Advantages and challenges of using soil water isotopes to assess groundwater recharge dominated by snowmelt at a field study located in Canada
Bibliographic record
Abstract
Seasonal signals of stable isotopes in precipitation, combined with measurements of isotope ratios in soil water, can be used for quantitative estimation of groundwater recharge rates. This study investigates the applicability of using the piston flow principle and the peak shift displacement method to estimate actual groundwater recharge rates in a humid Nordic region located in the province of Quebec, Canada. Two different sites with and without vegetation (C1 and C2) in an unconfined aquifer were tested by measuring soil water isotope ratios (18O/16O and 2H/1H) and volumetric pore water content. Core samples were obtained along the vadose zone down to the groundwater table at the two sites (2.45 m for Site C1 and 4.15 m for Site C2). The peak shift method to estimate groundwater recharge rates was shown to be accurate only in certain specific conditions inherent to the soil properties and the topographical situation of the investigated sites. Indeed, at Site C2, recharge from the snowmelt could not be estimated because of heterogeneity in the lower part of the vadose zone. At this same site the later recharge after the snowmelt (in the period from late spring to early autumn) could be estimated accurately because the upper part of the vadose zone was homogeneous. Furthermore, at site C1, runoff/runon phenomena hampered calculations of actual infiltration and thus produced inaccurate results for recharge. These two different site effects (heterogeneity in the first site and runoff/runon in the other site) were identified as being limiting factors in the accurate assessment of actual recharge. This study therefore recommends the use of the peak shift method for (1) humid Nordic regions, (2) homogeneous and thick vadose zones, and (3) areas with few or limited site effects (runoff/runon).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".