A field project was conducted to observe and measure smoke plumes from wildland fires in Alberta. This study used handheld inclinometer measurements and photos taken at lookout towers in the province. Observations of 222 plumes were collected from 21 lookout towers over a 6-year period from 2010 to 2015. Observers reported the equilibrium and maximum plume heights based on the plumes' final levelling heights and the maximum lofting heights, respectively.
Observations were tabulated at the end of each year and matched to reported fires. Fire sizes at assessment times and forest fuel types were reported by the province. Fire weather conditions were obtained from the Canadian Wildland Fire Information System (CWFIS). Assessed fire sizes were adjusted to the appropriate size at plume observation time using elliptical fire-growth projections.
Though a logical method to collect plume observations in principle, many unanticipated issues were uncovered as the project developed. Instrument limitations and environmental conditions presented challenges to the investigators, whereas human error and the subjectivity of observations affected data quality. Despite these problems, the data set showed that responses to fire behaviour conditions were consistent with the physical processes leading to plume rise.
The Alberta smoke plume observation study data can be found on the Canadian Wildland
Fire Information System datamart
(Natural Resources Canada, 2018) at
Some of the most severe air quality events in Canada are due to smoke from
forest fires. Each year dozens of communities are evacuated due to smoke and
health concerns, each evacuation disrupting the lives and livelihoods of
residents, their families, and their communities. Large-scale smoke events
can blanket major population centres affecting hundreds of thousands of
people, of whom approximately one-third are susceptible May 2001, when a plume from the Chisholm fire inundated Edmonton,
causing particulate matter readings to reach a concentration of approximately 260 13 July 2012, when Alberta Health Services issued a precautionary health
advisory regarding air quality in Edmonton due to fires from the BC interior.
Later that summer, on 24 September, Alberta Health Services issued a smoke
advisory for the Edmonton area due to fires in northern Alberta. June to August 2014, which was reputed to be the worst forest fire
season the Northwest Territories had experienced for at least two decades.
The smoke generated by the fires was blown into the Prairie provinces and created
a moderate health risk, leading Environment Canada to declare an air quality
advisory for southern Saskatchewan and Manitoba on 9 July. 5 July 2015, when Metro Vancouver issued an air quality advisory for
smoke from fires on British Columbia's Sunshine Coast, 50 July 2015, when Saskatchewan fires and smoke resulted in the evacuation of over 13 000
people in the La Ronge region and prompted health officials in Saskatchewan and
neighbouring Manitoba to issue health advisories due to smoke. August and September 2015, when smoke from the Okanogan Complex
fire in Washington state extended through much of the BC interior, affecting
cities such as Penticton and Kelowna. On 26 August, Alberta Health Services
issued air quality advisories for areas from the US border north to the Edmonton region because of smoke from these wildfires.
These are recent examples of smoke events that may only get worse given the
potential increase of wildland fire activity due to global warming.
A challenge in forecasting smoke events is predicting its transport and, more
specifically, the height to which a plume will rise. Drastically different
trajectories can result if a plume breaks through into the free atmosphere
compared to a plume that is confined within the mixing layer. Predicting the
possible penetration (or injection) heights of smoke plumes from wildland
forest fires is largely an unresolved problem
Modelling wildland fire smoke plumes is a relatively new research topic and
one that mixes a variety of disciplines. A forest fire's behaviour drives
the processes that lead to smoke emissions and concentrations while the
energy generated by the fire leads to the buoyancy, vertical lift, and plume
penetration height. Several models have been developed, ranging from simple,
empirical approaches
The following studies used remotely sensed data to evaluate smoke plume predictions:
BlueSky, a widely accepted smoke forecasting framework
While quality work, these studies lack ground observations of fire behaviour
characteristics (fire size, growth, and intensity). Satellite-based
information cannot substitute for ground observations as satellites do not
provide an accurate measure of fire size due to instrument resolution.
Likewise, FRP measurements do not discern between small, high-intensity fires
and larger, low-intensity fires at the sub-pixel level of the satellite
resolution. Finally, the timing of satellite passes is an issue, as often
these do not occur in the mid- to late afternoon when fire intensity is at its
maximum . These factors are important to determine the size and shape of the
plume, leading to the volume of the smoke column.
