Live fuel moisture content (LFMC) is a parameter that affects the flammability of plants, and the capacity to measure it remotely makes it an accessible variable for use in fire behaviour models. Although the effect of LFMC on the flammability of fuel particles has clear theoretical support however, the way in which this relates to fire behaviour is complex and difficult to quantify so that empirical studies of heath and forest fires at times yield weak or ambiguous results.
This study examines the way in which moisture affects fire behaviour by using a process-based conceptual framework (Zylstra 2011) to identify feedbacks and complexities that may confound empirical analysis.
Z11 links empirically-derived sub-models of flammability characteristics within a dynamic physical framework where heat is transferred convectively across spaces between leaves, branches, plants and plant strata. The ignitability, combustibility and sustainability of flame from burning leaves interacts with the geometry of the fuel array to determine whether flame will spread across horizontal spaces affecting rates of spread, and vertical spaces affecting flame heights.
Factors are identified that should be considered explicitly in experimental design if LFMC is a consideration, and physical arguments presented to show that where such a range of conditions is not present in the experimental design, the results are inadequate to draw conclusions. In some cases, practical considerations will prevent the lighting of experimental fires under the full range of necessary conditions so that the best understanding will be derived from modelling results. In such cases, misleading conclusions will be drawn unless the model can adequately reflect the complexity presented here.
There exists a strong physical argument for the effect of LFMC on fire behaviour, however this effect is not straightforward and will drive threshold changes and feedbacks. Such changes may represent sudden and dangerous escalations in fire behaviour, so understanding and quantifying these is important. Z11 is a model that can calculate such thresholds and can be used to both inform experimental design and risk-based decision making