Calibrating the Dynamic Reservoir Simulation Model (DYRESM) and filling required data gaps for one‐dimensional thermal profile predictions in a boreal lake
Bibliographic record
Abstract
One‐dimensional vertical heat transfer and mixing models, such as the Dynamic Reservoir Simulation Model (DYRESM), have seldom been applied to lakes in the boreal region even though this region houses the majority of global freshwater lakes. In order to employ DYRESM to predict the thermal structure of a boreal lake located near Sudbury, Ontario, Canada, we overcame two methodological challenges. First, we developed models to predict the vertical light extinction coefficient (Kd) from dissolved organic carbon (DOC) concentrations and hydraulic retention time. We also developed models to predict stream temperatures from local meteorology, and to predict the discharge of lake inflows and the lake outflow from runoff per unit area at gauged streams nearby. We then re‐calibrated several DYRESM parameters which had been tested previously primarily in the Southern Hemisphere, and explored the sensitivity of the re‐calibrated model to all of the remaining uncalibrated inputs implicated in heating and mixing processes. The mean difference between values predicted with the re‐calibrated model and field measurements (± 1 standard deviation), 1.09 m (± 0.89 m) for thermocline depth and 1.98°C (± 1.58°C) for bottom water temperature, was relatively small compared with other North American studies, and likely due to the model rather than our parameterization. Our calibration of DYRESM for Clearwater Lake, and supplementary models, provide a demonstration for and guidance to those wishing to simulate changes in thermal regimes of boreal lakes in response to climate change or other broad‐scale environmental stressors of importance both to local fisheries and freshwater resource 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".