Modeling of Low Impact Development Nutrient Reduction Performance in the Lake Simcoe Watershed
Bibliographic record
Abstract
Stormwater quality management has evolved from traditional centralized downstream control devices (e.g. ponds and wetlands) to distributed low impact development practices (LID) at the source (e.g. bioretention, porous pavement, greenroof). In order to develop master LID plans for municipalities in the Lake Simcoe watershed (3576 km2), a new modeling approach was developed. The challenge of modeling small scale LID practices over a watershed scale was resolved using unit response functions (URF) of different types of LID. The concept of URF is based on the linear assumption of LID performance on a watershed level where routing is not important. Detailed URF of runoff and nutrient reduction were developed on a lot level using US EPA SWMM models and linked with lot level characteristics such as imperviousness percentage. The process of modeling include: (1) screening of appropriate LID across the watershed based on identification of unsuitable areas (e.g. wellhead protection area, NaCl concentration, industrial land use) and prioritization suitable lots which maximize environmental benefits and demonstration potential; (2) development of hydrological unit response functions of each type of LID (i.e. average annual runoff and nutrient loading reduction) using US EPA SWMM models; (3) aggregation of the cumulative runoff and nutrient reduction of all appropriate LID at each municipalities; (4) cost-effective analysis of different combinations of LID (i.e. Pareto front); (5) recommendation of the preferred LID combinations for each municipal within the watershed . Results of the modeling indicate that (1) the average annual runoff volume reduction of implementing LID for the uncontrolled urban areas in Lake Simcoe watershed is estimated to be between 20% and 33%; and (2) the average annual phosphorus reduction of implementing LID for the uncontrolled urban areas in Lake Simcoe watershed is estimated to be between 2.0 to 2.7 tonnes per year. This study has demonstrated a new modeling approach of small scale LID over watershed scales.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".