Below are portions of a press release issued March 24 by the U.S. Forest Service’s Southern Research Station.
By Stephanie Siegel, Southern Research Station
March 24, 2022 – Much of what is known about planned fire comes from a burn manager’s memory.
“It takes years to get that kind of experience,” says Joseph O’Brien, fire research ecologist with the USDA Forest Service. “If things are changing, like invasive species or climate, or if you’re a new manager, you need help.”
O’Brien, writing in Fire Ecology with J. Kevin Hiers of Tall Timbers Research Station and others, identified a need for more science-based prescribed fire predictions and models. Fire researchers and managers can use these tools to test scenarios, teach new prescribed fire managers, and identify possible improvements in fire prescriptions and plans.
For predicting fire behavior, the Southern Research Station (SRS) team developed and is testing QUIC-Fire. The real-time modeling tool uses 3D maps of fuels and forest structure and accounts for how chemistry, material science, fluid mechanics, and heat transfer interact to influence fire behavior — yet it can run on a laptop computer. “It’s definitely a revolution in modeling and a quantum leap in fire management,” says O’Brien.
QUIC-Fire was created, evaluated, and improved by access to prescribed fire operations, “where we could measure conditions before, during, and after the burn in detailed and extensive ways,” adds O’Brien.
After ten years in development, QUIC-Fire is getting good results in testing.
“We have been building demonstration landscapes on Oconee National Forest and Piedmont National Wildlife Refuge,” says O’Brien. “We’re going to get feedback from the managers who know those lands best. Managers’ insights will mold the product to meet timber stand management objectives. “For example, a land manager could say, ‘We want to manage underbrush without scorching the pines.’”
The new WIFIRE Lab at the University of California, San Diego has integrated QUIC-Fire as the model behind its new prescribed fire decision support tool BurnPro3D.
QUIC-Fire’s developers organized themselves this year as a modeling hub for advanced forest and fire technology. They teamed up with partners from Tall Timbers, the Forest Service’s Pacific Northwest Research Station, Los Alamos National Laboratory, and the University of Georgia.
Based at the Athens Prescribed Fire Lab, the hub includes seven scientists who previously created the Prescribed Fire Science Consortium. The Consortium brought together various fire managers and scientists annually at a burn site to observe, network, share experiences, and vet ideas.
“Anybody who manages land that is prone to fire has insights that are valuable,” says O’Brien. “Our collaboration with Southern Region fire management gave us the exposure to fire operations that generated the insights we are pursuing. Fire managers have the knowledge we need, and there are gaps they need to fill. There’s respect for each other on both sides.”
“The goal of the modeling hub is to operationalize QUIC-Fire and the framework of required 3D inputs that also serve to revolutionize fire effects assessment and fuels treatment monitoring,” says O’Brien.
The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) has awarded 19 small businesses in 12 states a total of more than $4.4 million in grants to support innovative technology development. One of those grants, for $100,000, is to help build a system for automatically detecting and forecasting the spread of every wildfire in the continental United States and updating the forecasts as conditions change.
Reax Engineering Inc. of Berkeley, California, the company that received the grant, has a beta version of the forecasting tool online now just for the state of California. It is a work in progress and will eventually include data for fires in other states.
Wildfire forecasting is one of the four primary goals of Pyregence, a group of fire-science labs and researchers collaborating about wildland fire, where the forecasting tool now resides. The organization brings together initiatives and leading researchers from 18 institutions representing industry, academia, and government in an effort to transform how wildfire mitigation and adaptation measures are implemented. In addition to forecasting wildfire activity, wildfire scenario analyses will be produced to inform future wildfire risk and California’s 5th Climate Change Assessment, using open science and technology principles.
In order to predict the spread of wildfires, fire behavior models are run on computers. The versions that have been used for decades are not accurate for dealing with heavy dead and down fuels or fires spreading through the crowns of trees under extreme weather conditions. The goal of one of the four Pyregence workgroups, the Fire Behavior Workgroup, is to improve existing models or develop new ones. That effort is being led by Scott Stephens, Professor of Fire Science, and director of the University of California Center for Fire Research and Outreach.
Mark Finney, a researcher at the U.S. Forest Service Missoula Fire Sciences Laboratory, is part of the Fire Behavior Workgroup and will soon have access to a burn chamber much larger than the one in the photo above. It will reportedly be the size of a grain silo. These wind tunnel/combustion chambers are used to conduct burning experiments in a controlled environment under varying fuel, temperature, humidity, and wind conditions. It can lead to a better understanding of how vegetation burns, leading to improvements in predicting fire spread.
An article at Wired describes the planned burn chamber:
Once complete, that chamber will let him replicate wildfire fuel beds by piling logs and other material as much as a few feet deep. He will then ignite them, hit them with wind and moisture, and quantify their burn rate and energy-release rate—what he calls the “heat-engine part of mass fires.”
“Really what we’re looking for,” Finney says, “is how these things transition to flaming. Instead of just smoldering on the forest floor, how do they become actively involved in these large fires?”
If all goes well, Finney’s working group will eventually code three-dimensional digital simulations of various wildland fuel beds—digital cubes, in essence, not unlike Minecraft voxels—that can be stacked and arranged in infinite variation across landscapes generated by GIS mapping data.
