Statistical modeling of precipitation processes for gaged and ungaged sites in the context of climate change
Notice bibliographique
Résumé
Understanding the variations of precipitation process in time and in space is essential for the planning, design, and management of various water resources systems. Recently, climate change impacts on precipitation have been recognized as one of the most critical issues for water management in many regions around the world. The present study was therefore carried out in order to develop better methods for improving the accuracy of rainfall estimation at a gauged or ungauged local site in the context of a changing climate. This study can be divided into five main parts.The first part of the present research deals with the development of a Statistical Downscaling model for Rainfall (SDRain) for describing accurately the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local gauged site using a logistic regression model and a nonlinear model. The feasibility of the suggested SD was tested using the NCEP re-analysis data and the observed daily precipitation data available from a group of 26 raingages located in South Korea and in Canada. It was found that it is feasible to link large-scale climate predictors given by General Circulation Model (GCM) simulation outputs with daily precipitation characteristics at these stations.The second part proposed a statistical downscaling approach to describe the linkage between large-scale climate variables to Annual Maximum Precipitations (AMPs) for daily and sub-daily scales at a local site. The feasibility of the proposed downscaling method has been tested based on climate simulation outputs from CGCM3 and HadCM3 and using available AMPs for durations ranging from 5 minutes to 1 day at 9 raingage stations in Quebec (Canada). Results of the application has indicated that it is feasible to link large-scale climate predictors given by GCM simulation outputs with daily and sub-daily AMPs at a local site.The third part was concerned with the development of a new statistical regionalization method using the Ordinal Factor Analysis (OFA) and the daily precipitation occurrence data. The feasibility and accuracy of the proposed method has been assessed using the daily precipitation data available from a network of 63 raingage stations in South Korea. Results of the numerical application have indicated that the suggested method was more accurate and more robust than the Principal Component Analysis (PCA). The identified homogeneous precipitation regions were found physically consistent to the particular climatic features of South Korea.The fourth part proposed a stochastic estimation procedure for estimating the missing daily precipitation series at an ungauged site. The feasibility and accuracy of the proposed estimation approach have been assessed using the daily precipitation data available at 63 raingage stations in South Korea. Results have indicated that the proposed procedure could provide an accurate estimate of the daily precipitation series for ungauged locations.Finally, a statistical downscaling procedure was proposed for the downscaling of the daily precipitation process at an ungauged location. More specifically, the suggested approach consists of two components: a spatial-link function and a spatial downscaling. The feasibility and accuracy of the proposed SD procedure was assessed based on the NCEP re-analysis data and the observed and reconstructed daily precipitation series at the same raingage station. Results have indicated that the proposed procedure could provide comparable results as those given by the downscaling using real observed precipitation data at the local site.
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 | 0,002 | 0,002 |
| 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 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 ».