Pore water isotope fingerprints to understand the spatiotemporal groundwater recharge variability in ungauged watersheds
Bibliographic record
Abstract
Abstract Reliable groundwater recharge quantification at the regional scale (e.g., watershed or subwatershed) is fundamental to sustainable water resource management. Although modeling at the watershed scale is gaining wide support, the long‐term monitoring needed for model calibration is often not readily available, as many watersheds worldwide remain ungauged. In response to this situation, we propose a new approach to estimate groundwater recharge at the watershed scale. This approach is fast and accurate and takes into account the existing variability without requiring long‐term monitoring. Only a single field campaign to acquire soil water content and pore water isotopic composition depth profiles is needed. The principle is to extend a physically based, one‐dimensional unsaturated zone flow model from the local (i.e., profile) to the watershed scale, using an index method for distributed recharge based on a GIS. The methodology was validated in a gauged watershed, where previous studies have estimated recharge using a spatialized water balance model calibrated using long‐term discharge monitoring data. Scaling was investigated by comparing recharge values obtained using the local‐scale approach at 10 study sites within the watershed with coinciding values obtained at the watershed scale. Recharge values were similar in terms of both dynamics and quantity. Using the pore water isotopic fingerprint of ungauged watersheds is therefore confirmed to be a suitable approach for understanding spatiotemporal recharge variability.
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".