پیش نگری متغیرهای اقلیمی دهه های آینده در پهنه جنوب شرق ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آب و هواشناسی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران. ایران

2 دانشیار گروه جغرافیا، دانشگاه آزاد اسلامی. واحد اسلامشهر. تهران. ایران.

3 دانشیارگروه جغرافیا. دانشگاه آزاد اسلامی. واحد علوم و تحقیقات. تهران. ایران

4 استادیارگروه جغرافیا. دانشگاه آزاد اسلامی. واحد علوم و تحقیقات. تهران. ایران

چکیده

در چند دهه اخیر افزایش دمای زمین باعث بر هم خوردن تعادل اقلیمی کره زمین شده و تغییرات اقلیمی گسترده ای را در اغلب نواحی کره زمین موجب گردیده است که از آن به عنوان تغییر اقلیم یاد می شود. در این پژوهش ، به بررسی متغیر های اقلیمی در شش ایستگاه های سینوپتیک پهنه جنوب شرق ایران (زابل، زاهدان، خاش، ایرانشهر، سراوان و چابهار) از سال 1987 لغایت 2019 پرداخته شد و در دو دوره ی زمانی (2021-2040 و 2041-2060)، با استفاده از ﻣﺪل ﮔـﺮدش ﻛﻠـﻰ (HadCM2 ) بررسی و تحلیل گردید. پس از بررسی و ارزیابی مدل مقادیر متغیرهای اقلیمی در دو سناریو RCP2.6 و RCP8.5 در دوره های مورد مطالعه ریز مقیاس گردید. نتایج ارزیابی دقت مدل LARS-WG در شبیه سازی متغیرهای اقلیمی براساس شاخص های MAE ، RMSE،R2 ،NSE در پهنه جنوب شرق ایران در مرحله صحت سنجی نشان داد که انطباق زیادی بین مقادیر شبیه سازی شده و دوره پایه وجود داشته است. بر اساس نتایج خروجی مدل LARS-WG  افزایش دما در کلیه ایستگاه ها در دو دوره مورد مطالعه را نشان می دهد. میزان افزایش دما در مناطق ساحلی کمتر از سایر مناطق خشکی می باشد. در تمامی ایستگاه ها تغییرات دما به طور یکنواخت و تغییرات بارش، نوسان دارند. مقدار بارش طی فصول زمستان  و بهار در تمامی ایستگاه ها روند افزایشی دارد؛ شرایط اقلیمی آینده از قبیل تعداد روزهای یخبندان، تعداد روزهای داغ، خشک و روزهای تر محاسبه گردید و نتایج نشان داد که در دوره آینده تعداد روزهای داغ و تعداد روزهای خشک نسبت به دوره پایه افزایش می یابد و تعداد روزهای یخبندان کاهشی می باشد. در صورت عدم پایبندی به کاهش گازهای گلخانه ای افزایش متغیرهای اقلیمی دمای حداقل و دمای حداکثر و کاهش بارش در دهه 2041-2060 بیشتر خواهد شد. پیش بینی ماهانه برای دوره های آینده نیز در سناریو های مذکور ، بیانگر این است که گرمترین ماه در استان به طور میانگین در ماه تابستان (ژوئیه)، سردترین ماه در زمستان (دسامبر و ژانویه)، بیشترین بارش ها در زمستان و بهار (دسامبر، ژانویه و مارس) و تبخیر و تعرق در تابستان به بیشترین حد خود می رسد. در نهایت اینکه نتایج بیانگر روند افزایش دما و کاهش بارش در دهه های آینده نواحی جنوب شرق ایران خواهد بود.

