Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale
Bibliographic record
Abstract
Abstract Single or a limited number of point observations, such as from index stations, are commonly assumed to be representative for the snow cover of larger areas in many applications. This study presents a systematic investigation of the relationship between point observations and areal mean snow depths ranging from the scale of tens of metres to entire catchments. We analyse aerial snow depth information from four mountain regions in the European Alps, one in the Spanish Pyrenees and one in the Canadian Rocky Mountains, obtained from airborne laser scanning surveys. This rich data set allowed to compare point values with snow cover statistics, reflecting the real snow depth distribution of the investigation areas. We present two contrasting approaches in order to assess the representativeness of typical flat‐field snow depth measurements. In the first approach, we define potential index stations based on topographic characteristics as commonly applied for snow cover monitoring stations. The point observations of these index stations are then compared with the mean values in their vicinities. We show that most of the index stations strongly overestimate the snow depth of the catchment and of their surrounding area at distances of several hundreds of metres. Results confirm the expectation that the larger the support area, the smaller the difference to the mean of the complete catchment. The second approach was to analyse topographic characteristics of all cells with snow depths that deviated less than 10% from the catchment mean. It appears that these representative cells are rather randomly distributed and cannot be identified a priori. In summary, our results show large potential biases of index stations with respect to snow distribution and therefore also snow water equivalent. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".