Assessing variability in snowmelt bypass among thermokarst lakes using water isotope tracers, Northwest Territories, Canada
Bibliographic record
Abstract
Abstract. Snow represents the largest potential source of water for thermokarst lakes, but the runoff generated by snowmelt (freshet) can flow beneath lake ice and out of lakes without mixing with and replacing pre-snowmelt lake water. Although this phenomenon, called “snowmelt bypass”, is common in ice-covered lakes, it is unknown what lake and watershed properties cause variation in snowmelt bypass among lakes. Understanding the variability of snowmelt bypass is important because the amount of freshet that is mixed into a lake affects the limnological and biogeochemical properties of the lake. To explore lake and watershed attributes that influence snowmelt bypass, we sampled 17 open-drainage thermokarst lakes for isotope analysis before and after snowmelt. Isotope data were used to estimate the amount of lake water replaced by freshet and to observe how the water source of lakes changed in response to the freshet. A median of 25.2 % of lake water was replaced by freshet, with values ranging widely from 5.2 to 52.8 %. For every metre lake depth increased, the portion of lake water replaced by freshet decreased by an average of 13 %, regardless of the size of the lake’s watershed. Vertical mixing is more restricted in deeper lakes, which reduces the relative thickness of the layer where freshet can mix with lake water, leading to more snowmelt bypass at deeper lakes. We expect a similar relationship between increasing lake depth and greater snowmelt bypass could be present at all ice-covered open-drainage lakes, since the limited vertical mixing conditions that lead to this relationship are present at all ice-covered lakes. The water source of freshet that was mixed into lakes was not exclusively snowmelt, but a combination of snowmelt mixed with rain-sourced water that was released as the soil thawed after snowmelt. As climate warming increases rainfall and shrubification causes earlier snowmelt timing relative to lake ice melt, snowmelt bypass may become more prevalent with the water remaining in thermokarst lakes post-freshet becoming increasingly rainfall sourced. However, if climate change causes lake levels to fall below the outlet level (i.e., lakes become closed drainage) more freshet may be retained by thermokarst lakes as snowmelt bypass will not be able to occur until lakes reach their outlet level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".