A simulation of probabilistic wildfire risk components for the continental United States

Abstract

This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for 10,000 to 50,000 ''years'' of artificial weather. The fire growth simulations, when run repeatedly with different weather and ignition locations, produce burn probabilities and fire behavior distributions at each landscape location (e.g., number of times a ''cell'' burns at a given intensity divided by the total years). The artificial weather was generated for each land unit using (1) a fire danger rating index known as the Energy Release Component (ERC) which is a proxy for fuel moisture contents, (2) a time-series analysis of ERC to represent daily and seasonal variability, and (3) distributions of wind speed and direction from weather records. Large fire occurrence was stochastically modeled based on historical relationships to ERC. The simulations also required spatial data on fuel structure and topography which were acquired from the LANDFIRE project (http://www.landfire.gov). Fire suppression effects were represented by a statistical model that yields a probability of fire containment based on independent predictors of fire growth rates and fuel type. The simulated burn probabilities were comparable to observed patterns across the U.S. over the range of four orders of magnitude, generally falling within a factor of 3 or 4 of historical estimates. Close agreement between simulated and historical fire size distributions suggest that fire sizes are determined by the joint distributions of spatial opportunities for fire growth (dependent on fuels and ignition location) and the temporal opportunities produced by conducive weather sequences. The research demonstrates a practical approach to using fire simulations at very broad scales for purposes of operational planning and perhaps ecological research.

Generation of a global fuel data set using the Fuel Characteristic Classification System

“…One or two representative species of each type of vegetation were assigned to each individual fuelbed from the list available within FCCS, considering the vegetation form and foliage type most characteristic within every fuelbed. In the case of the crop fuelbeds, the 15 crops considered were grouped to 10 categories, according to their characteristics, and they were assigned the most similar agricultural fuelbed from the ones developed by French et al (2013).…”

A Model-Based Framework to Evaluate Alternative Wildfire Suppression Strategies

“…We chose the Large Fire Simulator (FSim) as it has these capabilities [64]. FSim has been widely used in hazard and risk assessments [3,30,65,66].…”

“…FSim has been widely used in hazard and risk assessments [3,30,65,66]. FSim was designed to simulate the ignition and growth of fires under weather conditions during which fires grow large, as these fires contribute the vast majority of burned area under current fire suppression strategies [64]. Simulation time is saved by The Sierra National Forest comprises about 525,000 hectares situated on the west side of the Sierra Nevada Mountains of California and varies from about 300-3700 m in elevation.…”

“…Inputs required by FSim are: (1) a list of recent large fires with their final area, ignition location and date of discovery; (2) daily weather data from a nearby weather station generally representative of the weather for the simulation area; and (3) a landscape file including fuel and topography information [64]. Daily large fire ignitions are modeled via logistic regression of a fire danger metric (the Energy Release Component for Fuel Model G) with historical large fire ignitions [64,67]. Once ignited, fire growth proceeds via a minimum travel time algorithm [68] based on the Rothermel rate-of-spread equations and a crown fire model.…”

“…In order to produce a map of burn probabilities, a modeled approach is necessary: the documentary record of large fire perimeters is too sparse [64]. To remedy this problem, FSim generates artificial yearly weather sequences whose statistics are representative of the local weather station records [70].…”