Volume 10, Issue 2 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(2): 1-20 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ahadi L, Asakereh H, Khosravi Y. Simulation of Zanjan temperature trends based on climate scenarios and artificial neural network method. Journal of Spatial Analysis Environmental Hazards 2023; 10 (2) :1-20
URL: http://jsaeh.khu.ac.ir/article-1-3350-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3350-en.html
1- , leila.ahadi70@gmail.com
Abstract: (3181 Views)
Simulation of Zanjan temperature trends based on climate scenarios and artificial neural network method
Abstract
Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.
Research Methods
The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.
results
According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.
Keywords: climate scenarios, simulation, extreme temperatures, artificial neural network, Zanjan
Abstract
Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.
Research Methods
The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.
results
According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.
Keywords: climate scenarios, simulation, extreme temperatures, artificial neural network, Zanjan
Type of Study: Research |
Subject:
Special
Received: 2022/12/7 | Accepted: 2023/05/27 | Published: 2023/09/20
Received: 2022/12/7 | Accepted: 2023/05/27 | Published: 2023/09/20
References
1. اسدی، اشرف. مسعودیان، ابوالفضل. منتظری، مجید. (1391). بررسی همدید گرماهای فرین ایران. پایان نامه کارشناسی ارشد، دانشگاه اصفهان، ص 3.
2. اعتمادی، هانا. شریفی کیا، محمد. صمدی، سیده زهرا. اسماعیلی ساری، عباس. دانه کار، افشین (1394)، شبیهسازی تغییرات اقلیمی آینده در منطقهی جاسک و تأثیر آن بر جنگلهای حرا، جغرافیا و توسعه، شماره 41، زمستان 1394، صص 104-87.
3. اکبری، مهری. اسدالهی، عنایت. (1400). گرمایش جهانی و تغییرات ضخامت جو طی دوره سرد سال در ایران. فصلنامه علمی دانشگاه گلستان، نشریه پژوهش های تغییرات آب و هوایی، سال دوم، شماره مسلسل هشتم، ص 83.
4. آذرخشی، مریم، فرزادمهر، جلیل، اصلاح، مهدی، صحابی، حسین (1392)، بررسی روند تغییرات سالانه و فصلی بارش و پارامترهای دما در مناطق مختلف آب و هوایی ایران، نشریه مرتع و آبخیزداری، مجله منابع طبیعی ایران، دوره 66، شماره 1، صص 16-
5. پناهی، حسین. اسمعیل درجانی، نجمه. (1399). بررسی اثر گرمایش جهانی و تغییرات اقلیمی بر رشد اقتصادی )مطالعه موردی: استان های ایران طی دوره 1390-1380). علوم و تکنولوژی محیط زیست، دوره بیست و دوم، شماره 1، صص 80-82.
6. پورمحمدی، محمدرضا، جمالی، فیروز، اصغری زمانی، اکبر، (1387) ارزیابی گسترش فضایی کالبدی شهر زنجان با تأکید بر تغییرات کاربری زمین، پژوهشهای جغرافیایی، دوره 61 ، شماره 61 ،21 -2.
7. جعفری، اعظم. ترابی آزاد، مسعود. سهرابی، شهاب. (1395). بررسی اثرات افزایش دمای ناشی از تغییر اقلیم جهانی بر میانگین سطح تراز آب (MSL) در سواحل شمالی خلیج فارس(سواحل استان هرمزگان). علوم و تکنولوژی محیط زیست، دوره هجدهم، شماره 3، ص 83.
8. حیدری، محمد امین، خوش اخلاق، فرامرز، مرادی مقدم، محمدامین، مولایی پارده، اصغر (1392)، شبیهسازی تغییرات رژیم دمای مراغه در اثر خشک شدن دریاچه ارومیه، جغرافیا و مخاطرات طبیعی، سال 2، شماره 8.
9. خادمی، معصومه. فضل اولی، رامین. عمادی، علیرضا. (1396)، بررسی توانمندی مدل شبکه عصبی مصنوعی در شبیه سازی فرآیند بارش-رواناب در شرایط تغییر اقلیم (مطالعه موردی: حوزه سد پاشاکلا بابل)، پژوهشنامه مدیریت حوزه آبخیز، پیاپی ۱۶ ، صص ۵۳ -۶۴.
