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Enregistrement W6888718845 · doi:10.22098/mmws.2023.12563.1252

The effects of climate change on increasing the risk of drought in Tehran using CMIP6 scenarios

2024· article· en· W6888718845 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHydrology and Drought Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClimate changeGlobal warmingVulnerability (computing)Effects of global warmingScale (ratio)Risk assessmentExtreme weatherGlobal change

Résumé

récupéré en direct d'OpenAlex

Introduction Global warming threatens human survival in today's scenario; and has become an environmental challenge. The climate data shows the warming trend in many parts of the world, which has led to a wide range of climate effects such as reduced precipitation, drought, and the occurrence of extreme events. The increasing vulnerability of many urban areas, especially in developing countries, has been one of the main concerns of life. Therefore, examining the risk of risks caused by global warming on a national and local scale is a fundamental step to increase the readiness of urban areas to reduce current and future risks caused by climate change. Droughts have effects on vegetation, soil and freshwater quality, and etc., and are a serious ecological problem around the world, its impact on crops and water availability for humans can jeopardize human life. Although drought has always been common, the drought risk has become increasingly prominent because of the climatic warming that has occurred during the past century. Therefore, these effects of are noticeable in all climates, and Iran, having a dry and semi-arid climate, is one of the countries that is always at risk of drought, and this causes great economic damage to the country every year.  in addition to this, the review of the history and climatic texts shows that the importance of the effects of climatic elements (rainfall and temperature) on the drought situation of the region in the coming period is essential. Until now, the study of global warming in terms of increasing the risk of drought using the sixth report and new climate data has not been studied in Tehran province. So this study aims to investigate the effects of climate change on increasing the risk of drought in Tehran province.   Materials and Methods This research, First, the parameters (temperature and precipitation) for the synoptic stations (Abali, Shemiran, Mehrabad) for the period (1988-2020) were received from the Tehran Meteorological Organization. To projection in the future, down-scaling methods (SDSM-DC model) and The Mann-Kendall test were used to investigate the changes in temperature and precipitation. Climatic parameters change in space and time scale for many reasons. that these changes should be checked based on observations and statistical methods. Rainfall is one of those climate parameters that is not normal, and methods such as Menn-Kendall should be used to deal with such conditions; For this purpose, using Macro Excel, the value of age slope and Z statistic was calculated in the period (1988-2020) for the stations (Mehrabad, Shemiran and Abali) to investigate the trend of precipitation and temperature from the past to the present at a confidence level of 95-99% computed. In the following NetCDF data together with CanESM5 predictors from the base period (1979-2014) according to the most recent SSP release scenarios of the IPCC 6th Report were obtained from the Canadian Climate Change website. Among them, only Mehrabad station had a complete basic period compared to other stations, it was chosen as the selected station in Tehran. Drought indices are used as an index to track and quantify drought, this research is SPI index with DIC software has been used to evaluate the drought. in this index Precipitation, which has been one of its main components, whose output and results are more consistent with reality.   Results and Discussion The results using the Mann-Kendall test showed that the examination of temperature changes in the stations (Mehrabad, Shemiran, Abali) was an increasing trend. Rainfall in most months of the year is a stable trend, only jumps are observed which can be justified by the increase in the frequency of rainfall. In the simulation with the CanESM5 model under the SSP5 scenario at Mehrabad station, the highest temperature was assigned to July and the highest rainfall will be in March. in this research, The drought survey showed that Mehrabad station experienced severe drought only during 1989-1993, while the duration of the drought period was longer in Abali and Shemiran stations and both of them faced very severe drought in 2014. in addition, this Investigation of three stations with a common period of 32 years in Tehran shows that Tehran has overcome the drought situation in recent years; But most of the time, the region may be in close to normal climatic conditions. But the risk of dry to very dry conditions will be very close. On the other, the results confirmed that drought depends on both rainfall and temperature.   Conclusion As drought depends on both precipitation and temperature, The more the drought is accompanied by the trend of increasing temperature, decreasing precipitation, and the prediction of climate models, the more likely it is that climate change will occur due to global warming. As a result, the temperature of Tehran will increase. And while the rainfall is decreasing, it will be fluctuating and torrential. so In the future, Tehran's climate will have more fluctuations in rainfall and will be warmer than the current conditions. In addition, rains will occur more randomly but with more intensity. Tehran has had a drought in recent years, but most of the time the region has been in near-normal climatic conditions, but is the risk of dry and very dry conditions. This factor will create environmental challenges in the future. In addition to this, it is necessary to have a plan for climate risk management in the future due to the new climate of Tehran, which is prone to drought.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0020,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,099
Tête enseignante GPT0,469
Écart entre enseignants0,370 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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