Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China
Notice bibliographique
Résumé
Abstract Based on a newly developed daily precipitation dataset of 740 stations in China and more robust trend detection techniques, trends in annual and seasonal total precipitation and in extreme daily precipitation, defined as those larger than its 95th percentile for the year, summer, and winter half years, have been assessed for the period 1951–2000. Possible links between changes in total precipitation and frequency of extremes have also been explored. The results indicate that there is little trend in total precipitation for China as a whole, but there are distinctive regional and seasonal patterns of trends. Annual total precipitation has significantly decreased over southern northeast China, north China, and over the Sichuan Basin but significantly increased in western China, the Yangtze River valley, and the southeastern coast. In western China, precipitation increase has been observed for both cold and warm seasons. However, trends differ from one season to another in eastern China. Spring precipitation has increased in southern northeast China and north China but decreased significantly in the midreach of the Yangzte River. The summer precipitation trend is very similar to that of annual totals. Autumn precipitation has generally decreased throughout eastern China. In winter, precipitation has significantly decreased over the northern part of eastern China but increased in the south. The number of rain days has significantly decreased throughout most parts of China with northwest China being an exception. Meanwhile, precipitation intensity has significantly increased. This suggests that the precipitation increase in western China is due to the increase in both precipitation frequency and intensity. In eastern China, the impact of reduced number of rain days seems to be more dominant in the north while the influence of enhanced intensity prevails in the south. Over regions with increasing precipitation trends, there have been much higher than normal frequency of precipitation extreme events. For example, significant increases in extreme precipitation have been found in western China, in the mid–lower reaches of the Yangtze River, and in parts of the southwest and south China coastal area. A significant decrease in extremes is observed in north China and the Sichuan Basin. Trends in the number of extremes and total precipitation from nonextreme events are generally in phase. An exception is southwest China where an increase of extreme events is associated with a decrease in total nonextreme precipitation.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| 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) | 0,001 | 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 ».