Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers
Bibliographic record
Abstract
Abstract Urbanization is characterized by increased impervious cover, artificial drainage via sewers, soil compaction, and vegetation removal, which fundamentally alters how water moves across landscapes. Less rainwater and snowmelt infiltrate the subsurface and stream responses to precipitation are faster and larger compared to natural environments. While the impacts of catchment imperviousness on hydrologic processes have been widely investigated, the relationship between stream hydrologic response and infrastructure‐mediated hydrologic connectivity across a wide range of urban watersheds has not. This study examines the relative magnitude of control that catchment characteristics, total impervious area (TIA), and sewer‐corrected TIA have on broad‐scale spatial variability in hydrologic response. Sewer‐corrected TIA accounts for inter‐catchment water transfer via storm and/or combined sewer pipes and is feasible to estimate at the watershed‐scale using urban drainage asset data. Daily stream discharge data were used to calculate the Richards‐Baker Flashiness Index for 96 watersheds located across southern Ontario, Canada. For a subset of watersheds ( n = 39) with suitable stream discharge and rainfall data, watershed average event runoff ratio (RR) was calculated, and watersheds with spatial sewer network data ( n = 24), were used to assess if sewer‐corrected TIA is a better predictor of hydrologic response than TIA. As expected, stream flashiness and RR increased linearly as a function of TIA and sewer‐corrected TIA, however, sewer‐corrected TIA did not explain any additional variability in the response variables. Likewise, the relative control of catchment characteristics on the spatial variability in streamflow generation were negligible. Despite sewer‐corrected TIA not improving our understanding of broad scale variation in runoff response, it could be a useful metric to consider when assessing inter‐event variability in runoff response of, heavily urbanized sub‐catchments for improved understanding of flooding and water quality.
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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 | 0.000 |
| 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 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".