Land‐use comparison of depression‐focussed groundwater recharge in the Canadian prairies
Bibliographic record
Abstract
Abstract In the cold semiarid Canadian prairies, groundwater recharge is focussed under numerous topographic depressions, in which snowmelt runoff converges. Agricultural land uses on the uplands surrounding the depressions affect snow accumulation, snowmelt infiltration, evapotranspiration (ET) and soil moisture dynamics, thereby influencing snowmelt runoff and depression‐focussed recharge. The objective of this study is to compare the differences in hydrological processes under two common land uses in the Canadian prairies, namely grazed grass and annual crop, and examine how they affect groundwater recharge. A short‐term (3 years) paired catchment study was used for detailed observation of hydrological processes in two depressions, supplemented by a longer‐term (17 years) data set covering a larger scale to quantify the differences in snowmelt runoff between the two land uses. Compared to the grazed grassland, the cropland had a shorter and more intense period of ET, and root water uptake restricted to the shallower (top 0–80 cm) soil zone. The amount of snowmelt runoff was greater in the grazed grassland primarily due to a higher amount of snow accumulation, which was dictated by differences in topography. This finding was contrary to previous studies in the Canadian prairies that indicated substantially smaller snowmelt runoff in ungrazed grassland, but was consistent with the larger‐scale remote sensing results, which showed only a marginal difference between grazed grasslands and croplands. Groundwater recharge rates were estimated using the chloride mass balance method for the present condition using “modern” pore water containing tritium. The rates were similar between the grazed grassland and croplands, implying similarity in snowmelt runoff characteristics. These results suggest that groundwater recharge will continue to be focussed under depressions in the future, though the amount and seasonality of recharge may be influenced by warmer winters.
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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 | 0.000 | 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) | 0.002 | 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".