Simulating preferential flow and snowmelt partitioning in seasonally frozen hillslopes
Bibliographic record
Abstract
Abstract The infiltrability of frozen soils strongly influences snowmelt partitioning and redistribution in cold regions. Preferential flow in frozen soil can enhance infiltration, but dynamics are complicated by coupled water and heat transfer processes as well as landscape conditions prior to and during snowmelt. Hypothetical model simulations based on hydrological functioning and landscape properties of the Canadian Prairies were used to evaluate a dual‐domain (matrix and macropore) formulation of variably‐saturated flow in frozen soils, with distinct water and heat transport regimes in each domain. The description was incorporated into a fully‐integrated groundwater‐surface water model. Two‐dimensional hillslope simulations were able to capture the landscape hydrologic response to snowmelt fluxes observed in the prairies and similar landscapes, specifically: (1) enhanced infiltration into frozen soil due to preferential flow, (2) refreezing of infiltrated water and its effect on the evolution of runoff generation in frozen soils, and (3) groundwater recharge prior to ground thaw. Results showed that multiple meltwater input events progressively decreased frozen soil infiltrability and increased runoff generation. Simulations demonstrated that refreezing of infiltrated water along preferential flowpaths is an important process governing the timing and magnitude of both runoff generation and groundwater recharge in frozen soils, but that this behaviour can be highly counterintuitive and depends on soil structure. The modeling framework provides a physically‐based approach for describing these interacting preferential flow and soil freezing processes at the hillslope scale needed to simulate the hydrologic functioning of seasonally frozen landscapes.
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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) | 0.008 | 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".