Estimations of fuel moisture content for improved wildland fire spread prediction

Decision support systems for wildland fire behavior are essential for effective and efficient wildland fire risk assessment and firefighting. Together with the Center of Excellence for Advanced Technology Aerial Firefighting in Rifle, Colorado we are developing a wildland fire prediction system for the State of Colorado. The mission of the Center of Excellence is: "To protect citizens, land, and resources in Colorado, the Center of Excellence will research, test, and evaluate existing and new technologies that support sustainable, effective, and efficient aerial firefighting techniques." 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. Currently the coupled operational wildland fire prediction system is at Technical Readiness Level (TRL) 4. The coupled atmosphere–wildland fire spread model has been implemented in the prototype operational system and the available relevant data sets are assimilated in the operational forecasting process. The system is undergoing testing in real time at the scale of the State of Colorado, as required. The fuel data component of the operational system is at the TRL 2. A simple fuel model (Anderson 1982) has been implemented. This model does not allow for the observed variability in FMC. A more advanced (i.e. Scott and Burgan 2005) fuel model is being implemented. However, a dynamic, gridded FMC data set that can be assimilated in real-time in the operational system does not exist. Presently, the National Fuel Moisture Database (available via the Wildland Fire Assessment System) provides continuously updated information about FMC based on surface observations from Remote Automated Weather Stations (RAWS). To provide FMC data at any location in the CONUS, measurements are interpolated using an inverse distance squared interpolation. However, considering the sparse spatial distribution of RAWS any interpolation method can result in large errors in spatial FMC distribution. We therefore propose to use satellite remote sensing observations to develop more accurate gridded, dynamic, real-time FMC database product for use with the dynamic Scott and Burgan (2005) fire behavior fuel models. By combining vegetation index products from the polar orbiting MODIS instruments, we will develop a high temporal and spatial resolution FMC product. The vegetation indices will be combined using machine learning algorithms and calibrated using surface RAWS observations to produce a best estimate of the dead and live fuel moisture content. The dead and live FMC will be combined to derive total fuel moisture content in the widely used Rothermel (1972) fire spread model. The use of the new gridded FMC database will be demonstrated in our WRF-Fire coupled atmosphere wildland fire prediction model. More accurate accounting for live and dead FMC through assimilation of satellite observations will result in more realistic, dynamic representation of fuel heterogeneity and in improved accuracy of wildland fire spread prediction. The effectiveness of the coupled atmosphere wildland fire spread prediction model accounting for the FMC will be assessed in collaboration with the Center for Excellence for Advanced Technology Aerial Firefighting using observations of wildland fires over Colorado.

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