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سلطانی, کبری, معصوم پور سماکوش, جعفر, مجرد, فیروز, هادی پور, سحر, جلیلیان, عبداله. (1402). تحلیل روند و تنوع فضایی خشکی در اقلیم آینده ایران. , 55(2), 25-50. doi: 10.22059/jphgr.2023.361339.1007777
کبری سلطانی; جعفر معصوم پور سماکوش; فیروز مجرد; سحر هادی پور; عبداله جلیلیان. "تحلیل روند و تنوع فضایی خشکی در اقلیم آینده ایران". , 55, 2, 1402, 25-50. doi: 10.22059/jphgr.2023.361339.1007777
سلطانی, کبری, معصوم پور سماکوش, جعفر, مجرد, فیروز, هادی پور, سحر, جلیلیان, عبداله. (1402). 'تحلیل روند و تنوع فضایی خشکی در اقلیم آینده ایران', , 55(2), pp. 25-50. doi: 10.22059/jphgr.2023.361339.1007777
سلطانی, کبری, معصوم پور سماکوش, جعفر, مجرد, فیروز, هادی پور, سحر, جلیلیان, عبداله. تحلیل روند و تنوع فضایی خشکی در اقلیم آینده ایران. , 1402; 55(2): 25-50. doi: 10.22059/jphgr.2023.361339.1007777

تحلیل روند و تنوع فضایی خشکی در اقلیم آینده ایران

پژوهش های جغرافیای طبیعی
مقاله 2، دوره 55، شماره 2، مرداد 1402، صفحه 25-50    اصل مقاله (2.71 M)
نوع مقاله: مقاله کامل
شناسه دیجیتال (DOI): 10.22059/jphgr.2023.361339.1007777
نویسندگان
کبری سلطانی1؛ جعفر معصوم پور سماکوش* 1؛ فیروز مجرد1؛ سحر هادی پور2؛ عبداله جلیلیان3
1گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه رازی، کرمانشاه، ایران
2گروه مهندسی عمران، دانشکده مهندسی عمران، دانشگاه تکنولوژی مالزی، جوهر بهرو، مالزی
3گروه آمار، دانشکده علوم، دانشگاه رازی، کرمانشاه، ایران
چکیده
این تحقیق با هدف بررسی تنوع فضایی و روند زمانی خشکی ایران در آینده (2050- 2020) بر اساس سناریوهای SSP2-4.5 و SSP5-8.5 مدل‌های CMIP6 (MRI-ESM2 و GFDL-ESM4) نسبت به داده‌های دیدبانی (2014- 1992) با استفاده از شاخص‌های AI و IDM بر پایه متغیرهای بارش، تبخیر و تعرق و متوسط دما انجام شد. از ضریب تغییرات و تحلیل روند نوآورانه به ترتیب برای بررسی تغییرات و روند میانگین سالانه خشکی استفاده شد. نتایج نشان داد در دوره مشاهداتی به‌جز نواحی شمالی البرز و بخشی از شمال غرب، سایر مناطق کشور خشک و نیمه‌خشک بوده‌اند. اما در آینده، سناریوها نشان‌دهنده کاهش رطوبت در مناطق شمالی البرز، نواحی شمالی فلات داخلی ایران و بخش‌هایی از مناطق جنوبی رشته‌کوه زاگرس هستند. خشکی در شمال غرب و بخش‌هایی از مناطق مرکزی و شمالی زاگرس کاهش می‌یابد. در سایر نواحی مدل‌ها همچون گذشته شرایط خشک و نیمه‌خشک را پیش‌بینی کردند. بیشترین درصد تنوع فضایی میانگین سالانه خشکی (105%- 71%)، در دوره 2014- 1992 در جنوب شرق و سواحل جنوبی کشور مشاهده شد و بر اساس مدل‌ها درصد تنوع مکانی خشکی در آینده کاهشی می‌باشد. روند مقادیر میانگین سالانه خشکی ایران نشان داد که خشکی در سطوح معناداری 05/0 و 01/0 درگذشته افزایش‌یافته و در آینده بر اساس MRI-ESM2 (سناریوی SSP5-8.5 و شاخص IDM) در سطوح معناداری 05/0 و 01/0 افزایش خواهد یافت. در سایر شرایط خشکی در این سطوح معناداری، کاهش نشان می‌دهند. نتایج پژوهش می‌تواند در برنامه‌ریزی و کاهش آثار منفی تغییرات اقلیمی در ایران مفید باشد.  
