Effects of antecedent moisture and macroporosity on infiltration and water flow in frozen soil
Bibliographic record
Abstract
Abstract Infiltration into frozen soil plays an important role in soil freeze–thaw and snowmelt‐driven hydrological processes. To better understand the complex thermal energy and water transport mechanisms involved, the influence of antecedent moisture content and macroporosity on infiltration into frozen soil was investigated. Ponded infiltration experiments on frozen macroporous and non‐macroporous soil columns revealed that dry macroporous soil produced infiltration rates reaching 10 3 to 10 4 mm day −1 , two to three orders of magnitude larger than dry non‐macroporous soil. Results suggest that rapid infiltration and drainage were a result of preferential flow through initially air‐filled macropores. Using recorded flow rates and measured macropore characteristics, calculations indicated that a combination of both saturated flow and unsaturated film flow likely occurred within macropores. Under wet conditions, regardless of the presence of macropores, infiltration was restricted by the slow thawing rate of pore ice, producing infiltration rates of 2.8 to 5.0 mm day −1 . Reduced preferential flow under wet conditions was attributed to a combination of soil swelling, due to smectite‐rich clay (that reduced macropore volume), and pore ice blockage within macropores. In comparison, dry soil column experiments demonstrated that macropores provided conduits for water and thermal energy to bypass the frozen matrix during infiltration, reducing thaw rates compared with non‐macroporous soils. Overall, results showed the dominant control of antecedent moisture content on the initiation, timing, and magnitude of infiltration and flow in frozen macroporous soils, as well as the important role of macropore connectivity. The study provides an important data set that can aid the development of hydrological models that consider the interacting effects of soil freeze–thaw and preferential flow on snowmelt partitioning in cold regions.
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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) | 0.000 | 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".