پیش‌نمایی بارندگی مناطق مختلف آب و هوایی ایران با استفاده از مدل اقلیمی LARS WG7

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی آب، دانشکده آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

2 گروه مهندسی آب، دانشکده آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

3 بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی گرگان، گرگان، ایران

4 گروه مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی نوشیروانی بابل، ایران.

چکیده

هدف از این مطالعه، ارزیابی مدل ریزمقیاس‌نمایی (LARS-WG7) در پیش‌‌نمایی بارندگی در شهرهای گرگان، مشهد، شیراز، اهواز، کرمانشاه و ارومیه می‌باشد. به‌همین منظور اطلاعات بارندگی در دوره آماری (2020-1991) به‌عنوان دوره پایه در نظر گرفته شد. سپس برای پیش‌بینی تغییرات بارندگی در دوره‌های آتی (2040-2021، 2060-2041، 2080-2061) تحت سه سناریوی HadGem3 (SSP585، SSP126، SSP245)، مدل به اجرا درآمد. نتایج نشان می‌دهند که میزان بارندگی تحت سناریوهای اقلیمی در تمامی دوره‌های آتی در بعضی از ماه‌های سال کم‌تر از دوره پایه است. روند تغییرات میانگین بارندگی سالانه در دوره‌های آتی نسبت به دوره پایه افزایشی می‌باشد. اما فقط در شهر ارومیه، دوره آتی 2040-2021 (تحت سناریوی SSP126) و دوره آتی 2080-2061 (تحت سناریوی SSP585) نسبت به دوره پایه، روند کاهشی بارندگی را نشان داد. در شهر مشهد نیز دوره آتی 2080-2061 (تحت سناریوی SSP585) نسبت به دوره پایه، روند کاهشی بارندگی را نشان داد. هم‌چنین نتایج حاکی از آن بود که بیش‌ترین درصد اختلاف مقدار بارندگی (66/42 درصد) بین دوره پایه و آتی (تحت سناریوی SSP585) متعلق به شهر اهواز می‌باشد. کم‌ترین درصد اختلاف مقدار بارندگی (16/4 درصد) بین دوره پایه و آتی (تحت سناریوی SSP585) متعلق به شهر ارومیه می‌باشد. روند تغییرات بارندگی (ماهانه و سالانه) در دوره‌های آتی تحت سناریوهای مختلف SSP متغیر بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Forecasting Rainfall in Different Climatic Regions of Iran Using the LARS WG7 Climate Model

نویسندگان [English]

  • Marzieh Bagheri Khaneghahi 1
  • Aboutaleb Hezarjaribi 2
  • Mohammad Ismaeil Kamali 3
  • Fatemeh Zamani 4
1 Ph.D. Student, Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
2 Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
3 Department of Soil and Water Research, Agricultural and Natural Resources Research and Education Center of Golestan Province, Gorgan Agricultural Research, Education and Promotion Organization, Gorgan, Iran.
4 Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Noshirvani University of Technology, Babol, Iran.
چکیده [English]

Abstract
Objective: The objective of this study is to evaluate the performance of the downscaling model LARS-WG7 in projecting precipitation across six major Iranian cities: Gorgan, Mashhad, Shiraz, Ahvaz, Kermanshah, and Urmia.
Method: For this purpose, precipitation data from the statistical period 1991-2020 were considered as the baseline. Subsequently, the model was employed to predict precipitation changes for three future periods (2021-2040, 2041-2060, and 2061-2080) under three climate scenarios of HadGEM3 (SSP585, SSP126, SSP245).
Results: The results indicate that precipitation under the climate scenarios is projected to be lower than the baseline in certain months of all future periods. However, the trend of mean annual precipitation generally shows an increase compared to the baseline. Exceptions were observed in Urmia, where precipitation exhibited a decreasing trend during 2021-2040 (under SSP126) and 2061-2080 (under SSP585), and in Mashhad, where a decreasing trend was detected during 2061–2080 (under SSP585).
Conclusion: The findings revealed that the largest percentage difference in precipitation (42.66%) between the baseline and future periods (under SSP585) occurred in Ahvaz, while the smallest difference (4.16%) was observed in Urmia. Overall, both monthly and annual precipitation trends varied across the future periods depending on the applied SSP scenarios.
 
 
 

English Extended Abstract
 
Introduction
Water resources, agriculture and environment are among the important factors affected by climate change. Therefore, it is more important to examine the process of changes in parameters such as temperature and precipitation to know the future climate in order to plan and make management decisions.
The purpose of this study is to evaluate the microscale model (LARS-WG7) in forecasting rainfall in the cities of Gorgan, Mashhad, Shiraz, Ahvaz, Kermanshah and Urmia.
 