There have been plume studies involving detailed ground-based observations.
Lookout towers used in Alberta smoke plume observation study.
This study describes a field project conducted to observe and measure the
smoke plumes from wildland fires in Alberta. Observers at several lookout
towers in the province used handheld inclinometers to take height
measurements of smoke plumes. Plume observations were then linked to
ground-based fire reports to capture fire weather and fire behaviour
associated with the plumes and include them in the data set. The overall
purpose of this study was to create an extensive data set composed of
ground-based observations of smoke columns and related fire information to
validate a plume-rise model the authors are developing to improve smoke
forecasting models
The Alberta smoke plume study included 222 plume observations collected over a 6-year period from 2010 to 2015 (20, 10, 26, 29, 63, and 74 observations per year chronologically), involving 21 fire observation lookout towers (Table 1). Observations were tabulated at the end of each year and matched to reported fires. Fire sizes at assessment times and forest fuel types were recorded by the province. Fire weather conditions were obtained from the Canadian Wildland Fire Information System (CWFIS). Finally, assessed fire sizes at reported times were adjusted to sizes at plume observation times using elliptical fire-growth projections.
The wildfire branch of Alberta Agriculture and Forestry runs a network of about 127 lookouts (many of which are towers) for the detection of wildland fires. Observers at these towers monitor the forest and are well trained in recognizing plumes from wildland fires, reporting the azimuth for fire detection purposes. Two tower reports are used to triangulate to the fire location. Also, fire suppression resources report the precise location upon arrival using the Global Position System (GPS). From this and the lookout tower location, distance to the fire can be ascertained.
During the 6-year study, these observers were asked to take measurements
using a handheld Suunto PM-5 inclinometer. The inclinometer used is a simple
device, providing measurements in degrees above or below a level handheld
position. The device has a manufacturer specification of
Illustration of the plume height observation (
Figure 1 illustrates the technique used to measure the smoke plume height
based on the measured inclinometer angle. Taking the curvature of the Earth
into account, the equation for the smoke plume height,
Illustration of the equilibrium and maximum plume heights for observation. Zoomed-in photo taken from the Whitefish lookout tower on 19 June 2010.
Observers were asked to report equilibrium and maximum plume heights based on the plume's final levelling height and the maximum lofting height, respectively (Fig. 2). Due to buoyancy, a smoke plume will rise through the atmosphere until it reaches thermal equilibrium with the environment, typically spreading out laterally at this level. This is reported as the equilibrium height. Yet as it rises, the plume builds vertical velocity and thus will overshoot the equilibrium level, only to fall to the equilibrium level afterwards. This overshoot is reported as the maximum plume height.
In addition to the inclinometer measurements, the observers were asked to photograph the plume with and without the zoom feature. This gave the authors a rudimentary ability to assess the quality of the observations.
As fires are detected and actioned by fire-fighting resources, the province collects assessment data on the fire. Information includes the fire name and location, time and date of detection, the assessed size at the time when fire-fighting resources arrive, size and date at times of containment and of extinguishment, and several intermediate points. Additional information such as cause, fire characteristics and the fuel type are collected by teams at the fire location. These reports are tabulated annually at the Alberta Provincial Forest Fire Centre. Note that these reports are collected independently of the plume observations in this study.
For this study, plumes were matched with fire reports based on the time, date, and azimuth from the lookout tower. Distances to the fires and ground elevation above sea level at the fire locations were then determined.