کلیدواژه‌ها
ایران؛ تغییرات فضایی؛ خشکی؛ روند؛ CMIP6
عنوان مقاله [English]
Analysis of the Trend and Spatial Variation of Aridity in the Future Climate of Iran
نویسندگان [English]
Kobra Soltani1؛ jafar masoompour samakosh1؛ Firouz Mojarrad1؛ Sahar Hadi Pour2؛ Abdollah Jalilian3
1Department of Geography, Faculty of Literature and Human Sciences, Razi University, Kermanshah, Iran
2School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
3Department of Statistics, Faculty of Science, Razi University, Kermanshah, Iran
چکیده [English]
ABSTRACT
This research aims to investigate the spatial variability and temporal trends of Iran's aridity in the future (2020–2050) based on the SSP2-4.5 and SSP5-8.5 scenarios of CMIP6 models (MRI-ESM2 and GFDL-ESM4) compared to observational data (1992–2014) using AI and IDM indices based on precipitation, evapotranspiration, and average temperature variables. The coefficient of variation and innovative trend analysis were exerted to examine the changes and trends of the average annual aridity, respectively. The results showed that during the observation period, except for the northern areas of Alborz and a part of the northwest, other areas of the country were arid and semi-arid. However, in the future, the scenarios show a decrease in humidity in the northern areas of Alborz, the northern areas of the inner plateau of Iran, and parts of the southern areas of the Zagros mountains. Aridity decreases in the northwest and parts of the central and northern regions of Zagros. The models predicted arid and semi-arid conditions in other areas, as in the past. The highest percentage of annual average spatial variation of land (71%–105%) was observed in the southeast and south coasts of the country in the period 1992–2014, and according to the models, the percentage of spatial variation of land will decrease in the future. The trend of the average annual aridity values of Iran showed that the drought has increased at significance levels of 0.05 and 0.01 in the past and will increase in the future at significance levels of 0.05 and 0.01 based on MRI-ESM2 (SSP5-8.5 scenario and IDM index). At these significant levels, aridity shows a decreasing trend in other conditions. The results of this research can be useful in planning and reducing the negative effects of climate change in Iran.
Extended Abstract
Introduction
High temperatures and low precipitation characterize arid and semi-arid regions. Aridity is a permanent feature of a region's long-term hydrological and climatic conditions. Aridity is a function of precipitation, evapotranspiration, and temperature. Due to the diversity of climate, many numerical indicators have been proposed for different types of climate in different regions of the world. Indices based on the changes in precipitation and temperature, or indices calculated based on precipitation, evapotranspiration, and studying aridity or variables affecting it, are useful in environmental planning.
 
Methodology
Iran has an area of about 1,698,195 square kilometers, between 25 and 40 degrees north latitude and 44 and 64 degrees east longitude. An arid and semi-arid climate covers about 90% of the country. The most important reason for the arid climate of Iran should be related to its geographical location because it is close to the tropical region (receives more solar radiation) and is under the influence of sub-tropical high pressure. On the other hand, the Zagros and Alborz mountain ranges prevent moisture from entering the interior, arid regions. Another factor affecting the aridity of Iran is that a large part of its territory is far from seas and oceans.
In this study, SSP2-4.5 and SSP5-8.5 scenarios data from CMIP6 models (MRI-ESM2 and GFDL-ESM4) and observed data (1992–2014) in cloud of precipitation, average, maximum, and minimum temperature, wind speed at 2-meter height, sunshine duration, and several radiation variables (from CMIP6) were prepared. The data were converted into data with a 50 x 50 km resolution in the R software with the Resample command, and calculations were made using two aridity indices, AI and IDM. Due to the lack of sunshine hours data in CMIP6 models, this variable was calculated using the input (short and long wave) and output (short and long wave) data of the models and the radiation estimation method of Li et al. After calculating the indices, zoning maps were produced in ArcGIS 10.5.