Method
In this research, first, the daily rainfall information for the statistical period from 1991 to 2020 related to the synoptic stations of the studied cities was obtained from the National Meteorological Organization. Then, the LARS WG7 climate model was used in order to predict the precipitation of future periods.
After sorting the daily rainfall data of the study stations in the Notepad file and selecting the HadGem3 model and the SSP scenarios and future periods considered in LARS WG7, the software was implemented for the study stations. In the first step, the modeled data were compared with the observed data. In the next step, with the help of the base period data, the data of the future periods (2021-2040, 2041-2060, and 2061-2080) were produced under different scenarios (SSP126, SSP245, and SSP585), and finally, the changes in precipitation in the future periods compared to the base period were investigated.
 
Results
The results showed that the amount of rainfall under the climate scenarios in all future periods in some months of the year is less than the base period. The trend of changes in the average annual rainfall in future periods is increasing compared to the base period. But only in Urmia city, the future period of 2021-2040 (under the SSP126 scenario) and the future period of 2061-2080 (under the SSP585 scenario) compared to the base period showed a decreasing trend of rainfall.
In the city of Mashhad, the future period of 2061-2080 (under the SSP585 scenario) showed a decrease in rainfall compared to the base period. Also, the results indicated that the highest percentage of difference in rainfall amount (42.66%) between the base and future period (under SSP585 scenario) belongs to Ahvaz city.
The lowest percentage difference in rainfall amount (4.16%) between the base and future period (under SSP585 scenario) belongs to Urmia city. The trends of rainfall changes (monthly and yearly) in the future periods were variable under different SSP scenarios.
 
Conclusion
According to the obtained results, it was found that the climatic conditions in the coming periods will be significantly different from the current conditions and in the coming years, the studied cities will face more rainfall. Changing the precipitation pattern in the future in cities can be caused by atmospheric instability and global warming. With the increase in temperature, the polar ice melts and leads to the rise of the water level of the seas and rivers and finally leads to climate changes and increased rainfall.
The increasing trend of precipitation changes in the coming periods can lead to surface runoff and soil erosion in the future. Therefore, managing the crisis of urban floods in the coming years will be an important and necessary measure.
Also, managing the optimal use of water in the agricultural sector for rainfed crops, choosing the type of crop and paying attention to the crop calendar according to the amount of precipitation in different months of the year is more important than before.

کلیدواژه‌ها [English]

