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Modeling Fire

Authored By: D. A. Weinstein, P. B. Woodbury

There are several methodologies currently in use or those under development containing features for estimating fire occurrence risk assessment. Two major categories of fire risk assessment tools are: those that predict fire under current conditions, assuming that vegetation, climate, and the interactions between them and fire remain relatively similar to their condition during recent history, and those that anticipate changes in fire risk as climate and vegetation communities change through time. Three types of models have proven useful for predicting fire under current conditions: (1) biophysical models that predict fire from vegetation type, fuel load, and climate; (2) statistical models; and (3) fire behavior models. Programs such as LANDFIRE have great promise for using biophysical properties to estimate risk. Statistical models that use historical data to predict fire probabilities if landscape-fire relationships continue to remain relatively unchanged, are gaining interest as more data becomes available. Fire behavior models are producing accurate predictions of the ways individual fires will move across the landscape. For longer time periods, fire risk needs to be evaluated by models that predict the ways vegetation communities will change over time because these changes will alter fire probabilities. Models capable of being used to track changes in vegetation are identified and the resulting effect on changes in fire frequency. Risk systems need to be designed to track changes in fire susceptibility as the climate changes, using models such as MAPSS.

Prediction of fire occurrence is just the first part of a complete analysis of risks associated with fire. Fire occurrence risk needs to be combined with models that determine the risk of the effects of fire. Models that predict mortality, fuel consumption, smoke production, and soil heating caused by prescribed fire or wildfire should be used, as well as those capable of evaluating second order effects, such as changes in site productivity, animal use, insects, and disease. Fire must be looked at in the context of other stresses, such as invasive insects and pathogens, encroaching urbanization, and loss of critical habitat. There are interactions among stresses that play a role in affecting the frequency and intensity of fire, and fire, in turn, can affect the probability of those stresses. Consequently, risk evaluation systems need to be created that can simultaneously estimate the probability of other major stresses influencing ecosystem development.


Subsections found in Modeling Fire
  • Introduction : Methodologies are described here that may be useful for estimating fire occurrence risk assessment, including the probability of ignition and the spatial spread and intensity of the fire during its lifetime.
  • Biophysical Fire Risk Systems : Biophysical fire risk models traditionally use regional characteristics of weather patterns (temperature, humidity, wind), vegetation (fuel type, moisture level), and topography (elevation, slope) to produce a prediction index of fire risk based on historical correlations among these variables and fire.
  • Probability-Based Fire Statistical Models : An illustration of the general approach shared by these types of models is the probability-based fire risk model, which estimates fire probability by fitting a nonparametric logistic regression to data grouped in cells of 1 sq. km. with a temporal resolution of one day.
  • Fire Behavior Modeling : Fire behavior models predict the propagation of fire by assuming that the landscape is subdivided into cells, and each cell has a probability of burning that depends on conditions in the cell and in surrounding cells.
  • 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.
  • Models to Estimate the Effect of Climate Change on Fire Risk : Biophysical process models can be used to estimate the effect of vegetation change on fire risk.
  • Conclusions Concerning the Use of Fire Modeling Systems : Much effort has gone into creating a capability of predicting fires throughout the region, both in their likely location and frequency.

Encyclopedia ID: p3208



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