Ice Ice Maybe: Stream Hydrology and Hydraulic Processes During a Mild Winter in a Semi-Alluvial Channel
Bibliographic record
Abstract
Warm conditions during typically cold winters impact runoff and resulting hydraulic processes in channels where ice-cover would typically dominate. This field study on a short, low-slope reach in Southern Ontario, Canada, examined hydrologic and hydraulic processes with a focus on winter runoff events and subsequent bed shear stress variability. Through winter 2024, six cross-sections over a ~100 m reach were monitored near-weekly to measure hydraulic geometry and velocity profiles. These data characterized channel processes and estimated bed shear stress with law of the wall. In this channel, velocity increased more rapidly than width or depth with rising discharge and influenced bed shear stress distribution. Bed shear stress magnitudes were highest (means ranged ~2–6 N/m2) and most variable over gravel beds compared to the exposed bedrock (means ranged ~0.05–2 N/m2). Through a rain-on-snow (ROS) event in late January, bed shear stress estimates decreased dramatically over the rougher gravel bed, despite minimal changes in water depth and velocity. Pebble counts before, during, and after the event, showed that the proportion of finer-sized particles (i.e., <5 cm) increased while median grain size did not vary. These observations align with findings from both flume and field studies and suggest that milder winters reduce gravel-bed roughness through finer-sized sediment deposition, altering sediment transport dynamics and affecting gravel habitat suitability. Additionally, limited ice-cover leads to lower bed shear stresses and thus finer-sized materials are deposited, further impacting gravel habitat suitability. Results highlight the importance of winter hydrologic variability in shaping channel processes and inform potential stream responses under future climate scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".