That ambient winds influence fire behavior is well known. Less understood is how fire influences the winds and how the feedback affects the fire’s evolution.
The more knowledge firefighters have about the fluid dynamics of wildfires the better equipped they will be to take on the tasks of igniting prescribed fires and suppressing wildfires.
Below is an article written by Rod Linn, who leads development, implementation, testing, and application of computational models of wildfire behavior in the Earth and environmental sciences division at Los Alamos National Laboratory in New Mexico. From Physics Today 72, 11, 70 (2019). https://doi.org/10.1063/PT.3.4350
Fluid dynamics of wildfires
Wildland fires are an unavoidable and essential feature of the natural environment. They’re also increasingly dangerous as communities continue to spread away from urban areas. Unfortunately, a century of wildfire exclusion—the strategy of putting out fires as fast as they start—has led to a significant buildup of fuel in the form of overgrown forests. Continuing to keep wildfires at bay is simply not sustainable. In 2018, nearly 60,000 fires scorched parts of the continental US. California wildfires exemplify what can happen when they burn through communities: In November alone that year fires killed more than 90 people and destroyed some 14,000 homes and businesses.
Decision makers are striving to find ways to manage the consequences of those fires and yet still allow them to thin out dense, fuel-heavy forests and reset ecosystems. Among other things, the goal requires that land managers be able to predict the behavior of wildland fires and their sensitivity to ever-changing conditions. Many factors, including the interactions between fire, surrounding winds, vegetation, and terrain, complicate those predictions.
That ambient winds influence fire behavior is well known. Less understood is how fire influences the winds and how the feedback affects the fire’s evolution. As the fire rages, it releases energy and heats the air. The rising air draws in air below it to fill the gap in much the same way as air is drawn into a fireplace and rises up a chimney. The interaction between rising air and ambient winds controls the rate at which surrounding vegetation heats up and whether it ignites. The interaction thus determines how quickly a fire spreads.
The influence of the fire–atmosphere coupling is much greater in wildland fires than in building fires. Wildland fires are fed by fine fuels—typically grasses, needles, leaves, and twigs; often, tree trunks and large branches do not even burn. Buildings burn thicker fuels, such as boards, furniture, and stacks of books. The difference matters because fine fuels exchange energy more efficiently with surrounding hot air and gases. In those hot, fast-moving gases, the fuels’ temperature rises quickly to the point where they ignite.
But the converse is also true. Because wildland fuels are primarily fine, they are also efficiently cooled when the surrounding ambient air is cooler than they are. That means that the indraft of air caused by a fire may actually impede its spread. A rising plume can draw cool air over foliage and litter near a fire line and prevent those fine fuels from heating. The grasses just outside a campfire ring are a case in point: They are continuously exposed to the fire’s radiant heat, but the cool indraft effectively prevents them from reaching the point of ignition.
The spread of a wildfire is sometimes conceptualized as an advancing wall of flame that the wind forces to lean toward unburned fuels that then ignite in front of the fire. Although that wall-of-flame paradigm simplifies models of fire behavior, it is not correct. Convective cooling would prevent the wall of flame from spreading by radiation alone, and for convective heating to spread the fire, the wind would have to be strong enough to lean the flame to the point where it touches the unburned fuel. Were that true, the fires would be unable to spread in low-wind conditions because the buoyancy-driven updrafts would keep the flames too upright.
If you were to look upon an advancing wildfire from the front, you would actually see a series of strong updrafts, visible as towers of flame that are separated by gaps, as shown in figures 1 and 2. The towers are regions where the buoyancy-driven updrafts carry heat upward. They are fed by ambient wind drawn into the gaps between them, as described earlier. When the ambient wind is strong enough, it pushes air through the gaps between the towers, but that air is heated as it blows over burning vegetation. The motion of hot gases through the fire line disrupts the indraft of cool ambient air and ignites grasses and foliage in front of the fire. That’s the primary way a wildfire spreads.
A second factor that influences the spread is the shape of the fire line, because different parts of the blaze compete for wind. The headfire, the portion moving the fastest, often has trailing flanking fires that form a horseshoe shape and open up to the ambient wind. Part of that wind gets redirected toward the flanks of the horseshoe. The strength, length, and proximity of the flanking fires to each other thus help determine how much wind reaches the headfire. The narrower the horseshoe is, the larger the fraction of wind diverted to the flanks, the lower the wind speed reaching the headfire, and the slower it spreads.
Another factor to be considered is the spatial arrangements of fuels. The potential for wildfires spreading from the crown of one tree to another is reduced when the spacing between trees increases. In that case more horizontal wind is required for flames to jump between trees. Indeed, removing trees is a common fire-risk-management practice. But the strategy behind it is more complex than just removing fuel. Gaps in a forest canopy also make it easier for high-speed winds above the canopy to reach fires on the ground. So although reducing the number of trees might reduce the crown-to-crown fire activity, it might increase the spread rate of a surface fire.