کلیدواژه‌ها


عنوان مقاله [English]

Climatic Variable Forecasting for Future Decades in South East Area of Iran

نویسندگان [English]

  • mahsa farzaneh 1
  • Azadeh Arbabi Sabzevari 2
  • Jamalodin Daryabari 3
  • Farideh Asadian 4
1 Ph.D. Student, IslamicAzad , University, Science and Research Branch
2 َAssociate Prof., Islamic Azad University. Islamshahr Brancj,, Tehran, Iran
3 Associate Prof., Science and Research Branch-Islamic Azad University
4 Assistant Prof., Science and Research Branch-Islamic Azad University
چکیده [English]

 
In recent decades, rising global temperatures have upset the climate balance of planet and caused widespread climate change in most parts of the world, known as climate change Index. In the present study, climatic variables in six synoptic stations in the southeastern part of Iran (Zabol, Zahedan, Khash, Iranshahr, Saravan and Chabahar) from 1987 to 2019 were studied in two time periods (2021-2040 and 2041-2060), considering the uncertainty of the general circulation model (Hadcm2) was investigated and analyzed. After reviewing and evaluating the model, the values ​​of climatic variables in two scenarios, RCP2.6 and RCP8.5, were scaled in the studied periods. The results of LARS-WG model accuracy evaluation in simulation of climatic variables based on MAD, RMSE, MSE, MAPE indices in the southeastern part of Iran in the validation stage showed that there was a large correlation between the simulated values ​​and the base period. Based on the output results of LARS-WG model, it shows the increase of temperature in all stations in the two studied periods. The rate of temperature increase in coastal areas is less than other land areas. Temperature changes fluctuate uniformly and precipitation changes fluctuate at all stations. The amount of rainfall during the winter and spring seasons in all stations is increasing; Future climatic conditions such as number of frosty days, number of hot, dry and wet days were calculated and the results showed that in the next period, the number of hot days, the number of dry days will increase compared to the base period and the number of frosty days will decrease. If we do not adhere to the reduction of greenhouse gases, the increase of climatic variables, the minimum temperature and the maximum temperature and the decrease of precipitation will increase in the 2041-2060 decade. Monthly forecasts for future periods in the mentioned scenarios also indicate that the warmest month in the province is on average in summer (July), the coldest month in winter (December and January), the most precipitation in winter and spring (December, January and March) and evapotranspiration peaks in summer. Finally, the results show the trend of increasing temperature and decreasing rainfall in the coming decades in the southeastern regions of Iran.

کلیدواژه‌ها [English]

  • climate change
  • Forecasting
  • climate variables
  • South- east area
  • Model LARS-WG
  1.  

