Is there snow on that tree? Citizen Science helps shed light on the impact of snow on summer water supply

Newswise – The snow that falls in the mountains is good for skiing, snowshoeing and great views. The resulting mass of snow will eventually melt, and this water can be used for hydraulic energy, irrigation and drinking water.

Researchers want to predict how much water we will get later in the year based on the snow mass. But in forested areas, trees have an impact on the calculations. When falling snow is caught by trees, it is sometimes never made to the ground, and current patterns make it difficult to predict what will happen.

To improve models and investigate what happens to this captured snow, researchers at the University of Washington created a citizen science project called Snow Spotter. Participants viewed pastime photos of Colorado and Washington and tagged photos taken when the trees had snow on their branches. This information looked at how snow-tree interactions can change between climates and how this can affect summer water supply forecasts.

The group published these findings on May 18 in AGU Water Resources Research.

“We, the skiers or snow enthusiasts, know that the snow in Colorado is really different from Washington. But so far, it hasn’t been an easy way to see how these differences occur in the treetops,” the leader said. Author Cassie Lumbrazo, a UW PhD in civil engineering and environmental engineering. “This project leverages volunteers to get some hard data on these differences. Another benefit is to let our volunteers know how research works and what snow hydrology is all about.”

There are three possible scenarios for the snow caught by the trees. It can fall to the ground as snow, adding to the current mass of snow. It can explode and turn into water vapor, so nothing is added to the snow mass. Or it can melt the snow and drip it to the ground, and depending on the conditions, the total amount of water in the snow mass may or may not be added.

One of the current problems with the mathematical models that describe these processes is that researchers do not know the timing: how many times a year there is snow in the trees, and what happens to it? – and how this time changes in different climates.

But time-lapse cameras can record what’s happening in remote locations by taking pictures every hour, every day, year after year, creating a large set of image data.

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

Using Snow Spotter, a total of 6,700 scientists scanned 13,600 images from parts of the western United States. The group focused on four areas for this research: Mount Hopper, Washington; Niwot Ridge, Colorado; and two different sites at Grand Mesa, Colorado.

“When the project started, I don’t think anyone knew how successful it would be,” said Lumbrazo, who is currently researching in Norway as part of the Valle Scholarship & Scandinavian Exchange Program. “But citizen scientists were processing so fast that we ran out of pictures to classify people. We said this task was really relaxing. Citizen scientists can take these pictures on the Zooniverse app and sit down on the couch and click very quickly.”

Each photo is categorized by nine to 15 different volunteers, and the volunteers agreed on between 95% and 98% of the time. From there, the researchers were able to collect what the snow in the trees was like for each site during the year.

“Our data physically show the difference in snow,” Lumbrazo said. .

The researchers used this data set to evaluate current snow patterns. One limitation, however, is that right now the team only knows when there is snow in the trees. This method does not tell you how much snow there is in the trees, another component needed to make the patterns even better.

“But a limitation that doesn’t exist is the number of citizen scientists willing to process those images,” Lumbrazo said. “We’ve signed up for a lot of volunteer hours for students, and they have great discussions about some of the images and it turns out to be a scientific conversation.”

In addition, the data set generated by these volunteers could be used in the future to train an algorithm to learn the image classification machine, the team said.

Researchers are working to expand their image data set to include photos from around the world so that they can continue to learn how different climate and precipitation patterns affect the snow set, which will help make the patterns more accurate.

Additional co-authors are Andrew Bennett and William “Ryan” Currier, both of whom completed this research as UW PhD students in civil and environmental engineering; and Bart Nijssen and Jessica Lundquist, both professors of civil and environmental engineering at UW. Snow Spotter was created by Max Mozer, and he started this project as a UW undergraduate student studying civil engineering and the environment. This research was funded by the National Science Foundation and the Steve and Sylvia Burges Endowed Presidential Fellowship.


Grant number: CBET-1703663

Video / Photos / B-roll / PDF PDF available:

Name Pronunciation guide: Cassie Lumbrazo – Kass-EE Lum-BRAY-zo (click on the link to hear the correct pronunciation)


Leave a Comment