New remote sensing methods are quickly evolving for fast and accurate measurement of snow depth in forested mountain regions, but depths must be converted to snow water equivalent (SWE) in order to predict the amount and distribution of stored water in the watershed.
The collection of SWE is more difficult. Traditional methods involving manual snow course sampling are labour intensive and lead to the question of how many snow courses are needed to allow for accurate prediction of SWE across a watershed given snow depth measurements.
Establishing a robust relationship between snow depth and SWE across Kootenay region mountain watershed will improve outcomes of studies intended to quantify the impacts of forest disturbance and regeneration on the water balance of a watershed.
Proposed Study method:
- Literature review on factors contributing to SWE variability across elevations and aspects
- Field data collection: Set-up snow courses across elevations and aspects in Rover Creek and establish depth to SWE relationships through time between March and May 2020
- Determine a predictive relationship between snow depth and SWE that can be used to model the SWE across the watershed
- Determine the minimum number of snow courses necessary to adequately characterize depth to SWE in future studies.
Data from sampling program and report with literature review, analysis on depth to SWE relationships in Rover Creek and recommendations for snow course density for adequate modeling of SWE across a watershed.
Education/Skills and Abilities Required
BSc in Physical Geology, Earth Sciences, Forestry or similar. Experience with back-country travel including snow shoeing and skiing. Understanding of avalanche and natural hazards assessment. Ability to undertake full day surveying activities and willingness to work outside in all weather conditions. First Aid training is preferred.
This position will be compensated at $21.59/hour plus 4% vacation pay. The posting closes February 14, 2020. Please submit your resume and cover letter to Dr. Kim Green: firstname.lastname@example.org.