Fire weather conditions were obtained from the Canadian Wildland Fire
Information System
Weather conditions at each plume location were interpolated from the gridded
CWFIS maps using an inverse distance weighting scheme. These included noon
values of the temperature, relative humidity, wind speed and direction, and
precipitation over the past 24
A forest fuel type, a classification based on tree species and vegetative
ground cover used to predict potential fire behaviour in the CFFDRS, was
selected for each plume based on the priority approach. In Canada, the forest
protection agencies of the provinces, territories, and national parks are
responsible for fire management and fuel-type mapping. Fuels are mapped from
various sources, typically forest inventory, Landsat imagery, or a
combination of the two. A fuels map used in this study was provided by
Alberta Agriculture and Forestry at 100
Fire behaviour conditions presented in the study were calculated using the
Canadian Fire Behaviour Prediction (FBP) System
Values for the area burned at the time of plume observations were derived
from fire sizes at the time of assessment from the fire assessment reports
(for example, an adjustment of the fire size must be made when a fire
assessed at 14:00 MDT and the plume is observed at 15:00 MDT). Fires typically
follow a diurnal growth cycle peaking in the late afternoon and subsiding
overnight; hence, the fires in this study were assumed not to grow between
20:00 and 06:00 MDT of the next day; sizes could then be used for adjacent
dates if required or deemed appropriate (e.g., a fire size reported late in
the evening could be used as the fire size for a plume observation early the
next day). For large, multi-day fires, sizes were based on fire mapping
techniques using infrared satellite imagery from polar-orbiting satellites
with the Moderate Resolution Imaging Spectroradiometer sensor
Based on the daily area growth and fuel consumption, the energy of the fire was calculated as
It is important to note that not all of a fire's energy enters the buoyant plume. Large amounts of energy are spent propagating the fire forward (heating the fuel ahead of the fire and evaporating moisture), as well as being injected into the ground (released into the atmosphere but at a time much later than the primary plume development).
The Alberta smoke plume observation study data provide the smoke plume
observations for the Alberta smoke plume study, as well as information on the
associated fire and observing station. During the study, 222 observation
reports were collected. One report (plume observation 10) was a blend of two
observations and thus was separated (10a, 10b); one report (204) was a
duplicate (of 203). Of the remaining reports, 14 observations were rejected:
three had no associated reported wildland fires (29, 30, 50), one fire in neighbouring Saskatchewan had no certain fire report (21), five had camera malfunctions (111, 112, 113, 114, 115), and five had poor observation conditions due to looking into the Sun (181, 182, 183, 184, 185).
Of the remaining 208 observed plumes, eight adjusted plume heights following
Eq. (1) were negative (2, 44, 59, 65, 74, 76, 83, 117). These were also
rejected and the final number of acceptable plume observations used in the
study was 200.
A summary of plume observation statistics.
Multiple tower observations.
Table 2 summarizes statistics on the observed plumes. Excluding negative
plume heights, there were 197 observed equilibrium plumes and 158 maximum
plume heights (4, 66, and 214 were missing equilibrium height but had maximum
height observations, while 42 were missing maximum height observations).
Observed equilibrium plume heights varied from 27 to 8833
Histogram of equilibrium and of maximum plume heights (m).
The distribution of plume heights (Fig. 3) shows the majority of
equilibrium heights are below 2000
There were 60 reported fires in the study (some over multiple days) and 88 days with plume observations (87 with equilibrium heights, 64 with maximum heights). There were 39 cases of plumes being observed multiple times over the course of the day. For example, on 28 June 2015, fire LWF161 was observed 11 times from 14:05 to 18:30 MDT. To reduce possible bias, the subset of 88 observations (48 of single and 40 of multiple observations) was used to create a set of daily peak equilibrium and maximum plume heights. The benefit of such a subset is that it reflects the intended conditions of the fire weather measurements, that is of conditions at the time of peak burning (typically at 17:00 LST). Also, by selecting the peak values, any indirect problems, such as changes in afternoon weather or the impact of fire suppression efforts, are avoided.
Finally, there were six cases where two towers reported the same plume at approximately the same time (Table 3): SWF120 on 22 June 2010, PWF068 on 11 July 2012, GBZ002 on 6 August 2014, LWF161 on 24 June 2015, and PWF131 on 2 July and again on 19 July 2015. An examination of these cases helps to quantify the uncertainty of all observations in this study.
