Reconstructed Precipitation Tritium Leads to Overestimated Groundwater Recharge
Bibliographic record
Abstract
Abstract Time series of precipitation tritium contents are often used for groundwater recharge estimation based on the tritium mass ratio of the (un) saturated zones to precipitation. But records of the precipitation tritium measurements are sparse and often need to be reconstructed. Uncertainties associated with reconstructed records can affect the recharge estimates, but this has received little attention. Here we leverage four >15 m deep, exceptionally well preserved unsaturated zone tritium profiles to quantify the uncertainty in Tritium mass balance from precipitation tritium reconstruction. The tritium profiles have bomb tritium peaks at 7.2–10.5 m below the surface and peaks ranging from 46 to 235 tritium unit. We first estimate the diffuse recharge by the tritium peak method. Since this method is independent of any precipitation tritium reconstruction, the estimated recharge was used as truth to evaluate four precipitation tritium reconstruction methods: two interpolation methods and two reference curve methods. Direct comparison between the observed and simulated precipitation tritium showed that the reference curve methods performed more poorly than the interpolation methods. Indirect evaluation by comparing the recharge rates estimated by the tritium storage method with our truth measurements showed that tritium storage method overestimated recharge by 100% to 200%. Our results suggest that the atmospheric tritium flux was greatly underestimated, especially by the reference curve methods. As such, groundwater recharge at our sites would be overestimated. This has implications for using these standard approaches elsewhere.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".