Background

Fuel moisture content is a key driver of fuel flammability and subsequent fire activity and behavior worldwide. Dead fuels passively exchange moisture with the atmosphere while live fuel moisture is confounded by a mixture of seasonal carbon and water cycle dynamics. Despite the significance of live fuel moisture content (LFMC) on wildland fire potential, attempts to model its variations seasonally and between species are often inconclusive or unsuccessful.

Results

Here we present a mechanistic LFMC model that uses easily measured live fuel physiological and morphological traits that are rooted in either plant ecophysiology or combustion science. These traits serve as proxies for important components of the seasonal water and carbon cycle or they capture inter-species plant morphology variations. The model decomposes LFMC based into leaf mass area (LMA), relative water content (RWC), surface-area-to-volume ratio (SAV), and the volumetric saturated water holding capacity (κ). We test 10 simplifications or variations of the mechanistic model using combinations of fixed and time-varying inputs of the four variables. A simplified mechanistic model version that uses the time-varying RWC and LMA with foliage age class-specific SAV and κ medians accounted for most of the seasonal variation in Douglas fir LFMC across two growing seasons (r2=0.91, MAE = 12.9%). Further, this same model applied to 11 Intermountain Western US conifers adequately captured the seasonality and inter-species differences in live fuel moisture dynamics across an entire growing season and foliage age classes (r2=0.89, MAE = 12.5%).

Conclusions

This pyroecophysiology-based approach to live fuel moisture content modeling provides a more robust way to characterize seasonal variations in both fuel availability and water stress while building on decades of plant ecophysiology and combustion research. The model can be used to more appropriately represent live fuels in process-based models, it can be used to better parameterize multi-dimensional fire behavior models to represent the combined effects of biomass and moisture variations on live fuel flammability, and it can improve our ability to more accurately monitor live fuel variations with remote sensing. This new model harmonizes decades of disparate live fuel moisture research and lays a foundation for more fruitful live fuel dynamics explorations worldwide.