Hydrodynamic Modelling of Great Slave Lake Using NEMO
Bibliographic record
Abstract
The subarctic region of northern Canada, including the Mackenzie River basin is deeply impacted by the changing climate. Unprecedented rates of warming in Canada’s North, up to four times the global average, have been observed in the region over the past few decades. This brings significant implications for regional hydrology, ecosystems, and human activities. A major controlling feature in the Mackenzie River Basin is Great Slave Lake, which is the second largest lake in the Northwest Territories of Canada and the deepest lake in North America. With over 60% of the population of Northwest Territories living along its shores, Great Slave Lake is a vital ecological and societal asset in the region. This study aims to further our understanding of water circulation and stratification patterns in Great Slave Lake through numerical simulation. Despite the status of Great Slave Lake as one of the largest and deepest lakes in North America, comprehensive numerical modelling has proven difficult due to lack of accurate bathymetric data. To address this gap, we collaborated with the Department of Fisheries and Oceans to develop the first complete bathymetric map of Great Slave Lake. Historical naval charts and field sheets were integrated with additional sounding data to produce a simulation domain tailored to the Nucleus for European Modelling of the Ocean (NEMO) at a horizontal resolution of 1km with 30 vertical layers. The NEMO model was chosen for application in this large lake for consistency with the existing model setup being used by Environment and Climate Change Canada (ECCC) for its operational forecasting system in the Laurentian Great Lakes. Atmospheric reanalysis is provided by ECCC’S Regional Deterministic Reanalysis System (RDRS), while surface runoff entering the lake is driven by the Community Environmental Modelling System – Surface & Hydrology (MESH) outputs from the Global Water Futures reanalysis efforts. The resulting NEMO model shows good capability of simulating lake processes with preliminary results indicating that the lake exhibits seasonal thermal stratification, consistent with dimictic behaviour, where full vertical mixing occurs twice annually. Our results also show that wind-induced mixing appears to also play a significant role in lake circulation, and a counterclockwise circulation pattern is observed, with prominent gyres in the main basin of the lake. Ongoing work focuses on further validation of the temperature profiles at select locations and sensitivity analysis to improve the overall simulation capabilities of the model for future water resource management needs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".