Event Based Characterization of Hydrologic Change in Urbanizing Southern Ontario Watersheds via High Resolution Stream Gauge Data
Bibliographic record
Abstract
Tracking and quantifying hydrologic change in urbanizing watersheds is a complex problem which can vary spatially and temporally throughout the effective catchment area as change occurs. Hydromodification due to urbanization usually results in a larger peak event stream discharge, a change in typical event volume, a reduced lag time between rainfall and stream discharge events, and a more complex falling hydrograph. Recently extracted Environment Canada data have allowed the creation of a high resolution instantaneous stream flow dataset dating to the late 1960s for many Ontario gauge stations. Hydrometric data were obtained for fifteen urban and semi-urban catchments within Southern Ontario ranging in size from ~50km² to 300 km² with urbanized land use assemblages varying from <5% to 80%. Utilizing automated methods, each individual runoff event from the hydrographic record was identified and characterized. Temporal changes to urban land area, land use, and road length were quantified for each watershed from aerial photography spanning the period of record at approximately 8 year intervals allowing identified trends in event hydrograph parameters to be correlated quantitatively with the alteration of the catchment over time. <br> \nIncreasing trends in event peak discharge were identified in all but one study catchment. Event volume was found to be consistently increasing in most of the urban watershed, while trends in event duration were observed but with no clear increasing or decreasing trend. The lack of consistent trends in the timing and distribution of flow during runoff events suggest that build-out, drainage network design, and stormwater management systems play differing roles in the neighbouring urban catchments. Changes to flood recurrence intervals through the period of urbanization were also investigated; peak magnitude of high frequency events is affected to a greater extent than low frequency or flood events. The relative change in return frequency distribution is not consistent between catchments, also the degree of alteration can differ between various recurrence intervals at a gauge. Peak discharge of some return periods appeared to decrease with urban development suggesting that the increased detention brought with urban stormwater management systems have effectively offset the increased runoff due to additional impervious area and improved drainage efficiency. A consistent relationship defining the change in geomorphically significant return periods (i.e. channel forming flow) with urbanization was identified in neighbouring urban catchments.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".