Water Security Assessment of the Grand River Watershed in Southwestern Ontario, Canada
Bibliographic record
Abstract
Water security is the capability of a community to have adequate access to good quality and a sufficient quantity of water as well as safeguard resources for the future generations. Understanding the spatial and temporal variabilities of water security can play a pivotal role in sustainable management of fresh water resources. In this study, a long-term water security analysis of the Grand River watershed (GRW), Ontario, Canada, was carried out using the soil and water assessment tool (SWAT). Analyses on blue and green water availability and water security were carried out by dividing the GRW into eight drainage zones. As such, both anthropogenic as well as environmental demand were considered. In particular, while calculating blue water scarcity, three different methods were used in determining the environmental flow requirement, namely, the presumptive standards method, the modified low stream-flow method, and the variable monthly flow method. Model results showed that the SWAT model could simulate streamflow dynamics of the GRW with ‘good’ to ‘very good’ accuracy with an average Nash–Sutcliffe Efficiency of 0.75, R2 value of 0.78, and percentage of bias (PBIAS) of 8.23%. Sen’s slope calculated using data from over 60 years confirmed that the blue water flow, green water flow, and storage had increasing trends. The presumptive standards method and the modified low stream-flow method, respectively, were found to be the most and least restrictive method in calculating environmental flow requirements. While both green (0.4–1.1) and blue (0.25–2.0) water scarcity values showed marked temporal and spatial variabilities, blue water scarcity was found to be the highest in urban areas on account of higher water usage and less blue water availability. Similarly, green water scarcity was found to be highest in zones with higher temperatures and intensive agricultural practices. We believe that knowledge of the green and blue water security situation would be helpful in sustainable water resources management of the GRW and help to identify hotspots that need immediate attention.
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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 | 0.001 |
| 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".