5th Symposium on Fire and Forest Meteorology and the 2nd International Wildland Fire Ecology and Fire Management Congress

Wednesday, 19 November 2003: 11:00 AM
Long lead statistical forecasts of wildland fire suppression costs
Anthony L. Westerling II, SIO/Univ. of California, La Jolla, CA; and K. Gebert, G. Jones, K. Abt, J. Prestemon, and A. Gershunov
A substantial increase in the inter-annual variability of wildfire season severity over the past two decades has greatly increased the complexity and expense of wildland fire management on Federal lands. Timely, skillful forecasts of wildfire season severity have the potential to assist Federal land management agencies in planning for fire management and in reducing associated costs. We present ongoing research to produce skillful forecasts of seasonal area burned and suppression costs in a format useful for wildland fire management.

A recently developed statistical forecast for seasonal wildfire area burned shows significant skill for the Western United States, particularly in the Great Basin and Sierra Nevada. Canonical Correlation Analysis of pre-filtered time series of seasonal area burned and leading values of the Palmer Drought Severity Index is used to construct linear forecast models of area burned a season or more in advance. Experimental forecasts for the 2003 fire season were issued in January, February and early April. The earlier forecasts showed the most skill in the Great Basin. All three forecasts anticipated an above normal fire season in the Colorado Rockies and in mountain ranges in Arizona and New Mexico, and a below normal fire season in desert basins throughout the West.

We are extending this work to include forecasts of seasonal suppression costs for the USDA Forest Service by Forest Service administrative regions. This work compares linear regression models estimating suppression costs using area burned forecasts as predictors to Canonical Correlation Analysis models that estimate suppression costs directly from leading climate indices.

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