Climate change impacts on urban stormwater best management practices
Bibliographic record
Abstract
Total maximum daily load (TMDL) studies determine the amount of contaminant(s) that can be discharged daily from point (waste load allocation -WLA) and nonpoint (load allocation -LA) sources including a margin of safety (MOS) and then layout the path for achieving these levels by reductions in loadings.This has caused environmental agencies to require best management practices (BMPs) for control of urban stormwater contributions.Design storms for volume-based and peak discharge BMPs are typically determined from historic precipitation and runoff records that do not adequately address the impacts of climate change.We examine a 10-year period of predicted flows in the Spokane River watershed under 2050 climate predictions to determine the amount of additional LA removal required to meet water quality goals.While the current TMDL proposes a 50% reduction of nonpoint loading, our results indicate this will not be adequate.The implication is that urban BMPs are currently inadequately designed to handle nonpoint pollution in areas projected to experience increased precipitation events.The problem is particularly acute for rain on snow events where BMP performance is already impaired.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".