Uncovering the Ground Thermal Regime of Coastal Labrador: The Influential Effects of Snow and Vegetation on Ground Temperatures

dc.contributor.authorForget, Anika
dc.contributor.departmentGeography and Planning
dc.contributor.supervisorWay, Robert
dc.date.accessioned2023-08-23
dc.date.accessioned2023-10-10T19:42:06Z
dc.date.available2023-08-24
dc.date.available2023-10-10T19:42:06Z
dc.degree.grantorQueen's University at Kingston
dc.description.abstractThe ground thermal regime of Arctic and Subarctic regions is impacted by climate and local scale factors. Snow cover has been linked to differences in ground temperatures over short distances (< 5 m) because of its thermal buffering properties which slows energy exchanges between the ground and atmosphere. Accurate derivation and characterization of the effects of snow cover on the ground thermal regime is essential for predicting the future impacts of climate change in northern Canada. This thesis presents a machine learning-based method for estimating snow cover from local ground surface temperature (GST) and air temperature measurements and was tested using modelled and in situ data. Results were compared against two other commonly used snow prediction methods, which select thresholds of either 1) the standard deviation of GST or 2) the difference between the standard deviations of air and surface temperatures. The machine learning method showed better performance for the modelled data and comparable performance with the in situ data compared to the other techniques. Variations in snow and ground temperatures were further analyzed with extensive field investigations at two field sites in coastal Labrador which includes a permafrost probability analysis. Results showed that mean annual ground surface temperatures (MAGST) was significantly correlated (p < 0.05) at our southern site with microclimate indices (freezing n-factor [r=-0.70], surface offset [r=0.99], nival offset [r=0.71]) while our northern site had significant correlations with both in situ ecosystem focused indices (snow depth [r=0.78], snow water equivalent [r=0.77]) and microclimate indices (freezing n-factor [r=-0.93], surface offset [r=0.99], nival offset [0.93]). Permafrost probability results showed a 10% likelihood across all 35 logger locations with all probable permafrost locations within our northern site at tundra and wetland ecotypes of low snow accumulation. Overall, this research will improve our ability to model snow-ground interactions and offers a step forward in our understanding of ground thermal heterogeneity in coastal environments. This work will support the next generation of permafrost and ground thermal modelling in coastal Labrador.
dc.description.degreeM.Sc.
dc.identifier.urihttps://hdl.handle.net/1974/31954
dc.language.isoeng
dc.relation.ispartofseriesCanadian theses
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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dc.rightsCopying and Preserving Your Thesis
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owne
dc.subjectGround temperatures
dc.subjectPermafrost
dc.subjectCoastal
dc.titleUncovering the Ground Thermal Regime of Coastal Labrador: The Influential Effects of Snow and Vegetation on Ground Temperatures
dc.typethesis
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