What Proglacial Aufeis Formations Tell Us about the Evolution of Hydrological Processes in a Glacier Decline Context
Bibliographic record
Abstract
Alpine environments in cold regions are undergoing rapid transformations due to shifting climates, raising concerns about the future of ecosystems and downstream water supplies. Traditionally, hydrological studies have focused on visible cryospheric elements, often overlooking buried features like permafrost, ice-cored moraines, and rock glaciers, as well as the significance of groundwater in alpine valleys. Recent investigations, however, highlight the pivotal role of groundwater in shaping local watershed dynamics and regional water resources in these cold regions. Understanding hydrogeology in cold region alpine environments presents challenges due to remote and inaccessible study sites and harsh winter conditions. Recent studies suggest that the winter hydrogeological dynamics of proglacial areas are captured within proglacial aufeis, ice formations persisting during winter despite extended sub-zero temperatures. This study focuses on aufeis formation within a tributary of the Duke Valley, a glacierized catchment in the green belt of Mount St-Elias on the Kluane First Nation territory in Yukon. Employing a field-based approach at the Shar Shäw Tágà study site and remote sensing analysis using satellite imagery, our research aims to delineate key aufeis growth stages and identify contributing water sources. The field study, conducted from 2018 to 2023, utilizes time-lapse imagery, hydrochemical tracers, and meteorological records to reveal aufeis growth variability. Observations in Shar Shäw Tágà show aufeis forming consistently across most years, spanning from the canyon exit to the meander of the Grizzly Creek River. However, results indicate a three-year absence of aufeis formation and variability in formation dates when present. Notably, aufeis growth deceleration or cessation in March, despite sustained sub-zero temperatures, suggests groundwater's role. Remote sensing analysis dating back to 1974 indicates a declining trend in aufeis occurrences. Statistical analysis on relative frequencies suggests a potential link between non-formation and meteorological conditions from the preceding summer, supporting the hypothesis that aufeis occurrence is influenced by factors impacting groundwater behavior. The correlation between aufeis formation and longer-term factors implies aufeis as a valuable indicator of groundwater evolution in alpine cold regions, sensitive to processes beyond immediate seasonal variations. These findings contribute to our understanding of broader changes in groundwater behavior over extended periods in these dynamic environments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".