Applying GIS and SWAT to understand the stream network, hydrology, sediment and nutrient export from the Grand River watershed into Lake Erie
Bibliographic record
Abstract
Headwater streams are important lotic systems that represent more than 80% of the total stream lengths in watersheds. The dynamic coupling of hydrological and biogeochemical processes in headwaters is responsible for regulating the chemical form, residence time and longitudinal transport of nutrients. Over time, stream modification (e.g. to enhance drainage in agricultural watershed) has altered natural stream flow-paths and thus, stream functionality. Such alteration has resulted in degradation of habitat and water quality, both in upland and downstream waters. Currently, nutrients exported from the Grand River (Ontario) watershed are contributing to eutrophication and Harmful Algal Blooms in Lake Erie. With respect to the Grand River watershed, this thesis examined (1) the impact of agriculture on the existing stream network, (2) the utility of the Soil and Water Assessment Tool to simulate hydrology, sediment and nutrient export that closely correlate with measured data, and (3) the application of Best Management Practices in the watershed with the intent of meeting provincial and transnational nutrient targets. The results showed that compared to the actual ground-truthed stream network, the predicted stream network based on topography underpredicted a total of 2,535 km of actual channel present in the watershed. Channels not anticipated by topography were mostly first-order, with low sinuosity, and were most common in areas with high agricultural land use, and are likely excavated extensions to headwater streams to facilitate drainage. Then, the sediment and nutrient loading at Dunnville, discharging to entering Lake Erie, was predicted to be 2.3[superscript⁻1] 105 t yr[superscript-1] of total suspended sediment, 7.9 [superscript⁻1] 103 t yr-1 of total nitrogen, and 2.5 ⁻1 102 t yr-1 of total phosphorus. Finally, implementing wide buffer strips, stabilizing channel banks and grassed waterways were found to be the most effective practices for reducing sediment and phosphorus loading into Lake Erie.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".