Performance of HYDRUS-1D for simulating water movement in water-repellent soils
Bibliographic record
Abstract
Soil water repellency affects soil water movement during infiltration significantly. The HYDRUS software has been popularly applied in soil water dynamics simulation for many years, but its performance in water-repellent (WR) soils has not been assessed thoroughly. Our objectives are to assess the performance of HYDRYUS-1D for cumulative infiltration (CI), wetting front (Zf), and volumetric soil water content (θv) during horizontal imbibition and vertical infiltration in wettable, slightly WR, and strongly WR soils. The key parameters of α and n in water retention curves were inversely estimated by RETension Curve software. The α and n were calibrated inversely until the observed data fitted the simulated values well enough. The α and n were then used for validation using three statistical parameters including relative root-mean-square error, R2, and Nash–Sutcliffe efficiency coefficient. The performances of calibration and validation for wettable, slightly, and strongly WR soils were good enough to be used for further simulations (RRMSE ≤20.2% for calibration and ≤21.1% for validation). Soil water movements for strongly WR soils of variable ponded depth during vertical infiltration were simulated. For Lou soil, as the ponded depth increased from 4 to 10 cm, the CI and Zf increased 2.08 and 5.5 cm, respectively. The simulations for the other three soils also showed gradually increased CI and Zf values. In conclusion, the performances of HYDRUS-1D in four different soil types with changing WR levels were good, which confirmed the application of HYDRUS-1D in WR soils.
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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 | 0.000 |
| 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".