Spatial Assessment of Overland Flow, Pollutant Concentration, and First Flush Using a 2D Non-Point Source Pollution and Hydrological Model for Urban Catchments
Bibliographic record
Abstract
Modeling high-resolution spatial distribution of overland flow and total suspended solids concentration (TSS) is becoming more feasible due to parallel computing and gridded physiographic and rainfall data available. In this article, we developed a fully distributed model based on the methods of Green-Ampt for infiltration loss, non-linear reservoir for flow routing, and build-up and wash-off approach for TSS accumulation. The model was applied in a parking lot catchment (7.85 ha) at the University of Texas at San Antonio. Calibration results indicate that the model can predict overland flow (NSE = 0.89, R2 = 0.95) and TSS concentration (NSE = 0.91, R2 = 0.96) at the outlet of the catchment. Moreover, 54% of the normalized TSS accumulated mass were carried out in the first 30% of normalized accumulated volume, characterizing the first flush at the outlet by the m(v) curve. The first flush spatial analysis, however, showed that other hotspots had more intense m(v) rates for 30% of the normalized accumulated volume, indicating possible areas to allocate low impact development techniques to treat first flush. A topographic influence analysis was performed assessing the role of topographic slope, total volume, total pollutant mass, and event mean concentration (EMC) for all cells in the grid by a descriptive statistical analysis via Pearson and Spearman correlations. Only the total pollutant mass and total volume had a strong Pearson correlation, and EMC, total mass, and total volume had a strong Spearman correlation compared to each other. These results suggest that these variables follow a non-linear monotonic relationship.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".