Optimization of over-summer snow storage at mid-latitude and low elevation
Bibliographic record
Abstract
Abstract. Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using wood chip insulation, has been successfully employed as a climate change adaptation strategy by high elevation and/or high latitude ski centers in Europe and Canada. Such storage has never been attempted at low-elevation, mid-latitude sites nor have studies quantified snowmelt rate through the summer or correlated snow melt rate with environmental characteristics including ground and air temperature, humidity, wind, and solar radiation. Such data, along with tests of different insulation strategies, are needed to optimize snow storage strategies. Here, we assess the melt rates of two snow piles (each ~ 200 m3) emplaced during spring 2018 in Craftsbury, Vermont (45° N and 360 m asl). We monitored volume change over the melt season using terrestrial laser scanning. We continually logged air-to-snow temperature gradients under different insulating layers including rigid foam, open cell foam, and wood chips both with and without an underlying insulating blanket and an overlying reflective cover. We also measured ground temperatures to a meter depth both under and adjacent to the snow piles and used a snow tube to measure snow density. Snow volume of the two piles changed similarly over the summer, with minimum rates of change (−0.29 m3 day-1 and −0.88 m3 day-1) in September and maximum volumetric loss rates in July of −1.98 m3 day-1 and −2.81 m3 day-1. Snow density changed little over time indicating that most volume reduction was the result of melting. Wet wood chips underlain by an insulating blanket and covered with a reflective cover was the most effective combination for minimizing melt, likely because the surface reflected incoming shortwave radiation while the wet wood chips provided significant thermal mass, allowing much of the energy absorbed during the day to be lost as blackbody radiation at night. Together, the data we collected demonstrate the feasibility of over-summer snow storage even at mid latitudes and low altitudes and suggest efficient insulation strategies.
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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 | 0.000 | 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 | 0.001 | 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".