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TitleA model for quantifying ground-ice volume, Yukon coast, Western Arctic Canada
AuthorCouture, N J; Pollard, W H
SourcePermafrost and Periglacial Processes 2017.
Alt SeriesEarth Sciences Sector, Contribution Series 20150342
PublisherWiley & Sons
Mediapaper; on-line; digital
File formatpdf
AreaClarence Lagoon; Komakuk Beach; Hershel Island; Kay Point; King Point; Shingle Point
Lat/Long WENS-142.0000 -136.0000 70.0000 68.2500
Subjectsground ice; permafrost; modelling; coastal environment; ice content; excess ice; cryostratigraphy
Illustrationslocation map; tables; photographs; plots
ProgramCoastal Infrastructure, Climate Change Geoscience
AbstractA morphological model for estimating ground-ice contents of various landscape units is presented to address the gap between large-scale, general studies and small-scale, site-specific case histories. The model considers different groundice types and cryostratigraphic relations between ice bodies within a terrain unit. Input parameters needed for the model are described. Derived variables and algorithms used to determine the quantity of each ground-ice type within a terrain unit are presented. Examples of the application of the model are provided for the Yukon Coastal Plain, northwest Canada. The uncertainties and limiting assumptions of the model are discussed.
Summary(Plain Language Summary, not published)
Permafrost in the Canadian western Arctic contains large amounts of ground ice and the volume of ice is of interest, since it is a major contributing factor in the response of the permafrost system to environmental changes or to infrastructure development. A geomorphological model was used to estimate ground ice content in different terrain units along the Yukon coast. The overall volume of ground ice is assessed based on the geometric relationships between various types of ground ice. The inputs needed to drive the model are described and the equations used to determine the extent each ground ice type are provided. This study includes a discussion of the uncertainties and potential errors associated with the model assumptions.