Implications of stubble management on snow hydrology and meltwater partitioning
Bibliographic record
Abstract
Spring snowmelt is the most important hydrological event in agricultural cold regions, recharging soil moisture and generating the majority of annual runoff. Melting agricultural snowcovers are patchy, which leads to melt rate enhancement by energy advection from warm moist snow-free surfaces to cool dry snowcovers. Agricultural snowmelt is also impacted by crop residue. Adoption of zero-tillage agricultural practices means vast areas of the Canadian Prairies are now characterized by standing crop stubble. Stubble influences snow accumulation through blowing snow processes and snowmelt through the impact of emerging stubble upon the surface energy balance. Unfortunately, stubble emergence and advection to patchy snowcovers are unaccounted for in snow hydrology models and a complete process description has been unavailable. Here, both advection and stubble influences on snowmelt hydrology are modelled by coupling new stubble-snow-atmosphere surface energy balance and advection models to existing blowing snow and frozen soil infiltration models. Long-term meteorological datasets from sub-humid and semi-arid locations in Saskatchewan, Canada are used to quantify the influence of stubble characteristics on accumulation, melt, and meltwater partitioning processes with respect to interannual variability, antecedent soil moisture, and climatic differences on the Canadian Prairies. The hydrological response to increasing stubble height is increased meltwater, melt rate, infiltration, and runoff, and negligible changes in melt duration. The response of these processes to changes in stubble height was more pronounced at a semi-arid site versus a sub-humid site as stubble more effectively suppresses blowing snow sublimation in the windier, drier semi-arid environment of southwestern Saskatchewan. Recommendations for stubble management to meet specific runoff or infiltration objectives are summarised; stubble management can be an effective tool to influence infiltration where soils are dry and runoff where soils are wet. This framework allows the diagnosis of the influence of stubble management on meltwater partitioning in cold agricultural regions.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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 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".