 
Results and Discussion
The results showed that during the observation period, except for the northern areas of Alborz and small parts of northwestern Iran, which according to AI included semi-humid to humid areas and according to IDM included Mediterranean to very humid areas, other regions of the country were placed in dry and semi-arid classes. An increase in aridity in 6.4% and 5.4% of the country's area based on the AI index and under the conditions of SSP2-4.5 and SSP5-8.5 scenarios and 4.2% and 3.4% based on the IDM index, according to the mentioned scenarios of The MRI-ESM2 model, will occur in the northern areas of Alborz (especially the southern coasts of the Caspian Sea) and the northern areas of the inner plateau of Iran in the future. According to the GFDL-ESM4 model, the increase in aridity in 4.4% and 7.1% of the country's regions based on AI and 3.3% and 6.3% of the country's regions under the SSP2-4.5 and SSP58.5 scenarios based on the IDM index and according to the mentioned scenarios, as predicted by the MRI-ESM2 model, it will happen in the Caspian coasts and the northern areas of Alborz, and in addition, in the southern areas of Zagros, in 2020–2050. 17.2% and 22.4% of the country's area under the conditions of SSP2-4.5 and SSP5-8.5 of the MRI-ESM2 model in the central Zagros areas, parts of the northeast and northwest based on AI, and also 17.2% and 20.4% The entire area of the country located in the central Zagros regions to the northwest of Iran will experience a decrease in aridity, according to the IDM index and according to the mentioned scenarios.
The highest amount of changes in the observation period and the future, based on the indicators (and scenarios of both models), related to the southeastern regions and the southern coasts of the country, especially the coasts of the Oman Sea, by 71%–105% and related to the observational data (1992–2014), which is most likely related to changes in humidity in this area following the beginning and end of monsoon activities in this area. According to the models, the number of aridity changes in the future (2020–2050) will decrease. The most decrease in aridity changes in the future is related to the coasts of the Oman Sea.
The trend of average aridity values in Iran showed that aridity increased at significance levels of 0.05 and 0.01 in the past (2014–1992), and in 2020–2050 only under SSP5-8.5 of the MRI-ESM2 model and the IDM index at significance levels of 0.05 and 0.01 will increase. In other conditions, aridity will decrease at significance levels of 0.05 and 0.01. Both models showed a decrease in humidity in the northern regions of Alborz and a decrease in aridity in the country's northwestern regions. The models had the same performance in depicting the country's future climate based on the average annual aridity for the period 2020–2050.
 
Conclusion
The results showed that during the observed period, except for the northern areas of Alborz and small parts of the northwest of Iran, other regions were classified as arid and semi-arid. However, in the future, the scenarios of the models show a decrease in humidity in the northern areas of Alborz, the northern areas of the inner plateau of Iran, and parts of the southern areas of the Zagros mountain range. Dryness decreases in the northwest and parts of the central and northern regions of Zagros. The models did not show change in other regions of Iran, and arid and semi-arid conditions will continue in these areas. The highest percentage of annual average spatial variation of aridity (71%–105%) was observed in observational data (1992–2014) in the southeast areas and south coasts of the country, and according to the models, the percentage of spatial variation of aridity will decrease in the future. The trend of average values of aridity in Iran showed that aridity increased at significance levels of 0.05 and 0.01 in the past. During 2020–2050, it will increase based on MRI-ESM2 (SSP5-8.5 scenario and the IDM index) at the significance levels of 0.05 and 0.01. In other conditions, aridity shows a decrease at these significance levels. The research results can be effective for long-term planning to reduce the negative effects of climate change in Iran, especially in its eastern and southern parts.
 
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.
کلیدواژه‌ها [English]
Aridity, CMIP6, Iran, Spatial Changes, Trend
مراجع
  1. بختیاری، بهرام؛ مهدوی، نکیسا و سیاری، نسرین. (1400). تحلیل حساسیت و بررسی تغییرات شاخص خشکی (AI) در چند نمونه اقلیمی ایران. تحقیقات منابع آب ایران، 17 (56)، 15- 1.
