Probabilistic fire spread forecast as a management tool in an operational setting.

Area of Science:

• Wildfire Science
• Geospatial Analysis
• Environmental Modeling

Background:

• Operational fire management requires accurate wildfire growth prediction tools.
• Traditional deterministic models lack uncertainty quantification, hindering effective decision-making.
• Integrating probabilistic approaches is crucial for enhanced wildfire planning and suppression.

Purpose of the Study:

• To evaluate a probabilistic approach for predicting wildfire growth in an operational setting.
• To quantify the uncertainty in fire spread model predictions.
• To assess the combined utility of fire spread modeling and satellite data for forecasting.

Main Methods:

• Utilized FARSITE software for wildfire growth simulations.
• Performed 100 probabilistic simulations by varying input parameters to generate burn probability maps.
• Conducted deterministic simulations for comparative analysis.
• Investigated the integration of satellite active fire data with modeling.

Main Results:

• Probabilistic fire spread predictions demonstrated significant potential as a fire management tool.
• Generated spatial-temporal burn probability maps offering valuable insights.
• Probabilistic simulations proved advantageous over deterministic ones.
• Re-initializing simulations with satellite data did not enhance prediction accuracy as anticipated.

Conclusions:

• Probabilistic wildfire growth information aids in anticipating landscape fire progression with associated probabilities.
• This enhanced forecasting capability can help mitigate adverse environmental, social, and economic impacts.
• The approach provides critical data on the timing, location, and likelihood of fire spread.