Extended HYDRUS-1D freezing module emphasizes thermal conductivity schemes for simulation of soil hydrothermal dynamics
Bibliographic record
Abstract
• 24 soil thermal conductivity ( λ ) schemes are evaluated by incorporating into the HYDRUS-1D freezing module. • Eight λ schemes performed better than the built-in λ schemes in HYDRUS-1D. • Importance of choosing appropriate λ schemes for soil water and heat simulations. Soil thermal conductivity (λ) is required to investigate coupled heat and water transport in disciplines such as agriculture, hydrology and engineering. Parameterization schemes or models of λ are also the critical input parameter for various numerical simulation programs like the widely used HYDRUS, one of the most commonly used models for mimicking water, heat, and solute transport. However, λ has not received enough attention in HYDRUS, and it remains unclear how different λ schemes affect the simulated soil water and thermal regimes. Thus, we programmed 24 λ schemes (including two built-in schemes) used in mainstream land surface, hydrological, and soil–vegetation–atmosphere transfer models into HYDRUS-1D (freezing module) to assess the effects of different λ schemes on soil temperature and water content simulations under freezing–thawing. The results showed that the 24 λ schemes performed differently in the simulation of soil temperature within 1 m below ground, with eight λ schemes, i.e., de Vries 1963 scheme/DV1963 (R=0.99), Camillo and Schmugge 1981 /CS1981 scheme (R=0.98), Desborough and Pitman 1998 /DP1998 scheme (R=0.97), Cass et al. 1984 /CS1984 scheme (R=0.96), Shmakin 1998 /SA1998 scheme (R=0.95), Dharssi et al. 2009 /DI2009 scheme (R=0.95), Becker et al. 1992 /BB1992 scheme (R=0.95), Hubrechts 1998 /HL1998 scheme (R=0.94), performing superiorly to the built-in λ schemes. This study highlights the importance of choosing appropriate λ schemes in soil water and heat simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".