Water age in stormwater management ponds and stormwater management pond‐treated catchments
Bibliographic record
Abstract
Abstract Expansion of impervious surface cover results in “flashy” hydrologic response, elevated flood risk, and degraded water quality in urban watersheds. Stormwater management ponds (SWMPs) are often engineered into stream networks to mitigate these issues. A clearer understanding of how water is stored and released from SWMPs and SWMP‐treated catchments is required to better represent these engineered systems in hydrological and water quality models of urban and urbanizing watersheds. Stable water isotopes were used to compare water age in SWMPs and SWMP‐treated catchments in an urbanizing watershed. We sampled water biweekly from two SWMPs and five stream sites with varying land cover and stormwater control in their catchments. Two inverse transit time proxies (damping ratio and young water fraction) were computed along with the mean transit time (MTT) by sine–wave fitting for each SWMP and stream site using the δ18O and δ2H data. Water entering the SWMPs was consistently older (224 and 177 days) than water in or exiting the ponds (ranging from 46 to 91 days and 39 to 67 days, respectively). This finding is likely due to a combination of groundwater infiltration into broken sewer pipes that transport water into the ponds and a bias toward baseflow sampling. At the catchment scale, detention provided by SWMPs was not found to be more significant than the interactive effects of impervious cover, surficial geology, land use proportions, and catchment size in determining MTT. Overall, surficial geology explained the most variation in MTT among the seven sites. This study illustrates the potential for isotope‐based approaches of water age to provide information on individual SWMP functioning and the influence of SWMPs on catchment‐scale water movement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".