Whether you love it or hate it, snow — or more accurately, snowpack — is critical to the survival of about a sixth of the world’s population. That includes Idaho, a highly arid state dependent on an extensive canal system for the irrigation of crops.
But determining how much water is in the snowpack is much more complicated than sticking a pole into a snowdrift to measure its depth.
Lejo Flores, an associate professor of geosciences, has been awarded a three-year, $295,577 grant from NASA’s highly competitive Research Opportunities in Space and Earth Sciences (ROSES) program to investigate the use of remote sensing to better predict annual runoff.
ROSES Terrestrial Hydrology investigations are designed to do three things:
- Apply available remote sensing assets to better describe the snowpack environment.
- Provide NASA with future approaches for snow remote sensing, which may include use of models and data assimilation.
- Improve existing approaches to observe hydrological variables, in particular surface and groundwater, root-zone soil moisture, and evapotranspiration.
“Snow is complex,” Flores said. “It melts, refreezes, and snowflakes have different grain sizes and orientations, and so on. And the structure of snowpacks are changing due to climate warming.”
In the past, scientists have had to hike, ski and snowshoe out to snowpacks to do physical depth and weight measuring (which is not always possible due to the dangers inherent in shifting snow or the physical difficulties of traveling in deep snow), or rely on satellite and airborne remote sensing methods using microwave, LiDAR, gravimetric, or other methods that don’t take into account the nature of the snow.
“NASA satellites use the electromagnetic spectrum, and it’s easy to pick out points on the spectrum and take snapshots of the Earth to identify things like greenness or snow cover, etc.,” he said. “But often these missions focus on just one or two wavelengths, or points on the spectrum, and even when multiple wavelengths are used they often are close together.”
Flores insists that snow is sufficiently complex that we need more points along different parts of the spectrum.
“In particular, we are looking at four microwave wavelengths of the spectrum and focusing on active radar remote sensing techniques. Think of using radar as having a flash on a camera — the flash serves to illuminate what’s being photographed. We’re doing this ‘microwave flash photography’ at four wavelengths that can penetrate to different depths of deep snowpacks,” he said. “The cumulative data from these four wavelengths will give us a better idea of the structure of the snowpack. Is it melting? How deep is it? Is it wet or dry? Heavy or light? And most importantly, how much water does it hold?”
This project will use modeling to determine the best technology to answer these questions, with data obtained from the Fraser Experimental Forest in Colorado using both ground-based and airborne observations.
Flores is particularly interested in how future snow remote sensing missions could be designed to capitalize on existing satellite assets to improve retrieval of snow information.
“To the extent possible, we’d like to show NASA and the snow remote sensing community that there are a number of satellites already in orbit that can be supplemented so that together we have a novel platform to measure snow,” he said.
Data collected will be made available in a public archive.