Surface melt and the importance of water flow – an analysis based on high-resolution unmanned aerial vehicle (UAV) data for an Arctic glacier
Bibliographic record
Abstract
Abstract. Models of glacier surface melt are commonly used in studies of glacier mass balance and runoff; however, with limited data available, most models are validated based on ablation stakes and data from automatic weather stations (AWSs). The technological advances of unmanned aerial vehicles (UAVs) and structure from motion (SfM) have made it possible to measure glacier surface melt in detail over larger portions of a glacier. In this study, we use melt measured using SfM processing of UAV imagery to assess the performance of an energy balance (EB) and enhanced temperature index (ETI) melt model in two dimensions. Imagery collected over a portion of the ablation zone of Fountain Glacier, Nunavut, on 21, 23, and 24 July 2016 was previously used to determine distributed surface melt. An AWS on the glacier provides some measured inputs for both models as well as an additional check on model performance. Modelled incoming solar radiation and albedo derived from UAV imagery are also used as inputs for both models, which were used to estimate melt from 21 to 24 July 2016. Both models estimate total melt at the AWS within 16 % of observations (4 % for ETI). Across the study area the median model error, calculated as the difference between modelled and measured melt (EB = −0.064 m, ETI = −0.050 m), is within the uncertainty of the measurements. The errors in both models were strongly correlated to the density of water flow features on the glacier surface. The relation between water flow and model error suggests that energy from surface water flow contributes significantly to surface melt on Fountain Glacier. Deep surface streams with highly asymmetrical banks are observed on Fountain Glacier, but the processes leading to their formation are missing in the model assessed here. The failure of the model to capture flow-induced melt would lead to significant underestimation of surface melt should the model be used to project future change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".