10. خلفی خطبسرا، محمد جواد، نهتانی، محمد، دهمرده، محمدرضا، نوروزی انگنایی، امید (1397)، تخمین بارندگی با استفاده از روش شبکه های عصبی مصنوعی در استان گیلان، دومین همایش ملی دانش و فناوری علوم کشاورزی، منابع طبیعی و محیط زیست ایران
11. رضائی، مریم. نهتانی، محمد. مقدم نیا، علیرضا. آبکار، علیجان. رضائی، معصومه. (1395)، پیشبینی بلندمدت بارش با استفاده از مدل ریزمقیاس نمایی آماری، نشریه دانش آب و خاک، جلد 26، شماره 1/2، صص 115-127.
12. زارعی، کبری. محمدی، حسین. بازگیر، سعید.(1398). پیش بینی عناصر اقلیمی دما و بارش ایستگاه سینوپتیک گرگان براساس سناریوهای RCP . پژوهش های جغرافیای طبیعی، دوره 51،شماره 4، ص579-563
13. سبحانی، بهروز، اصلاحی، مهدی، اکبرزاده، یونس (1395)، مقایسه عملکرد مدلهای SDSM و LARS-WG در شبیهسازی متغیرهای هواشناسی در منطقه شمال غرب ایران، نشریه علمی – پژوهشی هواشناسی کشاورزی، دوره 4، شماره 2 - شماره پیاپی 8، تیر 1395
14. سبزی پرور، علی اکبر، سیف، زهرا، قیامی، فرشته (1392)، ﺗﺤﻠﻴﻞ روﻧﺪ دﻣﺎ در ﺑﺮﺧﻲ از اﻳﺴﺘﮕﺎه ﻧﻴﻤﻪ ﻫﺎی ﻣﻨﺎﻃﻖ ﺧﺸﻚ و ﻛﺸﻮر ﺧﺸﻚ، ﺟﻐﺮاﻓﻴﺎ و ﺗﻮﺳﻌﻪ ﺷﻤﺎره 30
15. شیدائیان، مجید. ضیاء تبار احمدی، میرخالق. فضل اولی، رامین. (1393)، تأثیر تغییر اقلیم بر نیاز خالص آبیاری و عملکرد محصول برنج (مطالعه موردی: دشت تجن)، نشریه آب و خاک (علوم و صنایع کشاورزی)، جلد 28، شماره 6، بهمن – اسفند 1393، صص 1297-1284.
16. صلاحی، برومند. حسینی، سید اسعد. شایقی، حسین. سبحانی، بهروز. (1389)، پیش بینی دماهای حداکثر با استفاده از مدل شبکه عصبی مصنوعی (مطالعه موردی: شهرستان اردبیل)، تحقیقات جغرافیایی، پاییز 1389، دوره 25، شماره 3، (پیاپی 98). صص 78-57.
17. عساکره، حسین . مسعودیان، سید ابوالفضل. ترکارانی، فاطمه (1400)، تغییرپذیری نقش عوامل مکانی مؤثر بر بارش در ارتباط با تحولات دههای بارش سالانۀ ایران زمین. جغرافیا و برنامه ریزی محیطی دورۀ 32 شمارۀ 3: صفحات 146-129
18. عساکره، حسین، کیانی، حدیث (1397)، ارزیابی کارایی مدل SDSM در شبیهسازی میانگین دمای شهر کرمانشاه، فصلنامه علمی - پژوهشی اطلاعات جغرافیایی، دوره 27، شماره 105، بهار 97
19. عساکره، حسین، یوسفی زاده، رحیم (1394)، شبیهسازی تغییرات میانگین درجه حرارت ماهانه و سالانه با استفاده از الگوسازی مدل های سری زمانی، اولین کنگره علمی پژوهشی توسعه و ترویج علوم کشاورزی، منابع طبیعی و محیط زیست ایران.
