Ground–atmosphere interaction modelling for long-term prediction of soil moisture and temperature
Bibliographic record
Abstract
Land surface and subsurface variables, such as soil moisture–suction and temperature, are among the most important components to study the behaviour of expansive soil, geothermal energy, and climate change. A more accurate and long-term series of soil moisture and temperature prediction, due to ground–atmosphere interaction, is very important for real-time drought monitoring for understanding and improving the behaviour of soil, buried structures, and climate prediction. In this study, ground–atmosphere interaction is numerically modelled using Vadose/W software for two instrumented sites in Melbourne, Australia. Soil moisture and temperature down to 2 m depth were monitored over 2 years at discrete locations and the meteorological variables including air temperature, air humidity, wind speed, precipitation, and solar radiation were measured from a weather station installed at the sites. Further, laboratory and field tests were performed to establish initial conditions and soil characteristics such as the soil-water characteristic curve (SWCC ), hydraulic conductivity, and thermal conductivity functions. The numerical model results were calibrated with the field data, indicating good agreement between numerical and field results. The calibrated numerical model was used to compute the long-term moisture and temperature variations into the immediate future using the past 20 years of weather data in Melbourne.
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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 | 0.001 |
| 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".