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Enregistrement W3043201931 · doi:10.22034/gp.2020.10790

ارزیابی اثرتغییر اقلیم بر بارش در مناطق فاقد آمار مشاهداتی با استفاده از بسته نرمافزاری CCT مطالعه موردی: حوضه دریان

2020· article· fa· W3043201931 sur OpenAlexaboutno aff
الهام محمد علیزاده فرد, سید حسین میرموسوی, جمشید یاراحمدی, عبدالله فرجی

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languefa
DomaineEnvironmental Science
ThématiqueHydrology and Drought Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCruGCM transcription factorsEnvironmental scienceGeographyGeneral Circulation ModelClimate changeMeteorologyBiologyPrecipitationEcology

Résumé

récupéré en direct d'OpenAlex

Introduction Climate change is one of the most important phenomena of the present century, which has created many problems and challenges both globally and regionally and nationally. In the second half of the twentieth century, global warming relative to The first half of this century has increased and it is predicted that this increase in temperature will continue in future periods, resulting in changes in the level of climatic conditions in different parts of the world. Due to the lack of atmospheric precipitation, due to the increase in temperature, the rate of evaporation has increased significantly and can greatly affect the aggravation of water shortage conditions in surface currents, especially evaporation from the surface of lakes behind dams. Percentage by evaporation leads the country to higher values ​​(Farajzadeh and Ghasemifar, 1398). Regarding the changes in Iran's water resources in the horizon of 2100, few studies have been done and most have been case studies (Fahmi, 1393). Although the results of these studies, based on the climatic models and different scenarios used, sometimes show contradictions, so it is necessary to do more studies in this field. Methodology The present research has been done in three specific sections and the output of each section has been used as the input of the next section. In the first part, climate change in the form of precipitation variables in the study area is detected and subsequently, rainwater runoff in the Daryan catchment is simulated. Then, while identifying the characteristics of hydrological drought periods in the basin, the probability of occurrence, intensity and duration of hydrological drought periods are calculated based on the fit of different statistical distributions for different return periods in the third section. Results and discussion Climate change is one of the most important environmental problems of this century. Thus, evaluating the phenomenon of climate change and reducing its effects on both global and regional scales has attracted the attention of many researchers, planners and legislators (Yohe et al., 2007). Proper assessment of these effects requires the existence of climatic information with appropriate spatial distribution and long-term time series, as well as a thorough understanding of its future trends at the regional and local scale. Despite the fact that today the output of public circulation models (GCM) are the main sources of future climate data production. One of the most important consequences of climate change includes changes in the hydrological cycle and river flow regime of watersheds. Therefore, the present study aimed to investigate the possible effects of climate change on rainfall and runoff in the Daryan catchment area north of Lake Urmia. In this study, statistical method (SDSM) and data of CanESM2 Canadian climate model in the form of three scenarios RCP2.6, RCP4.5 and RCP8.5 in order to micro-scale the precipitation data of five synoptic stations adjacent to the sea basin and changes Its future is used. Here, the basic period (1961-2005) and future periods (2049-2020), (2079-2050) and (2080-2100) were selected. In this research, the threshold level method has been used to identify hydrological drought periods and extract its characteristics. The results of the analysis of the last 35 years of hydrological droughts in the Daryan Basin showed that 44 drought events occurred in this basin, which in total, led to a reduction in surface flow volume of about 140 million cubic meters in this basin. Conclusion The simulation results of SWAT model showed that the annual average runoff of the sea basin in the first period (2020-49) in all three scenarios increases by 3.7 and 6%, respectively, compared to the base period. While in the rest of the periods of all scenarios, runoff reduction is predicted compared to the base period. Accordingly, a decrease in surface runoff compared to the base period is predicted for five months of the year (April to August) and an increase in the remaining months. Future changes in precipitation at Tabriz station, which is the basis for modeling runoff in the Daryan basin, are not very noticeable compared to the base period, and only in the period (2049-2020) all three scenarios are predicted to increase by 5, 2 and 8%, respectively, compared to the base period. In the other periods, in all three scenarios, a decrease in rainfall is predicted compared to the base period. Results of evaluating the effects of climate change on rainfall and surface runoff in the Daryan Basin with the results of other researchers in the catchment area of ​​Lake Urmia, including: Goodarzi and Fatehifar (2010) in the Azarshahrchai Basin, Qaderpour et al. (2016), Dariane et al. (2019) ), Sobhani et al. (2015), Goodarzi et al. (2015) and Salehpour and Malekian (2019) are consistent.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil0,221

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,002
Communication savante0,0040,002
Science ouverte0,0000,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0660,035

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 Opus0,233
Tête enseignante GPT0,524
Écart entre enseignants0,290 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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