An assessment of the tracer‐based approach to quantifying groundwater contributions to streamflow
Bibliographic record
Abstract
The use of conservative geochemical and isotopic tracers along with mass balance equations to determine the pre‐event groundwater contributions to streamflow during a rainfall event is widely used for hydrograph separation; however, aspects related to the influence of surface and subsurface mixing processes on the estimates of the pre‐event contribution remain poorly understood. Moreover, the lack of a precise definition of “pre‐event” versus “event” contributions on the one hand and “old” versus “new” water components on the other hand has seemingly led to confusion within the hydrologic community about the role of Darcian‐based groundwater flow during a storm event. In this work, a fully integrated surface and subsurface flow and solute transport model is used to analyze flow system dynamics during a storm event, concomitantly with advective‐dispersive tracer transport, and to investigate the role of hydrodynamic mixing processes on the estimates of the pre‐event component. A number of numerical experiments are presented, including an analysis of a controlled rainfall‐runoff experiment, that compare the computed Darcian‐based groundwater fluxes contributing to streamflow during a rainfall event with estimates of these contributions based on a tracer‐based separation. It is shown that hydrodynamic mixing processes can dramatically influence estimates of the pre‐event water contribution estimated by a tracer‐based separation. Specifically, it is demonstrated that the actual amount of bulk flowing groundwater contributing to streamflow may be much smaller than the quantity indirectly estimated from a separation based on tracer mass balances, even if the mixing processes are weak.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".