Statistical Modeling of Daily Precipitation Process in the Context of Climate Change
Notice bibliographique
Résumé
Information on the variability of precipitation process is essential for the planning, design and management of various water resources systems. Furthermore, recent assessment reports on climate change have indicated a worldwide increase in the frequency of extreme storm events for the late 20th century because of global warming. Consequently, research on developing innovative approaches for limiting and adapting climate change impacts on water infrastructures is highly critical due to the substantial investments involved. Global Climate Models (GCMs) have been commonly used in various studies for assessing these potential impacts. However, outputs from these GCMs (generally greater than 200 km) are considered too coarse and hence are not suitable for climate change impact studies at a given site or over a catchment area. As a result, several downscaling techniques have been proposed to downscale these GCM outputs to the precipitation series at a given location of interest. Nevertheless, there is still no general agreement about which downscaling method is the best approach for describing accurately the observed precipitation characteristics at a given site in the climate change context, depending mainly on the study objectives and the climatology of the study area. The present study is therefore carried out in order to develop appropriate methods for improving the accuracy of precipitation estimation at a local site in the context of a changing climate. This study therefore proposes a new statistical model, herein referred to as SDGAM, using the Generalized Additive Models (GAM) to address the shortcomings of existing downscaling methods. The feasibility and accuracy of the proposed new approach were evaluated using the observed daily precipitation records available at two rain-gauge stations located in Quebec Province, and the National Center for Environmental Prediction (NCEP) re-analysis data that are interpolated for two GCMs (Canadian CanESM2 and UK HadCM3). Results of this numerical application have indicated that the proposed SDGAM model was able to describe well many features of the daily precipitation process, including its amounts, occurrence frequency, intensity, and extremes. In addition, it has been demonstrated that the suggested SDGAM model could provide more accurate results than the popular Statistical Downscaling Model (SDSM) in the modeling of the daily precipitation process based on both numerical and graphical performance criteria. Finally, the proposed SDGAM can generate daily precipitation series for future periods under different climate change scenarios: RCP2.6, RCP4.5 and RCP8.5 for CanESM2, as well as A2 and B2 for HadCM3. These generated precipitation series are useful for various climate change impact studies in practice
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».