Logo with a link to the homepage.


Mississippi Fire Potential Publications PDF

Synopsis:  Fire behavior and fire potential are characterized by the Fire Environment Triangle.  Exhibits 1 and 2 describe techniques for integrating the fuel and weather components of the triangle.  Exhibit 1 examines pre- and post-Katrina fuel loadings of forest species and age combinations coupled with 3 categories of damage (no damage, defoliation, broken tops, and downed timber).  Remotely sensed satellite imagery was used to define areas of damage and high-resolution airborne imagery sub samples were used to identify categories of damage.   Often fuel loadings are treated as static, but when disaster strikes fuel conditions can change rapidly.  Methods like these aimed at describing the dynamic change in fuel loadings are an essential component for the accurate characterization of regional fire potential.   Exhibit 2 examines   the weather component of the Fire Environment Triangle.  Surface water balance is driven by a system of inputs and outputs where water is accumulated through precipitation and lost through evaporation, transpiration, and runoff.  This study examines the spatial distribution of pan evaporation stations and offers alternatives for intensifying the number of locations at which pan evaporation can be estimated. Combining these evaporation estimates with precipitation data from Doppler weather radar sites  enables the calculation of a cumulative water budget.  By comparing these calculations with historic water budget averages, periods of sustained water stress can be spatially depicted and compared to fire activity.

Exhibit 1: William H. Cooke, III, Katarzyna Grala, David L. Evans and Curt Collins (In Press, Journal of Forestry). Assessment of pre- and post-Katrina fuel conditions as a component of fire potential modeling for southern Mississippi.


Exhibit 2: William H. Cooke III, Katarzyna Grala, Charles L. Wax, (Submitted, Southeastern Geographer). A Method for Regional Estimates of Evaporation for Use in GIS-Based Dynamic Forest Fire Potential Models.