  • Climate Change
  • SSP Scenarios
  • Downscaling
  • HadGem3 Model
[1] Al-Kakey O, Al-Mukhtar M, Berhanu Sh, Dunger V. Assessing CFSR climate data for rainfall-runoff modeling over an ungauged basin between Iraq and Iran. Kuwait Journal of Science. 2023 Jul; 50(3): 405-414. https://doi.org/10.1016/j.kjs.2022.12.004.
[2] Arias P, Bellouin N, Coppola E, Jones R, Krinner G, Marotzke J, Naik V, Palmer M, Plattner GK, Rogelj J, Rojas M. Climate Change 2021: The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary.
[3] Stocker T, editor. Climate change 2013: The physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge university press; 2014 Mar 24.
[4] Zamani R, Akhond Ali AM, Roozbahani A. Evaluation of adaptation scenarios for climate change impacts on agricultural water allocation using fuzzy MCDM methods. Water Resources Management. 2020 Feb; 34: 1093-1110. https://doi.org/10.1007/s11269-020-02486-8.
[5] Gidden MJ, Riahi K, Smith SJ, Fujimori Sh, Luderer G, Kriegler E, Van Vuuren DP, Van Den Berg M, Feng L, Klein D, Calvin K, Doelman JC, Frank S., Fricko O, Harmsen M, Hasegawa T, Havlik P, Hilaire J, Hoesly R, Horing J, Popp A, Stehfest E, Takahashi K. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: A dataset of harmonized emissions trajectories through the end of the century. Geoscientific model development. 2019 Apr 12; 12(4): 1443-1475. https://doi.org/10.5194/gmd-12-1443-2019.
[6] O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K., Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M, Solecki W. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 2017 Jan; 42: 169-180. https://doi.org/10.1016/j.gloenvcha.2015.01.004.
[7] Fakhimi S, Zeinali B, Bromand S. Forecasting the effects of climate change on precipitation in East Azarbaijan Province. Journal of Environmental Sciences Studies. 2024 May; 9(4): 9560-9576. https://doi.org/10.22034/jess.2023.421560.2157 [In Persian].
[8] Iranshahi M, Ebrahimi B, Yousefi H, Moridi A. Investigating the effects of climate change on temperature and precipitation using neural networks and the sixth IPCC report (Case study: Al-Shatar and Khorramabad stations). Water and Irrigation Management. 2023 Jan 21; 12(4): 821-845. https://doi.org/10.22059/jwim.2022.346796.1009 [In Persian].
[9] Kazemi R, Khazaei MR. Prediction of future climate change in Tehran and Yazd under RCPs scenarios and using LARS-WG model. Environmental Science and Technology. 2022 Aug 23; 24(6): 31-44 [In Persian].
[10] Hejazizadeh Z, Hosseini SA, Karbalaee A, Barabadi RP, Mousavi SM. Spatiotemporal variations in precipitation extremes based on CMIP6 models and Shared Socioeconomic Pathway (SSP) scenarios over MENA. Arabian Journal of Geosciences. 2022 Oct 11; 15, 1601. https://doi.org/10.1007/s12517-022-10887-9.
[11] Semenov MA, Brooks RJ, Barrow EM, Richardson CW. Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate research. 1998 Aug 14; 10(2): 95-107. https://doi.org/10.3354/cr010095.
[12] Semenov MA, Barrow EM. LARS-WG: A stochastic weather generator for use in climate impact studies. Rothamsted Research: Hertfordshire, UK, 2002 Aug.
[13] Izadi N, Amirabadizadeh M, Dastourani M. Investigating temperature changes in Tabas City in the future using the sixth climate change report. Journal of Meteorology and Atmospheric Sciences. 2023 Apr; 6(1): 1-14. https://doi.org/10.22034/jmas.2023.415661.1210 [In Persian].
[14] Koohi S, Azizian A, Mazandarani Zadeh H. The effects of climate change on drought conditions using fuzzy logic under SSP3 and SSP5 scenarios. Iran-Water Resources Research. 2022 Dec; 18(3): 1-17. https://doi.org/20.1001.1.17352347.1401.18.3.1.3 [In Persian].
[15] GhorbaniMinaei L, Mosaedi A, Zakerinia M, Kalbali E, Ghabaei Soogh M. Study of future climate change on the temperature and precipitation trends in Qarasu basin based on the CMIP6 models. Iranian Journal of Soil and Water Research. 2024 May; 55(2): 245-268. https://doi.org/10.22059/ijswr.2024.369146.669613 [In Persian].
[16] Sarabi M, Dastorani MT, Zarrin A. Investigating impact of future climate changes on temperature and precipitation condition (Case Study: Torogh Dam Watershed, Mashhad). Journal of Meteorology and Atmospheric Sciences. 2020 Springer; 3(1): 63-83. https://doi.org/10.22034/jmas.2021.278862.1129 [In Persian].
[17] Jahangir MH, Jahanpanah M, Abolghasemi M. Drought forecasting for future periods using LARS-WG model (Case Study: Shiraz Station). Environment and Water Engineering. 2020 Apr; 6(1): 69-82. https://doi.org/10.22034/jewe.2020.215515.1343 [In Persian].
[18] Karimi S, Ghazanfarpour H, Pour mousa R, Jahani Babadi M. Predictive and analysis of probable heavy rainfall in Khuzestan Province using HadCM3 and SDSM. Climate Change Research. 2022 Nov; 3(11): 65-78. https://doi.org/10.30488/ccr.2022.356661.1088 [In Persian].
[19] Heshmati S, Ramezani Etedali H. Drought forecasting for future periods using LARS-WG model: The Case study of Kermanshah City. Town And Country Planning. 2021 Oct; 13(2): 647-669. https://doi.org/10.22059/jtcp.2021.332432.670263 [In Persian].
[20] Ebrahimi N, Zarrin A, Mofidi A. Dadashi-Roudbari AA. Projected precipitation extremes in Lake Urmia Basin under climate change. Journal of Water and Soil. 2024 Dec-Jan; 37(5): 769-785. https://doi.org/10.22067/jsw.2023.81891.1270 [In Persian].
دوره 1، شماره 1
فروردین 1404
صفحه 27-38
  • تاریخ دریافت: 07 آذر 1403
  • تاریخ بازنگری: 08 دی 1403
  • تاریخ پذیرش: 16 دی 1403
  • تاریخ اولین انتشار: 16 دی 1403
  • تاریخ انتشار: 05 فروردین 1404