Impacts of Future Climate and Agricultural Land Use Changes on Groundwater Nitrate Concentrations in Southern Ontario
Bibliographic record
Abstract
Contamination of groundwater by nitrate is a critical concern in agricultural subwatersheds in southern Ontario. Groundwater quality may further deteriorate due to socio-economic pressures and extreme future climate change conditions. The focus of this study was to assess the impacts of future climate changes and potential changes in agricultural land use on groundwater nitrate concentrations. Three research sites with contrasting hydrogeological settings, located in agricultural subwatersheds, were investigated: Norfolk County, Guelph, and Acton. The Norfolk (sandy aquifer) and Guelph (fractured bedrock aquifer) sites were examined in the most detail. Two different vadose zone models, DRAINMOD-NII and RZWQM2, were evaluated to select the most suitable model to represent the soil conditions encountered in southern Ontario. The selected vadose zone model, RZWQM2, was used to quantify the shallow nitrate concentrations and leaf area index (LAI) under different crop types. These data were used as input to fully integrated numerical models, developed using HydroGeoSphere software, for groundwater flow and contaminant transport for both sites. The HydroGeoSphere models were calibrated and validated for the 2014-2016 period using field data collected at both sites. Following calibration of the groundwater models, three different climate change models (2040-2059) and three different crop rotations (corn-soybean, continuous corn, corn-soybean-winter wheat-red clover) were applied (i.e., nine scenarios for each site) to evaluate the potential impact of future climate changes on groundwater quality. The selected climate change scenarios yielded less water availability for hydrologic processes. There was less impact on groundwater elevations at the Norfolk site compared to the Guelph site. The nitrate concentrations were lower significantly during the future period at both sites. However, the continuous corn land use scenario had much higher nitrate concentrations compared to base case scenario (corn-soybean rotation). Further, the best management practices (BMP) scenario (corn-soybeans-winter wheat-red clover rotation) produced significantly lower groundwater nitrate concentrations at both research sites. BMPs, such as the crop rotation tested herein, should be adopted to reduce the potential negative impacts of future climate change on groundwater quality, especially in vulnerable fractured bedrock aquifer settings and shallow sandy aquifers. These findings are important for water and land managers in agricultural settings, to mitigate future impacts of nutrient transport on groundwater quality under a changing climate.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".