Document Type

Thesis

Degree Name

Master of Science (MSc)

Department

Geography & Environmental Studies

Program Name/Specialization

Geomatics

Faculty/School

Faculty of Science

First Advisor

Dr. Homa Kheyrollah Pour

Advisor Role

Supervisor

Abstract

With the expected rise in air temperature, it becomes important to understand how snow will respond in different climate scenarios. The presence of snow over lake ice largely influences the ice thickness, and as Canada’s Arctic and sub-arctic regions are experiencing warming at twice the global rate, concerns rise as changes in the snowpack will significantly impact northern communities that rely on lake ice as a means of transportation, source for drinking water, and feeding their families. The distribution of snow depth is highly sensitive to changes in climate over time, as such a slight increase in air temperature or change in precipitation can substantially alter snowpack dynamics, which in-turn, directly impacts the rate of lake ice growth. The heterogeneity of snow depth over lake ice is driven by wind redistribution and snowpack metamorphism which creates an inconsistent ice thickness across the lake. Currently, daily snow depth measurements are represented as one value, collected at a weather station on land, near lake shorelines, but previous studies show that this data is not representative of the distribution of snow across different landscapes, more specifically lake ice. Due to the exposed nature of lakes, it is shown that snow depth will be redistributed greatly over lake ice, as there is a lack of vegetation compared to land surfaces with differences in topography. To identify the snow spatial distribution, extensive snow depth measurements must be collected across the entire lake. However, the collection of accurate snow depth measurements over lake ice is challenging and requires a great deal of time spent in the field. Studies have explored the use of remote sensing techniques to map snow distribution over land, however our understanding of such over lake ice is minimal.

Accurate measurements of the spatial distribution of snow depth over lake ice is limited due to logistical difficulties in manual measurement techniques (i.e., ruler, snow depth probe). This study presents the use of ground-penetrating radar (GPR) and in-situ observations (snow depth and density) to develop a systematic method to estimate the spatial distribution of snow depth over lake ice. Focused on four lakes located in the North Slave Region, Northwest Territories (Landing Lake, Finger Lake, Vee Lake, Long Lake) the snow depth is derived using GPR two-way travel time. Through utilizing a combination of ground-based techniques, this study proposed a methodology to ease the collection process required to get accurate snow depth measurements on a larger spatial scale than current methods allow. The findings of this thesis will benefit the snow and ice community as we can increase our availability of accurate snow depth data over lake ice through an efficient method of collecting larger snow depth datasets. Specifically, with the availability of snow depth data over lake ice, the accuracy of thermodynamic lake ice model can be improved significantly.

Convocation Year

2023

Convocation Season

Spring

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