Modelling rainfall interception by urban trees
Bibliographic record
Abstract
Trees in the urban environment have significant effects on the hydrological cycle by aiding in the reduction of stormwater runoff through rainfall interception. Factors such as wind exposure, relative humidity and leaf area index in the urban environment differ from those in a forest and affect the processes occurring within the canopy of conifers and deciduous tree species differently. This study focused on the interception losses of trees in an urban setting with a view to providing some information on tree selection in urban environments. An analytical model was formulated based on a rainfall interception model developed for sparse canopy forests and preliminary data on water losses from tree canopy interception. The model was validated using empirical data, and an assessment of the performance of the model for four deciduous tree species (white oak, Norway maple, green ash and Prunus sp.). Model-calculated values of interception losses and throughfall were congruent with measured empirical values. Sensitivity analysis with respect to model parameter values revealed that evaporation and rainfall rates were the most sensitive parameters for model output. The ratio of evaporation rate to rainfall rate used in the model was identified as the most dynamic parameter. To measure independently the two components requires further analysis, and a more reliable measurement of leaf area index.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".