  2. تیموری، مهدی؛ عبداللهی مایوان، محبوبه؛ نژاد حسن، بتول و گرایی، پرویز. (1390). بررسی روند شاخص خشکی در ایران. اولین کنفرانس ملی خشک‌سالی و تغییر اقلیم.
  3. جعفری، غلامحسن و آوجی، مینا. (1396). بررسی خشکی هیدرولوژیکی حوضه‌های داخلی ایران. جغرافیای طبیعی، 10 (1)، 100- 87.
  4. رنجبر، فیروز و طباطبایی، حسن. (1401). بررسی روند شاخص خشکی در ایستگاه‌های نوار شمالی ایران طی دوره 2019- 1982، پژوهش‌های تغییرات آب‌وهوایی، 3 (9)، 24- 12. https://doi.org/30488/ccr.2022.327870.1070
  5. زرین، آذر. و صالح‌آبادی، نسرین. (1398). پیش‌آگاهی مخاطره خشک‌سالی در تهران بر اساس برونداد مدل‌های CMIP6. ششمین کنفرانس بین‌المللی - منطقه‌ای تغییر اقلیم.
  6. طاوسی، تقی. (1397). بررسی روند تغییرات بارندگی و شاخص خشکی یونپ در پهنه‌های آب‌وهوایی غرب و شمال غرب ایران. اطلاعات جغرافیایی (سپهر)، 27 (105)، 96- 85. https://doi.org/10.22131/sepehr.2018.31475
  7. طاوسی، تقی؛ محمودی، پیمان و سرگلزایی مقدم، فرزانه. (1389). مقایسه گسترش مکانی اقلیم‌های خشک و نیمه‌خشک در ایران طی دوره 2005-1976. تحقیقات مرتع و بیابان ایران، 17 (1)، 105- 94.
  8. علیجانی، بهلول؛ محمودی، پیمان و کلیم، دوست‌محمد. (1394). تحلیل آماری زمینه‌های آب‌وهوایی بیابان‌زایی در ایران. فضای جغرافیایی، 15 (51)، 32- 19.
  9. نادری، مصطفی. (1399). مخاطرات سیل و خشک‌سالی در مناطق خشک و نیمه‌خشک تحت شرایط تغییر اقلیم: شمال استان فارس. پژوهش آب ایران، 14 (1)، 97- 85.
  10. نوری، میلاد؛ همایی، مهدی و بنایان، محمد. (1395). بررسی روند تغییرات شاخص خشکی طی دوره 2100- 1966 در شمال غرب ایران. مهندسی و مدیریت آبخیز، 8 (4)، 453– https://doi.org/10.22092/ijwmse.2016.107187
  11. نوری، میلاد؛ همایی، مهدی و بنایان، محمد. (1396). بررسی تغییرات تبخیر و تعرق مرجع طی سده بیست‌ویک در برخی مناطق نیمه‌خشک ایران. تحقیقات آب‌وخاک ایران، 48 (2)، 252- 241. https://doi.org/22059/ijswr.2017.62578
  12. Ali, R., Kuriqi, A., Abubaker, S., & Kisi, O. (2019). Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen’s innovative trend method. Water, 11 (9), 18-55. https://doi.org/10.3390/w11091855.
  13. Alijani, B., Mahmoudi, P., & Kalim, D. M. (2015). Statistical Analysis of Climatic Histories of Desertification in Iran. Geographic space, 15 (51), 19-32. [In Persian].
  14. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). FAO Irrigation and drainage paper. 56. Rome: Food and Agriculture Organization of the United Nations, 56(97), e156.‌
  15. Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., & Ismail, M. (2020). Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Systems and Environment, 4 (2), 297-320. ‌ https://doi.org/10.1007/s41748-020-00157-7.
