Dual‐permeability modeling of preferential flow and snowmelt partitioning in frozen soils
Bibliographic record
Abstract
Abstract The infiltrability of frozen soils modulates the partitioning of snowmelt between infiltration and runoff in cold regions. Preferential flow in macropores may enhance infiltration, but flow dynamics in frozen soil are complicated by soil heat transfer processes. We developed a dual‐permeability model that considers the interacting effects of freeze–thaw and preferential flow on infiltration and runoff generation in structured soils. This formulation was incorporated into the fully integrated groundwater–surface water model HydroGeoSphere, to represent water–ice phase change in macropores such that porewater freezing is governed by macropore–matrix heat exchange. Model performance was evaluated against laboratory experiments and synthetic test cases designed to examine the effects of preferential flow on snowmelt partitioning between infiltration, runoff, and drainage. Simulations were able to reproduce experimental observations of rapid infiltration and drainage behavior due to macropores very well, and approximated soil thaw to an acceptable degree. Simulation of measured data highlighted the importance of macropore hydraulic conductivity, as well as macropore–matrix heat and water transfer, on controlling preferential flow dynamics. Test cases replicated a range of snowmelt partitioning behavior commonly observed in frozen soils, including subsurface conditions that produce rapid infiltration and deeper drainage, the contrast between limited vs. unlimited infiltration responses to snowmelt, and the temporal evolution of runoff generation. This study demonstrates the important influence that water freezing along preferential flowpaths can have on infiltrability and runoff characteristics in frozen soils and provides a physically based description of this mechanism that links infiltration behavior to hydraulic and thermal properties of structured 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 | 0.000 | 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) | 0.006 | 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".