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Record 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 on OpenAlexaboutno aff
الهام محمد علیزاده فرد, سید حسین میرموسوی, جمشید یاراحمدی, عبدالله فرجی

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languagefa
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCruGCM transcription factorsEnvironmental scienceGeographyGeneral Circulation ModelClimate changeMeteorologyBiologyPrecipitationEcology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0660.035

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.233
GPT teacher head0.524
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
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