Capturing temporal variability for estimates of annual hydrochemical export from a first‐order agricultural catchment in southern Ontario, Canada
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Bibliographic record
Abstract
Abstract This 2‐year study (2000, 2001) reports annual nutrient (phosphorus, nitrate) export from a first‐order agricultural watershed in southern Ontario based on an intensive monitoring programme. The importance of storm and melt events in annual export estimates is demonstrated and the temporal variability in nutrient loading during events is related to processes occurring within the catchment. The feasibility of predicting event‐related nutrient export from hydrometric data is explored. The importance of sampling frequency throughout events is also shown. Export of total phosphorus (TP), soluble reactive phosphorus (SRP) and nitrate ( $NO_{3}^{-}$ ) for 2000 and 2001 averaged 0·35 kg ha −1 year −1 , 0·09 kg ha −1 year −1 , and 35 kg ha −1 year −1 (as N) respectively. Approximately 75% of annual TP export, 80% of annual SRP export and 70% of annual $NO_{3}^{-}$ export occurred during 28 events per year. A small number of large‐magnitude events (>34 mm) accounted for 18–42% of annual TP export, 0–61% of annual SRP export and 13–33% of annual NO $NO_{3}^{-}$ export. Our results show that temporal variability in nutrient export is largely governed by discharge in this basin, and $NO_{3}^{-}$ export can be predicted from discharge. SRP and TP export can also be predicted from discharge, but only for events that are not large in magnitude. The sampling interval throughout events is important in obtaining precise estimates of nutrient export, as infrequent sampling intervals may over‐ or under‐estimate nutrient export by ± 45% per event for P. This study improves our understanding of $NO_{3}^{-}$ and P export patterns and our ability to predict or model them by relating temporal variability in event nutrient export to discharge and processes occurring within the basin, and also by exploring the significance of sampling interval in the context of the importance of individual events, season and temporal variability during events. Copyright © 2006 John Wiley & Sons, Ltd.
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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 it