Comparison of algorithms and parameterisations for infiltration into organic-covered permafrost soils
Bibliographic record
Abstract
Abstract. Infiltration into frozen and unfrozen soils is critical in hydrology, controlling active layer soil water dynamics and influencing runoff. Few Land Surface Models (LSMs) and Hydrological Models (HMs) have been developed, adapted or tested for frozen conditions and permafrost soils. Considering the vast geographical area influenced by freeze/thaw processes and permafrost, and the rapid environmental change observed worldwide in these regions, a need exists to improve models to better represent their hydrology. In this study, various infiltration algorithms and parameterisation methods, which are commonly employed in current LSMs and HMs were tested against detailed measurements at three sites in Canada's discontinuous permafrost region with organic soil depths ranging from 0.02 to 3 m. Field data from two consecutive years were used to calibrate and evaluate the infiltration algorithms and parameterisations. Important conclusions include: (1) the single most important factor that controls the infiltration at permafrost sites is ground thaw depth, (2) differences among the simulated infiltration by different algorithms and parameterisations were only found when the ground was frozen or during the initial fast thawing stages, but not after ground thaw reaches a critical depth of 15 to 30 cm, (3) despite similarities in simulated total infiltration after ground thaw reaches the critical depth, the choice of algorithm influenced the distribution of water among the soil layers, and (4) the ice impedance factor for hydraulic conductivity, which is commonly used in LSMs and HMs, may not be necessary once the water potential driven frozen soil parameterisation is employed. Results from this work provide guidelines that can be directly implemented in LSMs and HMs to improve their application in organic covered permafrost soils.
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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 | 0.001 | 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 | 0.001 |
| 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".