The impact of urban heat island on snow properties and stratigraphy in the Moscow region
Bibliographic record
Abstract
The urban heat island (UHI) effect is common in large cities during both summer and winter. In winter, heat is not only retained by infrastructure, buildings, and roads, but also actively released during their use, with substantial losses to the surrounding environment. This leads to higher urban temperatures compared to nearby rural areas. While the phenomenon is well documented, its impact on snow cover properties remains understudied. In this study, we examine the influence of the UHI on snow cover by comparing snow properties and stratigraphy between an urban site (Moscow) and adjacent rural site (Khotkovo) over the 2014–2022 period. Our methodology included in-situ measurements of snow depth and density, analysis of meteorological station data on snow depth, temperature and precipitation, and satellite-based assessment of land surface temperature using MODIS (MOD11A1) imagery. Results show that snow cover duration was shorter at the urban site due to later onset and earlier melt. Despite slightly higher winter precipitation in Moscow, snow depth and snow water equivalent were consistently lower than in Khotkovo. Urban snowpacks had higher average density. Stratigraphic analysis revealed thicker melt-freeze layers in Moscow and thinner layers of faceted crystals and depth hoar compared to the rural site. These findings highlight the role of the urban heat island in altering snow cover properties and stratigraphy. They provide valuable insights for improving snowpack modeling and assessing hydrological and ecological conditions in urban environments.
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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".