Anne Nolin headshot

Anne Nolin

Professor, Department of Geography She/her/hers

Summary

Dr. Anne Nolin is a Professor in the Geography Department at the University of Nevada, Reno. Her research focuses on snow hydrology and climate, snow-forest interactions, and mountains as social-ecological systems. Nolin has over three decades of experience in remote sensing, field measurements, and modeling of changing snow and ice. She and her students have published on “at-risk” snow, wildfire impacts on snow, new snow metrics in a warming world, melting snow and glaciers from Greenland to Alaska to the Andes, and new ways to map snow and ice from space. In new work, Nolin and students are exploring how declining snow cover in mountain watersheds across the West influences forest health and fire vulnerability, and how post-fire conditions affect snow and watershed hydrology.

Research interests

  • Snow hydrology
  • Climatology
  • Snow-forest interactions
  • Remote sensing

Courses taught

  • GEOG 491/691, Snow Hydrology
  • ATMS/GEOG 121, Climate Change and Its Environmental Impacts
  • GEOG 438/638, Western Water Resources and Management

Education

  • Ph.D, Geography, University of California-Santa Barbara, 1993

Selected publications

  • Nolin, A. W., Sproles, E. A., Rupp, D. E., Crumley, R. L., Webb, M. J., Palomaki, R. T., & Mar, E. (2021). New snow metrics for a warming world. Hydrological Processes, 35(6), e14262
  • Koshkin AL, Hatchett BJ and Nolin AW (2022) Wildfire impacts on western United States snowpacks. Front. Water 4:971271. doi: 10.3389/frwa.2022.971271.
  • Tarricone, J., Webb, R. W., Marshall, H.-P., Nolin, A. W., and Meyer, F. J. (2023). Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR), The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023.
  • Hatchett, B. J., Koshkin, A. L., Guirguis, K., Rittger, K., Nolin, A. W., Heggli, A., et al. (2023). Midwinter dry spells amplify post-fire snowpack decline. Geophysical Research Letters, 50, e2022GL101235. https://doi-org.unr.idm.oclc.org/10.1029/2022GL10123
  • Jennings, K.S., Collins, M., Hatchett, B.J., Heggli, A., Hur, N., Tonino, S., Nolin, A. Yu, G., Zhang, W., and Arienzo, M. M. Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology. (2025). Nat Commun 16, 2929. https://doi.org/10.1038/s41467-025-58234-2

Professional certifications

  • Wilderness First Responder