Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada
Bibliographic record
Abstract
Water percolation in snow plays a crucial role in the avalanche risk assessment. Liquid water content and wetting front are hard to measure in the field; hence, accurate simulation of the phenomena can be of great help to forecasters. This study was the first to evaluate water percolation simulations with the SNOWPACK model using Richards’ scheme on Mount Fidelity, Glacier National Park, Canada. The study highlights that, at this site, an updated configuration on precipitation phase transition and new snow density can significantly improve simulations of the snow cover, and water percolation in particular, which can be relevant in an era of an increased occurrence of rain-on-snow (ROS) events. More specifically, emphasis was put on the quality of the input data and parameters. The analysis of the precipitation phase temperature threshold showed that a value of 1.4°C was the best suited to track the rain/snow transition on site. A 10-year analysis of 24-h precipitation measured using the rain gauge and 24-h new snow water equivalent showed an excellent correlation. New snow density sub-models were evaluated using the 24-h new snow density values taken by the park technicians. The BELLAIRE model performed best and was used to drive the snow simulations. Two SNOWPACK snow simulations were evaluated using 1) rain gauge precipitation amount (PCPM) and 2) automatic snow height measurement (HS) at the same site. Both runs simulated the main snowpack layers observed during the dry season (i.e., before spring percolation was observed), and both simulated the snow properties with good accuracy. The water equivalent of snow cover, used as a proxy for a first-order characterization of the simulations generated by both simulations, was slightly underestimated compared with four manual measurements taken on-site during the winter. Nevertheless, the comparison of both measured density and modeled bulk density showed great correspondence. The percolation timing and wetting front depth were evaluated using field measurements from field campaigns and continuous observations from on-site instruments. The main percolation events were correctly simulated and were coincident with the observed wet avalanche cycles. The results highlight the need for accurate input data on valid simulation of the wetting front and percolation timing on site. Good percolation information generated using the SNOWPACK model and Richards’ scheme could be used to assess the snowpack stability by forecasters in areas where such data are available.
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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 | 0.001 |
| 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".