Wildfire scientists have unveiled a new rapid risk-assessment tool that shows substantial wildfire danger to homes and buildings in three major Arctic communities, revealing that between 14% and 26% of structures in Anchorage, Fairbanks, and Whitehorse face high or very high risk from approaching flames. The study, led by Jennifer I. Schmidt of the University of Alaska Anchorage together with Robert H. Ziel, Monika P. Calef, Anna Varvak, and Julio C. Postigo, was published in the International Journal of Disaster Risk Science. Rather than relying on complex fire-behavior simulations that demand specialist analysts and expensive software, the approach combines datasets that most local governments already possess: building footprints, zoning maps, and readily available vegetation layers. The result is an updatable, affordable portrait of which buildings face the greatest threat and where mitigation dollars can do the most good.

The urgency behind the work is hard to overstate. Wildfire costs and damages in the United States alone are now estimated at between USD 394 billion and 893 billion annually, and the 2019 to 2020 Australian fire disasters cost nearly USD 100 billion. The Arctic, often imagined as too cold and wet to burn, is not immune: since 1980, wildfires have cost Alaska between USD 2 billion and 5 billion, with projections suggesting costs could rise 68% by the end of the century. Canadian suppression expenses are expected to climb by 60 to 110% relative to the 1980 to 2009 baseline. Meanwhile, structure loss from wildfire has doubled over the past two decades in the western United States, and western Canada has suffered major structure-loss events in four of the last eight fire seasons. Nearly half the global population now lives within the wildland-urban interface, the zone where people and forests meet or intermingle, making tools that translate fire science into actionable local maps increasingly vital.

The researchers’ central premise is conceptually simple but technically consequential: for a structure to be at high risk, a flammable hazard must exist nearby, within roughly 500 meters, and the surrounding landscape must exhibit high wildfire exposure. The method begins with a flammability hazard layer at 30-meter resolution, built from a modified version of NASA’s ABoVE Landcover dataset in which generic evergreen classes were refined to reflect locally flammable species such as black spruce, white spruce, lodgepole pine, and subalpine fir, each assigned hazard ratings from 0 to 100. Critically, the team treated structures themselves as fuel. Each building contributes three fuel units to the surrounding flammability score, a value chosen after systematic testing of one through ten fuel units; three proved optimal, nudging densely built environments up a hazard category without causing wholesale reclassification. Structure density was computed at 90-meter resolution, multiplied by three, and added to the vegetation-based hazard to produce an enhanced flammability hazard layer.

Wildfire exposure, the second pillar of the assessment, captures the potential for hazard to travel. It is calculated as the average flammability hazard within a 100-meter or 500-meter radius, representing the likelihood that fire can reach and burn a given location even if the local fuels themselves are sparse. Exposure values, also ranging from 0 to 100, are classified into five tiers from very low to extreme, and concentrated pockets of flammable vegetation drive scores upward. The third ingredient is structure vulnerability, produced by multiplying the enhanced flammability hazard by a structure importance factor derived from zoning data: residential buildings and critical infrastructure such as hospitals, schools, and nursing homes receive the highest weight, followed by commercial and other structures. Where zoning was unavailable, as on military installations like Joint Base Elmendorf-Richardson in Anchorage and Fort Wainwright and Eielson Air Force Base in Fairbanks, the team collaborated with base personnel or interpreted aerial imagery to classify neighborhoods. The final risk score synthesizes hazard, exposure, and vulnerability into six categories, from extremely low to very high.

The results paint a nuanced picture of Arctic fire danger. Fairbanks showed the greatest proportion of structures in the top two risk categories at 26%, followed by Whitehorse at 22% and Anchorage at 14%. Anchorage, paradoxically, had the highest share of buildings in the top vulnerability tiers, 42%, yet the lowest risk, a consequence of its large urban footprint where dense housing raises vulnerability but fragmented, less-flammable vegetation and abundant developed ground suppress exposure. Fairbanks and Whitehorse, with structures woven into spruce- and pine-dominated boreal landscapes, showed closer alignment between vulnerability and risk. Anchorage was also the only study area where more than 1% of cells jumped two or more flammability categories when buildings were counted as fuel, at 7%, reflecting its 27% share of structure area containing seven or more buildings per cell. The analysis also confirmed the primacy of exposure: when a structure’s wildfire exposure was extreme, at least 87% of such buildings landed in the highest risk category across all three communities.

