Predicted Contribution of Snowmelt to Subsurface Drainage Discharge in Two Subsurface-Drained Fields in Southern Quebec and Ontario
Bibliographic record
Abstract
Highlights RZ-SHAW model delivered a satisfactory simulation for winter subsurface drainage discharge. Snowmelt dominated the drainage discharge during the winter and spring snowmelt seasons in southern Quebec. Snowmelt contributed to 29% and 18% of winter and annual drainage discharge, respectively, in southern Ontario. Abstract. Late winter/early spring has been recognized as a critical period for subsurface drainage discharge and associated nutrient losses due to snowmelt and rainfall in croplands under cold, humid climates in North America and Europe. Although the detachment and transport processes of soil particles under snowmelt and rainfall are known to be different, studies quantifying the contribution of snowmelt to winter drainage discharge are limited. This study aims to investigate the contribution of snowmelt to subsurface drainage discharge at two winterized experimental sites in southern Quebec and Ontario, Canada, using observed data and the Root Zone Water Quality Model-Simultaneous Heat and Water model (RZ-SHAW model). The RZ-SHAW model was calibrated and validated against the measured snow depth and year-round subsurface drainage discharge data from 2021 to 2023 at St. Emmanuel, Quebec, and from 2000 to 2003 at Harrow, Ontario. RZ-SHAW demonstrated satisfactory Nash-Sutcliffe model efficiency values (NSE = 0.50) for simulating snow depth, soil temperature, and winter subsurface drainage discharge. The calibrated RZ-SHAW model was used to simulate the contribution of snowmelt to subsurface drainage for the two sites from 1990 to 2022. The snowmelt was predicted to contribute 55% and 44% of the winter and annual subsurface drainage discharge for the cropland in Southern Quebec. In contrast, for the southern Ontario site, the contribution of snowmelt to winter and annual subsurface drainage discharge was 29% and 18%, respectively. Considering that snowmelt in Southern Quebec contributed a significant fraction of winter and annual subsurface drainage discharge, more attention should be devoted to adapting and developing new winter management for nutrient loss in future research in the region. Keywords: Cold region, Cropland drainage, Drainage partition, RZ-SHAW, RZWQM2, Snowmelt, Soil freeze and thaw, Winter subsurface drainage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".