On 22 June 2010, Trout Mountain and Teepee Lake both observed the plume from
fire SWF120 (from 72 and 94
Evolution of observed plume heights for PWF068
from Hotchkiss at 24
Zoomed-out photograph of plume from fire PWF068
from Hotchkiss at 24
On 11 July 2012, the plume from fire PWF068 was observed by both Hotchkiss
and by Saddle Hills lookouts (from 23 and 173
Similar comparisons can be drawn for the other cases. Fire GBZ002 was
observed on 6 August 2014 by Pinto (40
In summary, if we assume that Saddle Hills mistook maximum plume heights for
equilibrium heights for fires PWF-068 and GBZ-002, the overpredictions range
from 16 to 77
A summary of weather and fire weather characteristics.
FFMC is the Fine Fuel Moisture Code; DMC is the Duff Moisture Code; DC is the Drought Code; ISI is the Initial Spread Index; BUI is the Buildup Index; FWI is the Fire Weather Index; DSR is the Daily Severity Rating.
A summary of fire behaviour characteristics.
ROS is the rate of spread; SFC is the surface fuel consumption;
TFC is the total fuel consumption; HFI is the head fire intensity; CFB is the crown fraction
burned;
Correlation of fire behaviour and observed plume heights.
Energy of the fire (J) on a logarithmic scale compared with observed equilibrium and maximum plume heights (m). Correlation coefficients shown next to trend line; regression equations shown in the legend.
Fire weather conditions were sampled at all 200 plume locations. Because these weather values represent noon conditions, a subset of data limited to the 88 plume observation days was created. This provided 88 fire weather values, valid at noon each day.
A summary of the statistics of fire weather conditions associated with the
plumes is shown in Table 4. This table shows that the mean noon weather
conditions associated with smoke plumes reflect a typical summer day in
Alberta with a temperature of 21.2
Linear regressions were conducted to test for any relationships between plume
heights and fire weather conditions, comparing each of the variables in Table 4
individually against observed equilibrium and then maximum plume heights.
Regressions were conducted first against all observations and then against
the subset of 88 daily peak heights. No practical correlations were observed
with the only
Fire behaviour conditions were modelled for all 200 plumes; results are presented in Table 5. Unlike the noon-based fire weather, these values reflect conditions at the plume observation time, making each plume observation unique.
As was done with fire weather conditions, linear regressions were conducted
to test for any relationships between plume heights and the fire behaviour
variables listed in Table 5. Regressions were conducted first against all
observations and then against the daily peak heights to remove bias resulting
from multiple observations of the same plume (Table 6). Moving from fire
weather to fire behaviour, clear correlations begin to emerge. Of these,
total fuel consumption, hourly and daily growth, and energy of the fire
consistently showed relationships with P values
Figure 6 shows a scatter plot of the energy of the fire (on a logarithmic
scale) versus the daily peak equilibrium and maximum plume heights, presented
to illustrate the degree of scatter in the data set. The regression lines
through the data provide coefficients of determination (
The project set out to collect smoke plume heights as observed from lookout towers in Alberta. Already trained in recognizing smoke plumes for fire detection purposes, observers were asked to measure, photograph, and document the plume heights they saw. In principle, this seemed a logical method to collect plume observations, yet many unanticipated issues arose as the project developed.
Observation errors were possibly the largest source of error in this study. It was apparent from the written reports that not all information was complete or accurate. Given the occasional wrong dates or missing times scattered throughout the reports, one can assume that errors in reported inclinations would also be embedded in the reports, whether due to reading the device improperly or incorrectly copying the data. This assumption is supported by the seven cases of negative plume heights when calculated using Eq. (1) and the observed inclinations. Determining which observations were in error was not possible.
As the observer from Keg Tower wrote, “I was able to use it [the inclinometer] on two smokes/fires but they were fairly small and distant so there was not much height difference from my location to the smoke plume height and I found it difficult to hold the inclinometer steady enough for a really accurate reading”.
Another source of systematic error lies in the subjectiveness of plume observations. This is apparent when considering that on average the maximum heights were nearly 4 times higher than the equilibrium, which seems greater than would be expected. While the plume characteristics and reporting techniques were described to the observers, precisely how the observers judged these levels comes into question. A significant source of this uncertainty lies in the fact that what one observer may see as an equilibrium plume height another observer may believe to be a maximum height, especially when one observation is close to the plume and a second observer is distant and unable to see the lower equilibrium level. This was certainly the case for PWF068 in 2012 (Figs. 4, 5) and GBZ002 in 2014.