20. عساکره، حسین. اکبرزاده، یونس (1396)، شبیهسازی تغییرات دما و بارش ایستگاه سینوپتیک تبریز طی دوره (2100-2010) با استفاده از ریزمقیاس نمایی آماری (SDSM) و خروجی مدل CanESM2، مجله جغرافیا و مخاطرات محیطی، دوره 6، شماره 1 - شماره پیاپی 21، بهار 1396، صفحه 153-174
21. عساکره، حسین. غلامی، آوا (1400)، شبیهسازی دمای بیشینه ی ایستگاه سینوپتیک قزوین با استفاده از ریزمقیاس نمایی آماری خروجی مدل CanESM2، فصلنامه علمی - پژوهشی اطلاعات جغرافیایی (سپهر)، دوره 30، شماره 118، تابستان 1400، صفحات 41-25.
22. عساکره، حسین. مازینی، فرشته (1389)، بررسی احتمال وقوع روزهای خشک در استان گلستان با استفاده از مدل زنجیره ی مارکف، جغرافیا و توسعه، شماره 17، بهار 1389، ص 44-29
23. علوی نیا، سید حسن. زارعی، مهدی (1399) واکاوی روند تغییرات فرین های دما تحت تأثیر سناریوهای آینده به منظور ارزیابی نوسانات اقلیمی (مطالعه موردی: ایستگاه های همدیدی سنندج و سقز)، مطالعات جغرافیایی مناطق خشک، دوره یازدهم، شماره چهل و یکم، پاییز 1399، صفحات 16-1.
24. علیجانی، بهلول. روشنی، احمد. پرک، فاطمه. حیدری، روح الله (1391)، روند تغییرپذیری فرین های دما با استفاده از شاخص های تغییر اقلیم در ایران، نشریه علمی جغرافیا و مخاطرات محیطی، دوره 1، شماره 2، شماره پیاپی 2، شهریور 1391، صفحه 17-28
25. علیجانی، بهلول. طولابی نژاد، میثم. کربلائی درئی، علیرضا. (1398). رفتارسنجی اثر گرمایش جهانی بر پرفشار جنب حاره. پژوهشهای جغرافیای طبیعی، دوره 51، شماره 1، ص 34.
26. ماه آورپور، زهرا (1393)، احتمال وقوع بارش های روزانه ایران و پیش بینی آن با مدل زنجیره مارکوف، تحقیقات جغرافیایی، زمستان 1393 , دوره 29 , شماره 4 (پیاپی 115) ; از صفحه 229 تا صفحه 240 .
27. محمدی، پروین، علی پور، حسن (1396)، شبیهسازی پارامترهای دما و بارش بر اساس سناریوهای تغییر اقلیم (مطالعه موردی: کرج)، کنفرانس بین المللی مدیریت منابع طبیعی در کشورهای در حال توسعه.
28. مرادی، روح الله. پورقاسمیان، نسیبه. (1396). بررسی انتشار گازهای گلخانهای و پتانسیل گرمایش جهانی ناشی از مصرف نهادههای شیمیایی در زراعت محصولات مهم استان کرمان. نشریه بوم شناسی کشاورزی، جلد 9، شماره 2، ص 389.
29. مرکز آمار ایران، سرشماری عمومی نفوس و مسکن، شهرزنجان، سال های 1395-1390
30. مروتی، رضا. شکوهی، علیرضا (1393)، ارزیابی روش های مختلف درون یابی داده های دمایی NCEP/NCAR در سطح حوزه های آبریز درجه 2 کشور ایران، فصلنامه علمی پژوهشی مهندسی آبیاری و آب، سال پنجم، شماره هجدهم، زمستان 1393
31. ملکی، زهرا. (1398). تاثیربرندسازی شهری در کیفیت فضاهای معماری و شهری (مطالعه موردی: شهر زنجان). نخستین همایش ملی کیفیت در فضاهای معماری و شهری.
32. ناصری، محسن. احدی، محمد صادق (1395)، ارزیابی سیاست های جمهوری اسلامی ایران در خصوص تغییر اقلیم، فصلنامه راهبرد اجتماعی – فرهنگی، سال پنجم، شماره 21، زمستان 95، صص 48-21.
33. یزدان پناه، حجت الله. علیزاده، تیمور (1390)، برآورد احتمال وقوع امواج گرمایی با دوره های تداوم مختلف در استان کرمان به کمک زنجیره مارکف، فصلنامه تحقیقات جغرافیایی، سال 26، شماره سوم، پاییز 1390، شماره پیاپی 102، ص 71-51.
