Novel methods for estimating extreme design rainfalls at gauged and ungauged locations in a changing climate
Notice bibliographique
Résumé
Information on the variability of extreme rainfalls in time and in space is of critical importance for many types of hydrologic studies. In addition, in recent years, climate change has been recognized as having a profound impact on the hydrologic cycle at different temporal and spatial scales. The present study is therefore was carried out to develop appropriate methods for improving the accuracy of design rainfall estimation at gauged and ungauged locations in the current climate as well as in the context of climate change. This study can be divided into five primary parts.The first part presents a general procedure for assessing systematically the performance of different commonly used probability distributions in extreme rainfall frequency analyses based on their descriptive as well as predictive abilities. To test the feasibility of the proposed procedure, an illustrative application was carried out using annual maximum rainfall data from a network of 21 raingages located in the Ontario region in Canada. Results have indicated that the Generalized Extreme Values (GEV), Generalized Normal (GNO), and Pearson Type 3 (PE3) models were the best models for describing the distribution of daily and sub-daily annual maximum rainfalls in this region.The second part introduces a new probability-weighted-moment-based scaling Generalized Extreme Value (GEV/PWM) distribution model for modeling rainfall extremes across a wide range of time scales. A comparative study was then carried out to asses the performance of the proposed model using the available extreme rainfall data from a network of 74 raingages located across Canada. Results of this comparative study have indicated the superior performance of the proposed GEV/PWM model as compared to the existing models based on an extensive set of graphical and numerical comparison criteria.The third part proposes an innovative spatio-temporal statistical downscaling approach for establishing the linkage between daily extreme rainfalls at regional scales and daily and sub-daily extreme rainfalls at a given local site. The performance of the proposed method was assessed for a case study in Ontario using observed extreme rainfall data from seven raingages and climate simulation outputs from 21 different Global Climate Models that have been downscaled to a regional 25-km scale. Results based on various graphical and numerical comparison criteria have indicated the feasibility and accuracy of the proposed downscaling approach. The fourth part introduces new scale-invariancce models for modeling rainfall extremes across a wide range of time scales. The present study presented some general mathematical frameworks for three commonly-used probability distributions in hydrologic frequency analyses such as the Generalized Logistic (GLO), GNO, and PE3 using both non-central moment (NCM) and PWM estimation methods. Results of an illustrative application using the observed IDF data from a network of 74 raingages located across Canada have indicated the feasibility and accuracy of these new scale-invariance models. Finally, the fifth part consists of developing a convenient decision-support tool for the construction of robust rainfall IDF relations in consideration of model uncertainty and potential climate change impacts for the design of urban water systems at a given location of interest. More specifically, this tool can readily be used to identify in an objective and systematic manner the most suitable probability models for accurate and robust estimation of design rainfalls. In addition, in the context of a changing climate, the proposed tool was able to establish the linkage between large-scale climate predictors given by GCMs and the daily and sub-daily extreme rainfalls at a given site
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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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».