Modelling the cooling potential of street trees at city-scale with COSMO-BEP-Tree
Bibliographic record
Abstract
Street trees are more and more regarded as a potential measure to mitigate the excessive heat in urban areas resulting from climate change and the urban heat island. However, the current knowledge of the cooling effect of street trees relies on studies at the micro-scale while potential interactions at the city-scale are yet to be understood. In fact, the vast majority of large-scale modelling studies only represent street trees outside the street canyon, neglecting important effects such as the shading and sheltering. In order to explicitly represent street trees in coupled urban climate simulation, the multi-layer urban canopy model BEP-Tree was coupled with the regional weather and climate model COSMO-CLM. The coupled model, named COSMO-BEP-Tree, enabled simulating the radiative, flow and energy interactions between street trees, canyon surfaces and the atmosphere during weather and climate simulations. In this study, COSMO-BEP-Tree is used to model the cooling potential of street trees during a heatwave event in Basel, Switzerland. The impact of street trees is explored in terms of near-surface air temperature and thermal comfort. The impact of greening scenarios is simulated and compared with other heat mitigation strategies. The results highlight contrasting urban climate effects of street trees during daytime and night-time, where different processes become dominant. The daytime cooling was primarily a local effect and proportional to the local density of street trees. In contrast, the impact was more widespread at night, where city-scale interactions become important. Beside air temperature, the model results suggest a significant impact of street trees on wind speed and canyon surface temperature. Owing to these effects, street trees produced a larger impact on thermal comfort than on air temperature. Finally, the need for further model development with respect to urban hydrology is outlined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".