Modeling of Coupled Water and Heat Transfer in Freezing and Thawing Soils, Inner Mongolia
Bibliographic record
Abstract
Accurate simulation of soil water and heat transfer is critical to understand surface hydrology under cold conditions. Using an extended freezing code in HYDRUS-1D (freezing module), this study was conducted: (1) to evaluate the freezing module using field data collected in a grazed steppe of Inner Mongolia; and (2) to further simulate grazing effects on frozen soil hydrological processes. The experimental data consisted of soil water and temperature profiles measured during freeze-thaw cycles from 2005 to 2006 in two plots (ungrazed since 1979 (UG79) and winter grazing (WG)). To check the sensitivity of the freezing module, a model without a freezing scheme (normal module) was used for comparison. We found that while the normal module can only simulate soil water and heat transfer under unfrozen conditions, the freezing module can simulate well under both frozen and unfrozen conditions. The freezing module can reasonably compute water phase change and, therefore, substantially improved the simulation of the evolution of liquid water and temperature in frozen soil. It overestimated liquid water content during spring snowmelt and, thus, underestimated surface runoff from underlying frozen soil layers. Furthermore, the weak prediction of soil moisture at the WG site, compared with the UG79 site, might relate to the less than ideal parameterization of soil hydraulic properties. Our results confirmed that the freezing module was able to accurately predict behaviors of soil freezing and thawing, as well as the effects of land management. We suggest that detailed knowledge of the soil-atmosphere processes is needed to improve the surface runoff algorithm in the frozen soil module.
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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) | 0.001 | 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 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".