GPS receivers installed to measure the movements of tectonic plates and help you find tacos could find yet another purpose: measuring snow depth.

By analyzing the way the GPS signals are transformed as they travel through snowpack, a team of scientists from the University of Colorado, Boulder, may have found a cheap, easy way to optimize an important variable in climate models.

"It'll be hard, but it looks pretty good so far," said Kristine Larson, a GPS specialist at CU, and lead author of a paper on the technique published earlier this month in Geophysical Research Letters.

Her team, basically, is using waste data from the global positioning process. When a wave leaves a GPS satellite heading for a receiver, not all of it reaches the antenna directly. Some of it bounces off the ground around the actual antenna. For standard GPS purposes, people try to suppress this "multipath" signal, but Larson realized it could yield valuable information about the surfaces it's hitting. A few years ago, they trialed measuring soil moisture data based on the reflectivity of the ground. Snow, theoretically, should reduce the frequency of the multipath wave.

"It's one thing to say that you used a signal from a GPS satellite to estimate the depth of the snow, but unless I go out there with a yardstick and say this is 18 inches of snow, you don't know," Larson said.

So, after securing a seed grant and setting up the experiment, she went out there with a yardstick, husband and 10-year old son in tow. They drove out on Highway 36, past the Costco, and took measurements of snow depth and density. Though it might not have been the most glamorous fieldwork, it revealed two very important problems with existing measurement systems like the National Weather and Climate Center's SNOTEL sensor network.

First, point measurements — no matter how accurate — do not account well for snow-depth variability due to wind or other hyperlocal conditions.

"The snowpack can be 12 inches here and you walk one foot and it can be 18 inches there," Larson said. "That's your signal plus or minus 50 percent."

GPS can measure a wider area than any current ground-based method, allowing it to account for that variability. Theoretically, one could measure snow depth from space and get very broad area coverage. Unfortunately, no current satellite can do it. And any space-based system would be vastly more expensive and have a relatively limited life-span, maybe five years.

Second, climate modelers aren't interested in snow depth in the way that a skier might be. Really, what they're after is how much water is locked up in the snow. To calculate that, you need both the depth and the density of the pack. Right now, the best way to get snow density remains low-tech.

"We literally took plastic piping that you'd use in your house and you put that into the snow, then cover up the bottom, then you pull it up," Larsen said. "Then you wait for it to melt."

A little bit of high school math later, and you've got a density measurement for the snow in the PVC pipe. By matching up GPS signals with those density measurements, you can calibrate a model that yields snow density from GPS information alone. With the depth and density in hand, you can calculate the snow-water equivalent data that climate modelers covet.

Someday soon, the hundreds of GPS receivers scattered around the world's wintery wonderlands could be sending out snow data. It's about as close to getting something for nothing as a scientist could hope for.

"You've got to convince people that are measuring what you say you're measuring. You have to have people with sticks. You have to have ultrasonic snow-depth sensors," she said. "I'm not there yet, but it's a good first step."

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