Publications

Displaying 1 - 20 of 62
By year of publication, then alphabetical by title
  1. Wilcox, Evan J., et al. “Bridging Gaps in Permafrost-Shrub Understanding”. PLOS Climate, vol. 3, 2024, https://doi.org/10.1371/journal.pclm.0000360.
  2. Tao, Jing, et al. “Evaluating the Impact of Peat Soils and Snow Schemes on Simulated Active Layer Thickness at Pan-Arctic Permafrost Sites”. Environmental Research Letters, vol. 19, 2024, https://doi.org/10.1088/1748-9326/ad38ce.
  3. Huang, Xiang, et al. “How Does Humidity Data Impact the Land Surface Modeling of Hydrothermal Regimes at a Permafrost Site in Utqiaġvik, Alaska?”. Science of The Total Environment, vol. 912, 2024, https://doi.org/10.1016/j.scitotenv.2023.168697.
  4. Fiolleau, Sylvain, et al. “Insights on Seasonal Solifluction Processes in Warm Permafrost Arctic Landscape Using a Dense Monitoring Approach across Adjacent Hillslopes”. Environmental Research Letters, vol. 19, 2024, https://doi.org/10.1088/1748-9326/ad28dc.
  5. Wang, Chen, et al. “Local-Scale Heterogeneity of Soil Thermal Dynamics and Controlling Factors in a Discontinuous Permafrost Region”. Environmental Research Letters, vol. 19, 2024, https://doi.org/10.1088/1748-9326/ad27bb .
  6. Berner, Logan T. “The Arctic Plant Aboveground Biomass Synthesis Dataset”. Scientific Data, vol. 11, 2024, https://doi.org/10.1038/s41597-024-03139-w.
  7. Renner, Caleb, et al. “The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: A Tool to Understand the Effects of Changing Rainfall Patterns in the Arctic”. Hydrology Research, vol. 55, 2024, https://doi.org/10.2166/nh.2023.146.
  8. 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.
  9. 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.
  10. Rowland, Joel. “Drainage Network Response to Arctic Warming”. Nature Communications, vol. 14, 2023, https://doi.org/10.1038/s41467-023-40796-8.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. Miraglio, Thomas, et al. “Mapping Canopy Traits over Québec Using Airborne and Spaceborne Imaging Spectroscopy”. Scientific Reports, vol. 13, no. 1, 2023, https://doi.org/10.1038/s41598-023-44384-0.