Investigation of Precipitation-Based Indices in the Mazandaran Region Under Climate Scenarios of the Sixth Report
Notice bibliographique
Résumé
Background: One of the important effects of global warming is the increase in extreme atmospheric phenomena, the most important of which are sudden changes in temperature, excessive heat, abnormal cold, heavy rains and floods, drought, and dust caused by the drying up of wetlands.Changes in climatic components due to climate change have impacted the intensity and frequency of extreme rainfall events and, subsequently affecting society and the natural environment.Therefore, examining the future modifications of different precipitation indices is more necessary to implement management plans, especially for Iran, which needs progress in climate modeling studies aiming at improving knowledge about climate change and its effects on the whole country.In this study, changes in the trend and breaking point of rainfall-based indices were studied using the CMIP6 series model under three optimistic, medium, and pessimistic scenarios in Mazandaran province for two periods: the near future (2021-2060) and the far future (2061-2100).Methods: In this research, the spatiotemporal trend and breaking point of the precipitation in Mazandaran province (15 stations) were investigated in the future period from the precipitation data of SSP scenarios (126, 245, and 585) in the two 2021-2060 and 2061-2100 periods.Using the R-Climdex software in the R software environment, the precipitation limit indices were determined for different scenarios and periods.Then, the trend and breaking point were examined using the Mann-Kendall, Sen, and Pettitt tests.Precipitation indexes were defined by an expert group under the supervision of the World Meteorological Organization as limit indexes and climate change indexes.After receiving the output of models and their micro scaling, data were grouped in the future period based on three scenarios in all studied stations.The daily rainfall indexes were extracted using R programming developed by Zhang and Yang at the Canadian Meteorological Service Climatic Research Branch.Therefore, 11 and 16 indices out of 27 existing indexes were related to precipitation and temperature, respectively.Results: Under the optimistic scenario (SSP126), the indexes of the number of consecutive wet days, days with heavy rain, and very wet days in the eastern half of the province will increase significantly.Based on the pessimistic scenario (SSP585), the profile of wet days and the annual amount of rainfall will decrease in different areas of the province.In terms of the occurrence of the breaking point, the daily intensity of precipitation and wet days will have a breaking point in the order of increasing in the 2060s, rising in 2061, and decreasing in 2051 in the eastern half of the region only in the pessimistic scenario of the profile of consecutive wet days.Under the other scenarios, however, no sudden changes will be observed in the data series.According to the median scenario, there will be no changes in precipitation indexes regionally, and only the daily intensity index of precipitation will increase in a limited number of coastal stations in the near future.Under a pessimistic scenario, however, changes in limit indexes will often be reduced so that the index of the number of wet days (with 1 mm rainfall) will decrease in the near future in most parts of the province with high intensity.The annual amount of rainfall on wet days in coastal and western regions, as well as two indexes of the number of days with heavy and very heavy rain, will decrease in the high stations of the western areas.However, the risk of flooding events may be reduced in the near future, and the probability of widespread droughts increases due to Sari Agriculture Sciences and NaturalResources University
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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 | 0,001 |
| 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 | 0,000 |
| Communication savante | 0,001 | 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 ».