The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Academic Article


  • Observation-based long-term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long-term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow-2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow-2 SWE when compared against the gamma SWE. However, GlobSnow-2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested-type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow-2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.
  • Authors

  • Cho, Eunsang
  • Jacobs, Jennifer
  • Vuyovich, Carrie M
  • Status

    Publication Date

  • January 2020
  • Has Subject Area

    Published In


  • airborne gamma radiation
  • land cover types
  • long‐term record
  • remote sensing
  • seasonal snow classification
  • snow water equivalent
  • Digital Object Identifier (doi)

    Start Page

  • e2019WR025813
  • Volume

  • 56
  • Issue

  • 1