,A firefighter passes a burning home as the Dixie Fire flares in Plumas County, California on Saturday, July 24, 2021. As this year’s fire season flares in the western US, Calkin is charging ahead with his research agenda, hoping to produce insights on everything from where planes should drop fire retardant to how hotshot crews – elite firefighting units – spread out during a blaze. — AP
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LOS ANGELES: Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California’s Klamath National Forest, overrun a firebreak and raced towards his hometown, he got a frustrating email.
It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze.
“They had spent so many resources building that useless break,” said Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe.
“The index showed it had no chance,” he told the Thomson Reuters Foundation in a phone interview.
The Suppression Difficulty Index (SDI) is one of a number of analytical tools Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data and forecasting to the world of firefighting.
As climate change and gaps in forest management create more intense and deadly wildfire seasons, firefighting resources are increasingly stretched to the limit.
Researchers like Dunn hope their tools can help ease that pressure by making sure scarce fire resources are deployed as efficiently as possible.
Dunn said so far firefighters at half of national forests are using one popular analytical tool he helped develop called Potential Operational Delineations (PODs).
It combines local firefighter know-how with advanced spatial analytics to help teams plan where to take on a fire even before it breaks out.
The tool superimposes a number of statistical models – such as the SDI – over a map of a region so fire managers and communities can plan out their control lines and plans of attack in advance.
“You will never take the personal element out of fighting fires,” said Brad Pietruszka, a fire manager at the 1.8-million-acre (728,000-hectare) San Juan National Forest who has been using advanced analytical tools like PODs since 2017.
“But people make bad decisions under stress – they can’t crunch all this data on their own. This is about reducing the uncertainty, and helping firefighters make better decisions.”
For decades, firefighters have relied on analytics to predict the possible behavior of fires, pulling on a range of data from weather patterns to satellite footage of potential fire fuels and historical fire behaviour.