Repeated Subsurface Thermal Profiling to Reveal Temporal Variability in Deep Groundwater Flow Conditions
Bibliographic record
Abstract
Abstract Reliably quantifying groundwater fluxes to and from confined aquifers in sedimentary basins is increasingly recognized as a critical challenge that impedes sustainable groundwater management. One approach to quantify such fluxes is through the analysis of deep (e.g., >50 m) borehole thermal profiles penetrating through aquifer‐aquitard systems. Recently developed methods to interpret such data exploit the relationship between vertical groundwater flow and the downward propagation of surface temperature disturbances resulting from climate warming. In this note, we advance beyond prior studies that assumed steady‐state groundwater flow by demonstrating how hydrogeological regime shifts on decadal time scales can be quantitatively inferred from temperature‐depth profiles (TDPs). We use a set of repeated temperature‐depth profiles from one site in an unconsolidated sedimentary aquifer system, recorded in 1980 and 2016/2018 to tentatively infer a minimum of a threefold increase in groundwater downwelling to deeper aquifers (i.e., from 100 to 350 mm/year). The enhanced flux likely results from intensified, deep groundwater abstraction in the vicinity since the mid‐1980s. We reach this conclusion through analyzing the occurrence and downward propagation of the minimum temperature in the profiles as well as the temporal trend in deeper groundwater temperatures. We conclude that repeated temperature‐depth profiles can be suitable to archive hydrogeological changes. Our results provide the impetus for more systematic collection of present‐day TDPs to provide a historical benchmark from which to assess future groundwater flow alterations, especially in areas that lack traditional aquifer monitoring via hydraulic head measurements.
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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) | 0.001 | 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".