A sprinkling experiment to quantify celerity-velocity differences at the hillslope scale
Bibliographic record
Abstract
Abstract. The difference between celerity and velocity of hillslope water flow is poorly understood. We assessed these differences by combining a 24-day hillslope sprinkling experiment with a spatially explicit hydrologic model analysis. We focused our work at Watershed 10 at the H. J. Andrews Experimental Forest in western Oregon. δ2H label was applied at the start of the sprinkler experiment. Maximum event water (δ2H labeled water) contribution was 26 % of lateral subsurface flow at 20 h. Celerities estimated from wetting front arrival times were generally much faster (on the order of 10–377 mm h−1) than average vertical velocities of δ2H (on the order of 6–17 mm h−1). In the model analysis, this was consistent with an identifiable effective porosity (fraction of total porosity available for mass transfer) parameter, indicating that subsurface mixing was controlled by an immobile soil fraction, resulting in an attenuated δ2H in lateral subsurface flow. Furthermore, exfiltrating bedrock groundwater that mixed with lateral subsurface flow captured at the experimental hillslope trench caused further reduction in the δ2H input signal. Our results suggest that soil depth variability played a significant role in the velocity-celerity responses. Deeper upslope soils damped the δ2H input signal and played an important role in the generation of the δ2H breakthrough curve. A shallow soil (~ 0.30 m depth) near the trench controlled the δ2H peak in lateral subsurface flow response. Simulated exit time and residence time distributions with the hillslope hydrologic model were consistent with our empirical analysis and provided additional insights into hydraulic behavior of the hillslope. In particular, it showed that water captured at the trench was not representative for the hydrological and mass transport behavior of the entire hillslope domain that generated total lateral subsurface flow, because of different exit time distributions for lateral subsurface flow captured at the trench and total lateral subsurface flow.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".