Untangling transient groundwater mixing and travel times with noble gas time series and numerical modeling
Bibliographic record
Abstract
The quality and quantity of alluvial groundwater in mountainous areas are particularly susceptible to the effects of climate change, as well as increasing pollution from agriculture and urbanization. Understanding mixing between surface water and groundwater as well as groundwater travel times in such systems is thus crucial to sustain a safe and sufficient water supply. We used a novel combination of real-time, in-situ noble gas analysis to quantify groundwater mixing of recently infiltrated river water (Frw<!-- Please note that everything in “$$” will look differently once submitted -->) and regional groundwater, as well as travel times of Frw during a two-month groundwater pumping test carried out at a drinking water wellfield in a prealpine valley in Switzerland. Transient groundwater mixing ratios were calculated using helium-4 concentrations combined with a Bayesian end-member mixing model. Having identified the groundwater fraction of Frw consequently allowed us to infer the travel times from the stream to the wellfield, estimated based on radon-222 activities of Frw. Additionally, we compared and validated our tracer-based estimates of Frw using a calibrated surface water-groundwater model. Our findings show that (i) mean travel times of Frw are in the order of two weeks, (ii) during most of the experiment, Frw is substantially high (~70\%), and (iii) increased groundwater pumping only has a marginal effect on groundwater mixing ratios and travel times. The high fraction of Frw in the abstracted groundwater and its short travel times emphasize the vulnerability of mountainous regions to present and predicted environmental changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".