34. B. Ustaoglu H. K. Cigizoglu M. Karaca (2008), Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods, METEOROLOGICAL APPLICATIONSMeteorol. Appl. 15: 431–445, Published online 9 July 2008 in Wiley InterScience
35. Charpentier, Arthur (2010), On the return period of the 2003 heat, Ecole Polytechnique, Centre National De La Recherche Scientifique, January 2010, 1-22
36. Demuth, H., Beale, M., 2000. Neural Network Toolbox User’s Guide, Copyright 1992-2002, BT The Math Works, Inc, Version 4, 840P.
37. Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of ownscaling methods and hydrologic models. Journal of Hydrology, Volume 307, Issues 1–4, 9 June 2005, Pages 145-163
38. F. Creutzig, P. Agoston, J.C. Minx, J.G. Canadell, R.M. Andrew, C.L. Quere, G.P. Peters, A. Sharifi, Y. Yamagata, S. Dhakal (2016), Urban infrastructure choices structure climate solutions, Nature Clim. Change, 6 (12) (2016), pp. 1054-1056
39. K.R. Gurney, P. Romero-Lankao, K.C. Seto, L.R. Hutyra, R. Duren, C. Kennedy, N.B. Grimm, J.R. Ehleringer, P. Marcotullio, S. Hughes, S. Pincetl, M.V. Chester, D.M. Runfola, J.J. Feddema, J. Sperling (2015), Climate change: track urban emissions on a human scale, Nature, 525 (7568) (2015), pp. 179-181
40. Konstantia, Tolikaa, Christina, Anagnostopouloua, Panagiotis, Maherasa, Margaritis, Vafiadisb (2008), Simulation of future changes in extreme rainfall and temperature conditions over the Greek area: A comparison of two statistical downscaling approaches, Global and Planetary Change Volume 63, Issues 2–3, September 2008, Pages 132-151
41. Kumar Goyal, Manish, Ojha, C. S. P. (2012), Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks, INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 32: 552–566 (2012)
42. Maduako ID, Yun Z, Patrick B (2016) Simulation and Prediction of Land Surface Temperature (LST) Dynamics within Ikom City in Nigeria Using Artificial Neural Network (ANN). J Remote Sensing & GIS 5:158. doi:10.4172/2469-4134.1000158
43. Marschütz, Benedikt; Bremer, Scott; Runhaar, Hens; Hegger, Dries; Mees, Heleen; Vervoort, Joost; Wardekker, Arjan (2020), Local narratives of change as an entry point for building urban climate resilience, Climate Risk Management, Volume 28, 2020, 100223
44. McEvoy, Darryn; Mitchell, David; Trundle, Alexei (2019), Land tenure and urban climate resilience in the South Pacific, Climate and Development Volume 12, 2020 - Issue 1, 1-11
45. Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2013). Climate change on Twitter: topics, communities and conversations about the 2013 IPCC report. PLoS ONE.
46. Ricardo M. Trigo, Jean P. Palutikof (1999), Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach, Inter-Research, Climate Research, Vol. 13: 45–59.
47. Schoof, J.T.; S.C., Pryor (2001), Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks, International Journal OF Climatologyint. J. Climatol. 21: 773–790 (2001)
48. Twigger-Ross, Clare; Orr, Paula; Brooks, Katya; Saduaskis, Rolands. (2016). Citizen Involvement in flood risk governance: flood groups and networks, E3S Web of Conferences, FLOOD risk .2016 - 3rd European Conference on Flood Risk Management, DOI: 10.1051/e3sconf/20160717006
49. UNEP (2022), Emissions Gap Report 2022. 27 OCTOBER 2022
50. Yonas B. Dibike, Paulin Coulibaly (2006), Temporal neural networks for downscaling climate variability and extremes, Neural Networks, Earth Sciences and Environmental Applications of Computational Intelligence, Volume 19, Issue 2, March 2006, Pages 135-144
51. Asadi, Ashraf. Masoudian, Abolfazl. Montazeri, Majid. (2011). Synopsis of Iran's extreme heat. Master's thesis, Isfahan University, page 3.
52. Etamadi, Hana. Sharifi Kia, Mohammad. Samadi, Seyyed Zahra. Ismaili Sari, Abbas. Danehkar, Afshin (2014), simulation of future climate changes in Jask region and its effect on mangrove forests, Geography and Development, No. 41, Winter 2014, pp. 87-104.
