Winter water quality modelling under ice-affected conditions in rivers and lakes: A comprehensive review
Bibliographic record
Abstract
• Water quality modelling studies in ice-affected waterbodies were critically examined. • Seven water quality models for rivers and lakes with ice capabilities were reviewed. • Models typically consider ice as a barrier to exchanges with environment. • Most models with ice capabilities neglect its effects on flow resistance. • Winter water quality modelling is constrained by model limitations and winter data scarcity. Numerical models are important tools for studying and predicting water quality in aquatic environments. Although there are many water quality models that are available (both freely and commercially), very few have capabilities to consider the effects of an ice cover in the simulation of water quality conditions in winter. This paper provides a comprehensive review of water quality models used to study winter water quality in rivers and lakes, focusing on their capabilities to simulate ice and how the ice interacts with the aquatic environment. Seven models are reviewed: CE-QUAL-W2, WASP, EFDC/EFDC+, MIKE HYDRO River, TELEMAC-MASCARET, ELCOM-CAEDYM/AEM3D, and HEC-RAS. The paper also reviews the modelling studies on seasonally ice-affected water bodies worldwide based on some of these models, coupled models, and other study-specific models. This review highlights the challenges associated with water quality modelling in ice-affected rivers and lakes, due to both model limitations and a scarcity of winter water quality data. The review also underscores the need for the collection of water quality data with equal emphasis on winter data, as this is not only necessary for accurate calibration of models during ice-affected periods, but also for developing a better understanding of how ice affects water quality and vice versa. This will allow important interactions between water quality and ice to be identified and integrated into models.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".