Vertical distribution of excess ice in icy sediments and its statistical estimation from geotechnical data (Tuktoyaktuk Coastlands and Anderson Plain, Northwest Territories)

Excess ice, found as massive ice and within icy sediments, is an important variable to quantify as it is a dominant control on the terrain and geotechnical response to permafrost thaw. A large amount of permafrost borehole data are available from the Tuktoyaktuk Coastlands; however, field geotechnic...

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Bibliographic Details
Published in:Arctic Science
Main Authors: Castagner, A., Brenning, A., Gruber, S., Kokelj, S.V.
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2023
Subjects:
Ice
Online Access:http://dx.doi.org/10.1139/as-2021-0041
https://cdnsciencepub.com/doi/full-xml/10.1139/as-2021-0041
https://cdnsciencepub.com/doi/pdf/10.1139/as-2021-0041
Description
Summary:Excess ice, found as massive ice and within icy sediments, is an important variable to quantify as it is a dominant control on the terrain and geotechnical response to permafrost thaw. A large amount of permafrost borehole data are available from the Tuktoyaktuk Coastlands; however, field geotechnical assessments typically only involve the estimation of visible ice. To add significant value to these data sets, a cryostratigraphic data set collected along the Inuvik–Tuktoyaktuk Highway (566 boreholes) is used to develop a beta regression model which predicts the excess ice content of icy sediments based on interval depth, visible ice content, material type, and Quaternary deposits. The resulting predictions are compared to recorded massive ice intervals and show that ground ice within icy sediments can contribute up to 65% of the excess ice and potential thaw strain within the first 10 m from the surface in this area. This study shows the general applicability of this approach and indicates that comparable, quantitative data on ground ice conditions should be collected with drilling programs to derive geotechnical variables and reduce modelling uncertainties so that ground ice data are available for quantitative analysis.