Urban Tree Rainfall Interception Measurement and Modeling in WinSLAMM, the Source Loading and Management Model
Bibliographic record
Abstract
Recently, the role of urban trees in stormwater management has received increasing interest.The interception of rainfall by urban trees has been proposed to provide substantial benefits by reducing runoff rates and quantities.However, few data are available for rainfall interception of trees in typical urban settings, in contrast to research from natural forests that have dense standings of trees.Additional needed information includes:• how interception changes for different seasonal changes in urban tree canopies for different types of trees,• how these interception values vary for different rains; and• how interception affects urban stormwater for typical urban settings.This paper describes a series of direct interception (throughfall) measurements under urban trees and calibrated modeling using WinSLAMM to provide some data to address these questions.This study used a standard rain gauge located in an open area and rain gauges under deciduous water oaks (Quercus nigra) and evergreen loblollly pines (Pinus taeda) trees.A total of 85 rain events were monitored from early December 2018 through January 2020 and were statistically evaluated.It was found that tree type had the most important effect on tree canopy interception, followed by rain amount, while seasonal effects were not as important.The interception under the pine was only important for the smallest rain events, while interception under the oaks varied from about 30% to 50%, depending on the rain amount.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".