Projection of precipitation variability over the highlands of Yemen by statistical down-scaling for the period 2026–2100
Notice bibliographique
Résumé
Climate change significantly affects the management of environmental resources, particularly through changes in the amount and variability of local climate variables, such as precipitation. However, current projections from Global Climate Models (GCMs) are not directly applicable to local-scale impact modeling due to their coarse spatial resolution and inherent biases. To address this challenge, the Statistical Down-Scaling Model (SDSM) is employed to downscale daily precipitation, a crucial input for impact assessment models. This study focuses on the highlands of Yemen, a region highly vulnerable to climate change and precipitation variability. Due to limited and incomplete local climate data, we utilized the best available precipitation datasets, including the Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), to fill in missing station data. Historical and future predictors derived from the National Center for Environmental Prediction (NCEP) reanalysis and the Canadian Earth System Model Phase 2 (CanESM2) were used to generate future precipitation scenarios, which were compared with the ensemble means from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We also used the Shared Socioeconomic Pathways (SSPs) scenarios, specifically SSP126 and SSP585, to evaluate potential future changes in precipitation. Results indicate a projected increase in seasonal precipitation during the 2030s (2026–2050), 2060s (2051–2075), and 2090s (2076–2100). The western highlands, including Al Mahwit, Rymah, and parts of Sana'a governorate, are expected to experience precipitation increases of up to 55 %. Under RCP2.6, the short rainy season (March–May) is projected to increase up to 14 %, while under RCP8.5, this increase could reach 24 %. The long rainy season (June–August) is expected to increase by 6 % under RCP2.6 and 27 % under RCP8.5. The dry season (December–February) could see increases of 18 % under RCP2.6 and 46 % under RCP8.5, while the autumn season (September–November) may experience a substantial rise of 61 %–101 %. At the annual timescale, precipitation is projected to increase up to 34 % higher than the baseline period (1991–2020) across the region. These projections indicate that the highlands of Yemen will experience wetter conditions in the 21st century. The findings provide valuable insights for developing adaptation strategies for water and environmental resource management, considering the potential future impacts of climate change in the region. • Projection of future precipitation trends in the highlands of Yemen using the Statistical Downscaling Model (SDSM) for the period 2026–2100. • Significant seasonal and annual increases in precipitation projected under both RCP2.6 and RCP8.5 climate scenarios. • The highest projected precipitation increases, mainly in autumn, occur in the western highlands of Yemen. • Results provide critical insights for water resource management and climate adaptation strategies in a region highly vulnerable to climate change.
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 | 0,000 |
| 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 ».