A radiative–conductive–convective approach to calculate thaw season ground surface temperatures for modelling frost table dynamics
Bibliographic record
Abstract
Abstract The frost table depth is a critical state variable for hydrological modelling in cold regions as frozen ground controls runoff generation, subsurface water storage and the permafrost regime. Calculation of the frost table depth is typically performed using a modified version of the Stefan equation, which is driven with the ground surface temperature. Ground surface temperatures have usually been estimated as linear functions of air temperature, referred to as ‘ n ‐factors’ in permafrost studies. However, these linear functions perform poorly early in the thaw season and vary widely with slope, aspect and vegetation cover, requiring site‐specific calibration. In order to improve estimation of the ground surface temperature and avoid site‐specific calibration, an empirical radiative–conductive–convective (RCC) approach is proposed that uses air temperature, net radiation and antecedent frost table position as driving variables. The RCC algorithm was developed from forested and open sites on the eastern slope of the Coastal Mountains in southern Yukon, Canada, and tested at a high‐altitude site in the Canadian Rockies, and a peatland in the southern Northwest Territories. The RCC approach performed well in a variety of land types without any local calibration and particularly improved estimation of ground temperature compared with linear functions during the first month of the thaw season, with mean absolute errors <2 °C in seven of the nine sites tested. An example of the RCC approach coupled with a modified Stefan thaw equation suggests a capability to represent frozen ground conditions that can be incorporated into hydrological and permafrost models of cold regions. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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) | 0.000 | 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".