  16. Araghi, A., Martinez, C. J., Adamowski, J., & Olesen, J.E. (2018). Spatiotemporal variations of aridity in Iran using high‐resolution gridded data. International Journal of Climatology, 38 (6), 2701-2717. https://doi.org/10.1002/joc.5454
  17. Asadi Zarch, M. A., Sivakumar, B., Malekinezhad, H., & Sharma, A. (2017). Future aridity under conditions of global climate change. Journal of Hydrology, 554, 451-469, https://doi.org/10.1016/j.jhydrol.2017.08.043
  18. Bakhtiari, B., Mahdavi, N., & SAYARI, N. (2021). Variations and Sensitivity Analysis on Aridity Index (AI) in Some Climate Samples in Iran. Iran-water resources research, 17 (1), 1-15. [In Persian].
  19. Baltas, E., (2007). Spatial distribution of climatic indices in northern Greece. Meteorological Applications: A journal of forecasting, practical applications. training techniques and modelling, 14 (1), 69-78. https://doi.org/10.1002/met.7
  20. Begueria, S., & Vicente-Serrano, S. M. (2017). Package ‘SPEI’. URL: https://CRAN.R-project.org/web/packages/SPEI/ version 1.7.
  21. Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2017). shiny: Web Application Framework for R. URL: https://CRAN.R-project.org/package=shiny. r package version 1.0.3. ‌
  22. Cherlet, M., Hutchinson, C., Reynolds, J., Hill, J., Sommer, S., & Von Maltitz, G. (Eds.). (2018). World atlas of desertification: Rethinking land degradation and sustainable land management. Publications Office of the European Union,‌ Luxembourg.
  23. Choudhary, A., Mahato, S., Roy, P. S., Pandey, D. N., & Joshi, P. K. (2023). Analyzing the long-term variability and trend of aridity in India using non-parametric approach. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-023-02483-4.
  24. Croitoru, A. E., Piticar, A., Imbroane, A. M., & Burada, D. C. (2013). Spatiotemporal distribution of aridity indices based on temperature and precipitation in the extra-Carpathian regions of Romania. Theoretical and applied climatology, 112 (3-4), 597-607, https://doi.org/10.1007/s00704-012-0755-2.
  25. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9 (5), 1937-1958, https://doi.org/10.5194/gmd-9-1937-2016.
  26. Gebremedhin, M. A., Kahsay, G. H., & Fanta, H. G. (2018). Assessment of spatial distribution of aridity indices in Raya valley, northern Ethiopia. Applied Water Science, 8 (8), 217, https://doi.org/10.1007/s13201-018-0868-6.
  27. Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., & Lyu, K. (2020). Insights from CMIP6 for Australia's future climate. Earth's Future, 8, e2019EF001469, https://doi.org/10.1029/2019EF001469.
  28. HadiPour, S., Abd Wahab, A. K., & Shahid, S. (2020). Spatiotemporal changes in aridity and the shift of drylands in Iran. Atmospheric Research, 233, 104704, https://doi.org/10.1016/j.atmosres.2019.104704.
  29. Huang, J., Yu, H., Guan, X., Wang, G., Guo, R. (2016). Accelerated dryland expansion under climate change. Nature Climate Change, 6(2), 166-17. https://doi.org/10.1038/nclimate2837
  30. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F. D., Qin, G. K., Plattner, M., Tignor, S.K., Allen, J., Boschung, A., Nauels, Y., Xia, V. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535, https://doi.org/10.1017/CBO978110741532.
  31. IPCC. (2021). Summary for policymakers. in: Climate change 2021: The physical science basis. Masson-Delmotte, V. P., Zhai, P., Pirani, S. L., Connors, C., Péan, S., Berger, N., ... & Scheel Monteiro, P. M. contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change.‌ https://doi.org/10.1017/9781009157896.001.
  32. Jafari, Gh., & Avaji, M. (2017). Investigating the hydrological aridity of the interior basins of Iran. Iranian Journal of Natural Geography, 10 (1), 87- 100. [In Persian].
  33. Jafari, M., Tavili, A., Panahi, F., Esfahan, E. Z., & Ghorbani, M. (2018). Reclamation of Arid lands. Springer International Publishing. https://doi.org/10.1007/978-3-319-54828-9
  34. Karamouz, M., Nazif, S., & Falahi, M. (2012). Hydrology and hydroclimatology: principles and applications. CRC Press.
