Impacts of climate change on streamflow in the McKenzie Creek watershed in the Great Lakes region
Bibliographic record
Abstract
Introduction This study explored streamflow dynamics of the McKenzie Creek watershed in Southern Ontario, Canada under a changing climate. The Creek is located in the southern portion of the Grand River watershed in the Great Lakes region and is an important water and ecosystem service provider for the Six Nations of the Grand River reserve, the largest (by population) Indigenous community in Canada and the fourth largest in North America. Methods The Coupled Groundwater and Surface-Water Flow Model (GSFLOW) was used to simulate streamflow from 1951 to 2020 using observed gridded meteorological data from Natural Resources Canada (NRCANmet) and in situ data from Environment and Climate Change Canada (ECCC). Downscaled data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for two Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) climate warming scenarios, RCP 4.5 and RCP 8.5 were used to run GSFLOW for the historic (1951–2020) and projected (2021–2099) period. Results Results suggested that streamflow in the McKenzie Creek will be significantly impacted by climate change in winter months when streamflow is projected to increase due to higher temperatures causing early melting of snowpack and increasing winter precipitation. Consequently, spring streamflow is expected to decrease and little or no change in streamflow in the summer and autumn. These changes in streamflow dynamics may lead to more flooding incidents in the winter, while at the same time, the region may face reduced water availability or dry conditions in late spring and summer due to warm temperatures. Discussion This study provides important information about streamflow and hydrologic dynamics of this watershed that will help managers and planners to better manage water resources and be prepared to deal with climate change and its impacts on water availability and security not only for the Six Nations area but also for Southern Ontario which houses one-third of Canada’s population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".