SWMM Simulation of the Stormwater Volume Control Performance of Rainwater Harvesting Systems
Bibliographic record
Abstract
EPA’s stormwater management model (SWMM) has been widely used in the planning and design of stormwater management facilities. SWMM is capable of simulating the hydrologic performance of low-impact development (LID) practices. However, on account of the default parameter setting of the constant water use rate throughout the entire simulation period in the SWMM’s LID module for rain barrels (denoted as SWMM-LID), there is a limitation of using it for some design cases where water use occurs only during dry periods. This study aims to investigate the accuracy of the SWMM-LID for modeling the rainwater harvesting system when water is consumed only in dry periods. Rain barrels were also modeled with two alternative methods and their long-term average runoff capture efficiency was evaluated based on simulated data. The first alternative method (SWMM-SC) represents a rain barrel as an equivalent subcatchment in SWMM, while the second (Self-Coded Simulation) uses a self-coded continuous simulation algorithm based on water balance equations. Comparing the Self-Coded Simulation results with the results obtained from SWMM-SC and SWMM-LID for a number of design cases at Atlanta and Billings, US, the SWMM-SC is shown to provide more accurate results than the SWMM-LID. It is therefore concluded that SWMM’s LID module for rain barrels may need to be improved to properly model cases where water use occurs only during dry periods, or the proposed way of representing rain barrels as subcatchments may be used for assessing the stormwater volume control performance of rain barrels.
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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".