William Mell

A comparison of level set and marker methods for the simulation of wildland fire front propagation

Simulating an advancing fire front may be achieved within a Lagrangian or Eulerian framework. In the former, independently moving markers are connected to form a fire front, whereas in the latter, values representing the moving front are calculated at points within a fixed grid. Despite a mathematical equivalence between the two methods, it is not clear that both will produce the same results when implemented numerically. Here, we describe simulations of fire spread created using a level set Eulerian approach (as implemented in the wildland–urban interface fire dynamics simulator, WFDS) and a marker method (as implemented in FARSITE). Simulations of surface fire spread, in two different fuels and over domains of increasing topographical complexity, are compared to evaluate the difference in outcomes between the two models. The differences between the results of the two models are minor, especially compared with the uncertainties inherent in the modelling of fire spread.

Probability of surface fire spread in Brazilian rainforest fuels from outdoor experimental measurements

This paper describes the development of a logistic model to predict the probability of surface fire spread in Brazilian rainforest fuels from outdoor experimental measurements. Surface fires spread over litter composed mostly of dead leaves and twigs. There were 72 individual outdoor experiments in eighteen sites. The fire propagated in 49% of the experiments. In each experiment, the litter height, litter temperature, unburned litter mass, wet and dry litter mass, soil temperature, wet and dry soil mass, ambient wind velocity, ambient air temperature, ambient air relative humidity and duration of fire spread were measured. Using these data, the rate of fire spread, litter bulk density, litter and soil moisture content, litter load and litter residue fraction were determined. For the sake of analysis, experimental results were classified into two groups: one for which the fire propagated and the other one for which the fire self-extinguished. Analyses of a logistic regression model showed that the relevant parameters for fire propagation are litter height and litter moisture content. Concerning the probability of successful fire propagation, the model showed a true positive rate of 71% and a true negative rate of 84%. The outdoor experiments also served to gather data to improve the understanding of surface fires and to provide input data for future computer simulations.

Numerical study of the interaction between a head fire and a backfire propagating in grassland

One of the objectives of this paper was to simulate numerically the interaction between two line fires ignited in a grassland, on a flat terrain, perpendicularly to the wind direction, in such a way that the two fire fronts (a head fire and a backfire) propagated in opposite directions parallel to the wind. The numerical simulations were conducted in 3D using the new fuel element module recently implemented in WFDS. We chose a grassland for the fuel layer, because it represents certainly one of the most studied ecosystem, in the frame of experimental campaigns to study the behaviour of surface fires at large scale. The aim of this numerical study is to understand what are the physical phenomena and the favorable conditions of ignition of a counter fire, during a fire suppression operation. The preliminary results highlighted that the two fire fronts interacted only at a relative short distance (10-20 m), following this scenario: • The thermal plume above the head fire (representing the main fire front) formed a sort of shelter, protecting the backfire to the direct effect of the wind flow • Before the merging between the two fire fronts, an in-draft flow was observed ahead of the head fire, promoting and accelerating the propagation of the backfire During the last step of the merging of the two fire fronts, a sudden increase of the heat release rate was observed, indicating a significant interaction between the two fires, which can potentially represent a safety problem for people in charge of this kind of operation.

Modeling thinning effects on fire behavior with STANDFIRE

Key messageWe describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model.ContextThinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts.AimsThe study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models.MethodsThe modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs.ResultsThe study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management.ConclusionThis system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.

Forest Fire Research: The Latest Advances Tools for Understanding and Managing Wildland Fire

1University of Corsica Pascal Paoli, Campus Grimaldi, SPE UMR 6134 CNRS, BP 52, 20250 Corte, France 2CSIRO Ecosystem Sciences and CSIRO Climate Adaptation Flagship, GPO Box 1700 Canberra, ACT 2601, Australia 3Aix-Marseille Universite, UNIMECA, 60 Rue Joliot Curie, 13453 Marseille cedex 13, France 4U.S. Forest Service, Pacific Wildland Fire Sciences Lab, 400 N. 34th St., Suite 201, Seattle, WA 98103, USA

Clarifying the meaning of mantras in wildland fire behaviour modelling: reply to Cruz et al. (2017)

In a recent communication, Cruz et al. (2017) called attention to several recurring statements (mantras) in the wildland fire literature regarding empirical and physical fire behaviour models. Motivated by concern that these mantras have not been fully vetted and are repeated blindly, Cruz et al. (2017) sought to verify five mantras they identify. This is a worthy goal and here we seek to extend the discussion and provide clarification to several confusing aspects of the Cruz et al. (2017) communication. In particular, their treatment of what they call physical models is inconsistent, neglects to reference current research activity focussed on combined experimentation and model development, and misses an opportunity to discuss the potential use of physical models to fire behaviour outside the scope of empirical approaches.

Correction to: “Modeling thinning effects on fire behavior with STANDFIRE”

The original article shows unit errors in Table 2: The torching index (TI) and crowning index (CI).