Representation of Non-Directly Connected Impervious Area in SWMM Runoff Modeling
Bibliographic record
Abstract
The overland flow runoff algorithm used in the USEPA SWMM model (Huber and Dickinson, 1988) has been a leading method for dynamic runoff simulation for over 30 years.The Runoff module in SWMM divides drainage catchments into two principal compartments, one each for impervious and pervious surfaces.Runoff discharges via a non-linear reservoir discharge equation to a drainage inlet, from where it can be routed through a collection system or downstream drainage subcatchments (Figure 18.1).SWMM44H, developed in 2002, allowed routing onto another drainage subcatchment (Huber, 2001).SWMM5, released in 2004, introduced new parameters that improve representation of typical urban runoff.The new parameters partition directly-connected impervious area (DCIA) and non-directly-connected impervious area (NDCIA) within a single catchment (Figure 18.2).The traditional representation of an urban watershed can be quite effective in environments where impervious area dominates and runoff from pervious area is of minor importance.However, in other settings, this approach suffers from its failure to directly represent impervious areas that drain onto pervious areas, such as the cases that roofs drain onto lawns, or high impervious lands drain onto the low impact development areas or other BMP infrastructures.Explicit representation of DCIA and NDCIA better
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".