Theory, tools, and multidisciplinary applications for tracing groundwater fluxes from temperature profiles
Bibliographic record
Abstract
Quantifying groundwater fluxes to and from deep aquifers or shallow sediment is a critical task faced by researchers and practitioners from many environmental science disciplines including hydrology, hydrogeology, ecology, climatology, and oceanography. Groundwater discharge to inland and coastal water bodies influences their water budgets, thermal regimes, and biogeochemistry. Conversely, downward water flow from the land surface or from surface water bodies to underlying aquifers represents an important water flux that must be quantified for sustainable groundwater management. Because these vertical subsurface flows are slow and typically diffuse, they cannot be measured directly and must rather be estimated using groundwater tracers. Heat is a naturally occurring groundwater tracer that is ubiquitous in the subsurface and readily measured. Most of the academic literature has focused on groundwater temperature tracing methods capitalizing on the propagation of diel temperature sine waves into sediment beneath surface water bodies. Such methods rely on temperature–time series to infer groundwater fluxes and are typically only viable in the shallow subsurface and in locations with focused groundwater fluxes. Alternative methods that utilize temperature–depth profiles are applicable across a broader range of hydrologic environments, and point‐in‐time measurements can be quickly taken to cover larger spatial scales. Applications of these methods have been impeded due in part to the lack of understanding regarding their potential applications and limitations. Herein, we highlight relevant theory, thermal data collection techniques, and recent diverse field applications to stimulate further multidisciplinary uptake of thermal groundwater tracing methods that rely on temperature–depth profiles. This article is categorized under: Water and Life > Methods Science of Water > Methods Water and Life > Nature of Freshwater Ecosystems
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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 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".