A decade of winter supraglacial lake drainage across Northeast Greenland using C-band SAR
Bibliographic record
Abstract
Abstract. This study presents a comprehensive, multi-year assessment of winter supraglacial lake drainages on the Northeast Greenland Ice Sheet, detailing cascading drainage events, examining links to melt-season conditions, and evaluating their potential impact on ice dynamics. Supraglacial lakes can drain rapidly, delivering meltwater to the ice-sheet bed, increasing basal water pressure, reducing friction, and accelerating ice flow. Such drainage events are well-documented across Greenland during the melt season using optical satellite imagery. Recent studies using satellite and airborne radar data reveal that many supraglacial lakes persist beyond summer and may also drain during winter, potentially affecting ice dynamics in a manner similar to melt-season drainages. Here, we use C-band synthetic aperture radar imagery from Sentinel-1 and RADARSAT Constellation Mission spanning ten consecutive winters (2014/2015–2023/2024), to detect winter lake drainages. We develop a normalization method to integrate images from varying acquisition geometries, enabling high-temporal-resolution monitoring. Our analysis identifies 90 winter drainage events from 55 unique lakes, exhibiting substantial interannual variability – from a maximum of 18 events in winter 2018/2019 to a minimum of four events in both 2020/2021 and 2021/2022. Drainages occurred most frequently in early winter, with decreasing frequency as winter progressed. Approximately half of the observed drainages were part of 13 cascading events, each involving two to seven lakes over distances up to ~33 km. Comparisons with preceding melt-season conditions reveal negative correlations between winter drainage frequency and both melt-season intensity and melt-season drainage frequency. Ice velocity analyses over the ten-year period show no sustained seasonal or annual increases attributable to winter drainages, although isolated short-term increases (6–12-day) were observed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".