Abstract
Abstract. A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event) in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.
Highlights
Current computational wildfire dynamics simulators are not fast enough to provide valid predictions on time (Sullivan, 2009) and require input parameters that are difficult to acquire and sense during an emergency situation
The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time in the order of 10 min for a spatial scale of 100 m
Inverse modelling, which is the core of data assimilation techniques, consists of studying measurements from sensors to gain information about the physical phenomena using a variety of mathematical models and algorithms
Summary
Inverse modelling, which is the core of data assimilation techniques, consists of studying measurements from sensors to gain information about the physical phenomena using a variety of mathematical models and algorithms. In the field of wildfire, Mandel et al (2009) explored this technique to predict the time–temperature curve of a sensor placed in the way of an advancing wildfire They examined a reaction– diffusion equation and a semi-empirical fire line propagation model coupled with an Eulerian level-set-based equation. The propagating model uses two components: the rate of spread (RoS) is represented by a product between the fuel depth (δ) and a constant ( ) to be quantified as part of the forecasting problem (RoS = · δ) Their model uses a level-set-based equation to cast the fire perimeter. Rochoux et al (2013) and Mandel et al (2008) use one single parameter at a time and do not emphasize lead times They seem tailored more towards supercomputing platforms than to mobile systems for field deployment. Another highlight of our method is the incorporation of automatic differentiation into the inverse model, which is accurate and fast, further decreasing the computational expense of a forecast
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