Abstract
Abstract. This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.
Highlights
Real-time prediction of the direction and speed of a propagating wildfire has been identified as a valuable research objective with direct applications in both fire risk management and fire emergency response (Noonan-Wright et al, 2011)
The ensemble Kalman filter (EnKF) algorithm is sequentially applied over an assimilation window [t − 1, t]; each assimilation cycle decomposes into two successive steps for each member of the ensemble indexed by the exponent k as illustrated in Fig. 4: 1. a prediction step, in which the system is evolved from time (t − 1) to time t (t being the observation time) through an integration of FIREFLY to forecast the fire front position yt given some uncertainty ranges in the control vector xt
The variations in the x and y coordinates of this marker are represented with respect to variations in the control parameter P : black crosses indicate the simulated marker positions associated with the Nquad = 5 quadrature roots (i.e., FIREFLY model integrations) corresponding to the first step of the polynomial chaos (PC)-EnKF algorithm; and blue circles indicate the forecast estimates obtained through the surrogate model evaluation combined with a Monte Carlo sampling (Ne = 1000) corresponding to the second step of the PC-EnKF algorithm
Summary
Real-time prediction of the direction and speed of a propagating wildfire has been identified as a valuable research objective with direct applications in both fire risk management and fire emergency response (Noonan-Wright et al, 2011). The objective of this study is to show the feasibility of this approach for wildfire spread forecasting under several assumptions, i.e., a minimalist treatment of the fire front (idealized as an interface and consistent with the limited knowledge on the environmental conditions); a semi-empirical formulation of the ROS; Gaussianity of the errors on the input parameters of the ROS model and on the observations; prior values for the control parameters specified based on user-defined mean and error standard deviation (STD) In this first part, both the EnKF and PC-EnKF algorithms are limited to the estimation of spatially uniform parameters of the ROS model due to computational cost constraints and a lack of high-resolution data on the environmental conditions. The performance of the data-driven wildfire spread capability using the reduced-cost approach is demonstrated in a validation test corresponding to a controlled grassland fire experiment
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