moisture content differences, thereby justifying our sampling protocol. Visual curing assessment predominantly resulted in an over-prediction bias of curing level and failed to capture the effect of the senescing process on fuel availability to combust due to misclassification of fuel components (e.g. senescing fuels with high fuel moisture content were classified as dead fuels because of their colouration). Models were developed to estimate the: (1) proportion of senescing and green fuels from knowledge of the current year’s dead fuel proportion; and (2) actual curing level from fuel moisture content and soil dryness level.

The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.

Prescribed burning can be an integral part of tallgrass prairie restoration and management. Understanding fire behaviour in this fuel is critical to conducting safe and effective prescribed burns. Our goal was to quantify important physical characteristics of southern Ontario’s tallgrass fuel complex prior to and during prescribed burns and synthesise our findings into useful applications for the prescribed fire community. We found that the average fuel load in tallgrass communities was 0.70 kg m–2. Fuel loads varied from 0.38 to 0.96 kg m–2. Average heat of combustion did not vary by species and was 17 334 kJ kg–1. A moisture content model was developed for fully cured, matted field grass, which was found to successfully predict moisture content of the surface layers of cured tallgrass in spring. We observed 25 head fires in spring-season prescribed burns with spread rates ranging from 4 to 55 m min–1. Flame front residence time averaged 27 s, varying significantly with fuel load but not fire spread rate. A grassland spread rate model from Australia showed the closest agreement with observed spread rates. These results provide prescribed-burn practitioners in Ontario better information to plan and deliver successful burns.