Much uncertainty exists regarding the possible role that gaps in forest canopies play in modulating fire–atmosphere interactions in otherwise horizontally homogeneous forests. This study examines the influence of gaps in forest canopies on atmospheric perturbations induced by a low-intensity fire using the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization. A series of numerical experiments are conducted with a stationary low-intensity fire, represented in the model as a line of enhanced surface sensible heat flux. Experiments are conducted with and without forest gaps, and with gaps in different positions relative to the fire line. For each of the four cases considered, an additional simulation is performed without the fire to facilitate comparison of the fire-perturbed atmosphere and the background state. Analyses of both mean and instantaneous wind velocity, turbulent kinetic energy, air temperature, and turbulent mixing of heat are presented in order to examine the fire-perturbed atmosphere on multiple timescales. Results of the analyses indicate that the impact of the fire on the atmosphere is greatest in the case with the gap centered on the fire and weakest in the case with the gap upstream of the fire. It is shown that gaps in forest canopies have the potential to play a role in the vertical as well as horizontal transport of heat away from the fire. Results also suggest that, in order to understand how the fire will alter wind and turbulence in a heterogeneous forest, one needs to first understand how the forest heterogeneity itself influences the wind and turbulence fields without the fire.
Wildland fires and the atmosphere interact across a range of spatial and
temporal scales from macroscale (10
Before proceeding to discussion of the current state of knowledge of
fire–atmosphere interactions inside forest gaps, some discussion of the
simpler case of fire–atmosphere interactions in homogeneous canopies is
warranted. The impact of homogeneous forest canopies on simulated
fire-perturbed variables (e.g., temperature, turbulent kinetic energy (TKE))
and processes affecting such variables (e.g., turbulent mixing, shear
production) was examined in
With regard to fire–atmosphere interactions within gaps, although some
modeling studies have examined fire propagation in discontinuous fuels beds
Studies that focus on flow within forest gaps in the absence of fire are
somewhat greater in number
In this study, we use the ARPS-CANOPY model
The remainder of this paper is organized as follows. A description of the
model and experiment design are included in Sect.
The development of ARPS-CANOPY is described in detail in
First, modifications to the ARPS model equations were made to account for the
drag force of vegetation elements, via a drag force term added to the
momentum equation, and the enhancement of turbulence dissipation in the
canopy air space, via a sink term added to the sub-grid-scale
TKE equation
Subsequently,
It is important to note that ARPS-CANOPY does not resolve the flow around
individual trees or the heating/cooling of individual branches or leaves. In
all aspects of the model, the canopy is represented as a height-varying plant
area density profile (
As stated in the previous section, a 1.5-order sub-grid-scale turbulence
closure scheme with a prognostic equation for TKE is utilized. Radiation
physics following
A one-way nesting procedure is utilized with two three-dimensional
computational domains, and a periodic boundary condition is applied at the
lateral boundaries of the outer domain. The outer domain consists of
The outer-domain simulation is initialized at noon local time, with a uniform
net radiation flux of 520
Following a 30 min spin-up period, a 25
The portion of the domain surrounding the fire line is divided into three
zones, delineated by their position relative to the fire center:
upstream, center, and downstream (Fig.
Experiment design summary, with
Perturbation wind components (
Vertical cross sections of the
Examination of the four cases begins with vertical cross sections of the
spatiotemporal mean
As in Fig.
Proceeding to vertical cross sections of vertical wind velocity (Fig.
When examining total (resolved plus sub-grid-scale) TKE in the absence of fire or
gap (Fig.
With analysis of mean wind and TKE complete, some brief discussion of potential interactions between the gap-induced and fire-induced circulations, and the possible influence of the circulations on turbulence above the fire, is in order. As evidence of such interactions is difficult to assess from difference fields of 30 min mean fire and no-fire simulation wind components, vertical cross sections of 1 min mean wind components from the fire and no-fire simulations have also been examined (not shown). For most of the 30 min period that the fire heat source is applied, there is no evidence of the gap-induced circulation. However, there are brief intervals when the gap circulation is more dominant, including the minute after the fire is switched on and sporadically throughout the 30 min period. Thus, on short timescales (e.g., 1–2 min), TKE and wind gusts are potentially influenced by the gap circulation, but over longer timescales (e.g., 30 min) the influence of the gap circulation is likely minimal.
As in Fig.
As in Fig.
As in Fig.
Proceeding to temperature in the absence of fire or gap (Fig.
As in Fig.
As this is a study of fire–atmosphere interactions in heterogeneous forests,
a relevant question to ask is, what role, if any, do gaps play in vertical
heat transport? In other words, do gaps act as vents for hot gases? To help
answer this question, we examine vertical (horizontal) turbulent mixing as
represented by the vertical (horizontal) gradient of vertical (horizontal)
turbulent heat flux, in a vertical cross section (Figs.
Before concluding this section, some discussion of possible advantages and
disadvantages of the idealized modeling framework used in this study is in
order. The fire is represented herein as a static line of enhanced surface
vertical turbulent heat flux that is independent of both temporal and spatial
variability in the atmosphere and the presence or absence of vegetation in
its vicinity. Unlike coupled fire–atmosphere models such as FIRETEC and
WRF-Fire
Acknowledging that fire–atmosphere interactions occur on a variety of
temporal and spatial scales, we proceed from analysis of time- and
space-averaged quantities to analysis of instantaneous variables inside the
canopy in the presence of fire. The analysis begins with the instantaneous
We next proceed to instantaneous vertical velocity (Fig.
Box and whisker plots of instantaneous
As in Fig.
As in Fig.
Examination of instantaneous TKE (Fig.
Lastly, we examine instantaneous temperature (Fig.
In this study, ARPS-CANOPY has been utilized to examine the sensitivity of
fire-perturbed variables to the presence of gaps in forest cover, and to the
position of such gaps relative to the fire line. A single plant area density
profile was used to represent a canopy with a moderately dense overstory and
sparse trunk space, and a 25
As in Fig.
Conceptual model of fire–atmosphere interactions with different
forest gap configurations. Background (i.e., no fire) state indicated with
black arrows and grayscale shading: black horizontal arrows indicate the
background mean
In the absence of fire (Fig.
In all cases, implementation of the fire (Fig.
This study has provided insight into the sensitivity of fire-induced
perturbations to the presence of gaps in forest cover. The results presented
herein suggest that, in order to understand how the fire will alter wind and
turbulence in a heterogeneous forest, one needs to first understand how the
forest heterogeneity itself influences the wind and turbulence fields
Despite the progress documented herein, much work remains. Future efforts planned include implementing a moving fire through a forest gap; exploring the sensitivity of fire–atmosphere interactions to canopy profile shape; and performing statistical significance testing with a larger suite of experiments, including experiments with higher-intensity fires.
The data for this paper are stored at the NCAR-Wyoming Supercomputing Center (NWSC). For data requests, please contact Michael T. Kiefer at mtkiefer@msu.edu.
Support for this research was provided by the USDA Forest Service via Research Joint Venture Agreements 13-JV-11242306-055 and 11-JV-11242306-058. We wish to thank Daisuke Seto and two anonymous reviewers for their helpful comments and suggestions regarding the manuscript. We acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Edited by: Y. Qian Reviewed by: D. Seto and two anonymous referees