Overcoming Obstacles in Applying SWMM to Large-Scale Watersheds
Bibliographic record
Abstract
(SWMM) was used to simulate the watershed hydrology and water quality contaminant loadings for the Schuylkill River Basin. The Schuylkill River is over 130 mi. (21 0 km) long, includes over 180 higher-order tributaries draining more than2,000 square mi. (5,100 squarekm). For the model ofthis basin, simulated runoff loadings were accumulated at major jtmctures, or inlet points, for up to 14 land use categories in each of the 356 sub-watersheds, resulting in over 3,000 RUNOFF module sub-basins. This allows results to be summarized by land use, by model basin, by accumulated groupings of sub-watersheds, and for the entire watershed study area. Continuous SWMM was applied using a 15-min simulation time step, with full implementation of the snowmelt and groundwater subroutines, for execution periods of 30 y, to generate estimates of seasonal and annual watershed discharge and loadings. We believe this to be one of the largest applications ofSWMlVI RUNOFF in terms of the combination of the size of the drainage area, the number of sub-basins, and the number of simulation time steps. The use ofSWMM at this scale uncovered nmnerous limitations of the model requiring either modifications to the SWMM code or the implementation of functional work-arounds. For example, anay bounds set for the input and simulation data required modifica-tions to the SWMM code expanding the limits. Also, during the calibration of the model, SWMM did not allow retrieval of daily flows at select inlet points,
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".