A key innovation lies in how the team redrew the wildland-urban interface itself. Traditional WUI maps distinguish urban, interface, and intermix zones; the researchers added a fourth category, the transmission zone, marking areas roughly 2.4 kilometers beyond the WUI boundary where fire could move into a community and where fuel breaks might be sited. Within the 500-meter buffer around structures, urban areas were defined by exposure values of 20 or less, interface zones by 21 to 60, and intermix by values above 60, cross-referenced with Alaska and Yukon wildfire management designations that prioritize suppression from critical down to unplanned or wilderness levels. Fairbanks claimed the largest overall WUI footprint, and the transmission zone proved the largest category in every study area, spanning 23,347 square kilometers around Anchorage, 6,180 square kilometers around Fairbanks, and 665 square kilometers around Whitehorse. Across all sites, most structures fell within interface zones, 52 to 61%, followed by intermix at 20 to 35%. Thousands of buildings sit in the high-risk interface and intermix zones where mitigation should concentrate: 15,537 structures in Anchorage, 16,212 in Fairbanks, and 3,734 in Whitehorse.

The method faced its most demanding test against a real disaster. On 17 August 2019, during southcentral Alaska’s hottest and driest summer on record, the likely human-caused McKinley wildfire was driven by high winds across 13.3 square kilometers in three days, destroying 52 residential structures, 84 outbuildings, and three commercial buildings, affecting 1,028 properties and forcing roughly 350 to 400 people to evacuate. By comparing pre-fire aerial imagery from 2017 with post-fire imagery from 2020 at 0.3-meter resolution and linking vanished buildings to the Matanuska-Susitna Borough structure database, the team identified exactly which structures burned. The verdict was statistically compelling: burned structures had significantly higher wildfire exposure than those that survived, and the proportion of burned structures with exposure values above 60 was significantly larger than among unburned ones. Structure risk scores above 60 were also significantly associated with burning. Notably, the test held even under conditions so extreme that normally resistant, low-flammability fuels were igniting.

The study’s design was shaped by sustained community engagement. Through workshops in 2019, 2021, and 2023, along with 15 to 20 meetings organized by Anchorage community councils and landowner associations and public events ranging from wildfire awareness days to Arbor Day, residents and agency staff reshaped everything from color schemes to model parameters. One consequential example: Anchorage residents and fire managers insisted that the maps reflect the spruce beetle outbreak first detected in 2016 and peaking around 2020, which killed and removed many spruce trees. The team responded with rules derived from aerial detection surveys, reclassifying white spruce cells in areas of beetle intensity of 11% or greater to mixed forest, and shifting mixed forest cells to open shrub, while a succession model updated vegetation on areas burned within the last 30 years. This iterative dialogue ensured outputs that both wildfire practitioners and the general public can interpret, a prerequisite the authors see as central to building fire-adapted communities.

The practical implications for planning are direct. Because mitigation funding is often tied to officially designated WUI areas, the ability to rapidly recalculate boundaries from updated building layers expands funding opportunities and keeps pace with community growth. The framework also points to different interventions in different zones: structures in intermix areas, surrounded by vegetation, benefit most from fuel treatments, while those in interface zones, where flammable vegetation is scarcer, are better served by home hardening measures such as fire-resistant roofs, siding, decks, screens, and unburnable buffers. Given that wildfire activity tends to occur where exposure exceeds 60, the authors recommend concentrating resources on the top two risk categories. Where national products like wildfirerisk.org rely on generalized fuel layers that can overestimate unburnable area in WUI zones and are updated too slowly for annually burning landscapes, this approach trades complexity for transparency, using local data that governments already collect. The underlying wildfire exposure toolbox is publicly available on GitHub, and the team argues that empowered residents, equipped with accurate, current, neighborhood-scale information, are crucial to reducing wildfire risk, particularly where communities may prefer action without government intervention.

