Experimental and numerical study on flow and heat transport in partially frozen soil
Bibliographic record
Abstract
Frozen soil has a major effect in many hydrologic processes, and its impacts are difficult to predict. A prime example is flood forecasting during spring snowmelt within the Canadian Prairies. One key driver for the extent of flooding is the antecedent soil moisture and the possibility for water to infiltrate into (partly) frozen soils. Therefore, these situations are crucial for accurate flood prediction at every spring. The main objective of this study was to evaluate the water flow and heat transport within available hydrological models to predict the impact of frozen and partly frozen soil on infiltration and percolation. A standardized data set was developed for water flow and heat transport into (partial) frozen soil by laboratory experiments using fine sand within a one-dimensional (1-D) soil column. A 1-D soil column having a length of 107 cm and diameter of 35.6 cm was built and equipped with insulation to limit heat exchange only through the soil surface. A data logger collected the moisture content and temperature by five FDR sensors which have been installed at a distance of 15 cm from each other. During the experiments, temperature, soil moisture, and percolated water was observed at different freezing conditions (-5°C, -10°C, and -15°C) as well as at thawing conditions when the air temperature was increased to +5°C. Distribution of soil moisture and soil temperature in the soil column was plotted for the experimental data over the freezing and thawing period. As some of the water in the soil begins to freeze, a decrease in water content was observed with a sudden increase in soil temperature near 0°C or slightly below of 0°C. This was, in fact, only a decrease in unfrozen water, not a decrease in total water content and was caused by the latent heat during freezing. Soil temperature showed noticeable differences at the top and the bottom of soil column during the change of state of water. The heat flux at the lower soil column was strongly limited due to iii the overlying soil. Thus, the soil temperature at the lowest sensors stayed in a freezing condition over several days and was not changing the temperature due to the latent heat which was released during the freezing process. Significant variation in soil moisture content was found between the top and the bottom of the soil column at the starting of the thawing period. However, with increasing temperature, the lower depth of the soil column showed higher moisture content as the soil was enriched with moisture with higher transmission rate due to the release of heat by soil particles during the thawing cycle. The soil system did not remain in the isothermal state during the thawing cycle. Although gravitational gradient was mainly responsible for the infiltration rate into the partially frozen soil, the distribution of moisture was greatly influenced by the temperature gradient. Vadose zone modeling using HYDRUS-1D was applied to the data set. Numerical results of the modeling were calibrated using the experimental results. It showed that the newly developed benchmark data set were useful for the validation of numerical models. The use of such a validated freezing and thawing module implemented into larger scale hydrologic models will directly reduce the prediction uncertainty during flood forecasting. Moreover, these benchmark data sets will be useful for the validation of numerical models and for developing scientific knowledge to suggest potential code variations or new code development in numerical models.
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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 | 0.001 |
| 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) | 0.001 | 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".