Wildfire Prediction Sees Improvements In Modeling

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house in need of wildfire prediction

A single battle with a large wildfire can easily burn $1 million a day in firefighting costs alone. National and state costs combined have exploded to nearly $3.5 billion annually, and firefighting costs are not the end of the story. In an environment of global heating and prevailing droughts, firefighting costs are just the beginning of a nightmarish reality. From homeowners to housing developers, forest fighters to forestry management teams, and from state to federal government agencies, everyone stands anxiously in need of improved wildfire prediction. Fortunately, improvements in modeling wildfire behavior are now being made by a dedicated team at the University of Alabama Huntsville.

Current Wildfire Prediction Is Poor, While Fire Incidents Are Rising

The total number of acres burned annually in the U.S. is rising due to increasing effects of climate change. The average size of each fire is continuing to increase, and with this comes an increase in the number of U.S. Forest Service firefighters on call. The costs to battle this increase in wildfires are rising higher and higher, and exacting a terrible toll on human life and property, as well as wildlife and wilderness terrain. And, unfortunately, all of this is coming at a time when answers to global warming are slow and confidence in current reliability of wildfire prediction is low.

“If a fire began in the forest, where would the perimeter be in two hours, four hours or six hours?” asks Dr. Shankar Mahalingam, dean of the UAH College of Engineering, and professor of Mechanical and Aerospace Engineering. He continues, “That currently is about the range of prediction ability that we have with operational fire behavior models, for low intensity fires. If we can better understand scientifically how wildland fires behave, we’ll have a better chance to accurately predict the spatial and temporal evolution of high intensity wildfires.”

Envisioning Wildfire Forecasting As Accurate As Weather Forecasting

Dr. Mahalingam is studying how wildfire propagates. He foresees a future of accurate wildfire prediction through physically based computational models. He and his team’s wildfire modeling and prediction efforts were recently published by the University of Alabama Huntsville. Dr. Mahalingam says he believes the day will come when wildfire behavior will be forecasted just as accurately as computer models now forecast tomorrow’s weather.

“My vision is that, just like you have fairly reasonable weather predictions today for what is going to happen tomorrow that have evolved to be very accurate compared to where they started out in the 1940s and ’50s, we can have that with fires,” Dr. Mahalingam says. “We look at the weather forecast every day to tell us how to prepare for tomorrow, and that is because we can predict the weather with a large degree of confidence.”

UAH Fire Modeling Team (credit: Michael Mercier, UAH)

Dr. Mahalingam says the “pure experience” of firefighters and forest managers is guiding current wildfire prediction efforts. He and his collaborator, UAH Mechanical and Aerospace Engineering faculty member Dr. Babak Shotorban, are currently supervising a team of four doctoral students in the MAE department. With funding from the U.S. Department of Agriculture’s U.S. Forest Service Division, they are performing desperately needed forest fire combustion research. They aim to upgrade wildfire prediction from the experiential realm to scientific, mathematical models and longer-range computational forecasts.

Using Computers To Model The Fluid Dynamics Of Fire

“In a computer model we are using very small volumes of space, on the order of one cubic millimeter on one end to a cubic meter on the other end” Dr. Mahalingam says. “We model these on a grid as a region of space. Fire is a process in which the energy release will drive the airflow around it and the resulting fluid dynamics will in turn drive the fire.” A necessary precondition to sustaining fire on the leading edge is continual warming. This heat releases the chemicals in the fuels that are needed for combustion.


“I was studying marginal burning behavior, which I call a fire transition phenomena,” Dr. Mahalingam says. “Fire is losing heat through radiative and convective heat transfer and it is gaining heat as energy is produced as a result of combustion, so it is an energy balance problem.”

While at the University of California Riverside, Dr. Mahalingam studied marginal burning in close association with the U.S. Forest Service. “When they go out and do these prescribed fires, sometimes on day one the fuels don’t ignite easily and spread, but they can come back there on day two and it will light and spread,” he says. “This situation is termed marginal burning. I began to study why the prescribed fire spreads. Under what conditions does it spread and when does it not spread?”

The Most Sensitive Variables For Prediction of Wildfire Spread Are Wind And Moisture

Wildfire fuel is provided mostly by undergrowth, so Dr. Mahalingam’s research focused on three prevalent species of shrub or small tree in southern California. He studied the chamise bush; the manzanita, which can grow as a bush or small tree; and the scrub oak, a small tree. Each fuel type was modeled through an annual seasonal cycle, seeking marginal burning impacts. The team experimented with fuel type and moisture content, wind, relative humidity, and ground slope as impacting variables.

“We found that one of the most sensitive elements that is required for fire to spread is wind and the other is moisture,” Dr. Mahalingam says. Rising temperatures in California occur with the seasonal Santa Anna winds. This flammable environment combines to both dry the fuel, facilitating ignition, and fan the flames to sustain combustion.


In 2010, Dr. Mahalingam came to the University of Alabama in Huntsville, continuing his work in collaboration with Dr. Shotorban. Even though U.S. wildfires are associated mostly with the western region, in 2013 Alabama experienced 1,284 wildfires, including some large springtime blazes. Almost 26 thousand acres, or roughly 40 square miles of land were burned in one year alone. There has been a similar significant increase of wildfires in every region of the country.

Comparing Controlled Burnings With Computer Model Wildfire Predictions

The UAH scientists are specifically studying the interaction of fires in shrubs near each other. This interaction can result in energy hot spots in a blazing wildfire. They are determining how proximity and wind can influence shrub combustion factors. Shrubs burned in controlled settings are then being compared to computer-modeled shrub fires to assess the quality of the wildfire prediction.

“We light the shrub, then figure out how much time it takes to burn out,” Dr. Mahalingam says. “We calculate how much mass is consumed so that we can then replicate that in our model. We are also interested in the details of the fire, like the vortices created.”


Working To Predict Rates And Areas Of Fire Spread Days In Advance

The quest for reliable, scientific, computerized wildfire prediction is driving Dr. Mahalingam. “My hope is that the time period of fire prediction can be extended to several day and nighttime cycles ahead,” he says. “You have to include the nighttime cycles separately because they present a very different set of atmospheric circumstances for the fire. I want us to be able to predict fire behavior with a high degree of confidence.”

The researchers believe when we understand better the scientific factors behind fire behavior, we’ll have a better chance for accurate wildfire prediction. Several of the keys are assessing fuel distribution, combustion factors and intense energy production regions. When these assessments can be made, Dr. Mahalingam’s team believes the rate and area of a fire’s spread can be predicted days in advance.

Associated Technological Advancements May Be Anticipated

Whole new markets could emerge from technological progress in wildfire prediction. Many new improvements may be anticipated, from new tools to assist the firefighters, to advanced logistical services planning for staging crews and equipment, and in forest management’s ability to assess potential threats, as well as governmental agencies’ budgetary planning for firefighting costs.

Even developers of residential subdivisions may anticipate accumulated wildfire prediction data influencing their future designs. Lending institutions and property insurers will certainly take interest in this data, as well. But, with families and properties sitting on the burning edge of real and terrible danger, it is future homeowners and buyers who will likely be the ones benefiting the earliest and the most from improved wildfire prediction. For all of us, this improvement can not come soon enough, and will be very welcome when it arrives.


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