Enhancing River1D for Simulating Water Quality in Ice-Affected Rivers
Bibliographic record
Abstract
Abstract The current landscape of water quality models offers a limited lens through which to view the complex interactions of water quality, hydrodynamics, and ice within ice-affected rivers. While existing models may account for ice as a static barrier to heat, light, and gas exchange, they often fall short in representing the resistive and displacement effects of ice on river hydrodynamics, which in turn may influence water quality. This simplification may hold for low velocity environments, like lakes or reservoirs, but it overlooks the complex dynamics within flowing rivers during winter conditions. This paper introduces enhancements to the University of Alberta’s river ice processes model, River1D, by integrating a specialized module for simulating dissolved oxygen and nutrients. A multi-year field-scale simulation demonstrates the enhanced River1D model’s ability to simulate water quality under varying conditions, including both open water and ice-covered periods. Model performance is assessedby comparing simulated values of dissolved oxygen, nutrients (ammonia and nitrate), water temperature, and water levels against observational data from the river. The paper discusses model enhancements and findings from the field-scale application, illustrating the facilitated simulation of water quality in ice-affected rivers. This extension of River1D’s modelling capabilities supports future integrated studies on cold-region river systems, providing a research tool for examining interactions between water quality and river ice processes, with potential applicability in environmental assessments and management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".