Long before anyone built a kiln on purpose, the ground itself may have shown people how. A new study argues that a wave of prehistoric wildfires in the southern Levant, more than 9,000 years ago, scorched the region so thoroughly that it left behind naturally baked clay — and that Neolithic communities, watching the aftermath, […]

A University of St. Andrews-led research team has proposed that the red wavelengths of flickering light emitted by early human ancestors’ campfires helped reprogram the human brain to boost social and verbal communication, leading to modern language and storytelling behaviors found only in humans.

The team, which included researchers from Dartmouth College, said the study builds on earlier theories linking fire and language to the extra hours it added to the day.

However, their findings suggest that the biological changes caused by fire use may have enabled complex language as much as fire-associated behavioral changes, accelerating the evolution of verbal communication away from the ape-like systems still used by modern apes toward more complex, “uniquely human” interactions.

Researchers Wondered if Firelight Reprogrammed the Human Brain

Catherine Hobaiter, a Professor of the School of Psychology and Neuroscience at the University of St Andrews and the lead author of a paper detailing the team’s idea, said she has always been fascinated by the origins of language.

“What was the first word?” the professor asked. “The first story?”

Unfortunately, Professor Hobaiter noted, many questions about the origin of humanity’s complex and diverse forms of verbal communication remain unanswered.

Some earlier research has explored a possible link between the first spoken words and the advent of controlled fire use. However, the team notes that most studies have focused primarily on the extra hours of practical light added to the end of each day.

Curious whether other biological mechanisms also contributed to the evolution of language and complex storytelling behavior, Professor Hobaiter and colleagues examined the effects of sitting around a campfire to see whether the emitted light could reprogram the human brain.

Potential Effects of Red Wavelengths and Flickering Lights

First, the team noted that one potential catalyst emitted by firelight that could help reprogram the human brain for complex language is the color of the light wavelengths emitted by campfires. Specifically, Hobaiter and colleagues noted that campfire light emits almost no blue wavelengths but instead mostly in the visible red spectrum.

Many modern studies have focused on the potentially adverse effects of exposure to blue light wavelengths from modern technology on sleep patterns and overall health. Conversely, the paper’s authors described the red wavelengths emitted from fire as ‘uniquely suited’ for human socializing, “allowing an extension to the day without negatively impacting sleep.”

Another component of firelight the study authors suggest could have reprogrammed the human brain for speech is the way a campfire behaves. Unlike a modern, steady light source, the authors noted that firelight “flickers.”

Hobaiter and colleagues said that neurons in the human brain synchronize firing patterns with external flickering lights. For example, flickering lights in movies and TV shows often come with warnings against possible neurological effects on vulnerable viewers. The research team said this mechanism might also help synchronize the behavior of those seated around the campfire, resulting in a much deeper feeling of connection.

Finally, the researchers noted that firelight emits infrared wavelengths, which may have also contributed to helping firelight reprogram the human brain for complex verbal communication.

Are We the Storytelling Ape?

When discussing the potential impact of their proposal, study co-author Nathaniel Dominy, the Charles Hansen Professor of Anthropology at Dartmouth College, said many of the chemical properties have been well researched. However, Professor Dominy also notes that understanding the properties of light that capture our attention and focus, thereby instilling “harmony and cooperation,” has received far less attention.

“Measuring these features is an exciting part of this project,” the study author added.

Professor Hobaiter agreed, while also noting the conundrum scientists face when explaining why humans speak, when “decades of research with other species has shown us that you don’t need language to learn from each other, to organize where and when to forage, to learn cultural knowledge about tools and songs, to co-ordinate hunting, or navigate social politics.”

“While other species share all these skills, there is a fundamental puzzle: we struggle to find evidence of key differences between human and ape communication,” Professor Hobaiter explained. “And yet, we are surrounded by evidence of human distinctiveness. For all their incredible, rich communication and cultures, no other ape is inventing algebra, writing King Lear, or heading out into interstellar space.”

