Rapid mobilization of old water during urban stormflow
Bibliographic record
Abstract
Abstract Urban landscapes do not easily fit into common conceptual models of streamflow generation because extensive impervious surfaces, artificial drainage via sewers, stormwater control measures, and the removal of vegetation substantially modify the pathways rainfall and meltwater take to streams. Hydrologic responses are well characterized for urban streams, however, the relative sources and flow pathways of water within urban landscapes are relatively understudied compared with undisturbed areas. Different water sources (e.g. groundwater, or ‘old’ water, rainfall, or ‘new’ water) can have distinct chemical characteristics whose mixing determines the quality of streamwater. In this study, we investigated the relative contribution of different sources of water (pre‐event water/groundwater, rainfall, wastewater, and tap water) to urban stormflow. Water samples were collected from three highly urban streams (65%–89% impervious cover) during 11 storm events and analysed for stable isotopes of oxygen and hydrogen in water (δ18O and δ2H). Precipitation samples were collected from a nearby precipitation collector to characterize the isotopic signature of new water inputs. Isotopic hydrograph separation (IHS) was used to estimate the relative proportion of new and old water in each event. The IHS results indicated that 25%–63% (δ18O) of the storm hydrograph was old water. For 9 of the 11 storms, the peak in old water contribution coincided with peak flow. These results are similar to findings from IHS studies in undisturbed catchments which suggests that under certain conditions (i.e. low intensity, long duration rainfall) the contribution of old water to stormflow in urban catchments is of a similar magnitude to undisturbed catchments. The use of tracer data is important for further exploring the conceptual model of streamflow generation that suggests that new water dominates stormflow in flashy and heavily urbanized catchments, and can be useful for characterizing the role of greenspaces and storm characteristics on water partitioning.
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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 | 0.000 |
| 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 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".