Quantifying Groundwater Recharge Dynamics and Unsaturated Zone Processes in Snow‐Dominated Catchments via On‐Site Dissolved Gas Analysis
Bibliographic record
Abstract
Abstract Snowmelt contributes a significant fraction of groundwater recharge in snow‐dominated regions, making its accurate quantification crucial for sustainable water resources management. While several components of the hydrological cycle can be measured directly, catchment‐scale recharge can only be quantified indirectly. Stable water isotopes are often used as tracers to estimate snowmelt recharge, even though estimates based on stable water isotopes are biased due to the large variations of δ 2 H and δ 18 O in snow and the difficulty to measure snowmelt directly. To overcome this gap, a new tracer method based on on‐site measurements of dissolved He, 40 Ar, 84 Kr, N 2 , O 2 , and CO 2 is presented. The new method was developed alongside classical tracer methods (stable water isotopes, 222 Rn, 3 H/ 3 He) in a highly instrumented boreal catchment. By revealing (noble gas) recharge temperatures and excess air, dissolved gases allow (i) the contribution of snowmelt to recharge, (ii) the temporal recharge dynamics, and (iii) the primary recharge pathways to be identified. In contrast to stable water isotopes, which produced highly inconsistent snowmelt recharge estimates for the experimental catchment, dissolved gases produced consistent estimates even when the temperature of snowmelt during recharge was not precisely known. As dissolved gases are not controlled by the same processes as stable water isotopes, they are not prone to the same biases and represent a highly complementary tracer method for the quantification of snowmelt recharge dynamics in snow‐dominated regions. Furthermore, an observed systematic depletion of N 2 in groundwater provides new evidence for the pathways of biological N‐fixation in boreal forest soils.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".