Improving seasonal fire predictions and information services in Alaska for regional and national fire resource planning

What this product does

  • Provides a forecast of the magnitude of the upcoming fire season for interior Alaska as a whole
  • Gives an estimate of certainty associated with the forecast

What this product does not do

  • Make predictions about the regions within Interior Alaska where fires will occur
  • Make predictions about what time of the season fires will occur

Predictive capacity for Alaska fire falls behind what is available in the lower 48 states. Increases in wildfire frequency, severity, duration, and total area burned are among the most significant expected ecological effects of climate warming. Two of the three most extensive wildfire seasons in Alaska’s 50-year record occurred in 2004 and 2005 and 60% of the largest fire years have occurred since 1990 (Kasischke et al. 2006).

In 2004, the largest fire season on record in Alaska, over 2.5 million hectares burned, costing state and federal fire agencies nearly $150 million. A Fairbanks neighborhood was evacuated multiple times and air quality in Fairbanks was classified as hazardous or unhealthy for nearly one quarter of the fire season.

Population growth, road-building and resource development are increasing the need for fire suppression by expanding the area of wildland-urban interface. Furthermore, increased fire activity in Alaska increases nationwide competition for limited and shared firefighting resources.

Designed in close collaboration with fire managers from a range of state and federal agencies participating in the Alaska Wildland Fire Coordination Group, this project takes advantage of the strong weather/fire link in Alaska to produce estimates for the severity of the 2009 and 2010 fire seasons. The regression model developed by Duffy et al. (2005) estimates the logarithm of annual area burned as a function of monthly weather and teleconnection indices with an R-squared of greater than 75%.

We extend this modeling framework through the application of gradient boosting models (GBM). Preliminary results show significant improvement over the already high R-squared from the regression model. The uncertainty associated with the forecasts will be quantified resulting in a set of possible values for area burned in Alaska and confidence intervals for the forecast.

These results will provide a web-based decision-support tool that will help Alaska fire managers adapt to a changing climate in their suppression and natural resource planning.