Continuous simulations of urban stormwater runoff and total suspended solids loads: influence of varying climatic inputs and catchment imperviousness
Bibliographic record
Abstract
Potential implications of climate change for future stormwater management were addressed by undertaking continuous simulations of runoff and total suspended solids (TSS) loads for three urban catchments, with imperviousness varying from 23 to 63%, which were exposed to five rainfall regimes during the snow-free part of the year: the current climate and four climate change scenarios projecting higher rainfalls. Simulated runoff volumes increased in all the future scenarios, particularly in the sub-arctic climate and the fixed uplift scenario (plus20) indicating appreciable rainfall increases. Simulated runoff volumes increased depending on the projected increases in rainfall and increasing runoff contributions from pervious areas when more intense future rainfalls exceeded hydrologic abstractions. The increased runoff volumes then contributed higher TSS loads, which were highly variable for the rainfall regimes tested. In cold climate regions, residues of solids from winter road maintenance may contribute to high initial accumulations of TSS on the catchment surface and high washed off loads. In general, the study suggests that intermediate design-life stormwater management facilities require flexible design allowing for future step-wise adaptation by gradually increasing design capacities and modifying treatment trains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".