Title: Estimations of fuel moisture content for improved wildland fire spread prediction
Presenting Author: Branko Kosovic
Organization: National Center for Atmospheric Research
Co-Author(s): Pedro Jimenez Munoz, Steven Massie, Amanda Siems-Anderson, Bill Petzke, David John Gagne, Domingo Munoz-Esparza, Sue Ellen Haupt

Abstract:
Decision support systems for wildland fire behavior are essential for effective and efficient wildland fire risk assessment and firefighting. We are developing a wildland fire prediction system for the State of Colorado. The wildland fire prediction system is based on the National Center of Atmospheric Research's Coupled Atmosphere Wildland Fire Environment (CAWFE) model, and the Weather Research and Forecasting — Fire (WRF-Fire) model. WRF-Fire is an extension of the widely used, community numerical weather prediction model WRF. In addition to atmospheric conditions and fuel type, fuel moisture content (FMC) is a critical factor controlling the rate of spread and heat release from wildland fires. Previous studies have shown that the intensity and frequency of occurrence of large fires are more highly correlated to reduced vegetation moisture than increased air or fuel temperature. Accurate information about FMC is therefore essential for more accurate wildland fire spread prediction. Presently, the National Fuel Moisture Database (available via the Wildland Fire Assessment System) provides continuously updated information about FMC based on relatively sparse surface observations and manual sampling. However, an effective wildland fire spread model requires regularly updated, high-resolution FMC data. We are there developing a dynamic, real-time, FMC data product using satellite remote sensing observations. By combining vegetation index products from the polar orbiting MODIS instruments, we are developing a high temporal and spatial resolution maps of FMC over the Continental United States (CONUS). The vegetation indices are combined using machine learning algorithms and calibrated using surface RAWS observations to produce a best estimate of the dead and live fuel moisture content. This new gridded product will provide more accurate, up to date estimates of FMC that can be effectively used in an operational wildland fire spread prediction system. The use of the new gridded FMC database will be demonstrated in our WRF-Fire coupled atmosphere wildland fire prediction model.