SWMM Modeling of a Rural Watershed in the Lower Coastal Plains of the United States
Bibliographic record
Abstract
The Storm Water Management Model (SWMM) has proven highly effective in modeling urban and suburban watersheds since its conception in 1969.While heavily implemented in the simulation of urban watersheds, its performance in strictly rural watersheds has been less frequently evaluated.Lower Coastal Plain watersheds are prevalent in the southern United States and the description of hydrological processes there is critical to the development of local water resources.This paper presents an ongoing research effort in a rural 2.90 km 2 site located in Pittsview, Alabama and owned by Alabama Associated General Contractors.A network of rain gauges, associated with a portable weather station, two monitoring wells and two weirs have been deployed and data is continuously recorded.Collected field data has then been used for initial development efforts of a PCSWMM model that provides representation of the rainfall-runoff processes at the watershed.As more data is obtained with regards to the local water cycle, it is anticipated that the PCSWMM model calibration will improve, which in turn will provide greater confidence in the software's ability to simulation such conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".