Jonah J. Colman

STUDYING WILDFIRE BEHAVIOR USING FIRETEC

A coupled atmospheric/wildfire behavior model is described that utilizes physics-based process models to represent wildfire behavior. Five simulations are presented, four of which are highly idealized situations that are meant to illustrate some of the dependencies of the model on environmental conditions. The fifth simulation consists of a fire burning in complex terrain with non-homogeneous vegetation and realistic meteorological conditions. The simulated fire behavior develops out of the coupling of a set of very complex processes and not from prescribed rules based on empirical data. This represents a new direction in wildfire modeling that we believe will eventually help decision makers and land managers do their jobs more effectively.

Modeling interactions between fire and atmosphere in discrete element fuel beds

In this text we describe an initial attempt to incorporate discrete porous element fuel beds into the coupled atmosphere–wildfire behavior model HIGRAD/FIRETEC. First we develop conceptual models for use in translating measured tree data (in this case a ponderosa pine forest) into discrete fuel elements. Then data collected at experimental sites near Flagstaff, Arizona are used to create a discontinuous canopy fuel representation in HIGRAD/FIRETEC. Four simulations are presented with different canopy and understory configurations as described in the text. The results are discussed in terms of the same two discrete locations within the canopy for each simulation. The canopy structure had significant effects on the balance between radiative and convective heating in driving the fire and indeed sometimes determined whether a specific tree burned or not. In our simulations the ground fuel density was the determining factor in the overall spread rate of the fire, even when the overstory was involved in the fire. This behavior is well known in the fire meteorology community. In the future, simulations of this type could help land managers to better understand the role of canopy and understory structure in determining fire behavior, and thus help them decide between the different thinning and fuel treatment strategies available to them.

Coupled influences of topography and wind on wildland fire behaviour

Ten simulations were performed with the HIGRAD/FIRETEC wildfire behaviour model in order to explore its utility in studying wildfire behaviour in inhomogeneous topography. The goal of these simulations is to explore the potential extent of the coupling between the fire, atmosphere, and topography. The ten simulations described in this paper include five different topographies, each run with two different ambient wind speeds of 6 and 12 m s–1. The five topologies explored are: an idealised hill (which serves as the base centerline for the other topographies), two variations of the hill with lateral gradients downwind from the ignition line (one sloping up from the ‘hill’ at the centerline to form an upward sloping canyon parallel to the ambient wind, and the other sloping down from the centerline to form a ridge parallel to the ambient flow), one with a second hill upwind of the ignition line such that the fire is ignited in the bottom of a canyon that runs perpendicular to the ambient wind, and finally a flat terrain. The four non-trivial topographies have the same profile along the centerline downwind of the ignition line to help assess the impacts of topographic gradients that are perpendicular to the ambient wind. It is hoped that analysis of these simulations will help reveal where point-functional models are sufficient, where topographically modified wind fields are needed, and where fully coupled fire and transport models are necessary to properly describe wildfire behaviour.

Separating combustion from pyrolysis in HIGRAD/FIRETEC

HIGRAD/FIRETEC is a coupled atmosphere/wildfire behavior model based on conservation of mass, momentum, species, and energy. It combines a three-dimensional transport model that uses a compressible-gas fluid dynamics formulation with a physics-based wildfire model, to represent the coupled behavior of the local atmosphere and wildfire. In its current formulation, combustion and pyrolysis are treated as a single process, which depends on the local densities of wood and oxygen, the levels of turbulent mixing, and a probability distribution function (PDF) for temperature in the solid. The PDF is employed to estimate the volume fraction that is hot enough to burn. This burning model is now being extended to deal with pyrolysis and combustion as separate processes. Some fire behaviors, such as flash events, crowning, and fire ‘whirls’, may depend on the ability of combustion to take place in a separate spatial location from the pyrolysis. We refer to this burning model as ‘non-local’. In the non-local burning model, pyrolysis is dealt with in roughly the same way as formerly, but now as an endothermic process. Instead of producing solely inert gasses, it now produces a mixture of inert and combustible gasses. Combustion is handled as a separate gas–gas reaction, which is highly exothermic. The basic premise of the HIGRAD/FIRETEC burning model is retained, i.e. that the rate of a reaction is limited by the rate at which the reactants can be brought together (mixing limited). In the non-local burning model, the reactants for pyrolysis can be thought of as heat and wood, for combustion: the reactive gas and oxygen. A few simple test cases that used idealised geometries were simulated with both burning models, and the results were compared. The non-local burning model was found to give results comparable to the local burning model in terms of the fire-line shape and the spread rate for these simple test cases.

On the variability of tropospheric gases: Sampling, loss patterns, and lifetime

and its global mean lifetime (t) has been used to estimate the t of atmospheric gases. This can prove quite useful if it is a unique relationship. Here a three-dimensional chemical transport model is used to investigate the variability-lifetime relationship of tropospheric gases with two types of sources and three types of losses. The effects of sampling time and location are also explored. The relationship is best described in the form s = atb , where a and b are variable depending on the sources, sinks, and time and location of averaging. When spatially averaging over the troposphere and temporally averaging over 1 year, the model results give a b of 0.77-0.79 for t between 0.9 and 7.0 years. The variability of a CH3Br-like gas is also analyzed using different weightings of chemical sinks. Photochemical (OH), ocean mixed layer, and soil losses are scaled separately to maintain t � 1 year. These different scalings result in a ±17% spread in s, which translates into a ±20% spread in t inferred from the variability-lifetime relationship found in the model. In addition, the model is used to simulate conditions of Pacific Exploratory Mission (PEM) Tropics A and B field campaigns. The variability-lifetime relationships derived from the model do not compare to the field observations, except that both demonstrate a seasonal dependence of variability. This study identifies some factors controlling the variability of trace gases in the troposphere, estimates the error in using variability-lifetime analysis to determine an unknown t, and shows that the variability-lifetime relation is not universal among trace gases. INDEX TERMS: 3210 Mathematical Geophysics: Modeling; 0368 Atmospheric Composition and Structure: Troposphere—constituent transport and chemistry; 0365 Atmospheric Composition and Structure: Troposphere—composition and chemistry; 0312 Atmospheric Composition and Structure: Air/sea constituent fluxes (3339, 4504); KEYWORDS: variability, lifetime, troposphere, methyl bromide, modeling, PEM-Tropics A and B