Calculating how much water is held in snowpack is vital for water resource management. Many regions, including much of the U.S. West, rely on melting snow—replenished by winter storms—for freshwater supplies, and thawing snowpack can contribute to extreme flooding events downstream. As Earth’s climate changes, when, where, and to what extent snowpack occurs are changing as well, and researchers need reliable tools to understand the impacts of these shifts and to plan accordingly.
Current methods for calculating snow water equivalent (SWE) come with inherent limitations. In a new study, Cho et al. compare three of these methods to a 40-year record of SWE observations taken from aircraft over the continental United States by the National Oceanic and Atmospheric Administration (NOAA). The NOAA observation method is based on measurements of natural gamma radiation emitted from soils and is very accurate in calculating the water content of snowpack, but its spatial coverage is limited to areas beneath flight paths. However, the method’s accuracy makes it valuable for checking other observational and modeling tools with better coverage.
The three observation-based methods the researchers studied are known as Special Sensor Microwave Imager and Sounder (SSMI/S) SWE, GlobSnow-2 SWE, and University of Arizona (UA) SWE. SSMI/S relies exclusively on measurements collected by passive microwave imagers aboard satellites operated by the Defense Meteorological Satellite Program and is known to have difficulty estimating SWE in excess of 200 millimeters and in regions with dense vegetation or wet snow. The GlobSnow-2 SWE relies on the same microwave imager data but combines them with ground-based observations of snowpack to obtain final SWE estimates. The UA SWE tool, which boasts the best resolution (4-kilometer) of the three methods, calculates SWE by assimilating station-based snow depth and SWE observations from thousands of sites in the Snow Telemetry and National Weather Service’s Cooperative Observer Program networks with modeled precipitation and temperature data.
The team found that overall, the UA SWE tool outperformed SSMI/S SWE and GlobSnow-2 SWE in most closely matching NOAA’s gamma radiation observations. Most of the gains in accuracy came from the UA tool’s ability to estimate SWE more accurately in heavily vegetated and/or mountainous regions like evergreen needleleaf forests, grasslands, tundra, and taiga environments, where the two other products greatly underestimated SWE. The SSMI/S and GlobSnow-2 SWE tools performed similarly overall, but GlobSnow-2 SWE performed better in mixed forest, deciduous broadleaf forest, warm forest, and maritime environments. (Water Resources Research, https://doi.org/10.1029/2019WR025813, 2020)
—David Shultz, Science Writer