Performance evaluation of different soil water retention functions for modeling of water flow under transient condition
Bibliographic record
Abstract
ABSTRACT: Description and prediction of water flow through unsaturated soils is necessary to understand their hydraulic properties, including soil water retention curve (SWRC). Many models have been developed for estimation of SWRC and many researchers compared water retention curve derived from these models with the measured values. In this paper, in addition to comparing measured and derived SWRC, a functional evaluation of SWRC for modeling of soil water movement was carried out using van Genuchten, Brooks-Corey, Campbell and Hutson-Cass models in three sites including Loamy sand, Loam and Clay loam soils. Therefore, the functional behavior of SWRC was quantitatively compared by applying mentioned SWRC to numerical code (HydroGeoSphere) to simulate soil profile drainage under steady-state and transient conditions. The agreement between simulated and measured free drainages values was evaluated using statistical criteria including mean absolute error (MAE), modified index of agreement (d’), modified coefficient efficiency (E’), and t-test. The results demonstrated that the van Genuchten model was slightly better than the other models for estimation of SWRC (MAE 0.014 – 0.016,E’ 0.80 – 0.87 and d’ 0.90 – 0.93) while according to t-test, it was found that the measured and estimated SWRC using various models did not differ significantly. Therefore, it is expected that the simulated free drainage using mentioned SWRC models did not differ significantly with observed values. But the results demonstrated that the simulated free drainage using Brooks-Corey model for Loamy sand soil and van Genuchten and Brooks-Corey models for Loam soil differed significantly (p ≤ 0.05) with measured values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".