Estimating Enhanced Fujita Scale Levels Based on Forest Damage Severity

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Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Christopher M. Godfrey, University of North Carolina, Asheville, NC; and C. J. Peterson
Manuscript (1.8 MB)

Handout (5.1 MB)

Enhanced Fujita (EF) scale estimates following tornadoes remain challenging in rural areas with few traditional damage indicators. In some cases, such as the 27 April 2011 tornadoes that passed through mostly inaccessible terrain in the Great Smoky Mountains National Park (TN) and the Chattahoochee National Forest (GA), traditional ground-based tornado damage surveys are nearly impossible. The present work describes a novel method to infer EF-scale categories from forest damage using levels of tree damage and a coupled wind and tree resistance model. High-resolution aerial photographs along both tornado tracks allow detailed analyses that include labeling nearly half a million trees with their geographic location and fall direction. Ground surveys that sampled over 2000 individual trees also provide details on the composition of tree species and tree diameters within each tornado track.

A statistical resampling procedure begins by randomly drawing a small sample of trees from this database of observed trees. The coupled wind and tree resistance model determines the percentage of trees in that sample that fall for a given wind speed increment ranging from light breezes to extreme wind speeds. By repeating this procedure a sufficient number of times, each wind speed increment corresponds with a Gaussian distribution of treefall percentages in the sampled plots. Comparing these results with the observed percentage of downed trees in small subplots along the entire tornado track allows an estimation of the most probable wind speed associated with each subplot. Maps of estimated EF-scale levels reveal the relationship between complex terrain and wind speeds and show the variability of the intensity of each tornado along both tracks. This approach may lead to methods for straightforward estimation of EF-scale categories in remote or inaccessible locations.