Modeling the Dynamics of Supraglacial Rivers and Distributed Meltwater Flow With the Subaerial Drainage System (SaDS) Model
Bibliographic record
Abstract
Abstract Meltwater produced at the surface of glaciers and ice sheets has important implications for basal sliding rates and therefore ice flow velocities. In order to determine the role of supraglacial water in ice dynamics and predict future changes, we first need to understand and be able to accurately predict moulin input rates. To this end, we present the Subaerial Drainage System (SaDS) model. SaDS is a dynamic model that couples supraglacial runoff in the bare‐ice ablation zone in a distributed sheet with flow in discrete channels. Flow in the distributed sheet drives melt through potential energy dissipation, allowing a channel network to form naturally with no prior assumptions about channel locations. We apply the model to a synthetic ice sheet margin and carry out a suite of sensitivity tests. Modeled moulin inputs show expected behaviors including large diurnal variability, multi‐hour lags following peak surface melt, and demonstrate complex and diverse seasonal dynamics. The sensitivity tests illustrate the range of possible model behaviors and constrain the parameter values for which the model predicts physically realistic moulin inputs. We also apply the model to a ∼20 × 27 km 2 catchment on the southwestern Greenland Ice Sheet using RACMO melt forcing and previously mapped moulin locations. Modeled supraglacial lake and stream locations match those mapped from Landsat 8 images, and moulin inputs show varied daily and seasonal dynamics. These results demonstrate that the model is a promising tool to provide moulin inputs for subglacial and ice dynamic studies.
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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 | 0.001 | 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) | 0.000 | 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 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".