Simulation of Water Movement in Layered Water‐Repellent Soils using HYDRUS‐1D
Bibliographic record
Abstract
Core Ideas HYDRUS‐1D performed well for the water movement in layered water repellent soils. Two scenarios of silt loam/sand and sand/silt loam with water repellent soils, were applied. HYDRUS‐1D simulated infiltration parameters by differing two layered water repellent scenarios. Water repellency has many negative influences on soil water movement. However, simulations of water movement in layered water repellent (WR) soils are limited. Our objectives are to calibrate and validate the infiltration parameters and simulate water movement in layered WR soils based on ponded infiltration experiments conducted in wettable, slightly WR, strongly WR, and severely WR soils. Our experiments were conducted in 50‐cm long soil columns with two layer scenarios: Silt loam overlying (/) sand and sand/silt loam. For WR treatments, the surface soil was all 5 cm. For the wettable treatments, surface soils with thicknesses of 10‐ and 20‐cm layer sequences were added. Calibrations were conducted based on cumulative infiltration (CI), distance of the wetting front ( Z f ), and volumetric soil water content (θ v ) in the wettable and WR silt loam/sand treatments. Validations were conducted via eight additional treatments. The 12 WR layered soil treatments were selected for simulation. Three statistical parameters including the relative root mean square error (RRMSE), were used to assess the HYDRUS‐1D performance. The RRMSE for calibration and validation, ranged from 3.2 to 10% and 2.5 to 13.6%, respectively, confirming that HYDRUS‐1D was able to accurately describe water movement in layered WR soils. For the severely WR treatments, infiltration time reached 2800 h in silt loam/sand scenario and 1000 h in sand/silt loam scenario when water infiltrated to a depth of 35 cm. Overall, soil water repellency was more important than the interlayer position in regard to affecting water movement in layered soils, especially in the sand/silt loam scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".