Using Bioretention Retrofits to Meet Virginia’s New Stormwater Management Regulations: A Case Study
Bibliographic record
Abstract
Virginia’s new stormwater regulations involve the use of the Runoff Reduction Method (RRM), a methodology to estimate a volume reduction in predicted runoff. Regulations require that for downstream erosion control, the product of the peak flow rate and runoff volume (Q*RV) from one-year storm events in the post-development condition be reduced to less than pre-development Q*RV. This study models different bioretention sizing scenarios in a developed watershed in Blacksburg, Virginia to determine the performance at both the sub-watershed and watershed levels. In addition, models of “optimal” bioretention cells, sized to meet the RRM for each sub-watershed, are evaluated. A direct relationship is observed between the size of the cell required to meet the RRM and the sub-watershed’s developed Natural Resources Conservation Service (NRCS) curve number, and a sizing analysis is provided. Modeling shows that the required size of “optimal” cells for many sub-watersheds exceeds conventional bioretention designs. Upon applying the RRM for all sub-watersheds, the resulting hydrograph at the watershed outlet more closely resembles the pre-development hydrograph than existing development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".