Hydrological footprints of urban developments in the Lake Simcoe watershed, Canada: a combined paired‐catchment and change detection modelling approach
Bibliographic record
Abstract
Abstract Urban sprawl and regional climate variability are major stresses on surface water resources in many places. The Lake Simcoe watershed (LSW) Ontario, Canada, is no exception. The LSW is predominantly agricultural but is experiencing rapid population growth because of its proximity to the Greater Toronto area. This has led to extensive land use changes that have impacted its water resources and altered run‐off patterns in some rivers draining to the lake. Here, we use a paired‐catchment approach, hydrological change detection modelling and remote sensing analysis of satellite images to evaluate the impacts of land use change on the hydrology of the LSW (1994 to 2008). Results show that urbanization increased up to 16% in Lovers Creek, the most urban‐impacted catchment. Annual run‐off from Lovers Creek increased from 239 to 442 mm/year in contrast to the reference catchment (Black River at Washago) where run‐off was relatively stable with an annual mean of 474 mm/year. Increased annual run‐off from Lovers Creek was not accompanied by an increase in annual precipitation. Discriminant function analysis suggests that early (1992–1997; pre‐major development) and late (2004–2009; fully urbanized) periods for Lovers Creek separated mainly based on model parameter sets related to run‐off flashiness and evapotranspiration. As a result, parameterization in either period cannot be used interchangeably to produce credible run‐off simulations in Lovers Creek because of greater scatter between the parameters in canonical space. Separation of early and late‐period parameter sets for the reference catchment was based on climate and snowmelt‐related processes. This suggests that regional climatic variability could be influencing hydrologic change in the reference catchment, whereas urbanization amplified the regional natural hydrologic changes in urbanizing catchments of the LSW. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 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) | 0.000 | 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".