Combining isotopic tracers (<sup>222</sup>Rn and δ<sup>13</sup>C) for improved modelling of groundwater discharge to small rivers
Bibliographic record
Abstract
Abstract In regions where aquifers sustain rivers, the location and quantification of groundwater discharge to surface water are important to prevent pollution hazards, to quantify and predict low flows and to manage water supplies. 222 Rn is commonly used to determine groundwater discharge to rivers. However, using this isotopic tracer is challenging because of the high diffusion capacity of 222 Rn in open water. This study illustrates how a combination of isotopic tracers can contribute to an enhanced understanding of groundwater discharge patterns in small rivers. The aim of this paper is to combine 222 Rn and δ 13 C DIC to better constrain the physical parameters related to the degassing process of these tracers in rivers. The Hallue River (northern France) was targeted for this study because it is sustained almost exclusively by a fractured chalk aquifer. The isotopes 222 Rn, δ 13 C DIC , δ 2 H and δ 18 O were analysed along with other natural geochemical tracers. A mass balance model was used to simulate 222 Rn and δ 13 C DIC . The results of δ 2 H and δ 18 O analyses prove that evaporation did not occur in the river. The calibration of a numerical model to reproduce 222 Rn and δ 13 C DIC provides a best‐fit diffusive layer thickness of 3.21 × 10 −5 m. This approach is particularly useful for small rivers flowing over carbonate aquifers with high groundwater DIC where the evolution of river DIC reflects the competing processes of groundwater inflow and CO 2 degassing. This approach provides a means to evaluate groundwater discharge in small ungauged rivers. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".