A University of Saskatchewan graduate student’s work with sound waves in snow may improve forecasts of floods and runoff for farmers’ use.
Previous measurements of a snowpack’s properties have involved shoveling, which wrecks the snowpack’s structure and layers, prevents measurement at the same site more than once, and also can’t be used to track changes within a snowpack over time.
Hydrology grad student Nicholas Kinar, however, has developed a “non-invasive” method of measuring snow water equivalent (SWE), using a loudspeaker to send sound waves into the snowpack, reflecting sound back to a microphone assembly from which data can be processed.
“Since much of our water originates from snow, being able to more accurately predict water availability will help prepare for future scenarios, particularly in regions where snowmelt contributions dominate inputs to rivers and streams,” Kinar said in a U of S release.
“Being able to measure snow properties as the snowpack evolves and changes over the winter season will allow for models to more adequately predict future weather and climate,” said Kinar, studying on a three-year Alexander Graham Bell Canada Graduate Scholarship from the National Sciences and Engineering Research Council.
While working on his master’s degree, Kinar previously developed a handheld “snow sonar” device to measure the depth-integrated snow density. That device attracted interest from NASA in the U.S., for use in confirming satellite measurements.
“Ultimately, Kinar’s research could save time and money, limit the disruption of snowpacks, and provide continual monitoring,” the U of S said.
“Being able to directly measure snowpack parameters without disturbing the snowpack, and being able to make multiple measurements over the winter season would greatly increase the ability for these models to predict future climate and weather patterns,” said Kinar.
“This implies better environmental predictions for our agricultural producers.”