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Tag: permafrost
  • Challenges and Limitations of Using Autonomous Instrumentation for Measuring In Situ Soil Respiration in a Subarctic Boreal Forest in Alaska, USA

    Abstract: Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH4) and carbon dioxide (CO2). Recent studies examining CO2 efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO2 efflux based on soil temperature and moisture estimates. The goal of this work was to acquire respiration rates from a boreal forest located near the town of Fairbanks, Alaska, and to provide in situ measurements for the future validation effort of the DRTSPORE model estimates of CO2 efflux in cold climates. Results show that soil temperature and seasonal soil thaw depth had the greatest impact on soil respiration. However, the instrumentation deployed significantly altered the soil temperature, moisture, and seasonal thaw depth at the survey site and very likely the soil respiration rates. These findings are important to better understand the challenges and limitations associated with the in situ data collection used for carbon efflux modeling and for estimating soil microbial activity in cold environments.
  • Advancing Engineering With Nature Initiatives in Point Hope, Alaska

    Purpose: Growing environmental risk threatens communities in cold regions, particularly as climate change contributes to permafrost thaw, a reduction in sea-ice extent, and some of the largest rates of coastal erosion on earth. In the context of these significant and growing risks, the Engineering With Nature® (EWN®) program formed its cold regions work unit in 2021 to explore the potential to apply EWN approaches in these areas to mitigate environmental risk while supporting resilient outcomes. The work unit’s objectives include working with communities to preserve the natural environment and traditions, advancing the work unit’s understanding of cold-region environments, and providing guidance on the implementation of natural and nature-based features (NNBF) and EWN in cold regions to increase resilience. This technical note (TN) provides an overview of the EWN in cold regions technical approach as applied to Point Hope, Alaska, which includes community engagement, the integration of traditional ecological knowledge (TEK) throughout the project, and the development of cold-regions-specific knowledge and tools.
  • Isolation and Characterization of Bacterial Isolates from Alaskan Permafrost for Synthetic Biology Applications

    Abstract: Operations in the Artic and other cold regions require technologies that can perform reliably under extreme cold conditions. Permafrost and frozen soils harbor a wide range of microorganisms that have adapted to extremely low temperatures and have unique metabolic capabilities relevant to military operations and that could be exploited to develop biotechnologies optimized for cold environments. Cold-tolerant bacteria (psychrophiles and psychrotrophs) are critical to the development of synthetic biology technologies meant to work in cold environments like the Arctic. Using bacteria isolated from Alaskan permafrost, we applied an experimental pipeline to test the best candidates for use as biological platforms, or chassis, for low-temperature synthetic biology. Since synthetic biology constructs will perform only as well as their chassis, it is critical that circuits expected to perform under extreme cold conditions are housed in chassis that are adapted to those conditions. We identified one permafrost isolate, PTI8, related to Rhodococcus fascians, that is capable of growing from −1°C to at least 25°C and which we experimentally confirmed to uptake and express the broad host range plasmid pBTK519, suggesting PTI8 is a candidate for use as a novel cold-adapted chassis for synthetic biology.
  • Testing Expedient Ground Anchor Solutions for Guyed Towers in Remote Cold Regions: Considerations for Cold Remote Regions with Limited Tools

    Abstract: Ground anchors connected to guy wires for tower structures in cold climates suffer from frost heaving, which causes loss of wire tension and subsequent structural instability. It is necessary to understand what ground anchors are available to resist this tendency yet are still capable of expedient installation in remote areas. To that end, three metal, traditional ground-anchor types (arrowhead, bullet, and penetrating auger) and one novel polyvinyl chloride (PVC) T-post anchor were evaluated in frozen gravels and frozen silts at a research facility in Fairbanks, Alaska. Criteria included installation capability, failure loading, and removal ability. Additionally, expedient installation techniques for use in field conditions were also demonstrated. All three traditional ground anchors failed to penetrate frozen gravels. The penetrating auger also failed to penetrate frozen silts, but the arrowhead and bullet anchors did penetrate frozen silts with difficulty. The PVC anchor is capable of being installed only in a predrilled pilot hole. Under flexural load, the arrowhead anchor cable failed at 3686.72 lb, and the bullet anchor cable failed at 1753.44 lb. The PVC slid out of its hole at a direct-pull force of 1978.24 lb and failed under flexural stress at 202.32 lb.
  • Ground-penetrating Radar Studies of Permafrost, Periglacial, and Near-surface

    Abstract: Installations built on ice, permafrost, or seasonal frozen ground require careful design to avoid melting issues. Therefore, efforts to rebuild McMurdo Station, Antarctica, to improve operational efficiency and consolidate energy resources require knowledge of near-surface geology. Both 200 and 400 MHz ground-penetrating radar (GPR) data were collected in McMurdo during January, October, and November of 2015 to detect the active layer, permafrost, excess ice, fill thickness, solid bedrock depth, and buried utilities or construction and waste debris. Our goal was to ultimately improve surficial geology knowledge from a geotechnical perspective. Radar penetration ranged between approximately 3 and 10 m depth for the 400 and 200 MHz antennas, respectively. Both antennas successfully detect buried utilities and near-surface stratified material to ~0.5–3.0 m whereas 200 MHz profiles were more useful for mapping deeper stratified and un-stratified fill over bedrock. Artificially generated excess ice which appears to have been created from runoff, water pooling and refreezing, aspect shading from buildings, and snowpack buried under fill, are prevalent. Results show that McMurdo Station has a complex myriad of ice-rich fill, scoria, fractured volcanic bedrock, permafrost, excess ice, and buried anthropogenically generated debris, each of which must be considered during future construction.
  • Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships Between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska

    Abstract: The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.