Evaluating the Effectiveness of Bioretention Systems for Managed Aquifer Recharge in Cold Climates
Bibliographic record
Abstract
Bioretention systems are a low impact development (LID) technology being utilized to mitigate the flooding and contamination impacts of urbanization on stormwater runoff. These systems are difficult to monitor in cold climates using traditional water-budget techniques when surface temperatures drop below freezing. A bioretention system in the Town of Okotoks, Alberta Canada was investigated to address four objectives. 1) Develop a method to monitor the system year-round. 2) Investigate seasonal changes in the infiltration and recharge characteristics of the system. 3) Determine if there was evidence of nutrient leaching from the system. 4) Develop a technique to estimate recharge from the system. The site was instrumented using a paired monitoring well set-up that allowed for characterization of the local groundwater mound induced by recharging waters. The solution of Hantush was used to calculate the recharge rates required to produce observed groundwater mounding heights and estimate the recharge produced by the bioretention system on a per-event basis. The method overestimated recharge by a factor of three compared to estimates obtained using the water budget technique, but could be corrected by finding an appropriate K value to match the water budget data. The system was estimated to recharge 2500 m3 of stormwater over two years. Winter snow-melt events produced recharge volumes comparable to those induced by a 4.5 mm summer precipitation event. Recharge volumes were found to be strongly correlated with precipitation magnitude, groundwater mound height, and the duration of the mounding event, and had no significant correlation with antecedent soil moisture. Nitrate leeching was observed beneath the bioretention system, with aquifer nitrate concentrations reaching 3.8 mg/L. While bioretention systems are effective at recharging aquifers in cold climates, groundwater plays an important role in the function of these treatment-infiltration systems and care must be taken to ensure nitrogen contamination does not lead to adverse ecological impacts for wildlife or humans. Future work should look at decreasing the uncertainty in recharges estimates associated with this method and accounting for the impact of heterogeneous and anisotropic aquifers with time-variant infiltration rates and recharge pond geometries at surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".