The perspective or point of view is also a concern. A smoke plume can look very different when viewed from close up or from afar, as was demonstrated by PWF068. The orientation of the plume is also associated with perspective. A plume approaching the viewer at an oblique angle or overhead creates a dilemma about where along the plume to assess the top and would likely result in a higher inclination being reported than for a plume viewed from the side. This might explain the excessive maximum plume heights of LWF191 observed by the May tower.
Finally, the clarity of the observations was also an issue. Observers were discouraged from reporting in hazy conditions or looking into the Sun (as noted by the observer for plume observations 181 to 185), but some observers may have persisted and reported questionable plumes – especially in the distance – or confused smoke plumes with cumulus clouds. Digital photographs were taken of each plume but in many cases the plumes were difficult to distinguish. In the future, photographs may need to be filtered or polarized to help in their clarity and usefulness.
In the case of the six plumes observed by two independent towers, the
observed heights varied considerably with an average difference of about
40
Regardless of the issues presented above, evidence of a relationship emerged
between observed plume heights and the fire behaviour parameters that would
drive such a process. As noted on Table 6, the strongest relationships were
with daily area burned, total fuel consumption, and energy of the fire. This
follows the relationship described by Eq. (3) whereby the weight of the fuel
consumed (
The purpose for collecting these data was to create an extensive data set
composed of ground-based observations of smoke columns and related fire
information to validate a plume-rise model the authors are developing
It is recommended that future studies of this nature use the lessons learned from this study to improve measurement procedures and technology, such as polarized filters for photography. Provincial agencies are also moving towards centralized fire detection using remote cameras in the forest. Accessing such photographic records could provide a more rigorous data set of plume observations. Another approach would be to employ cell phones along with GPS coordinates and calibrated angles of view. Given the ubiquity of cell phones, this would likely allow multiple views of the same fire at more frequent intervals.
Other studies have used remote sensing techniques to measure plume heights.
The Alberta smoke plume observation study data can be found
on the Canadian Wildland Fire Information System datamart
(Natural Resources Canada, 2018) at
A project was conducted to measure smoke plumes from wildland fires in Alberta. This study used handheld inclinometer measurements and photos taken at lookout towers in the province. Observations of 222 plumes were collected from 21 lookout towers over a 6-year period from 2010 to 2015. Observers reported the equilibrium and maximum plume heights based on the plumes' final levelling heights and the maximum lofting heights, respectively.
Observations were tabulated at the end of each year and matched to reported fires. Fire weather conditions and forest fuel types were then obtained from the Canadian Wildland Fire Information System. Assessed fire sizes were adjusted to the appropriate size at plume observation time using elliptical fire-growth projections.
In principle, this seemed a logical method to collect plume observations, yet many unanticipated issues arose as the project developed. Instrument limitations and less-than-optimal observing conditions challenged the observers. This, along with the expected likelihood of reporting errors, limited the quality of the final data. Regardless of the possible errors, this is still a very interesting and valuable data set. The data set showed that responses to fire behaviour conditions were consistent with the physical processes leading to plume rise and will be used in a future plume-rise model validation study.
The purpose for collecting these data was to create an extensive data set composed of ground-based observations of smoke columns and related fire information for the development of a wildfire plume-rise model. Our study indicates that this approach has potential but also that there are significant methodology issues to be overcome. It is our judgement that data from this study must be used judiciously with full knowledge of its shortcomings and should be supplemented with other data when confirming or supporting plume-rise models.
The authors declare that they have no conflict of interest.
We acknowledge Peter Englefield (Natural Resources Canada) for his assistance in collecting and sampling the CWFIS data as well as the outstanding work done by the managers and observers of Alberta Agriculture and Forestry, without whom this data set would not have been possible. Finally, we acknowledge Brian Wiens who helped conceive the idea of this project and who promoted its progress. Edited by: Alexander Kokhanovsky Reviewed by: two anonymous referees