Subsurface moisture storage patterns derived from hydrologic model and satellite measurements during extreme period of dryness
Notice bibliographique
Résumé
Spatially distributed subsurface soil moisture dataset with suitable temporal scale are needed for a better understanding of the mechanisms responsible for the recurrent drought outbreaks over the Canadian Prairie. However, there are no soil moisture data at depths exceeding a few centimeters over the Canadian Prairie sub-catchments thereby placing enormous constraints on the feasibility of studies that require such information. Hence, this thesis explores the use of a physically based, spatially distributed hydrologic model in reproducing the patterns of the spatial and temporal distribution of soil moisture over the drought-prone 13,000 km² Upper Assiniboine River Basin (UARB) in the Canadian Prairie. Prior to making any inferences on the spatial and temporal dynamics of the simulated subsurface soil moisture over this large domain, a necessary requirement was to validate the model's simulated output of other hydrologic variables. These validations were accomplished using observed measurements of streamflow, snow depth, moisture storage change from geological weighing lysimeters and estimates of the total water storage from the Gravity Recovery And Climate Experiment (GRACE) remote sensing satellite system. After an assessment of the simulated outputs from the Variable Infiltration Capacity (VIC) model, which were found to be of acceptable quality, this thesis thereafter focused on assessing the spatial distribution of the subsurface soil moisture over the large catchment. Therefore, in the first case study undertaken in this thesis, it was demonstrated that with a structurally sound model (one equipped with adequate land surface parameterization) such as the VIC model, it is possible to generate soil moisture datasets at different spatial and temporal scales for use in areas such as the Canadian Prairie and other geologically complex terrains where observed soil moisture measurements are lacking. -- Furthermore, retrieval of the terrestrial moisture storage dataset from the Gravity Recovery And Climate Experiment (GRACE) satellite remote sensing system is possible when the catchment of interest is of large spatial scale. These dataset are of paramount importance for the estimation of the total storage deficit index (TSDI), which enables the characterization of a particular drought event from the perspective of the terrestrial moisture storage over that catchment. Incidentally, the GRACE gravity signal over the 13000 km² Upper Assiniboine River Basin on the drought-prone Canadian Prairie is so poor therefore making the computation of the total storage deficit index for this basin infeasible. Consequently, the estimation of the terrestrial moisture storage from other reliable sources becomes imperative in order to enable the computation of the TSDI over this basin. -- In the second case study undertaken in this thesis, simulation of the total moisture storage over the Upper Assiniboine River Basin was accomplished utilizing the spatially-distributed land surface model, VIC, which was then employed in the estimation of the TSDI over this basin for subsequent characterization of the recent Prairie-wide drought. Interestingly, the resulting temporal patterns in the computed TSDI from the land surface model reveal a strong resemblance with the same drought characterization undertaken over the larger adjacent Saskatchewan River Basin, which was accomplished utilizing terrestrial moisture storage from the GRACE-based approach. In this second case study, it has been shown that in the computation of the total storage deficit index over small-scale catchments during anomalous climatic conditions that propagate extreme dryness through the terrestrial hydrologic systems, simulations of the total water storage from a structurally sound model such as the VIC model could be resourceful for the computation of the monthly total storage deficit index if no constraint is placed on the availability of accurate meteorological forcing. -- Finally, understanding the memory in land surface processes, such as that in the subsurface moisture storage has great implication for seasonal weather prediction over a catchment. However, given that there are no physical observations of soil moisture at depths of hydrological importance or measurements of the total water storage, it is infeasible to undertake studies on land-atmosphere interactions. In the last case study undertaken in this thesis, effort is focused on estimating the memory in the simulated deep soil moisture and total water storages over the 406,000 km² Saskatchewan River Basin (SRB) in the Canadian Prairies. Finally, given the similarity in the simulated deep moisture storage anomaly and the model-based TSDI, it was inferred that the former could serve as a descriptor of drought over this large Prairie sub-catchment.
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 | 0,000 |
| 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 | 0,000 |
| 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) | 0,000 | 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 ».