In February 2017, AWAE Research Hydrologist Kelly Elder will head to Grand Mesa, Colorado to lead a three-week ground campaign for a new project called SnowEx. This NASA-sponsored project will test a variety of sensors and techniques used to collect and improve airborne and ground-based measurements to determine the snow-water equivalent (SWE), or the amount of water held in snow, over different terrains.
This research is significant because much of the worlds’, including the western U.S.’s water supply is derived from snow in mountain environments. Better information on SWE can improve hazard forecasting, water availability predictions, and agricultural forecasting, among other things.
SnowEx is a multi-year airborne snow campaign that involves more than 100 scientists from universities and agencies across the U.S., Europe, and Canada. The overarching question that SnowEx will address is: How much water is stored in Earth’s terrestrial snow-covered regions? SnowEx is a little different than the typical science-only field campaign—and that’s part of what makes it exciting!
The team will investigate the distribution of snow-water equivalent (SWE) and the snow energy balance in different canopy types and densities and terrain. They will use a unique combination of sensors, including LiDAR, active and passive microwave, an imaging spectrometer and infrared sensors to determine the sensitivity and accuracy of different remote sensing techniques for measurement of SWE. Ground-based instruments, snow field measurements and modeling will all also be required to help address the science questions.
A large fraction of snow-covered lands are forested; however, most remote sensing techniques have found forested areas challenging. Recent developments, like LiDAR, have opened up new possibilities. And even passive microwave has shown to have more promise in forested areas than previously thought.
Since the SnowEx research community wants to fully understand the various techniques, focusing on the challenges presented by forests is the perfect opportunity to collect a unique dataset that will help address the science questions and enable snow mission design trade studies, which would otherwise be hard to justify.