53. Akbari, Mehri. Asdalahi, Enayat. (1400). Global warming and atmospheric thickness changes during the cold period of the year in Iran. Scientific Quarterly of Golestan University, Journal of Climate Change Research, 2nd year, serial number 8, p. 83.
54. Azarkhesh, Maryam, Farzadmehr, Jalil, Islah, Mehdi, Sahabi, Hossein (2012), Investigating the trends of annual and seasonal changes in precipitation and temperature parameters in different climatic regions of Iran, Pasture and Watershed Journal, Journal of Natural Resources of Iran, Vol. 66, No. 1, pp. 16-
55. Panahi, Hossein. Ismail Derjani, Najma. (2019). Investigating the effect of global warming and climate changes on economic growth (case study: Iranian provinces during the period 2010-2015). Environmental Science and Technology, Volume 22, Number 1, pp. 80-82.
56. Pourmohammadi, Mohammad Reza, Jamali, Firouz, Asghari Zamani, Akbar, (2007) Evaluation of physical spatial expansion of Zanjan city with emphasis on land use changes, Geographical Research, Volume 61, Number 61, 21-2.
57. Jafari, Azam. Torabi Azad, Masoud. Sohrabi, Shahab. (2015). Investigating the effects of temperature increase caused by global climate change on the mean water level (MSL) in the northern coasts of the Persian Gulf (coasts of Hormozgan province). Environmental science and technology, 18th volume, number 3, page 83.
58. Heydari, Mohammad Amin, Khosh Akhlaq, Faramarz, Moradi Moghadam, Mohammad Amin, Moulai Pardeh, Asghar (2012), Simulation of temperature regime changes in Maragheh due to the drying up of Lake Urmia, Geography and Natural Hazards, year 2, number 8.
59. Khademi, Masoumeh. Fazl Oli, Ramin Emadi, Alireza. (2016), Investigating the capabilities of the artificial neural network model in simulating the rainfall-runoff process under climate change conditions (case study: Pashakla dam area of Babol), watershed management research paper, 16, pp. 53-64.
60. Khalafi Khatbasra, Mohammad Javad, Nahtani, Mohammad, Dehmardeh, Mohammad Reza, Norouzi Angnai, Omid (2017), Rainfall Estimation Using Artificial Neural Networks Method in Gilan Province, Second National Conference on Knowledge and Technology of Agricultural Sciences, Natural Resources and Iran's environment
61. Rezaei, Maryam. Nahtani, Mohammad. Moghadamnia, Alireza. Abkar, Alijan. Rezaei, Masoumeh. (2016), Long-term forecasting of precipitation using statistical microscale exponential model, Danesh Water and Soil Science, Volume 26, Number 1/2, pp. 115-127.
62. Zarei, Kobra. Mohammadi, Hossein. Bazgir, Saeed. (2018). Prediction of climatic elements of temperature and precipitation of Gorgan synoptic station based on RCP scenarios. Natural Geography Research, Volume 51, Number 4, pp. 579-563
63. Sobhani, Behrouz, Islahi, Mehdi, Akbarzadeh, Yunus (2016), Comparison of the performance of SDSM and LARS-WG models in the simulation of meteorological variables in the northwest region of Iran, Scientific-Research Journal of Agricultural Meteorology, Volume 4, Number 2 - Serial Number 8, July 2015
64. Sabzi Parvar, Ali Akbar, Saif, Zahra, Qiami, Fereshteh (2012), Analysis of temperature trends in some stations of semi-arid regions and arid countries, Geography and Development, No. 30
65. Shidaian, Majid. Zia Tabar Ahmadi, Mir Khaleq. Fazl oli, Ramin. (2013), the effect of climate change on the net irrigation requirement and rice yield (case study: Tajen Plain), Water and Soil Journal (Agricultural Sciences and Industries), volume 28, number 6, February-March 2013, pp. 1284-1297.
66. Salahi, Broumand. Hosseini, Seyyed Asad. Shayghi, Hossein. Sobhani, Behrouz. (1389), prediction of maximum temperatures using artificial neural network model (case study: Ardabil city), Geographical Research, Fall 2019, Volume 25, Number 3, (series 98). pp. 57-78.
