Landscape Controls on Nutrient Export during Snowmelt and an Extreme Rainfall Runoff Event in Northern Agricultural Watersheds
Bibliographic record
Abstract
In the northern Great Plains, most runoff transport of N, and P to surface waters has historically occurred with snowmelt. In recent years, significant rainfall runoff events have become more frequent and intense in the region. Here, we examine the influence of landscape characteristics on hydrology and nutrient export in nine tributary watersheds of the Assiniboine River in Manitoba, Canada, during snowmelt runoff and with an early summer extreme rainfall runoff event (ERRE). All watersheds included in the study have land use that is primarily agricultural, but with differing proportions of land remaining as wetlands, grassland, and that has been artificially drained. Those watersheds with greater capacity for storage of water in surface depressions (noneffective contributing areas) exhibited lower rates of runoff and nutrient export with snowmelt. During the ERRE, higher export of total P (TP), but not total N, was observed from those watersheds with larger amounts of contributing area that had been added through artificial surface drainage, and this was associated primarily with higher TP concentrations. Increasing or restoring the storage of water on the landscape is likely to reduce nutrient export; however, the importance of antecedent conditions was evident during the ERRE, when small surface depressions were at or near capacity from snowmelt. Total P concentrations observed during the summer ERRE were as high as those observed with snowmelt, and N/P ratios were significantly lower. If the frequency of summer ERREs increases with climate change, this is likely to result in negative water quality outcomes.
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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 | 0.001 | 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".