A New Approach in Measuring Rainfall Interception by Urban Trees in Coastal British Columbia
Bibliographic record
Abstract
Abstract Interception loss plays an important role in controlling the water balance of a watershed, especially where urban development has taken place. The aim of this study was to illustrate the importance of urban trees as a form of ‘green infrastructure’ where they reduce stormwater runoff and rainwater intensity. In addition, trees cause a delay in precipitation reaching the ground. Interception loss was studied in the North Shore of British Columbia. We applied a unique methodology for measuring throughfall under six different urban trees using a system of long polyvinyl chloride pipes hung beneath the canopy capturing the throughfall and draining it to a rain gauge attached to a data logger. Different tree species (Douglas-fir [Pseudotsuga menziesii] and western red cedar [Thuja plicata]) in variable landscape sites (streets, parks, and natural forested areas) and elevations were selected to ensure that the system adequately captured the throughfall variability. Interception and throughfall were monitored over a one year cycle for which the results of seven discrete storm events for coniferous trees from the District of North Vancouver during 2007 to 2008 are presented. Cumulative gross precipitation for seven selected events was 377 mm. Average canopy interception during these events for Douglas-fir and western red cedar were 49.1 and 60.9%, where it corresponded to average net loss of 20.4 and 32.3 mm, respectively. The interception loss varied depending on canopy structure, climatic conditions, and rainfall characteristics.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".