Estimating Snowmelt-Driven Watershed Discharge in the High Arctic Using Remote Sensing and GIS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".