Modelling capillary hysteresis effects on preferential flow through melting and cold layered snowpacks
Bibliographic record
Abstract
Accurate estimation of the amount and timing of water flux through melting snowpacks is important for runoff prediction in cold regions. Most existing snowmelt models only account for one-dimensional matrix flow and neglect to simulate the formation of preferential flow paths. Consideration of lateral and preferential flows has proven critical to improve the performance of soil and groundwater porous media flow models. A two-dimensional physically-based snowpack model that simulates snowmelt, refreezing of meltwater, heat and water flows, and preferential flow paths is presented. The model assumes thermal equilibrium between solid and liquid phases and uses recent snow physics advances to estimate snowpack hydraulic and thermal properties. For the first time, capillary hysteresis is accounted in a snowmelt model. A finite volume method is applied to solve for the 2D coupled heat and mass transfer equations. The model with capillary hysteresis provided better simulations of water suction at the wet to dry snow interface in a wetting snow sample than did a model that only accounted for the boundary drying curve. Capillary hysteresis also improved simulations of preferential flow path dynamics and the snowpack discharge hydrograph. Simulating preferential flow in a subfreezing snowpack allowed the model to generate ice layers, and increased the vertical exchange of energy, thus modelling a faster warming of the snowpack than would be possible without preferential flow. The model is thus capable of simulating many attributes of heterogeneous natural melting snowpacks. These features not only qualitatively improve water flow simulations, but improve the understanding of snowmelt flow processes for both level and sloping terrain, and illuminate how uncertainty in snowmelt-derived runoff calculations might be reduced through the inclusion of more realistic preferential flow through snowpacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".