This project seeks to understand the strength and thaw consolidation characteristics of permafrost sediments through a comprehensive laboratory program. Starting with existing laboratory data, both published and from industry where available, we will build a geotechnical permafrost soils database in collaboration with the other projects in the network. The specific data gaps, the quality and reliability of the data will inform the direction of the experimental pro- gram, adding to previous databases that have compiled mostly geotechnical index properties.
This program will investigate a number of interrelated conditions that govern overall settlement of de-grading permafrost such as strength, consolidation behaviour and excess pore pressure dissipation. We aim to develop simple constitutive relations that describe the major geomechanical properties based on measurable and available parameters including soil type and ice content. Using the experimental data from the network, a synthesized model, which predicts frozen strength and thaw consolidation for soil type, temperature and ice content will be developed.
The Churchill Railway, which has experienced damage and failures due to flooding and permafrost thaw events, will provide the basis for a strategic case study. The PhD candidate will collect, synthesize and integrate a range of datasets (e.g. topographic, meteorological, hydrological, geotechnical, geophysical), which may be acquired through desktop studies, field investigations (e.g. electrical resistivity tomography field survey, boreholes) and remote sensing (e.g. satellite, drone) observations. Empirical and screening level computational tools will be advanced to generate risk estimates along the Infrastructure right of way. This improved knowledge base and tools will help to identify regions of interest where more detailed and localized investigations or assessments are required to refine the risk estimate. This will support owners in more effective decision making under uncertainty, within an evidence based risk framework, for the asset management of linear infrastructure in permafrost environments.
This project will improve methods for measuring and monitoring ground-ice content and permafrost thaw directly. This is important because changing soil characteristics govern the impacts of permafrost thaw on the natural and built environment, but permafrost temperature alone reveals these changes only incompletely. This project will advance (a) the understanding of soil thaw close to 0ºC, (b) in situ measurement of liquid water content in permafrost for tracking thaw, and (c) the geophysical detection of ground ice.
This project will develop methods and tools for evaluating permafrost models with
observational data. This is important because the lack of meaningful and quantitative evaluation of
permafrost simulation results impedes the improvement of simulation tools and the use of their
outputs for informing adaptation design or policy. This project will use the database compiled in
PermafrostNet (PINGO) as a source of observational evidence to provide confidence in
simulation-based permafrost climate services.
This position will elucidate controls on 1) the initiation of thaw lakes and ponds, 2) variation in rates of expansion, and 3) pathways of stabilization and permafrost recovery post stabilization. Field work will take place in thermokarst-affected lowlands between the Blackstone Uplands (YT) and Tuktoyuktuk (NWT). The position will develop predictive models based on the interacting effects of paleogeography (deposits and ice), climate, topography, snow cover, and vegetation growth within thaw ponds.
Permafrost conditions in the Hudson Bay Lowlands (HBL) are poorly understood and complicated by rapid isostatic uplift. This position will quantify relations between the aggradation of permafrost and ground ice, the emergence of the coast, and the development of the vegetation using field surveys and macrofossil analysis; and map ground-ice distribution in the HBL using this paleogeographic knowledge, remote-sensing, and field data.