The role of snow processes and hillslopes on runoff generation in present and future climates in a recently constructed watershed in the Athabasca oil sands region
Bibliographic record
Abstract
Mine reclamation in the Athabasca oil sands region Canada, is required by law where companies must reconstruct disturbed landscapes into functioning ecosystems such as forests, wetlands and lakes that existed in the Boreal landscape prior to mining. Winter is a major hydrological factor in this region as snow covers the landscape for 5 to 6 months and is ~25% of the annual precipitation, yet few studies have explored the influence of winter processes on the hydrology of constructed watersheds. One year (2017-2018) of intensive snow hydrology measurements are supplemented with six years (2013-2018) of meteorological measurements from the constructed Sandhill Fen Watershed to: 1) understand snow accumulation and redistribution, snowmelt timing, rate and partitioning, 2) apply a physically-based model for simulating winter processes on hillslopes and 3) evaluate the impact of soil prescriptions and climate change projections on winter processes in reclaimed systems. The 2017-2018 snow season was between November and April and SWE ranged between 40-140 mm. Snow distribution was primarily influenced by topography with little influence of snow trapping from developing vegetation. Snow accumulation was most variable on hillslopes and redistribution was driven by slope position, with SWE greatest at the base of slopes and decreased towards crests. Snowmelt on hillslopes was controlled by slope aspect, as snow declined rapidly on west and south-facing slopes, compared to east and north-facing slopes. Unlike results previously reported on constructed uplands, snowmelt runoff from uplands was much less (~30%), highlighting the influence of different construction materials. Model simulations indicate that antecedent soil moisture and soil temperature have a large influence on partitioning snowmelt over a range of observed conditions. Under a warmer and wetter climate, average annual peak SWE and snow season duration could decline up to 52 % and up to 61 days, respectively while snowmelt runoff ceases completely under the warmest scenarios. Results suggest considerable future variability in snowmelt runoff from hillslopes, yet soil properties can be used to enhance vertical or lateral flows.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".