Towards predictive simulation of wildfire spread at regional scale using ensemble-based data assimilation to correct the fire front position

Abstract

The objective of this study is to develop a prototype data-driven wildfire simulator capable of forecasting the fire spread dynamics. The prototype simulation capability features the following main components: A level-set-based fire propagation solver that adopts a regional scale viewpoint, treats wildfires as propagating fronts, and uses a description of the local rate of spread (ROS) of the fire as a function of vegetation properties and wind conditions based on Rothermel's model; a series of observations of the fire front position; and a data assimilation algorithm based on an Ensemble Kalman Filter (EnKF). Members of the EnKF ensemble are generated through variations in estimates of the fire ignition location and/or variations in the ROS model parameters; the data assimilation algorithm also features a state estimation approach in which the estimation targets (the control variables) are the two-dimensional coordinates of the discretized fire front. The prototype simulation capability is first evaluated in a series of verification tests using syntheticallygenerated observations; the tests include representative cases with spatially-varying vegetation properties and temporally-varying wind conditions. The prototype simulation capability is then evaluated in a validation test corresponding to a controlled grassland fire experiment. The results indicate that data-driven simulations are capable of correcting inaccurate predictions of the fire front position and of subsequently providing an optimized forecast of the wildfire behavior.