Modelling of rain interception by trees in outdoor urban climate
Bibliographic record
Abstract
Studies in building and urban physics can benefit from detailed modelling of outdoor climate conditions.Up to two-thirds of the rain, which a tree is exposed to, can be intercepted by tree foliage, branches and stem and evaporate without reaching the ground.However, the interception of rainwater by trees has not yet been considered in wind-driven rain studies of local urban climate.The aim of our work is to model rain interception by trees and implement it into a microclimate modelling suite (urbanMicroclimateFoam) based on OpenFOAM, in order to consider the outdoor environmental conditions more accurately.Field measurements are performed on a red oak for validation.Numerical results performed with the modelling tool which does not consider rain interception shows an overestimation of rain deposition on the ground, compared to measurements for two rain events of 2022 summer.Ongoing work leads to the addition of sink and source terms to account for interception and to close the presented gaps.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".