Effect of hysteresis of soil-water characteristic curves on infiltration under different climatic conditions
Bibliographic record
Abstract
This paper presents results of a numerical modelling exercise that investigates the effects of hysteresis of the soil-water characteristic curve (SWCC) on the infiltration characteristics of soils subjected to four different climatic conditions — from very dry to wet — within the Canadian province of Alberta. Multi-year climate datasets from four different natural regions and subregions of Alberta are compiled, classified, and applied as the soil–atmosphere boundary condition in one-dimensional finite element unsaturated flow models using Hydrus-1D software. Multi-year simulations are carried out with and without consideration of the SWCC hysteresis. Simulation results are analyzed in terms of water balance at the ground surface and temporal distribution and storage of water within the soil domain. It is demonstrated that hysteresis of the SWCC can significantly affect the prediction of flow, redistribution, and storage of water in the unsaturated zone. It is found that for soils that exhibit hysteretic SWCC, consideration of hysteresis in unsaturated flow modelling results in the prediction of lower infiltration and less movement of water through the soil. It is also found that the use of wetting parameters results in the prediction of increased infiltration and movement of water compared with the predictions using drying or hysteretic parameters. It is concluded that, for soils that exhibit a greater degree of SWCC hysteresis, it is important to measure both the drying and wetting branches of the SWCC accurately and that accurate simulation of hysteretic behaviour requires climate datasets at appropriate resolution. The results presented in this paper highlight the importance of considering SWCC hysteresis for a wide range of geotechnical problems, such as soil cover design, prediction of groundwater recharge, contaminant transport through unsaturated soils, soil erosion, slope stability, and swelling–shrinkage of expansive 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 | 0.002 |
| 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 | 0.001 |
| 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".