Application of an improved surface energy balance model to two large valley glaciers in the St. Elias Mountains, Yukon
Bibliographic record
Abstract
Abstract Glacier surficial melt rates are commonly modelled using surface energy balance (SEB) models, with outputs applied to extend point-based mass-balance measurements to regional scales, assess water resource availability, examine supraglacial hydrology and to investigate the relationship between surface melt and ice dynamics. We present an improved SEB model that addresses the primary limitations of existing models by: (1) deriving high-resolution (30 m) surface albedo from Landsat 8 imagery, (2) calculating shadows cast onto the glacier surface by high-relief topography to model incident shortwave radiation, (3) developing an algorithm to map debris sufficiently thick to insulate the glacier surface and (4) presenting a formulation of the SEB model coupled to a subsurface heat conduction model. We drive the model with 6 years of in situ meteorological data from Kaskawulsh Glacier and Nàłùdäy (Lowell) Glacier in the St. Elias Mountains, Yukon, Canada, and validate outputs against in situ measurements. Modelled seasonal melt agrees with observations within 9% across a range of elevations on both glaciers in years with high-quality in situ observations. We recommend applying the model to investigate the impacts of surface melt for individual glaciers when sufficient input data are available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".