Investigation of the Long-Term Trends in the Streamflow Due to Climate Change and Urbanization for a Great Lakes Watershed
Bibliographic record
Abstract
Climate change and rapid urbanization could possibly increase the vulnerability of the Great Lakes Basin, Canada, which is the largest surface freshwater system in the world. This study explores the joint impact of climate change and land-use changes on the hydrology of a rapidly urbanizing Credit River watershed which lets out into Lake Ontario 25 km southwest of downtown Toronto, Ontario (ON), Canada; we began by classifying the watershed into urban and rural sections. A non-parametric Mann–Kendall test and the Sen slope estimator served to detect and describe the annual-, seasonal-, and monthly-scale trends in the climate variables (temperature, precipitation, and evapotranspiration), as well as the streamflow characteristics (median annual streamflow, baseflow, Runoff Coefficients (RC), Flow Duration Curve (FDC), Center of Volume (COV), and Peak Over Threshold (POT)) since 1916 for four rural and urban sub-watersheds. The temperature, precipitation and evapotranspiration (1950–2019) showed significant increasing trends for different months and seasons. Furthermore, the results indicated that the median annual streamflow, 7-day annual minimum flow, and days above normal are increasing; meanwhile, the annual maximum streamflow is decreasing. A total of 230 datasets were tested for their trends; of these, 80% and 20% increasing and decreasing trends were obtained, respectively. Of the total, significant trends (<0.05%) of 32% and 2% increasing and decreasing, respectively, were observed. The results of the FDC analysis indicated a decline in the annual and winter 10:90 exceedance ratio over the years for the rural and urban sub-watershed gauges. The BFI results show that the BFI of the rural areas was, on average, 18% higher than that of the urban areas. In addition, the RC also showed the influence of land-use and population changes on the watershed hydrology, as the RC for the urban gauge area was 19.3% higher than that for the rural area gauge. However, the difference in the RC was the lowest (5.8%) in the summer. Overall, the findings from this study highlight the annual, seasonal, and monthly changes in the temperature, precipitation, evapotranspiration, and streamflow in the watershed under study. Based on the available monitored data, it was difficult to quantify the changes in the streamflow over the decade which were attributable to population growth and land-cover use and management changes due to municipal official planning in the watershed.
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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) | 0.000 | 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".