Spatiotemporal variability of water quality and stable water isotopes in intensively managed prairie watersheds
Bibliographic record
Abstract
Abstract Low‐relief and highly engineered prairie landscapes present many challenges for water quality modelling. Limited investigation has been carried out to determine whether drains, canals and diversions (artificial waterways) impact water quality relative to natural or naturalized systems or to assess the value of stable water isotopes as predictor variables for specific water quality metrics. The goal of this paper is to investigate the spatiotemporal variability of water chemistry in a typical prairie watershed by (1) comparing water quality and stable water isotopic ratios between naturalized and artificial waterways; (2) evaluating the relationships between topographic and land use characteristics, water quality and stable water isotopic ratios; and (3) expressing and predicting PO4concentrations as a function of stable water isotopic ratios. Focus was on the 2509 km2mostly agricultural Seine River Watershed located in southern Manitoba, Canada. Twenty‐four sampling sites located along naturalized river reaches, drains and diversions were visited once a week from May to June 2013. Surface water samples were collected and tested for physical water quality parameters and PO4concentrations, δ2H and δ18O. Kruskal–Wallis tests show no significant differences in water chemistry among waterway types, while strong correlations were found between physical water quality parameters, PO4concentrations, isotopic ratios and watershed characteristics. PO4concentrations were successfully predicted from isotopic ratios using linear regression models. These results bear significant importance for the mostly ungauged prairie watersheds, thanks to the relative ease and lower cost of measuring stable water isotopic ratios in comparison to nutrient concentrations. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".