Urban Tree Rainfall Interception Measurement and Modeling in WinSLAMM, the Source Loading and Management Model
Why this work is in the frame
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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 usingWinSLAMM 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it