Field measurements and modelling of climate-driven hydraulic flux, water content, and suction in a loess slope
Bibliographic record
Abstract
Rainfall-triggered shallow slope failures are common in New Zealand loess. While often unsaturated, rainfall infiltration reduces suction in the loess as the water content increases, resulting in reduction in shear strength and increased potential for slope instability. Here the hydraulic response of an in situ loess slope to natural rainfall and evapotranspiration is examined using long-term field instrumentation. Data from a 19-month monitoring period show that the loess’ hydraulic behaviour responds to change in seasonal climatic conditions, highlighting the relationship between moisture content and suction with evapotranspiration and infiltration. To simulate the field response, a one-dimensional steady infiltration/evapotranspiration model has been developed, exploiting the unique power laws which interrelate water content, suction and hydraulic conductivity which arise from a loess’s fractal particle and pore size distributions. Hydraulic hysteresis is also accounted for. Closed form expressions are derived for the subsurface suction profile, from which profiles of moisture content, hydraulic conductivity, and the suction’s contribution to strength may be obtained. Simulated subsurface water contents are in good agreement with field observations. Furthermore, fluxes across the loess surface used to obtain the modelled quantities are in general agreement with local climatic conditions and their variations during the monitoring period. Modelled and measured data are strongly correlated at shallow depths, having a Pearson correlation coefficient of 0.53.
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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 | 0.000 |
| 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) | 0.001 | 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".