Predicting stormwater nitrogen loads from a cold-region urban catchment in year 2050 under the impacts of climate change and urban densification
Bibliographic record
Abstract
Urban stormwater is a primary source of pollution for receiving water, but there is a shortage of studies on pollutant loads from urban catchments in cold regions. In this study, we coupled a build-up and wash-off model (in Mike Urban) with a climate change model to assess the impacts of climate change and urban densification on stormwater nitrogen loads (TN, TKN, NO x -N, and TAN) in an urban catchment in Canada. We calibrated and validated the Mike Urban model against observed event mean concentrations and nitrogen loads from 2010 to 2016. Results show that the nitrogen loads were mainly governed by rainfall intensity, rainfall duration, and antecedent dry days. Future precipitation data were downscaled using the Global Climate Models (GCMs), and three different Representative Concentration Pathways (RCP 2.5, RCP 4.5, and RCP 8.5) were used. Modeling results show that the TN, TKN, NO x -N, and TAN loads in 2050 will increase by 28.5 - 45.2% from May to September under RCP 2.5 compared to those from 2010 to 2016, by 34.6 - 49.9% under RCP 4.5, and by 39.4 - 53.5% under RCP 8.5. The increase of our projected TN load (from 1.33 to 2.93 kg·N/ha) is similar or slightly higher than the limited studies in other urban catchments. This study provides a reference for predicting stormwater nitrogen loads in urban catchments in cold regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".