Statistical Modeling of Precipitation Processes in the Context of Climate Change
Notice bibliographique
Résumé
Understanding the spatial and temporal variations of the precipitation process is essential for the planning, design, and management of various water resources systems. Recent years, climate change impacts on precipitation have been considered as one of the most critical issues for water resources management worldwide. Hence, it is essential to establish the linkage between the large- scale climate variables in the atmosphere with the precipitation characteristics at a local site of interest for impact and adaptation studies. The present study is therefore carried out to develop appropriate methods for improving the accuracy of precipitation 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 this research aims to develop a new statistical downscaling (SD) model for describing the linkage between large-scale climate predictors and observed daily precipitation characteristics at a local site. The proposed SD model, referred hereafter SDGAM, is based on the Generalized Additive Modeling (GAM) method. The feasibility and accuracy of the SDGAM are assessed using the National Center for Environmental Prediction (NCEP) re-analysis data and the observed daily precipitation data available for the 1961–2000 period at ten gauged sites located in Canada.The second part of this research is to propose a new statistical downscaling approach based on the combination of the spatial downscaling method to link large-scale climatic variables provided by Global Climate Models (GCMs) to daily extreme precipitations at a local site using the SDGAM and the temporal downscaling procedure to describe the relationships between daily extreme precipitations with sub-daily extreme precipitations using the scaling GEV distribution and the scaling behavior of the empirical Probability Weighted Moments. IDF relations were then constructed for historical period of 1961-2000 and future periods of 2030s, 2060s and 2090s for different Representative Concentration Pathways (RCP).The third part of this research aims to estimate daily precipitation series for ungauged sites in Vietnam. Initially, daily rainfall series data of 155 stations across Vietnam were employed to identify different homogeneous rainfall regions using the Principal Component Analysis (PCA) method. Daily precipitation series at ungauged sites were then estimated using a proposed two- stage interpolation method to describe the persistence in rainfall occurrences and amounts for the identified rainfall homogenous regions.The fourth part of this research is to investigate the presence of trends in daily annual maximum precipitation series using the historical rainfall records available from a network of 175 high- quality stations across Canada and the downscaled regional gridded data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The trends were computed for two different periods: historical period from 1950 to 2005 and future period from 2006 to 2100. The final part of this research is to perform a detailed analysis of the variability in time and in space of the daily annual maximum rainfalls and extreme temperatures over the Montreal region for the present and future climates using the data from two different sources: the Pacific Climate Impacts Consortium (PCIC) and the NEX-GDDP. More specifically, the evaluation was based on the climate simulation outputs from ten different Global Climate Models (GCMs) downscaled (i) by PCIC to a regional 1/12-degree grid using the BCCAQ and BCSD methods; and (ii) by NASA to a regional 1/4-degree grid. Historical data for the 1961-1990 period and future projections for the 2006 – 2100 period were also used for this evaluation
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 | 0,002 | 0,006 |
| 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 | 0,001 |
| 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) | 0,000 | 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 ».