Hydrological transit times in nested urban and agricultural watersheds in the Greater Toronto Area, Canada
Bibliographic record
Abstract
Abstract Watershed mean transit times (MTTs) are used to characterize the hydrology of watersheds. Watershed MTTs could have important implications for water quality, as relatively long MTTs imply lengthier water retention, thereby allowing more time for pollutant transformation and more moderate release of pollutants into streams. Although estimates of MTTs are common for undisturbed watersheds, only a few studies to date have applied MTT models to urbanized watersheds. In the present study, we use δ18O to compare estimates of MTTs for paired suburban‐industrial and agricultural watersheds in Toronto, Canada. Although differences in precipitation δ18O between the two watersheds were negligible, there were significant differences in stream δ18O, suggesting differences in water transport pathways. Less damping between input precipitation δ18O and output stream δ18O in the suburban‐industrial watershed indicated a larger streamflow contribution from quick‐flow transport pathways. We applied three transit time models to quantify MTTs. Considering overall model fit, root mean square error, and uncertainty in model parameters, the exponential model performed the best of the three models. Optimized MTTs using this distribution across the suburban‐industrial subwatersheds ranged from 2.1 to 2.9 months, whereas those in the agricultural subwatersheds ranged from 2.7 to 7.5 months. The relatively small difference between urban and agricultural MTTs coincides with observations elsewhere. Model efficiencies could potentially be improved, and MTTs estimated more reliably, with a higher sampling frequency that captures a greater volume of overall discharge. Overall, this work provides a distinct first glimpse into the separation of MTTs between paired watersheds with such a large contrast in their land use.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".