The importance of snow to water resources in U.S. cannot be overstated. In the western U.S., 85% of freshwater runoff originates as snowmelt. In the north-central and northeast U.S., most significant floods of the 20th century were directly related to snowmelt. Thus, accurate, timely estimates of the snowpack are required to help monitor and manage seasonal snow and melt.
The airborne gamma snow survey operated by the National Oceanic and Atmospheric Administration’s (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC) is designed to help hydrologists and flood forecasters in the National Weather Service (NWS) offices, regional river forecasting centers, and other U.S. and Canadian federal agencies improve operational flood predictions and water supply outlooks. Since 1979, the snow survey has collected areal mean snow water equivalent (SWE) data over a network of 2,400 flight lines covering 25 states and seven Canadian provinces (Figure 1).
The airborne gamma technique uses the attenuation of the gamma-ray signal by water in the snowpack (any phase). Because the degree of attenuation has a linear relationship with the amount of water stored in the snow, the SWE value can be calculated based on the difference between gamma radiation measurements over bare ground and snow-covered ground (Figure 2). The accuracy of the airborne gamma SWE observations provided the impetus to have NOAA operate continually the gamma snow program. Also, the gamma SWE data has been successfully used for operational flood forecasting during the last 40 years. Currently, the airborne gamma SWE observations are being used to produce the near-real-time, high spatial resolution national snow product, named Snow Data Assimilation System (SNODAS).
Because the gamma snow survey has a long-term and very accurate SWE record, this has substantial potential to evaluate other existing and new SWE datasets from satellites and modeling techniques. In my recent study, three of the long-term SWE products were evaluated using the gamma record (Cho et al., 2020). The three SWE datasets are Special Sensor Microwave Imager and Sounder (SSMI/S) SWE, GlobSnow-2 SWE, and University of Arizona (UA) SWE. SSMI/S relies on passive microwave imager measurements, which is sensitive to the water contents on the surface, collected by satellites operated by the Defense Meteorological Satellite Program (DMSP). The GlobSnow-2 SWE developed by the Finnish Meteorological Institute (FMI) relies on the same microwave imager data, but combines them with ground-based observations of snowpack to obtain better SWE estimates. The UA SWE data is calculated by assimilating station-based snow depth and SWE observations from thousands of sites with modeled precipitation and temperature data. Overall, the UA SWE outperformed SSMI/S and GlobSnow-2 SWE in most closely matching the gamma SWE observations (Figure 3). Most of the gains in accuracy came from the UA method to develop the gridded SWE more accurately in heavily vegetated and/or mountainous regions, where the two other products greatly underestimated SWE.
In the snow science community, long-term spatially distributed SWE products have been developed for hydrological and climate research as well as predictions of snow avalanche hazards, flooding, ecological issues, and impacts on winter tourism. However, an evaluation of the SWE products has been challenged by the lack of independent SWE data sets at a continental scale. Furthermore, as global land surface models and climate modeling with high-performance computing resources, producing SWE data as output, continue to evolve at a rapid pace, independent and reliable SWE data are required to evaluate the outputs’ accuracy and to identify potential limitations of snow physical processes involved in each model. The historical 40-year and ongoing NOAA airborne gamma-ray SWE record can be continually used as a reliable, long-term reference record across the U.S. and southern Canada.
Globally, the snowpack measurements observed by citizen scientists can be used as another independent snowpack dataset. In collaboration with the NOAA gamma survey, the CSO snowpack data can be used to validate existing and new gamma lines in U.S. and southern Canada (NOAA is continually developing new lines to gain wider snow information). Also, CSO can be effectively used to develop more robust snowpack datasets like, but beyond, the UA approach by combining the CSO data, especially in remote, high elevation locations, with existing station data along with remote sensing snow measurements.
Reference: Cho, E., J.M. Jacobs, C. Vuyovich (2020) The value of long-term (40 years) airborne gamma radiation SWE record for evaluating three observation-based gridded SWE datasets by seasonal snow and land cover classifications, Water Resources Research, 56(1), https://doi.org/10.1029/2019WR025813
Please contact Eunsang Cho with questions.