Improved Wildland Fire Spread Prediction

Project Tabs - Fuel Moisture

Decision support systems for wildland fire management are essential for effective and efficient wildland fire risk assessment and firefighting. The effectiveness of such a system depends on the quality and quantity of the available data including: environmental conditions, fuel types, and fuel moisture content (FMC). 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 prediction of wildland fire spread.

Development of machine learning models for the dynamic FMC estimation.
Development of machine learning models for the dynamic FMC estimation.

Presently, continuously updated information about FMC over the continental United States (CONUS) is based on sparse surface observations. Considering that FMC can vary significantly over relatively small area operational decision support systems for wildland fire management require frequently updated high-resolution FMC data. We are therefore developing a gridded real-time system for estimation of FMC based on satellite observations. Reflectances obtained by Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellite platforms are combined with surface observations of FMC and a numerical weather prediction model WRF-Hydro output and used as predictors in a machine learning model to produce daily one-kilometer gridded FMC data for CONUS.

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 high-resolution numerical simulations using observations of wildland fires over Colorado.

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