Groundwater recharge in the Canadian Prairies: mechanisms, constraints, and rates
Bibliographic record
Abstract
A combination of dry climate and extensive cover of low-permeability sediments reduces groundwater recharge in the Canadian Prairies to just a few percent of annual precipitation. Such recharge scarcity increases the importance of knowing recharge rates for adequate management of water resources and, simultaneously, complicates recharge rate evaluation. The present study addresses this problem by using a combination of surface conditions monitoring, geochemical methods, and remote sensing to identify recharge mechanisms and physiographic constraints on recharge, and to quantify recharge rates. The analysis of stable-isotopic data shows that groundwater recharge in the Canadian Prairies is dominated by the snow-melt-driven depression-focussed recharge pathway. The potential recharge through this pathway is limited by the volume of snow-melt that is retained within topographic depressions and, thus, is mostly constrained by just two factors: runoff generation and the available depression storage capacity. The latter is shown to be generally comparable in magnitude with typical runoff values and, thus, serves as a hard upper limit on recharge rates. The identified link between depression storage capacity values and specific types of surficial sediments allows to estimate limit on groundwater recharge rate based on the surficial geology maps. Unlike depression storage capacity, the limit on recharge rate associated with runoff volume varies over time and is sensitive to the atmospheric forcing with mid-winter melts, which are shown to be an important factor affecting both volume of snow-melt runoff and timing of groundwater recharge. The regional recharge rates are consistent with identified limiting factors and are estimated to be 8─14 mm per year in the parkland ecoregion and 3.5─6 mm per year in the grassland ecoregion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".