Quantify Piston and Preferential Water Flow in Deep Soil Using Cl− and Soil Water Profiles in Deforested Apple Orchards on the Loess Plateau, China
Bibliographic record
Abstract
Piston and preferential water flow are viewed as the two dominant water transport mechanisms regulating terrestrial water and solute cycles. However, it is difficult to accurately separate the two water flow patterns because preferential flow is not easy to capture directly in field environments. In this study, we take advantage of the afforestation induced desiccated deep soil, and directly quantify piston and preferential water flow using chloride ions (Cl−) and soil water profiles, in four deforested apple orchards on the Loess Plateau. The deforestation time ranged from 3 to 15 years. In each of the four selected orchards, there was a standing orchard that was planted at the same time as the deforested one, and therefore the standing orchard was used to benchmark the initial Cl− and soil water profiles of the deforested orchard. In the deforested orchards, piston flow was detected using the migration of the Cl− front, and preferential flow was measured via soil water increase below the Cl− front. Results showed that in the desiccated zone, Cl− migrated to deeper soil after deforestation, indicating that the desiccated soil layer formed by the water absorption of deep-rooted apple trees did not completely inhibit the movement of water. Moreover, there was an evident increase in soil water below the downward Cl− front, directly demonstrating the existence of preferential flow in deep soil under field conditions. Although pore water velocity was small in the deep loess, preferential water flow still accounted for 34–65% of total infiltrated water. This study presented the mechanisms that regulate movement of soil water following deforestation through field observations and advanced our understanding of the soil hydrologic process in deep soil.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".