Leveraging Weekly Snow Cover Time Series for Improved Glacier Monitoring and Modeling

Authors

Rainey Aberle, Boise State University
Ellyn M. Enderlin, Boise State UniversityFollow
David R. Rounce, Carnegie Mellon University
Shad O'Neel, U.S. Army Corps of Engineers
Brandon Tober, Carnegie Mellon University
Alexandra Friel, Boise State University

Document Type

Article

Publication Date

7-16-2025

Abstract

Seasonal snow and ice melt strongly influence glacier mass balance, yet sparse sub-annual observations limit our understanding of seasonal dynamics. Here we construct and analyze weekly snow cover time series for 200 glaciers across western North America from 2013 to 2023 using an automated image processing pipeline. Snow cover varied widely across the region: snow minima timing varied with latitude — from August from 62 to 64N to October from 48 to 50N—and accumulation area ratios ranged from near-zero to 0.92 (median of 0.52). A comparison of snowlines from observations and the PyGEM glacier mass balance model revealed seasonally evolving but spatially consistent biases in modeled snowlines: observed snowlines rose earlier, but at a slower rate throughout the melt season, than modeled snowlines. Beyond capturing glacier state, snowline observations efficiently provide sub-seasonal mass balance constraints and empirically represent unresolved processes like snow redistribution, refining model gradients and improving projections.

Comments

The corresponding dataset link can be accessed at https://doi.org/10.18122/cryogars_data.5.boisestate

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Publication Information

Aberle, Rainey; Enderlin, Ellyn M.; Rounce, David R.; O'Neel, Shad; Tober, Brandon; and Friel, Alexandra. (2025). "Leveraging Weekly Snow Cover Time Series for Improved Glacier Monitoring and Modeling". Geophysical Research Letters, 52(13), e2025GL115523. https://doi.org/10.1029/2025GL115523