Spatio-temporal variations in snow depth and associated driving mechanisms in a temperate mesoscale mountainous watershed

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.