Predicting Minnesota lake ice phenology with deep learning, explainable methods, and a physically based benchmark, 1980-2018
Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased towards few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational recor
Authors
- Samantha K. Oliver (ORCID: https://orcid.org/0000-0001-5668-1165)
- Gretchen J. A. Hansen (ORCID: https://orcid.org/0000-0003-0241-7048)
- Wallace Anderson McAliley (ORCID: https://orcid.org/0000-0002-8091-9767)
- Jordan S. Read (ORCID: https://orcid.org/0000-0002-3888-6631)
- Simon Topp (ORCID: https://orcid.org/0000-0001-7741-5982)
- Jeremy Diaz (ORCID: https://orcid.org/0000-0001-7087-7949)
Institutions
- United States Geological Survey (US)
- University of Minnesota System (US)
- Consortium of Universities for the Advancement of Hydrologic Science (US)
- Pacific Island Ecosystems Research Center
Publication Details
- Published
- 2025-09-05
- DOI
- https://doi.org/10.22541/essoar.175710698.87505589/v1
- Primary Topic
- Cryospheric studies and observations
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Geological Survey