Representation of Small Temporal and Spatial Changes in Rainfall Conditions by Analytical Probabilistic Stormwater Models
Bibliographic record
Abstract
ABSTRACT A set of analytical equations has been derived to directly quantify the average hydrologic performance of low‐impact development facilities such as bioretention cells and green roofs. These analytical equations, collectively referred to as the analytical probabilistic stormwater models (APSWMs), have been previously validated for representing regional rainfall conditions for regions across the US using selected example locations. This study evaluates APSWMs' capability to accurately represent small temporal changes in rainfall conditions at the same locations and small spatial changes in rainfall conditions between nearby locations. Results from the US EPA's Stormwater Management Model (SWMM), a continuous simulation tool representing rainfall conditions using long‐term observed series, are used as the basis for comparisons. For the 176 hypothetical cases of bioretention cells and 208 cases of green roofs, the temporal performance differences between early and recent periods as determined by APSWM and SWMM were within ±0.03 for green roofs and up to ±0.08 for bioretention cells, while the spatial performance differences between paired locations averaged about 3% in relative terms. These results demonstrate that APSWM is capable of consistently and accurately representing the small spatial and temporal rainfall‐condition changes. They also provide additional evidence of APSWMs' reliability and support jurisdictions to use APSWMs in the planning and design of LID facilities.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".