“So, it’s especially exciting to be able to generate testable predictions, to be able to ask if, at the end of the day, we are the storytelling ape,” the professor added.

The paper “Firelight and the Origins of Spoken Language” was published in Proceedings of the Royal Society. 

Christopher Plain has spent the last six years as Associate News Editor and Head Science Reporter at The Debrief. Follow and connect with him on X, learn about his novels at plainfiction.com, or email him at christopher@thedebrief.org.

Have you ever experienced a severe weather event, like a hurricane or a tornado? Researchers have shown that climate change is causing such weather events to become more extreme and more frequent, destroying communities and infrastructure. To determine which areas are most at risk, researchers from Canada studied the environmental effects of a powerful flooding event that occurred in southwestern British Columbia (BC) in November 2021. 

They focused on a type of severe flooding event that is characteristic of the North American Pacific coastal region, called an atmospheric river. An atmospheric river occurs when a long, narrow vapor cloud flows toward land from the Pacific Ocean, causing heavy rainstorms. In November 2021, an atmospheric river in southwestern BC destroyed numerous regional highways, pipelines, power lines, and railways within its path. Over 18,000 people were evacuated due to flooding, and 6 people died. 

The team explained that before the storm, BC experienced a severe drought and an unprecedented heatwave, followed by widespread thunderstorm activity. In 2021, lightning and human activity caused nearly 1,000 wildfires, which led to the third-worst wildfire season ever recorded in BC, partially because trees grow back slowly in forests that have been cut down or harvested. Poor vegetation recovery makes harvested forests more vulnerable to wildfires than old-growth forests. The November atmospheric river occurred shortly after this wildfire season. 

To determine how the atmospheric river impacted southwestern BC, the researchers analyzed approximately 70,000 square kilometers (27,027 square miles) of affected area. They compared satellite images of southwestern BC from Google Earth, Sentinel, and Landsat taken before and after the event to detect changes in river channels and identify geohazards, like landslides and debris flows. They also used satellite images and Canadian government data to identify which forests had been harvested within the study area. The researchers then traversed the area to confirm their remote analyses. 

The researchers used a type of 3D remote sensing, called light detection and ranging, or lidar, to measure how the 2021 atmospheric river redistributed sediment in the Coquihalla and Coldwater River valleys. With the help of lidar, the researchers discovered that both rivers had severely unstable banks due to erosion from the flooding event. Along the Coquihalla River, approximately 53,000 cubic meters (1,871,677 cubic feet) of sediment were eroded, while nearly 50,000 cubic meters (1,765,733 cubic feet) of sediment were eroded along the Coldwater River. 

The researchers also found that hillslope geohazards increased the volume of sediment in both rivers. And in some cases, upstream areas with a history of forest harvesting and wildfires also affected stable banks downstream. For example, they identified debris flows on an unstable hillslope that triggered a landslide on a riverbank downstream. They observed that the river and stream channels shifted due to a sudden addition of sediment from hillslope geohazards and floodwater during the event.

In total, the researchers documented over 1,300 geohazards along 47 kilometers (about 30 miles) of the Coquihalla River and 19 kilometers (about 12 miles) of the Coldwater River and nearby creeks. Nearly half of the geohazards originated in an area with a history of wildfires or forest harvesting. Of the 155 geohazards at the Coquilhalla River, 43% occurred below forest harvesting resource roads, and of the 117 geohazards observed at the Coldwater River, 29% originated in areas with past wildfires.

The researchers suggested that their findings could help scientists predict where severe geohazards will occur during extreme weather events, since both watersheds had geohazards originating in harvested forests. Before 2000, clear-cutting was a widespread practice in BC, and the team’s results demonstrate that unsustainable harvesting practices like this can have lasting environmental impacts.

The researchers concluded that both forest harvesting and wildfires contributed to the severe impacts of the November 2021 atmospheric river, causing catastrophic landslides and debris flows to occur in traditionally dry regions. They suggested that more accurate risk assessments can better equip communities for severe weather events like this. While the next atmospheric river that lands in the Pacific coastal region may be worse than the last, this study can inform scientists and communities about which areas it might affect most.

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