  35. Kousari, M., Dastorani, M. T., Niazi, Y., Soheyli, E., Hayatzadeh, M. & Chezgi, J. (2014). Trend Detection of Drought in Arid and Semi-Arid Regions of Iran Based on Implementation of Reconnaissance Drought Index (RDI) and Application of Non-Parametrical Statistical Method. Water Resour Manag, 28, 1857–1872. https://doi.org/10.1007/s11269-014-0558-6
  36. Landgren, O. A., Parding, K., Dobler, A., McSweeney, C. F., Benestad, R., Erlandsen, H. B., ... & El Zohbi, J. (2020). Effects of GCM selection for regional climate modelling illustrated by the interactive tool GCMeval. In EGU General Assembly Conference Abstracts (p. 13441), https://doi.org/10.5194/egusphere-egu2020-13441
  37. Li, Y., Yao, N., Sahin, S., & Appels, W.M. (2017). Spatiotemporal variability of four precipitation-based drought indices in Xinjiang, China. Theoretical and Applied Climatology, 129 (3-4), 1017-1034. https://doi.org/10.1007/s00704-016-1827-5
  38. Li, Y., Yu, W., Wang, K., & Ma, X. (2019). Comparison of the aridity index and its drivers in eight climatic regions in China in recent years and in future projections. International Journal of Climatology, 39(14), 5256-5272. https://doi.org/10.1002/joc.6137
  39. Lickley, M., & Solomon, S. (2018). Drivers, timing and some impacts of global aridity change. Environmental Research Letters, 13 (10), https://doi.org/10.1088/1748-9326/aae013
  40. Liu, C., Huang, W., Feng, S., Chen, J., & Zhou, A. (2018). Spatiotemporal variations of aridity in China during 1961–2015: decomposition and attribution. Science Bulletin, 63 (18), 1187-1199. https://doi.org/10.1016/j.scib.2018.07.007.
  41. Liu, L., Wang, Y., You, N., Liang, Z., Qin, D., & Li, S. (2019). Changes in aridity and its driving factors in China during 1961–2016. International Journal of Climatology, 39 (1), 50-60, https://doi.org/10.1002/joc.5781
  42. Madani, K. (2014). Water management in Iran: what is causing the looming crisis? Journal of environmental studies and sciences, 4 (4), 315-328. https://doi.org/10.1007/s13412-014-0182-z
  43. Majumder, M. (2015). Impact of urbanization on water shortage in face of climatic aberrations. ‌
  44. Marak, J. D. K., Sarma, A. K., & Bhattacharjya, R. K. (2020). Innovative trend analysis of spatial and temporal rainfall variations in Umiam and Umtru watersheds in Meghalaya, India. Theoretical and Applied Climatology, 142 (3), 1397-1412. https://doi.org/10.1007/s00704-020-03383-1
  45. Mehran, A., AghaKouchak, A., & Phillips, T. J. (2014). Evaluation of CMIP5 continental precipitation simulations relative to satellite‐based gauge‐adjusted observations. Journal of Geophysical Research: Atmospheres, 119 (4), 1695-1707. https://doi.org/10.1002/2013JD021152
  46. Miri, M., Masoompour, S. J., Raziei, T., Jalilian, A., & Mahmodi, M. (2021). Spatial and temporal variability of temperature in Iran for the twenty-first century foreseen by the CMIP5 GCM models. Pure and Applied Geophysics, 178 (1), 169-184. https://doi.org/10.1007/s00024-020-02631-9
  47. Miseckaite, O., Cadro, S., Tunguz, V., Lukashevich, V., Simunic, I., and Orlovic-Leko, P. (2018). Climate and aridity change. In 8TH Asian Regional Conference (8ARC): Irrigation in Support of Evergreen Revolution, Kathmandu, 143-152.
