Time‐lapse photogrammetry reveals hydrological controls of fine‐scale High‐Arctic glacier surface roughness evolution
Bibliographic record
Abstract
Abstract In a warming Arctic, as glacier snowlines rise, short‐ to medium‐term increases in seasonal bare‐ice extent are forecast for the next few decades. These changes will enhance the importance of turbulent energy fluxes for surface ablation and glacier mass balance. Turbulent energy exchanges at the ice surface are conditioned by its topography, or roughness, which has been hypothesized to be controlled by supraglacial hydrology at the glacier scale. However, current understanding of the dynamics in surface topography, and the role of drainage development, remains incomplete, particularly for the transition between seasonal snow cover and well‐developed, weathered bare‐ice. Using time‐lapse photogrammetry, we report a daily timeseries of fine (millimetre)‐scale supraglacial topography at a 2 m2 plot on the Lower Foxfonna glacier, Svalbard, over two 9‐day periods in 2011. We show traditional kernel‐based morphometric descriptions of roughness were ineffective in describing temporal change, but indicated fine‐scale albedo feedbacks at depths of ~60 mm contributed to conditioning surface topography. We found profile‐based and two‐dimensional estimates of roughness revealed temporal change, and the aerodynamic roughness parameter, z0, showed a 22–32% decrease from ~1 mm following the exposure of bare‐ice, and a subsequent 72–77% increase. Using geostatistical techniques, we identified ‘hole effect’ properties in the surface elevation semivariograms, and demonstrated that hydrological drivers control the plot‐scale topography: degradation of superimposed ice reduces roughness while the inception of braided rills initiates a subsequent development and amplification of topography. Our study presents an analytical framework for future studies that interrogate the coupling between ice surface roughness and hydro‐meteorological variables and seek to improve parameterizations of topographically evolving bare‐ice areas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".