High altitude outliers: when snow under-catch combined with altitudinal gradients yield unbelievable water balance results
Bibliographic record
Abstract
Determining the precipitation actually fallen in mountainous basins may turn out to be a nightmare, even before applying one single hydrological model. One way to illustrate this is to look at the water balance of catchments affected by snow. Indeed, they may present unrealistic physical behaviours: annual runoff could be (much)\ngreater than annual estimated precipitation on the catchment, or at least, annual measured precipitation.\nWe point out two main difficulties about our monster: on one hand, the\nunderestimation at the gauge stations and, on the other hand, the altitudinal effect on precipitation. These two hydro-meteorological aspects are enhanced by the scarcer point measurements at high altitudes than in valleys. Besides, they are correlated:\nthe higher the altitude, the more uncertain the altitudinal gradients (low stations density) and the more important the underestimation of the precipitation (higher quantity of snow).\nIn order to improve our precipitation knowledge on mountainous catchments and to try to bring a solution about this monstrosity, we start our work by the determination of altitudinal gradients for air temperature. This meteorological data present the advantage to be more spatially homogeneous than the precipitation, and it will be\nvery useful in the correction of solid precipitation losses. Then we work on the determination of altitudinal gradient for precipitation, despite the probable importance of very local influences. Finally, we look at the possible corrections of the solid precipitation underestimation. We present our results on three countries: Switzerland, Sweden and Canada(Québec).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".