SnowCloudMetrics: Snow Information for Everyone

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-10, Vol.12 (20), p.3341
• Main Authors: Crumley, Ryan L., Palomaki, Ross T., Nolin, Anne W., Sproles, Eric A., Mar, Eugene J.
• Format: Article
• Language: English
• Subjects: Climate change; Climate system; cloud computing; Critical components; Data storage; Datasets; Elevation; Forest management; Fresh water; Google Earth Engine; Mountain environments; Remote sensing; Scientists; Snow; Snow cover; snow cover frequency; snow covered area; snow disappearance date; Spectroradiometers; Tables (data); Telemetry; Water resources; Water resources management; Watersheds; Wildlife; Winter
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203341

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.