Catchment-scale assessment of groundwater discharge using ecological, thermal, and hydrochemical surveillance data in the Halton Region, Ontario
Bibliographic record
Abstract
• Aggregation of >21,000 interpolated groundwater and stream monitoring data • Ecological parameters reflective of larger-scale groundwater discharge patterns • Temperature but not hydrochemical gradients reflect groundwater discharge dynamics • Surface water surveillance data can guide groundwater discharge monitoring efforts Groundwater-surface water exchange is spatiotemporally variable and costly to assess at high-resolution across large areas. This study explores the application of readily available surface water monitoring data to identify potential groundwater discharge locations in 15 catchments within Conservation Halton's jurisdiction within the Halton Region, Ontario, Canada. We first compared two interpolated groundwater discharge models and subsequently contrasted these against stream ecology data (macrophyte and fish taxa) and surface- and groundwater quality measurements that were aggregated to derive temperature and hydrochemical (alkalinity, chloride) gradients across the hyporheic zone. Both groundwater models agreed in their prediction of discharge locations for only 52% of monitoring sites, corroborating the need for further reconnaissance of potential discharge areas. Fish temperature preferences and ecological temperature classifications aligned reasonably with the groundwater discharge models (<55% of sites) and air-to-stream temperature differences agreed better with groundwater discharge predicted by modeling (p<0.04, R 2 >0.40) than stream-to-groundwater gradients (p<0.1, R 2 <0.25). Instead, hydrochemical signatures of both chloride and alkalinity in the streams were more ambiguous and displayed poor correlation with groundwater discharge maps and other monitoring parameters. Finally, we amalgamated the various investigated parameters into a classification scheme to determine the likelihood of groundwater discharge at the monitoring locations . This work exemplifies how combining commonly available monitoring information may be used to provide additional insight into groundwater discharge dynamics and stream health across larger and diverse catchment types where lack of in-situ monitoring or unverified numerical models complicate a clear understanding of groundwater discharge patterns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".