IMPROVED FLOOD QUANTILE ESTIMATION THROUGH INTEGRATED HYDROLOGIC MODELING AND STATISTICAL ANALYSIS
Notice bibliographique
Résumé
Floods are one of the most destructive natural disasters. The annual flood damages in Canada are estimated in billions of dollars, which drain the Canadian economy and affect lives. A safe and cost-effective design of infrastructures, such as dams, bridges, and culverts, is essential to alleviate flood losses and requires accurate estimates of the magnitude and frequency of floods. This thesis comprehensively examines the methodologies of flood quantile estimation within the context of Canadian hydrologic characteristics, with a special focus on the Canadian Prairies. The conducted research aims to (1) evaluate existing flood frequency analysis distributions and suggest better alternatives, (2) characterize the complex generation mechanisms of peak spring streamflow, and (3) introduce an innovative methodology for enhanced regional flood frequency assessments. The methods are carefully set to comprehensively investigate each objective. First, a thorough analysis of 1088 streamflow records across Canada challenges the prevailing Generalized Extreme Value distribution (GEV) in flood frequency analysis. Alternative models are proposed (i.e., Burr type III and Burr type XII) and compared to the GEV model. The results show comparable performance between the models while highlighting the advantage of using Burr distributions. The assessment also raises questions about the reliability of the GEV in predicting extreme flows. Second, different flood generation mechanisms associated with historical floods at 109 Canadian Prairie basins are characterized using seven novel descriptors that account for key hydro-climatic basin conditions, which could trigger peak spring flows. The descriptors are set to be comprehensive enough to describe the complicated hydrologic processes of the Canadian Prairies. The descriptors are estimated using daily historical precipitation and temperature records to implicitly account for winter snowpack, overall basin wetness throughout the winter, rain-on-snow, soil wetness before soil freezing, basin connectivity, snowmelt rate, and losses in winter snowpack before the occurrence of peak spring flows. Additionally, a multivariate copula-based approach is proposed to describe the dependence structure between the proposed basin descriptors and historical peak spring flows using the t-copula. The characterization results in identifying nine different flood generation mechanisms and contributes to advancing the understanding of peak spring streamflow generation. The spatial and temporal distribution of the different flood generation mechanisms across the Canadian Prairies is found heterogeneous which challenges the association of mechanisms to sub-regions or sub-seasons across the Canadian Prairies. Furthermore, the t-copula is found feasible to probabilistically assess the interaction between the basin descriptors and peak spring flows, by describing the response of a basin according to its concurrent basin conditions, based on the knowledge obtained from historical basin responses. Third, a novel site similarity measure is introduced for regional flood frequency analysis to address land depressions. The new measure is based on hydrologic simulations and relates to land depressions. A case study that involves 109 sites across the Canadian Prairies is used to evaluate the pooling (cluster analysis) of the 109 sites using 30 combinations of site similarity measures. The index flood method is applied to regionally quantify flood quantities using several probability distributions. The results prove the need to consider the state of land depressions for pooling hydrologically similar sites and estimating accurate regional quantiles in prairie regions. It demonstrates the role of the proposed site similarity measure in enhancing groups' homogeneity and underscores the susceptibility of regionally estimated quantiles to the chosen site similarity measures. In summary, this research significantly contributes to enhancing the methodology for estimating flood quantiles, understanding flood generation mechanisms, and refining regional flood frequency analysis techniques, in the context of the Canadian Prairies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».