Publications

Yang, Dedi, et al. “ Fine-Scale Landscape Characteristics, Vegetation Composition, and Snowmelt Timing Control Phenological Heterogeneity across Low-Arctic Tundra Landscapes in Western Alaska”. Environmental Research Ecology, vol. 3, 2025, https://doi.org/10.1088/2752-664X/ad9eb8.

Bachand, Claire, et al. “Brief Communication: Monitoring Snow Depth Using Small, Cheap, and Easy-to-Deploy snow–ground Interface Temperature Sensors”. The Cryosphere, vol. 19, no. 19, 2025, https://doi.org/10.5194/tc-19-393-2025.

Kim, Kwansoo, et al. “Determination of Ground Subsidence Around Snow Fences in the Arctic Region”. Lithosphere, vol. 2025, 2025, https://doi.org/10.2113/2025/lithosphere_2024_215.

Berns-Herrboldt, Erin C., et al. “Dynamic Soil Columns Simulate Arctic Redox Biogeochemistry and Carbon Release During Changes in Water Saturation”. Scientific Reports, vol. 15, 2025, https://doi.org/10.1038/s41598-024-83556-4.

Torn, Margaret S., et al. “Large Emissions of CO2 and CH4 Due to Active-Layer Warming in Arctic Tundra”. Nature Communications, vol. 16, 2025, https://doi.org/10.1038/s41467-024-54990-9.

Hantson, Wouter, et al. “Scaling Arctic Landscape and Permafrost Features Improves Active Layer Depth Modeling”. Environmental Research Ecology, vol. 4 , 2025, https://doi.org/10.1088/2752-664X/ad9f6c.

Lathrop, Emma, et al. “Shrubs Strongly Influence Snow Properties in Two Subarctic Watersheds”. Permafrost and Periglacial Processes, 2025, https://doi.org/10.1002/ppp.2263.

Freitas, Nancy L., et al. “Substantial and Overlooked Greenhouse Gas Emissions from Deep Arctic Lake Sediment”. Nature Geoscience, vol. 18, 2025, https://doi.org/10.1038/s41561-024-01614-y.

Yuan, Fengming, et al. “An Ultrahigh-Resolution E3SM Land Model Simulation Framework and Its First Application to the Seward Peninsula in Alaska”. Journal of Computational Science, vol. 73, 2023, https://doi.org/10.1016/j.jocs.2023.102145.

Boike, Julia, et al. “Arctic Permafrost”. Encyclopedia of Soils in the Environment, Elsevier, 2023, pp. 410-8, https://doi.org/10.1016/b978-0-12-822974-3.00141-5.

Rowland, Joel. “Drainage Network Response to Arctic Warming”. Nature Communications, vol. 14, 2023, https://doi.org/10.1038/s41467-023-40796-8.

Painter, Scott L., et al. “Drying of Tundra Landscapes Will Limit Subsidence-Induced Acceleration of Permafrost Thaw”. Proceedings of the National Academy of Sciences, vol. 120, no. 8, 2023, https://doi.org/10.1073/pnas.2212171120.

Conroy, Nathan A., et al. “Environmental Controls on Observed Spatial Variability of Soil Pore Water Geochemistry in Small Headwater Catchments Underlain With Permafrost”. The Cryosphere, vol. 17, no. 17, 2023, https://doi.org/ttps://doi.org/10.5194/tc-17-3987-2023.

Uhlemann, Sebastian, et al. “Estimating Permafrost Distribution Using Co-Located Temperature and Electrical Resistivity Measurements”. Geophysical Research Letters, vol. 50, 2023, https://doi.org/10.1029/2023GL103987.

Thaler, Evan A., et al. “Estimating Snow Cover from High-Resolution Satellite Imagery by Thresholding Blue Wavelengths”. Remote Sensing of Environment, vol. 285, 2023, p. 113403, https://doi.org/10.1016/j.rse.2022.113403.

Thaler, Evan A., et al. “High-Resolution Maps of Near-Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning”. Earth and Space Science, vol. 10, 2023, https://doi.org/10.1029/2023EA003015.

Weber, Sören E., and Colleen M. Iversen. “How Deep Should We Go to Understand Roots at the Top of the World?”. New Phytologist, 2023, https://doi.org/10.1111/nph.19220.

Zhang, Lijie, et al. “Inhibition of Methylmercury and Methane Formation by Nitrous Oxide in Arctic Tundra Soil Microcosms”. Environmental Science and Technology, vol. 57, no. 14, 2023, pp. 5655-6, https://doi.org/10.1021/acs.est.2c09457.

Yang, Dedi, et al. “Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska”. Remote Sensing of Environment, vol. 286, 2023, p. 113430, https://doi.org/10.1016/j.rse.2022.113430.

Shirley, Ian A, et al. “Machine Learning Models Inaccurately Predict Current and Future High-Latitude C Balances”. Environmental Research Letters, vol. 18, no. 1, 2023, p. 014026, https://doi.org/10.1088/1748-9326/acacb2.