Hydrologic and water quality modelling of bioretention columns in cold regions
Bibliographic record
Abstract
Abstract Bioretention is widely used in urban sustainable stormwater management; however, limited numerical research has been conducted on its performance in cold regions, particularly for winter snowmelt, spring runoff and summer large storms (>50 mm) for urban flood mitigation. In this study, HYDRUS 1D was used to explore these knowledge gaps. The model was comprehensively calibrated and validated against 2‐year hydrologic and water quality data of four bioretention columns with different designs under lab‐simulated cold region conditions. The Morris method was used to measure the sensitivity and interaction of the calibrated hydraulic parameters. The model revealed that the effective hydraulic conductivity ( K S ) values of the soil media were similar for winter snowmelt and spring runoff when the soil temperature was around −0.5°C. Preferential flow is likely to occur in soil media during winter or spring in cold regions. The summer modelling showed that bioretention could substantially reduce peak flow, ponding depth and duration for large storm events (even for a 1:100 local storm with 83.4 mm in 4 h). The water quality modelling confirmed experimental results that the bioretention effectively removed phosphate and ammonium but had leaching issues for chloride and nitrate. Finally, optimization and recommendations for bioretention columns were provided.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".