Determination of BMPs to reduce soil and water pollution in tile-drained watersheds in Southern Ontario, Canada under changing climate
Bibliographic record
Abstract
Best Management Practices (BMPs) can be implemented on agricultural landscapes to manage water flows and reduce nonpoint source pollution.However, given the specificity of each landscape, there are presently no credible methods of determining, a priori, which BMP would work best under a given situation and, more importantly, where in the watershed should it be located.Furthermore, climate change in Ontario, Canada is going to cause non-uniform spatial and temporal distribution of precipitation, thereby causing and aggravating flooding, drought, and pollution problems.Hydrological simulation models are useful tools to understand how a change in global climate could affect the availability and variability of regional water resources.This research addresses this important issue in two different watersheds in Ontario.The main goal of this study is to develop an agricultural landscape assessment tool by simultaneously considering physical, chemical, and biological landscape parameters and carry out a holistic analysis of the agricultural and environmental state of the landscape.Our research team has developed SWATDRAIN, a watershed scale model for subsurface-drained agricultural landscapes, by fully integrating SWAT and DRAINMOD models.While the SWAT model has been used extensively around the world to simulate surface hydrology of watersheds, it leaves much to be desired when it comes to subsurface hydrology, specially for tile-drained landscapes.Therefore, DRAINMOD was fully incorporated into the SWAT model's subsurface hydrology module as an alternative method for simulating tile drainage, water table depth, and soil water status.The newly developed SWATDRAIN model is based on the DRAINMOD subsurface hydrology simulation and the SWAT surface hydrology simulation.SWATDRAIN computes the soil water balance for each HRU (Hydrologic Response Unit) in every sub-basin on a daily basis.In this paper, the impact of controlled drainage on watershed hydrology and sediment loadings will be presented.The effects of climate change on annual and seasonal water budgets, sediment loads will also be reported.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".