How Sensitive is Fire Behavior to Live Fuel Moisture Content and Other Fuel Attributes? Analysis Using the QUIC-Fire Model
Caleb Adams, University of Texas at Austin
The effects of climate change are altering wildfire behaviors, including frequency and severity, alongside changes to ecosystem structures and responses, making the empirical fire models of the past inadequate. Coupled atmospheric-fire behavior models provide a tool for both scientific research and operational prediction for wild and prescribed fires. These physics-based models, especially those that represent the 3-D structure of the forest canopy, offer a promising path to researching fire effects on vegetation and ecologic and hydrologic responses. QUIC-Fire is among the few fire behavior models that represent 3-D fuel structure. Fuel moisture content is a dominant driver of fire behavior, and thus a critical input parameter for these models. Fuel moisture content is also dynamic, changing as plants respond to changes in their environment (e.g., vapor pressure deficit and soil moisture).
As different plant species in the same environment may respond to these changes in different ways, live fuel moisture content can be heterogeneous across the landscape. Current operational fire behavior models do not consider 3-D fuel structure or vegetation water dynamics in three dimensions. To study the relationships between fire, fuel, and water, we are conducting a sensitivity analysis of canopy live fuel moisture content and canopy fuel density in the QUIC-Fire model. We expect to see that changes in canopy fuel moisture content will have a lesser effect on model output with smaller values of canopy fuel density, because low fuel density will result in less water mass to evaporate, even with relatively high fuel moisture content. As we increase fuel density, we expect the model to be more sensitive to moisture content. In addition to increasing our understanding of the role of water in fire, this analysis will inform the importance of estimating live fuel moisture content for use as a fire behavior model input.
Abstract Author(s): Caleb E. Adams, Adam Atchley, Ashley Matheny, Dev Niyogi, Rod Linn