Reply to comment by J.‐P. Renaud et al. on “An assessment of the tracer‐based approach to quantifying groundwater contributions to streamflow”
Bibliographic record
Abstract
[1] We welcome the comments of Renaud et al. [2007] on our recent paper [Jones et al., 2006]. This dialog provides us the opportunity to clarify some of the points we made regarding this important topic. [5] Regarding our division of the pre-event waters for the Borden rainfall-runoff experiment into separate unsaturated and saturated components, we would point out that one of our ancillary goals was is to determine the source zones of all of the waters contributing to streamflow generation and that this simply entailed adding an additional unsaturated zone tracer in the model that is used to tag the movement of the vadose zone water that existed prior to the rainfall event. As was shown in Figure 8 of Jones et al. [2006], the pre-event water initially residing below the water table (i.e., the saturated zone) did not significantly contribute to the streamflow produced after the onset of the rainfall event, at least on the basis of the hydraulic gradients that developed. Note that dispersive/diffusive processes were included in the simulation results provided in Figure 8 and that the concentration gradients in the saturated zone below the channel are clearly nonnegligible. The primary source of the hydraulically driven pre-event contribution to streamflow came from the unsaturated zone because of the capillary fringe effect, although this quantity was also small compared to the “lumped” pre-event estimate obtained from the direct application of (1a) and (1b) by Jones et al. [2006]. We do concede that these findings were not emphasized in the paper. [6] Renaud et al. [2007] also bring up a point concerning the use oxygen isotopes. When isotopically tagged water molecules diffuse (and mechanically disperse) from the subsurface to the surface, this does indeed represent the movement of these molecules; however, this does not necessarily represent the bulk movement of water as described by Darcy's law, and we again point out that the motion of the tagged water molecules comprise both advective (i.e., hydraulically driven) and dispersive/diffusive components. This latter quantity will impact the values of the Q terms inferred from lumped mass balance equations such as (1a) and (1b). That is, a large portion of the water appearing in the stream may appear to be “old” water, but not all of this pre-event water was hydraulically driven into the stream as many hydrologists and hydrogeologists commonly assume. [7] Finally, we concur with Renaud et al. [2007] that tracer-based research has significantly contributed to the advancement of hydrological sciences and will continue to do so in the future. It needs to be strongly emphasized here that we are not questioning the veracity of tracer data itself. Instead, we simply questioned the manner in which the data are commonly interpreted. We remain convinced that the use of lumped mass balance equations such (1a) and (1b) need to be adapted to account for (or at least approximate) dispersive/diffusive processes if they are to produce a direct estimate of the hydraulically driven pre-event contribution, and thus resolve the old water paradox. This is a topic of future work.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.038 | 0.056 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".