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dc.contributor.supervisor Hopkinson, Christopher
dc.contributor.author Cartwright, Kelsey
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2019-06-19T20:20:27Z
dc.date.available 2019-06-19T20:20:27Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10133/5415
dc.description.abstract Seasonal snow is a significant source of runoff in Western Canada. Mountainous snow depth distributions are challenging to quantify over large areas. Enhanced monitoring methods can provide the necessary data for more accurate flood and drought forecasts. Using multiple datasets, this research provides the foundation to optimize LiDAR snow depth data collection. Snow depth distribution consistency during mid-winter and melt onset was assessed and depth driver (elevation, aspect, slope, TPI and canopy cover) importance was determined. Consistent inter-annual relationships between aspect, TPI, elevation, treeline and snow depth distributions could be exploited in future sampling designs. Random forest models were utilized to predict depth over a 103 km2 area, based on high resolution (3m) watershed scale and partial datasets. Statistically significant correlations were found between parent and modelled datasets in all trials. This thesis illustrates that machine learning is a promising means of optimizing airborne LiDAR snow surveys in headwater environments. en_US
dc.language.iso en_US en_US
dc.publisher Lethbridge, Alta. : University of Lethbridge, Dept. of Geography en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science)
dc.subject Remote sensing en_US
dc.subject Snow en_US
dc.subject Snow surveys en_US
dc.title Spatio-temporal variations in snow depth and associated driving mechanisms in a temperate mesoscale mountainous watershed en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geography en_US
dc.degree.level Masters en_US
dc.proquest.subject Atmospheric sciences [0725] en_US
dc.proquest.subject Climate change [0404] en_US
dc.proquest.subject Hydrologic sciences [0388] en_US
dc.proquestyes Yes en_US
dc.embargo No en_US


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