In some regions of the US, land managers counter the threat of wildfires and promote ecosystem sustainability by purposefully lighting fires. Carefully controlled, prescribed burns, which clear duff and deadwood on the forest floor, are often lit at multiple locations; fire-induced indrafts at one location influence fires at other locations. For example, a single line of fire under moderate winds might reach spread rates and intensities that are undesirable or uncontrollable, but the addition of another line of fire upwind can influence how much ambient wind reaches the original fire and thus reduces its intensity.
The spread of the upstream fire line, ignited second, is purposefully limited, as it converges on the area downwind where the first fire has burned off fuel. Practitioners can manipulate the flow of wind between fire lines by adjusting the spacing between ignitions. Fire managers might tie the various ignition lines together—reducing the fresh-air ventilation, increasing the interaction between the lines, and causing fire lines to rapidly pull together—to give themselves more control over the spread.
The interaction between multiple fire lines can even stop a wildfire in its tracks. When firefighters place a new fire line downwind of a fire, they often hope that the indrafts will pull the so-called “counter fire” toward the wildfire and remove fuel in front of it. Unfortunately, the maneuver requires a good understanding of the wildfire’s indraft strength. Too weak an indraft could turn the counter fire into a second wildfire.
After realizing the huge significance of the wind interactions in wildfires over the past two decades, the science community is striving to better account for them. Those efforts should improve predictions of how a wildfire will behave in various conditions. To that end, some researchers, including me, use computer models to explicitly account for the motion of the atmosphere, wildfire processes, and the two-way feedbacks between them. Others perform experiments at scales ranging from meters (such as in wind tunnels) to kilometers (such as in high-intensity fires on rugged topography) for new insight on the nature of those fire–atmosphere interactions or to confirm existing models.
The [above] simulation illustrates the dynamics of wind fields in a vertical plane, located at the white horizontal line, as a wildfire approaches it. The colors mark the speed u of the wind perpendicular to the plane, with red indicating motion toward the viewer (out of the screen), and blue indicating motion away from the viewer. As the clip shows, the fire starts to influence the winds long before it reaches the plane, and the wind patterns change in scale and character as the fire approaches. As the fire crosses the plane, the towers and trough flow patterns become apparent. Some locations show strong upward motion, whereas others have strong horizontal or even slightly downward motion. The colors on the ground surface illustrate the convective cooling (blue) that occurs as a result of the movement of cool air over the fuel— grasses in this simulation—and locations in front of the fire where the fuels are being convectively heated (red).
A modern smartphone has many times the processing power of the computers on the Apollo spacecraft that took astronauts to the moon. Increasingly, wildland firefighters in the field are taking advantage of the smart brilliant devices in their pockets.
An article published in Fire Management Today (in the third quarter of 2015) covers two smart phone applications, or apps. After the user inputs the current weather and environmental conditions they can calculate various parameters and share them via mail or use various archiving options. One of the apps even uploads data to a remote computer server where advanced simulations can be performed which then return forecasts for the next 3 to 12 hours.
Fire Weather Calculator
(These images are screen shots from the app.)
Below, FDFM and PIG, are Fine Dead Fuel Moisture and Probability of Ignition. The app can harvest information from the smart phone and insert it into the fields, including time, date, latitude, longitude, and elevation.
From Fire Management Today:
This application allows the user to input traditional observations (e.g., dry bulb, wet bulb, etc.) and have the application calculate critical information, such as relative humidity and probability of ignition, which both saves time and ensures consistency between weather observers. More importantly, however, is the ability to archive and share these digital observations with other users and managers in real time. This application allows for more streamlined management of weather information, a critical aspect of any fire event. The ability to share observations, particularly if many users are archiving their observations, will lead to a very useful archive of crowd-sourced data that will be used to create value-added products, such as the calculations of 3-dimensional weather fields that could be shared with personnel to increase their situational awareness.
The Topofire Weather app takes the weather calculations to the next level, however it is no longer available. In searching for it we contacted one of the authors of the article, Matt Jolly, a research ecologist at the Missoula Fire Sciences Laboratory, who told us that it has been removed because they “are working on better options for displaying geographic information across all devices, rather than just a few platforms. We are almost ready to release it but development is going slowly right now.”
Topofire Weather looks like it was rather intriguing, as you can see from the description in Fire Management Today:
Similar to the Fire Weather Calculator app described above, this application allows users to enter a suite of fire weather observations that are normally collected on incidents. These observations, as well as the time and location, are sent directly to the TOPOFIRE server, where they are permanently archived and can be made available to users and fire weather forecasters. Weather information entered into the phone can then be used to parameterize the WindNinja simulation model, using either current observations or gridded data from the Real-Time Mesocale Analysis dataset (RTMA).
Users can also request forecasts for the next 3 to 12 hours, using data from the National Digital Forecast Database. Model simulations are then run on the TOPOFIRE server, and outputs are returned to the user’s phone in the form of a keyhole markup language (.KML) file that can be opened on the phone on GoogleEarth.
(The image above is from the article in Fire Management Today. Click on it to see a larger version.)
The Topofire Weather app apparently required access to government computer servers, which may prevent the ordinary user from being able to take advantage of its entire functionality.
We will look forward to the next generation of the app.