As climate change nudges weather in the eastern Cascades in extreme and volatile directions, forest managers in the region have a lot to juggle. Hotter, drier summers are contributing to bigger and more frequent wildfires. Meanwhile, warmer winters may cause the Cascades to lose 50% of its annual snowpack over the next 70 years. Mountain snow supplies the Yakima River Basin with 75% of its water supply, making it a crucial reservoir for both nature and agriculture . Less winter snow also leads to drier and more fire-prone forests in the summer.

To encourage fire resilience, forest managers use tried-and-true tools like controlled burning and the selective felling of trees to thin out the forest. Both methods remove fuel and help return forests to historical conditions 鈥� but less is known about their impact on snowpack.

To address this knowledge gap, a team of researchers at the 天美影视传媒 and The Nature Conservancy (TNC) embarked on an ambitious, multiyear study of snowpack along Cle Elum Ridge, an area of the eastern Cascades in the headwaters of the Yakima River Basin. The group experimentally thinned the forest to varying degrees in a roughly 150-acre area. Then, they measured the amount and duration of snowpack during the winter of 2023 and compared it to a previous winter before the forest treatment.听

The results were encouraging: Forest thinning efforts increased snowpack by 30% on north-facing slopes and by 16% on south-facing slopes. Thinning aided snowpack the most where it created a patchwork of gaps in the forest rather than a more even density; gaps of 4-16 meters in diameter seemed to be the 鈥渟weet spot鈥� for snow.听

The research points toward more refined forest management practices that can optimize for both wildfire resilience and snowpack.

in Frontiers in Forest and Global Change.

鈥淎t its core, this research shows that reducing wildfire risk and protecting water resources don鈥檛 have to be competing goals,鈥� said lead author , a postdoctoral researcher at the University of Alaska who completed this work as a UW doctoral student of civil and environmental engineering. 鈥淭hat鈥檚 genuinely good news for a place facing both growing wildfire threats and increasing water vulnerability. So much of the climate conversation focuses on loss, which makes findings like this especially meaningful.鈥�

Predicting snowpack in forested areas, especially those at higher altitudes, hinges on understanding how much snow reaches the ground and how much lands in the forest canopy. Snow on the ground is more likely to stick around through the season, whereas snow in the trees may either melt or sublimate back into water vapor. In either case, it wouldn鈥檛 add to the reservoir of water that melts in the spring and summer.听聽

鈥淭rees intercept snow and so can reduce snowpack, but trees also shade snow and so can retain snowpack,鈥� said senior author , a UW professor of civil and environmental engineering. 鈥淭he dominant effect depends on winter temperatures, and the Cascade crest near Cle Elum is right on the border where the effect flips from trees decreasing snow to trees saving snow.鈥澛�

found that natural gaps in the forests of the eastern Cascades accumulated more snow. This, combined with other research, gave the team reason to hope for a positive connection between forest thinning and snowpack, though it wasn鈥檛 a sure thing. have found that open areas elsewhere in the Western U.S. saw reduced snowpack.

Thus, it was time for a direct 鈥� and complex 鈥� study of managed forests.

Researchers picked Cle Elum Ridge for the work, where TNC鈥檚 forest managers were planning thinning treatments to improve forest health and wildfire resiliency. The orientation of the ridge allowed them to compare north- and south-facing slopes 鈥� southern slopes in the region see more sunshine and less snow retention on average. From October 2021 to September 2022, the researchers worked with TNC鈥檚 forest managers and local contract loggers to remove trees on both slopes in a gradient, from no thinning to extensive. The team also set up time-lapse cameras at several strategic points to measure snow depth over time.

Then, they waited for snow to fall.

By March 2023, the area was close to its peak snowpack, and the team returned with staff and equipment from the UW (RAPID). The RAPID crew flew a specialized drone that generated a detailed 3D map of the study area using a laser-mapping technology called lidar.听

By comparing the new 3D map and timelapse imagery to lidar data captured before the forest treatment, the team was finally ready to calculate two things: the change to the forest structure, and its effect on the snowpack.

Across the whole study area, the team found that thinning helped the forest recover 12.3 acre-feet (or about four million gallons) of water in the form of snow per 100 acres on north-facing slopes, and 5.1 acre-feet (or about 1.5 million gallons) per 100 acres on south-facing slopes.听

As expected, areas where the thinning opened gaps in the canopy were most effective at restoring snow storage that had been previously lost to environmental degradation and climate change. Gaps of 4-16 meters in diameter seemed to retain the most snow, though there were few gaps larger than 16 meters to evaluate.

One surprising result: The way forest managers thin forests doesn鈥檛 reliably create gaps. Forest managers map out their reductions using the density of trunks in an area, not canopies, as their primary measurement.

鈥淚magine a group of 100 people all holding umbrellas in the rain,鈥� said co-author , director of the UW Climate Impacts Group. 鈥淭hey鈥檙e standing close enough together that their umbrellas overlap, so none of the rain hits the ground. If you remove 10 of the umbrellas randomly, you鈥檇 still have plenty of coverage overall. But, if you remove 10 umbrellas that are right next to one another, you create a gap in the umbrella 鈥榗anopy,鈥� and you get a 10% increase in the amount of rain that hits the ground.鈥�

That realization adds a nuance to the findings. It鈥檚 likely that forest thinning can benefit both wildfire and snowpack resilience at the same time, but only if managers keep canopy gaps in mind.听

鈥淥ne thing we all learned was that snow people and tree people speak different languages,鈥� Lumbrazo said. 鈥淒ifferent experts look at totally different variables to help them decide whether or not to cut down a single tree. So an important goal is to get everyone speaking the same language. And I think this paper is one step towards better communication.鈥�

Overall, the results suggest practical changes to forest management practices in the eastern Cascades. For example, managers might consider more tree-thinning on north-facing slopes, since snowpack gains may be greater there. With further research, these learnings may also extend to other regions in the Pacific Northwest.听

The work could also aid collaboration between forest managers and hydrologists at a time when the region needs all the water it can get.

