Fine dead fuel moisture shows complex lagged responses to environmental conditions in a saw palmetto (Serenoa repens) flatwoods

Abstract

Fine dead fuel moisture has a major influence on wildland fire behavior yet the dynamics driving water exchange of fuel particles in forested environments remain poorly understood. Most fire behavior models rely on simple, stand-level fuel moisture estimates, ignoring potentially important variation occurring within fuelbeds that could influence fire behavior. This is especially true in surface fire regimes where variation in fine-scale fuel properties drive fire behavior and subsequent fire effects. Saw palmetto [Serenoa repens (Bartr.) Small] dominated fuelbeds in the pine forests of the southeastern United States have high within stand variation in one of the most fire prone habitats in the world. Pine needles and palmetto fronds dominate the biomass of fine dead fuel types that produce extreme fire behavior. To assess predictors of fine dead fuel moisture, we analyzed fuel moisture dynamics of these two fine dead fuel types over a two-year period in conjunction with under- and overstory forest meteorological data. Using multiple models and time lag analysis of within-stand moisture dynamics, the results indicate that saw palmetto and pine dramatically differ in drying regimes, primarily resulting from different responses to cumulative rainfall, net radiation, and antecedent atmospheric moisture content. Despite being responsive to changes in relative humidity, saw palmetto was significantly dryer than pine under nearly all meteorological conditions, and it was capable of maintaining extremely low fuel moisture despite high relative humidity or rainfall. Our results point to the need to capture additional drivers of microclimatic variation to aid fire managers in accurately predicting within-stand fuel moisture and subsequent fire behavior. Improving the scientific community’s understanding of variation in complex fuel beds is critical for effectively managing risk in fire prone ecosystems.