Experimental and numerical study on flow and heat transport in partially frozen soil
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».