Blue and Green Water Scarcity in the <scp>McKenzie</scp> Creek Watershed of the Great Lakes Basin
Bibliographic record
Abstract
ABSTRACT Climate change and extreme weather events affect hydrology and water resources in catchments worldwide. This study analysed Blue Water (BW) and Green Water (GW) scarcity in the McKenzie Creek watershed in Ontario, Canada, and explored how changes in temperature and precipitation may impact water scarcity dynamics. The McKenzie Creek is the main water source for agricultural activities for the Six Nations of the Grand River reserve (the largest Indigenous community in Canada) and other non‐Indigenous communities in the watershed. Data from the water use surveys and streamflow simulations performed using the Coupled Groundwater and Surface‐Water Flow Model (GSFLOW) under the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5, representing moderate and high greenhouse gas emissions and climate warming, respectively, were used to calculate BW and GW scarcity. Study results showed that BW scarcity may increase to ‘moderate’ levels if water users extract the maximum permitted water withdrawal allocation. This level of scarcity has the potential to cause ecological degradation and water quality issues in the watershed. GW scarcity will steadily increase throughout the 21st century due to climate warming with the western portion of the McKenzie Creek watershed projected to experience slightly higher levels of GW scarcity. This may cause users to withdraw more water resources, thereby decreasing BW available for downstream communities, including the Six Nations of the Grand River. This study provides water resource managers and regional planners with important information about potential challenges facing the watershed due to increased water use and changing climate conditions.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".