Models to Estimate Effect of Vegetation Change on Fire Risk

Fire risk over long time periods cannot be adequately evaluated without projecting the ways that the structure and composition of forest vegetation and fuels will change over time. Fire is very sensitive to vegetation structure and composition (Clark 1993, Swetnam 1997, Swetnam and Baisan 1996). Fire will, in turn, affect the rate and direction of vegetation change (Lenihan and others 1998).

Keane and colleagues classified 44 linked fire succession models, based on their complexity, principle mechanism, and stochasticity in their simulation design (Keane and others 2004). Most of these models fall into the category of fire behavior models because they attempt to simulate, at a fine scale, the dynamics that cause fire to spread across a landscape. These authors provide a handy table for deciding which models to use based on: (1) whether the purpose is management or research, (2) the importance of predicting fire patterns, (3) whether information is needed on specific species or just on stand characteristics, and (4) what spatial scale is of interest (Table: Model selection key).

Complex models such as Fire-BGC (Keane and others 1989) and LANDIS (Mladenoff 2004, Mladenoff and others 1996) simulate vegetation change as a complex function of either the development of nutrient cycling conditions or the driving force of individual life history characteristics. The most complex of these are the gap models such as ZELIG-SP (Miller and Urban 1999) that predict successional development by simulating the dynamics of each individual tree on representative plots in the forest. Simpler models, such as SIMPPLLE (Chew and others 2004), represent vegetation changes as a predictable succession of stages following a resetting disturbance. A few of these models that are in common use were selected for further discussion in the following sections.