Preferential flow of surface‐applied solutes: Effect of lysimeter design and initial soil water content
Bibliographic record
Abstract
Abstract Undisturbed lysimeters are widely used to study water and solute transport, where both natural and unnatural preferential flow can greatly influence leaching rates. The objective of this study was to use chemical and isotopic tracers to quantify the effect of initial soil water content and lysimeter design on preferential flows. Ten undisturbed lysimeters (900 cm 2 ) were collected from an agricultural field, with the gap between the soil and lysimeter casing sealed with petroleum jelly for five lysimeters. Lysimeters were subjected to two rainfall simulations (3.3 cm h −1 ) under contrasting initial soil water contents, and leachate near the soil‐casing interface was collected separately from leachate through the bulk soil. Three‐component hydrograph separation revealed that event water comprised 21–59% of total leachate irrespective of initial soil water content and lysimeter design. Sealing the edges of the lysimeters with petroleum jelly greatly reduced but did not eliminate edge flow during rainfall simulations. Although water and solute transport were similar in both sealed and unsealed lysimeters under dry antecedent conditions due to the formation of shrinkage cracks on the soil surface, edge flow was substantially greater for the unsealed lysimeters under wet antecedent conditions. Unsealed edges under wet antecedent conditions facilitated the preferential transport of both event and pre‐event water, resulting in greater solute leaching. Using multiple tracers to contrast lysimeter designs under different initial soil water contents not only allowed for rigorous testing of a commonly used edge‐flow suppression technique but also provided new insights into preferential flow processes and patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".