Estimating Snowmelt-Driven Watershed Discharge in the High Arctic Using Remote Sensing and GIS

Climate warming is rapidly altering Arctic tundra ecosystems, influencing seasonal processes like snowmelt and soil thaw, with significant implications for hydrological dynamics. Snowmelt is a critical hydrological event in the Canadian High Arctic, as it drives biogeochemical cycling, controls microclimate conditions, affects freshwater availability, and influences the distribution and phenology of Arctic flora and fauna. However, snowmelt dynamics remain under-researched and poorly understood in many regions of the High Arctic, where the extreme remoteness, inclement weather, limited infrastructure, and long polar nights limit the feasibility of in-situ hydrological research. This project proposes a method to address this knowledge gap using only remote sensing data and GIS techniques to estimate the onset and duration of snowmelt and associated discharge dynamics in High Arctic watersheds. The method involves using high-resolution Digital Elevation Models (DEMs), Landsat 8/9 satellite imagery, and global weather and climate data from Natural Resources Canada, the USGS, and NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). These datasets will be processed in Google Earth Engine, ArcGIS Pro, and RStudio to estimate snow accumulation, snowmelt timing, and runoff volumes throughout the 2019–2023 snowmelt seasons. Model estimates will be evaluated against in-situ discharge data from the Cape Bounty Arctic Watershed Observatory on Melville Island, NU, to assess their accuracy. By developing a remote sensing-based method for estimating discharge, this research seeks to improve hydrological monitoring in data-scarce Arctic environments. If successful, the approach could enhance understanding of snowmelt-driven hydrology and support future climate impact assessments in the Canadian High Arctic.