Observations of urban snow properties in Calgary, Canada
Bibliographic record
Abstract
Abstract Urban winter hydrology has garnered very little attention owing to the general notion that high‐intensity rainfalls are the major flood‐generating events in urban areas. As a result, few efforts have been made to research urban snow and its melt characteristics. This study investigates the characteristics of urban snow that differentiate it from rural snow, and makes recommendations for incorporating these characteristics into an urban snowmelt model. A field study was conducted from the fall of 2001 to the spring of 2002 in the city of Calgary, Canada. Snow depths and densities, soil moisture, soil temperature, snow albedo, net radiation, snow evaporation, and surface temperature were measured at several locations throughout the winter period. The combination of urban snow removal practices and the physical elements that exist in urban areas were found to influence the energy balance of the snowpack profoundly. Shortwave radiation was found to be the main source of energy for urban snow; as a consequence, the albedo of urban snow is a very important factor in urban snowmelt modelling. General observations lead to the classification of snow as one of four types: snow piles, snow on road shoulders, snow on sidewalk edges, and snow in open areas. This resulted in the development of four separate functions for the changing snow albedo values. A study of the frozen ground conditions revealed that antecedent soil moisture conditions had very little impact on frozen ground, and thus frozen ground very nearly always acts as a near impervious area. Improved flood forecasting for urban catchments in cold regions can only be achieved with accurate modelling of urban winter runoff that involves the energy balance method, incorporating snow redistribution and urban snow‐cover characteristics, and using small time steps. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.000 | 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".