PostBP: A Python library to analyze outputs from wildfire growth models.

Area of Science:

• Forestry science
• Computational modeling
• Geospatial analysis

Background:

• Wildfires are significant natural disturbances in Canadian forests, causing substantial economic damage.
• Spatial fire growth models are crucial for understanding wildfire dynamics and assessing hazards like burn probability.
• Existing metrics often fail to capture the directional spread and potential distances of wildfires.

Purpose of the Study:

• To introduce PostBP, an open-source Python package for processing fire growth model outputs.
• To extract directional fire spread information from raw simulation data.
• To generate practical wildfire risk summary metrics.

Main Methods:

• Developed PostBP, a Python package for post-processing fire growth model outputs.
• Processed simulated ignition locations and perimeters from multiple stochastic iterations.
• Generated a matrix of fire spread likelihoods between landscape patches.

Main Results:

• PostBP transforms complex raw fire growth data into actionable summary metrics.
• The package calculates directional fire spread likelihoods, source-sink ratios, and fire spread rose diagrams.
• Demonstrated practical application of PostBP to a forested landscape.

Conclusions:

• PostBP addresses the challenge of summarizing large fire growth model outputs for decision-making.
• The generated fire risk summaries enhance wildfire risk assessments and mitigation strategies.
• PostBP provides valuable tools for understanding and managing wildfire dynamics.