Isotopic evidence of increasing water abundance and lake hydrological change in Old Crow Flats, Yukon, Canada
Bibliographic record
Abstract
Abstract Lake-rich northern permafrost landscapes are sensitive to changing climate conditions, but ability to track real-time and potentially multiple hydrological responses (e.g. lake expansion, drawdown, drainage) is challenging due to absence of long-term, sustainable monitoring programs in these remote locations. Old Crow Flats (OCF), Yukon, is a Ramsar Wetland of International Importance where concerns about low water levels and their consequences for wildlife habitat and traditional ways of life prompted multidisciplinary studies during the International Polar Year (2007–2008) and led to the establishment of an aquatic ecosystem monitoring program. Here, we report water isotope data from 14 representative thermokarst lakes in OCF, the foundation of the monitoring program, and time-series of derived metrics including the isotope composition of input waters and evaporation-to-inflow ratios for a 13 year period (2007–2019). Although the lakes spanned multiple hydrological categories (i.e. rainfall-, snowmelt- and evaporation-dominated) based on initial surveys, well-defined trends from application of generalized additive models and meteorological records reveal that lakes have become increasingly influenced by rainfall, and potentially waters from thawing permafrost. These sources of input have led to more positive lake water balances. Given the documented role of rainfall in causing thermokarst lake drainage events in OCF and elsewhere, we anticipate increased vulnerability of lateral water export from OCF. This study demonstrates the value of long-term isotope-based monitoring programs for identifying hydrological consequences of climate change in lake-rich permafrost landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".