Simulation of the snowmelt runoff contributing area in a small alpine basin
Bibliographic record
Abstract
Abstract. Simulation of areal snowmelt and snowcover depletion over time can be carried out by applying point-scale melt rate computations to distributions of snow water equivalent (SWE). In alpine basins, this can be done by considering these processes separately on individual slope units. However, differences in melt timing and rates arise at smaller spatial scales due to the variability in SWE and snowpack cold content, which affects the timing of melt initiation, depletion of the snowcover and spatial extent of the snowmelt runoff contributing area (SRCA). This study examined the effects of variability in SWE, internal energy and applied melt energy on melt rates and timing, and snowcover depletion in a small cold regions alpine basin over various scales ranging from point to basin. Melt rate computations were performed using a physically based energy balance snowmelt routine (Snobal) in the Cold Regions Hydrological Model (CRHM) and compared with measurements at 3 meteorological stations over a ridge within the basin. At the point scale, a negative association between daily melt rates and SWE was observed in the early melt period, with deeper snow requiring greater energy inputs to initiate melt. SWE distributions over the basin (stratified by slope) were measured using snow surveys and repeat LiDAR depth estimates, and used together with computed melt rates to simulate the areal snowcover depletion. Comparison with observations from georeferenced oblique photographs showed an improvement in simulated areal snowcover depletion curves when accounting for the variability in melt rate with depth of SWE in the early melt period. Finally, the SRCA was characterized as the product of the snowcovered area and the fraction of the SWE distribution undergoing active melt and producing an appreciable runoff quantity on each slope unit. Results for each slope were then aggregated to give the basin scale SRCA. The SRCA is controlled by the variability of melt amongst slope units and over individual SWE distributions, the variability of SWE, and the resulting snowcover depletion patterns over the basin.
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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) | 0.000 | 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".