67. Asakere, Hossein. Masoudian, Seyyed Abulfazl. Tarkarani, Fatemeh (1400), Variability of the role of spatial factors affecting precipitation in connection with the decadal changes of annual precipitation in Iran. Geography and Environmental Planning, Volume 32, Number 3: Pages 129-146
68. Asakreh, Hossein, Kayani, Hadith (2017), evaluation of the efficiency of SDSM model in simulating the average temperature of Kermanshah city, Scientific-Research Quarterly of Geographical Information, Volume 27, Number 105, Spring 2017
69. Asakareh, Hossein, Yousefizadeh, Rahim (2014), Simulation of changes in average monthly and annual temperature using time series modeling, the first scientific research congress on the development and promotion of agricultural sciences, natural resources and environment in Iran.
70. Asakere, Hossein. Akbarzadeh, Younes (2016), simulation of temperature and precipitation changes of Tabriz synoptic station during the period (2010-2100) using statistical exponential microscale (SDSM) and CanESM2 model output, Journal of Geography and Environmental Hazards, Volume 6, Number 1 - Serial Number 21, spring 2016, page 153-174
71. Asakere, Hossein. Gholami, Ava (1400), simulation of maximum temperature of Qazvin synoptic station using CanESM2 output statistical microscale, Scientific-Research Quarterly of Geographic Information (Sephr), Volume 30, Number 118, Summer 1400, Pages 25-41.
72. Asakere, Hossein. Mazzini, Fereshte (2009), investigating the probability of dry days in Golestan province using the Markov chain model, Geography and Development, No. 17, Spring 2010, pp. 29-44
73. Alavinia, Seyyed Hassan. Zarei, Mehdi (2019) Analyzing the trend of temperature changes under the influence of future scenarios in order to evaluate climate fluctuations (case study: Sanandaj and Saqqez observation stations), Geographical Studies of Dry Areas, 11th Volume, Number 41, Fall 2019, Pages 1-16.
74. Alijani, Bahlul. Roshani, Ahmad Perak, Fatima. Heydari, Ruhollah (2013), (2013), the trend of variability of extreme temperatures using climate change indicators in Iran, Scientific Journal of Geography and Environmental Hazards, Volume 1, Number 2, Serial Number 2, September 2011, Pages 17-28
75. Alijani, Bahlul. Tulabinejad, Maysam. Karbalai Derai, Alireza. (2018). Behavior measurement of the effect of global warming on tropical subtropical high pressure. Researches of natural geography, volume 51, number 1, page 34.
76. Mah Avarpour, Zahra (2013), probability of occurrence of daily rainfall in Iran and its prediction with Markov chain model, Geographical Research, Winter 2013, Volume 29, Number 4 (115 series); From page 229 to page 240.
77. Mohammadi, Parveen, Alipour, Hassan (2016), Simulation of temperature and precipitation parameters based on climate change scenarios (case study: Karaj), International Conference on Natural Resources Management in Developing Countries.
78. Moradi, Ruhollah. Pourqasmian, Nasibeh. (2016). Investigation of greenhouse gas emissions and global warming potential due to the consumption of chemical inputs in the cultivation of important crops in Kerman province. Journal of Agricultural Ecology, volume 9, number 2, page 389.
79. Iran Statistics Center, General Population and Housing Census, Shahrzanjan, 2015-2016
80. Morovatti, Reza. Shakohi, Alireza (2013), evaluation of different NCEP/NCAR temperature data interpolation methods at the level of level 2 watersheds in Iran, Irrigation and Water Engineering Research Quarterly, Year 5, Number 18, Winter 2013
81. Maleki, Zahra. (2018). The impact of urban branding on the quality of architectural and urban spaces (case study: Zanjan city). The first national conference on quality in architectural and urban spaces.
82. Naseri, Mohsen. Ahadi, Mohammad Sadegh (2015), Evaluation of the policies of the Islamic Republic of Iran regarding climate change, Social-Cultural Strategy Quarterly, Year 5, Number 21, Winter 2015, pp. 21-48.
83. Yazdan Panah, Hojjatullah. Alizadeh, Timur (2013), estimating the probability of occurrence of heat waves with different durations in Kerman province using Markov chain, Geographical Research Quarterly, Year 26, Number 3, Autumn 2013, serial number 102, pp.
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
