A Method for Ensemble Wildland Fire Simulation

Abstract

An ensemble simulation system that accounts for uncertainty in long-range weather conditions and twodimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis is used to characterize the seasonal trend in ERC, autocorrelation of residuals, and daily standard deviation and stochastically generate artificial time series of afternoon fuel moisture. Daily wind speed and direction are sampled stochastically from joint probabilities of historical wind speed and direction for the date range of the fire simulation period. Hundreds or thousands of fire growth simulations are then performed using the synthetic fire weather sequences. The performance of these methods is evaluated in terms of the number of ensemble member simulations, one-versus twodimensional fire spread simulations, and comparison with results from 91 fires occurring from 2007 to 2009. Simulations were found to be in consistent agreement with observations, but trends indicate that the ensemble average of simulated fire sizes were consistently larger than actual fires whereas the farthest extent burned by fires was underestimated.

A Review of Recent Forest and Wildland Fire Management Decision Support Systems Research

“…Its Wildland Fire Decision Support System (WFDSS) which is described by Calkin et al (2011) [58••] is a comprehensive FMDSS that can be used to support and document wildfire risk analysis to enhance the management of large, escaped fires. Finney et al's (2011) [59] ensemble stochastic fire growth modelling system is an important advance in providing fire managers with advanced fire growth technology but it, and models like it, ultimately have to be linked to tractable and realistic suppression optimization models that can be used to develop and evaluate alternative large fire suppression strategies and tactics on an operational basis.…”

AEGIS: a wildfire prevention and management information system

“…WFDSS uses the National Fire Danger Rating System (NFDRS) (Burgan, 1988) for fire danger estimation, while data from RAWS (Zachariassen et al, 2003) are utilized for weather analysis. Potential wildfire spread from existing ignitions is forecasted for a maximum of 10 days using the Minimum Travel Time (MTT) algorithm (Finney, 2002) embedded within the FSPro simulation software (Finney et al, 2011a;McDaniel, 2016). WFDSS also provides damage assessment for the affected area based on the Rapid Assessment Values at Risk (RAVAR) model and the Stratified Cost Index (Gebert et al, 2007).…”

Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

“…This approach is limited in scope because of the large uncertainties associated with the accuracy of computer models since they do not account for the interaction between the fire and the atmosphere, and since they have a limited domain of validity resulting from a calibration procedure based on experiments (Perry, 1998;Sullivan, 2009;Viegas, 2011;Cruz and Alexander, 2013;Finney et al, 2013). This approach is also limited because of the large uncertainties associated with many of the input parameters to the fire problem (Jimenez et al, 2007;Finney et al, 2011).…”

“…The uncertainties inherent in wildfire spread modeling go beyond the limitations of deterministic forecast abilities of the dynamical model (also referred to as the forward model) and thus, suggest the use of ensemble forecasts to stochastically characterize the nonlinear response of the front-tracking simulator to variations in the input environmental parameters (D'Andrea et al, 2010;Finney et al, 2011). For instance, Finney et al (2011) describes an ensemble-based forecasting capability, in which a large number of fire spread scenarios (i.e., the ensemble members) are generated based on a probabilistic uncertainty in the weather conditions and in the moisture content of biomass fuels.…”

“…The uncertainties inherent in wildfire spread modeling go beyond the limitations of deterministic forecast abilities of the dynamical model (also referred to as the forward model) and thus, suggest the use of ensemble forecasts to stochastically characterize the nonlinear response of the front-tracking simulator to variations in the input environmental parameters (D'Andrea et al, 2010;Finney et al, 2011). For instance, Finney et al (2011) describes an ensemble-based forecasting capability, in which a large number of fire spread scenarios (i.e., the ensemble members) are generated based on a probabilistic uncertainty in the weather conditions and in the moisture content of biomass fuels. Model uncertainties are a combination of epistemic errors that express an imperfect knowledge of the input parameters of the ROS model (that could in theory be removed), and of aleatoric errors that result from natural and unpredictable stochastic variabilities of the physical system (that can be addressed by stochastic models, see for instance Reference by Pagnini and Mentrelli (2014), whose model relies on a stochastic component to represent the transport of firebrands).…”