鈥淎s we lose snowpack, everything becomes really squeezed,鈥� said co-author , a senior aquatic ecologist at TNC who earned her doctorate in aquatic and fishery sciences at the UW. 鈥淲e are currently in our third consecutive year of water restrictions in the Yakima River Basin, and are staring down one of the lowest snow years on record. However, our research shows that the treatments currently used for restoring fire resilient forests are compatible with the forest structure needed for supporting water security. And in a world where climate change is reducing water supplies and increasing wildfire severity, we are pleased to report that the same forest treatments can support both goals.鈥�

Co-authors include , a former UW graduate student of civil and environmental engineering; , a former UW undergraduate student of atmospheric and climate science; , a data processing specialist at the UW RAPID facility; and , director of Forest Conservation and Management at The Nature Conservancy.

This research was funded by The Washington Department of Natural Resources, The Nature Conservancy and the National Science Foundation.听

For more information, contact Lundquist at jdlund@uw.edu, Dickerson-Lange at dickers@uw.edu or Howe at emily.howe@tnc.org.听

The snow that falls in the mountains is good for more than just skiing, snowshoeing and breathtaking vistas. The snowpack it creates will eventually melt, and that water can be used for hydropower, irrigation and drinking water.

Researchers want to predict how much water we will get later in the year based on the snowpack. But in forested regions, the trees impact the calculations. When falling snow is intercepted by trees, it sometimes never makes its way to the ground, and the current models struggle to predict what will happen.

To improve the models and investigate what happens to this intercepted snow, 天美影视传媒 researchers created a citizen science project called . Participants viewed time-lapse photos from Colorado and Washington and labeled photos taken when trees had snow in their branches. This information provided the first glimpse of how snow-tree interactions could vary between climates and how that could affect predictions of summer water supplies.

The team May 18 in AGU Water Resources Research.

“We, as skiers or snow enthusiasts, know that the snow in Colorado compared to Washington is really different. But, until now, there hasn’t been an easy way to observe how these differences play out in the tree canopy,” said lead author , a UW doctoral student studying civil and environmental engineering. “This project leverages volunteers to get some hard data on those differences. Another benefit is that it introduces our volunteers to how research works and what snow hydrology is.”

There are three possible scenarios for snow that’s been caught by trees. It could fall to the ground as snow, adding to the current snowpack. It could be blown away and turn to water vapor, therefore not adding anything to the snowpack. Or the snow could melt and drip to the ground, which, depending on the conditions, may or may not add to the total amount of water in the snowpack.

One current issue with the mathematical models that describe these processes is that researchers don’t know the timing 鈥� over the course of a year, how often is there snow in the trees, and what happens to it? 鈥� and how this timing varies in different climates.

But time-lapse cameras can record what’s happening in remote locations by taking photos every hour, every day for years, creating a huge dataset of images.

That’s where the citizen scientists come in. Snow Spotter shows volunteers a photo, with the question: “Is there snow in the tree branches?” Volunteers then select “yes,” “no,” “unsure” or “it’s dark” before moving on to the next photo.

Using Snow Spotter, a total of 6,700 citizen scientists scanned 13,600 images from a number of sites across the western United States. The team focused on four sites for this study: Mount Hopper, Washington; Niwot Ridge, Colorado; and two different sites in Grand Mesa, Colorado.

“When the project started, I don’t think anybody really knew how successful it was going to be,” said Lumbrazo, who is currently doing research in Norway as part of the . “But citizen scientists were processing it so fast that we kept running out of images for people to classify. We’ve received feedback that this task is really relaxing. Citizen scientists can pull up these photos in the Zooniverse app and they can just sit on the couch and click through really fast.”

Each photo had between nine and 15 different volunteers classify it, and the volunteers agreed between 95% and 98% of the time. From there, the researchers could piece together what snow in the trees looked like over the course of the year for each site.

“Our data physically shows the difference in the snow,” Lumbrazo said. “You can see how the snow in Washington just becomes cemented in the canopy and never leaves, which is how it feels when you ski that snow. As opposed to the snow in Colorado where you get frequent snowfall, but it’s blowing away. It’s dry and dusty.”

The researchers used this dataset to evaluate current snow models. One limitation, however, is that right now the team only knows when snow is present in the trees. This method doesn’t say how much snow is in the trees, another component needed to make the models even better.

“But a limitation that does not exist is the number of citizen scientists who are willing to process these images,” Lumbrazo said. “We’ve signed off on countless volunteer hours for students, and they even end up having some great discussions about certain images and it becomes more of a scientific conversation.”

In addition, the dataset generated by these volunteers could be used to train a machine learning algorithm to classify images in the future, the team said.

The researchers are working to expand their image dataset to include photos from around the world so that they can continue learning about how different climates and precipitation patterns affect the snowpack, which will also help make the models more accurate.

Additional co-authors are and , both of whom completed this research as UW civil and environmental engineering doctoral students; and and , both UW professors of civil and environmental engineering. Snow Spotter was created by , who started this project as a UW undergraduate student studying civil and environmental engineering. This research was funded by the National Science Foundation and a Steve and Sylvia Burges Endowed Presidential Fellowship.

For more information, contact Lumbrazo, who is currently on Central European Time, at lumbraca@uw.edu.

Grant number: CBET-1703663