  48. Moral, F. J., Rebollo, F. J., Paniagua, L. L., García-Martín, A., & Honorio, F. (2016). Spatial distribution and comparison of aridity indices in Extremadura, southwestern Spain. Theoretical and applied climatology, 126 (3-4), 801-814. https://doi.org/10.1007/s00704-015-1615-7
  49. Moral, F.J., Paniagua, L.L., Rebollo, F. J., & Garcia-Martín, A. (2017). Spatial analysis of the annual and seasonal aridity trends in Extremadura, southwestern Spain. Theoretical and Applied Climatology, 130 (3-4), 917-932. https://doi.org/10.1007/s00704-016-1939-y
  50. Moran, M. S., Clarke, T. R., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote sensing of environment, 49 (3), 246-263. https://doi.org/10.1016/0034-4257(94)90020-5
  51. Naderi, M. (2020). The flood and drought events over arid and semi-arid regions under climate change: Northern Fars province. Iranian Water Research Journal, 14 (1), 85- 97. [In Persian].
  52. Nouri, M., & Bannayan, M. (2019). Spatiotemporal changes in aridity index and reference evapotranspiration over semi-arid and humid regions of Iran: trend, cause, and sensitivity analyses. Theoretical and Applied Climatology, 136 (3-4), 1073-1084, https://doi.org/10.1007/s00704-018-2543-0
  53. Nouri, M., Homaee, M., & Bannayan, M. (2017). An Assessment of reference evapotranspiration changes during the 21st century in some semi-arid regions of Iran.‌ Iranian Journal of Soil and Water Research 48 (2), 241-252. https://doi.org/22059/ijswr.2017.62578. [In Persian].
  54. Nouri, M., Homaee, M., & Bannayan, M. (2017). Assessing Trends of aridity index changes over 1966-2100 period in the Northwest of Iran. Watershed Engineering and Management, 8(4), 439- 453. https://doi.org/10.22092/ijwmse.2016.107187. [In Persian].
  55. Parding, K. M., Dobler, A., McSweeney, C. F., Landgren, O. A., Benestad, R., Erlandsen, H. B., ... & El Zohbi, J. (2020). GCMeval–An interactive tool for evaluation and selection of climate model ensembles. Climate Services, 18, https://doi.org/10.1016/j.cliser.2020.100167.
  56. Pravalie, Remus., & Bandoc, Georgeta. (2015). Aridity Variability in the Last Five Decades in the Dobrogea Region, Romania. Arid Land Research and Management, 29 (3), 265-287, https://doi.org/10.1080/15324982.2014.977459.
  57. Radakovic, M.G., Tosic, I., Bacevic, N., Mladjan, D., Gavrilov, M.B., & Markovic, S.B. (2018). The analysis of aridity in Central Serbia from 1949 to 2015. Theoretical and Applied Climatology, 133 (3_4), 887–898. https://doi.org/10.1007/s00704-017-2220-8.
  58. Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., & Taylor, K. E. (2007). Climate models and their evaluation. In Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR) (pp. 589-662). Cambridge University Press.‌
  59. Ranjbar, f., & Tabatabaii, H. (2022). Investigation of the trend of Aridity index in the northern stations of Iran during the period 1982-2019. Climate Change Research, 3 (9), 12- 24. https://doi.org/30488/ccr.2022.327870.1070. [In Persian].
  60. Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., & Lutz, W. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environmental Change, 42, 153-168, https://doi.org/10.1016/j.gloenvcha.2016.05.009.
  61. Sarlak, N., & Mahmood Agha, O. (2018). Spatial and temporal variations of aridity indices in Iraq. Theoretical and applied climatology, 133 (1-2), 89-99. https://doi.org/10.1007/s00704-017-2163-0.
  62. sen, Z. (2012). Innovative trend analysis methodology. Journal of Hydrologic Engineering, 17 (9), 1042-1046, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556.
  63. sen, Z. (2017). Innovative trend significance test and applications. Theoretical and applied climatology, 127 (3-4), 939-947. https://doi.org/10.1007/s00704-015-1681-x.
  64. Sivakumar, M.V.k., Raymond, P.M., & Haripada, P.D. (2005). Natural disaster and extreme events in agriculture. 367. Berlin, Germany: Springer.
