Publications

Displaying 181 - 200 of 342
By year of publication, then alphabetical by title
  1. Bisht, Gautam, et al. “Impacts of Microtopographic Snow Redistribution and Lateral Subsurface Processes on Hydrologic and Thermal States in an Arctic Polygonal Ground Ecosystem: A Case Study Using ELM-3D v1.0”. Geoscientific Model Development, vol. 11, no. 1, 2018, pp. 61-76, https://doi.org/https://doi.org/10.5194/gmd-11-61-2018.
  2. Zheng, Jianqiu, et al. “Impacts of Temperature and Soil Characteristics on Methane Production and Oxidation in Arctic Polygonal Tundra”. Biogeosciences Discussions, 2018, pp. 1-27, https://doi.org/10.5194/bg-2017-56610.5194/bg-2017-566-supplement10.5194/bg-2017-566-RC110.5194/bg-2017-566-RC210.5194/bg-2017-566-AC110.5194/bg-2017-566-AC2.
  3. Taş, Neslihan, et al. “Landscape Topography Structures the Soil Microbiome in Arctic Polygonal Tundra”. Nature Communications, vol. 9, no. 1, 2018, https://doi.org/10.1038/s41467-018-03089-z.
  4. Abolt, Charles J., et al. “Microtopographic Control on the Ground Thermal Regime in Ice Wedge Polygons”. The Cryosphere Discussions, 2018, pp. 1-26, https://doi.org/10.5194/tc-2018-210.5194/tc-2018-2-supplement10.5194/tc-2018-2-RC110.5194/tc-2018-2-RC210.5194/tc-2018-2-AC110.5194/tc-2018-2-AC2.
  5. Fisher, Joshua B., et al. “Missing Pieces to Modeling the Arctic-Boreal Puzzle”. Environmental Research Letters, vol. 13, no. 2, 2018, p. 020202, https://doi.org/10.1088/1748-9326/aa9d9a.
  6. Nicolsky, Dmitry J., and Vladimir E. Romanovsky. “Modeling Long-Term Permafrost Degradation”. Journal of Geophysical Research: Earth Surface, vol. 123, no. 8, 2018, pp. 1756-71, https://doi.org/10.1029/2018JF004655.
  7. Jafarov, Elchin E., et al. “Modeling the Role of Preferential Snow Accumulation in through Talik Development and Hillslope Groundwater Flow in a Transitional Permafrost Landscape”. Environmental Research Letters, vol. 13, no. 10, 2018, p. 105006, https://doi.org/10.1088/1748-9326/aadd30.
  8. Mekonnen, Zelalem A., et al. “Modelling Impacts of Recent Warming on Seasonal Carbon Exchange in Higher Latitudes of North America”. Arctic Science, vol. 4, no. 4, 2018, pp. 471-84, https://doi.org/10.1139/as-2016-0009.
  9. Chen, Hongmei, et al. “Molecular Insights into Arctic Soil Organic Matter Degradation under Warming”. Environmental Science & Technology, vol. 52, no. 8, 2018, pp. 4555-64, https://doi.org/10.1021/acs.est.7b05469.
  10. Bjorkman, Anne D., et al. “Plant Functional Trait Change across a Warming Tundra Biome”. Nature, vol. 562, no. 7725, 2018, pp. 57-62, https://doi.org/10.1038/s41586-018-0563-7.
  11. Vaughn, Lydia J. S., and Margaret S. Torn. “Radiocarbon Measurements of Ecosystem Respiration and Soil Pore-Space Carbon Dioxide in Utqiaġvik (Barrow), Alaska”. Earth System Science Data, vol. 10, no. 4, 2018, pp. 1943-57, https://doi.org/10.5194/essd-10-1943-2018.
  12. Lara, Mark J., et al. “Reduced Arctic Tundra Productivity Linked With Landform and Climate Change Interactions”. Scientific Reports, vol. 8, no. 1, 2018, https://doi.org/10.1038/s41598-018-20692-8.
  13. Tran, Anh Phuong, et al. “Spatial and Temporal Variations of Thaw Layer Thickness and Its Controlling Factors Identified Using Time-Lapse Electrical Resistivity Tomography and Hydro-Thermal Modeling”. Journal of Hydrology, vol. 561, 2018, pp. 751-63, https://doi.org/10.1016/j.jhydrol.2018.04.028.
  14. Lombardozzi, Danica L., et al. “Triose Phosphate Limitation in Photosynthesis Models Reduces Leaf Photosynthesis and Global Terrestrial Carbon Storage”. Environmental Research Letters, vol. 13, no. 7, 2018, p. 074025, https://doi.org/10.1088/1748-9326/aacf68.
  15. Lara, Mark J., et al. “Tundra Landform and Vegetation Productivity Trend Maps for the Arctic Coastal Plain of Northern Alaska”. Scientific Data, vol. 5, 2018, p. 180058, https://doi.org/10.1038/sdata.2018.58.
  16. Mekonnen, Zelalem A., et al. “Twenty-First Century Tundra Shrubification Could Enhance Net Carbon Uptake of North America Arctic Tundra under an RCP_8.5 Climate Trajectory”. Environmental Research Letters, vol. 13, no. 5, 2018, p. 054029, https://doi.org/10.1088/1748-9326/aabf28.
  17. Langford, Zachary, et al. “Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets”. 2018 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, 2018, https://doi.org/10.1109/icdmw.2018.00116.
  18. Ghimire, Bardan, et al. “A Global Trait-Based Approach to Estimate Leaf Nitrogen Functional Allocation from Observations”. Ecological Applications, vol. 27, no. 5, 2017, pp. 1421-34, https://doi.org/10.1002/eap.1542.
  19. Zhu, Qing, et al. “A New Theory of Plant-Microbe Nutrient Competition Resolves Inconsistencies Between Observations and Model Predictions”. Ecological Applications, vol. 27, no. 3, 2017, pp. 875-86, https://doi.org/10.1002/eap.1490.
  20. Rogers, Alistair, et al. “A Roadmap for Improving the Representation of Photosynthesis in Earth System Models”. New Phytologist, vol. 213, no. 1, 2017, pp. 22-42, https://doi.org/10.1111/nph.14283.