  65. Soltani, M., Laux, P., Kunstmann, H., Stan, K., Sohrabi, M. M., Molanejad, M., Sabziparvar, A. A., SaadatAbadi, A. R., Ranjbar, F., Rousta, I., Zawar-Reza, P., Khoshakhlagh, F., Soltanzadeh, I., Babu, C. A., Aziz, G., & Martin, M. V. (2016). Assessment of climate variations in temperature and precipitation extreme events over Iran. Theoretical and Applied Climatology, 126 (3-4), 775-795. https://doi.org/10.1007/s00704-015-1609-5.
  66. Tabari, H., & Aghajanloo, M.B. (2013), Temporal pattern of aridity index in Iran with considering precipitation and evapotranspiration trends. International Journal of Climatology, 33 (2), 396-409. https://doi.org/10.1002/joc.3432.
  67. Tabari, H., & Talaee, P. H. (2011). Analysis of trends in temperature data in arid and semi-arid regions of Iran. Global and Planetary Change, 79 (1-2), 1-10. https://doi.org/10.1016/j.gloplacha.2011.07.008.
  68. Tabari, H., Marofi, S., Aeini, A., Talaee, P. H., & Mohammadi, K. (2011). Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and forest meteorology, 151 (2), 128-136. https://doi.org/10.1016/j.agrformet.2010.09.009.
  69. Tavosi, T., Mahmoudi, P., & Moghadam, F. (2010). Comparison of spatial spreading of arid and semi-arid climates in Iran during 1976-2005. Iranian Journal of Range and Desert Research, 17 (1), 94- 105. [In Persian].
  70. Tavousi, T. (2018). Investigating the trend of fluctuations in annual precipitation and UNEP aridity index of clmatic zones in the west and northwest of Iran. Geogrphical data, 27 (105), 85-96. [In Persian]. https://doi.org/10.22131/sepehr.2018.31475.
  71. Teimouri, M., Abdollahi, M. , Nejadhasan, B., & Graie, P. (2011). Investigation of Aridity Index in Iran. The first national conference on drought and climate change. [In Persian].
  72. (1979). Map of the world distribution of arid regions: Explanatory note. M A B Technical Notes 7, Unesco.
  73. Wang, L., Cao, L., Deng, X., Jia, P., Zhang, W., Xu, X., Zhang, K., Zhao, Y., Yan, B., Hu, W., & Chen, Y. (2014). Changes in aridity index and reference evapotranspiration over the central and eastern Tibetan Plateau in China during 1960–2012. Quaternary International, 349, 280-286. https://doi.org/10.1016/j.quaint.2014.07.030.
  74. Wu, Y., Zhang, G., Shen, H., Xu, Y. J., & Bake, B. (2016). Attribute analysis of aridity variability in North Xinjiang, China. Advances in Meteorology, 2016 (3), 1-11, https://doi.org/10.1155/2016/9610960.
  75. Yin, Y., Ma, D., Wu, S., & Pan, T. (2015). Projections of aridity and its regional variability over China in the mid‐21st century. International Journal of Climatology, 35 (14), 4387-4398. https://doi.org/10.1002/joc.4295.
  76. Zarrin, A., & Salehabadi, N. (2019). Drought projection in Tehran based on CMIP6 models. 6th international-regional conference on climate change. Tehran. [In Persian].
  77. Zhao, Y., Zou, X., Cao, L., Yao, Y., & Fu, G. (2018). Spatiotemporal variations of potential evapotranspiration and aridity index in relation to influencing factors over Southwest China during 1960–2013. Theoretical and applied climatology, 133 (3-4), 711-726. https://doi.org/10.1007/s00704-017-2216-4.
  78. Zhou, Z., Wang, L., Lin, A., Zhang, M., & Niu, Z. (2018). Innovative trend analysis of solar radiation in China during 1962–2015. Renewable energy, 119, 675-689. https://doi.org/10.1016/j.renene.2017.12.052.
  79. Zolfaghari, H., Masoompour, J., Yeganefar, M., & Akbary, M. (2016). Studying spatial and temporal changes of aridity in Iran. Arabian Journal of Geosciences, 9(5), 375-389. https://doi.org/10.1007/s12517-016-2379-9.
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