    1. Asadi, A.  Jamshidi, O. Kalantari, Kh, 2017, Climate Change Adaptation Mechanisms for Small Farmers in Hamadan Province. Agricultural Extension Science and Education, 13(2), 109-130.
    2. Ashraf, B. Moosavi Beigi, M. Kamali, Gh. & Davari, K, 2011, Predicting Fossil Changes of Climatic Parameters in the Next Twenty Years Using the Statistical Exponential Scale of HadCM3 Model Data Case Study: Khorasan Razavi Province, Journal of Soil and Water, (Agricultural Science and Industry), V. 25, N. 4: 945-957.
    3. Babaeian, I. Najafi Nik, Z, 2006, Introducing and Evaluating the LARS - WG Model for Modelling the Meteorological Parameters of Khorasan Province, Statistical Period (1961- 2003). Nivar, 31(62-63), 50-67.
    4. Batool.b, 2018, Spatial Analysis of the Occurrence of Dust Phenomenon in Sistan and Baluchistan Province with Statistical Methods. Geography Quarterly (Regional Planning), 8 (3), 97-109.
    5. Dehghan, Z. Fathian, F. Eslamian, S, 2015, Comparative Evaluation of SDSM, IDW and LARS-WG Models for Simulation and Subscale of Temperature and Precipitation, Journal of Soil and Water, 29(5),1376-1390
    6. Ebrahimzadeh, I, 2010, Land Use Planning and Environmental Planning in Southeast of Iran, Tehran: Institute of Information.
    7. Faizi, Vahid and Farajzadeh, Manouchehr and Nowruzi, Rabab, 2010, Study of Climate Change in Sistan and Baluchestan Province by Man-Kendall Method, 4th International Congress of Geographers of the Islamic World, Zahedan.
    8. Fallah Ghalhari, Gh. Yusefi, H.  Hossein zade, A. Alimardani, M.  Reyhani, E, 2019, Climate Change Assessment of Bojnourd Station During 2016 to 2050 Using LARS WG and SDSM Microscale Exponential Models, Journal of Echo Hydrology, 6(1), 99-109.
    9. Fung, F. Lopez, A. and New, M, 2011, Modeling the Impact of Climate Change on Water Resources, Wiley-Blackwell, N, ISBN: 9781405196710. PP. 43-62.
      1. Goodarzi, M. Noori, A, 2018, Evaluation of LARS-WG Model and Method of Factor Change in Exponential Precipitation and Temperature Microscale. Quarterly Journal of Environmental Science and Technology.
      2. Goodarzi, M. Salahi, B. Hosseini, A, 2015, Evaluation of the Performance of LARS-WG and SDSM Microscale Models in Simulating Climate Change in the Catchment Area of Lake Urmia. Iranian Journal of Watershed Management Science and Engineering. 9 (31): 11-23.
      3. Hamidian pour, M. Ba-aghideh, M. Abbasnia, M, 2016, Evaluation of Temperature and Precipitation Changes in Southeastern Iran Using Microscale Output of Different Models of General Atmospheric Circulation in the Period 2011-2010, Quarterly Journal of Natural Geography Research, 48(95), 107- 123.
      4. Hamidian pour M. Hejazi zade, Z, 2012, Investigation of Temperature and Precipitation Changes Using Barley General Circulation Model, Case Study of Sistan and Baluchistan Province, National Conference on Border Cities and Security; Challenges and approaches, Zahedan.
      5. IPCC, 2007, Summary for Policy Makers Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report. Cambridge University Press.
      6. IPCC, 2007, Intergovernmental Panel on Climate Change. Fourth Assessment Report, Climate Change.
      7. IPCC, 2007, Climate Change. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Solomon, S., D. Qin, M. Manning.
      8. Khalili Aghdam, N. Mosaedi, A. Soltani, A. Kamkar, B,  2012,  Evaluation of LARS-WG Model Capability in Predicting Some Atmospheric Parameters of Sanandaj, Soil and Water Conservation Researches; Period 19, Number 4.
      9. Khosravanian, J. Onegh, M. Goodarzi, M. Hejazi, S,  2015,  Application of LARS-WG Model in Predicting Meteorological Parameters of Qarasu Basin in Golestan Province. Journal of Geography and Planning 93-115, 19(53).
      10. Kathiri, Maryam, Goodarzi, Masoud, Janbaz Ghobadi, Gholamreza, Motavi, Sadr al-Din,2020, Outlook for Remperature and Precipitation Changes on the Southern Shores of the Caspian Sea. Natural Geography, 13 (47), 35-51.
      11. Khalili, Najmeh, Davari, Kamran, Alizadeh, Amin, Ansari, Hossein, Rezaei Pajand, Hojjat, Kafi, Mohammad, Ghahraman, Bijan ,2016, Evaluation of the performance of LARS-WG and ClimGen models in the production of rainfall and temperature time series in Sisab rainfed research station, North Khorasan. Water and Soil, 30 (1), 322-333.
      12. Lopes, p, 2009, Assessment of Statistical Downscaling Methods. Application and Comparison of Two Statistical Methods to a Single Site in Lisbon.
      13. Meshkovati, A. Kord jazi, M. Babaeian, I, 2010, Investigation and Evaluation of Lars Model in Simulation of Meteorological Data of Golestan Province in the Period of 1993-1997. Journal of Applied Research in Geographical Sciences. 1389; 10 (13): 81-96.
      14. Mohammadloo, M. Haghizadeh, A. Zeynivand, H. Tahmasbi pour, N, 2016,  Evaluation of the Effects of Climate Change on the Trend of Temperature and Rainfall Changes in Baranduzchay Watershed in West Azerbaijan Province Using General Atmospheric Rotation Models. 16 (56): 151- 168.
      15. Mosayebi, M. Movahedi, S, 1995, the Role of Humans in Climate Change. Journal of Geographical Information "Sepehr", 4 (16), 6-11.
      16. Paymard, P. Banayan Aval, M. Sadrabadi Haghighi, R, 2015, Meteorology: Evaluation of Two Models of General Circulation of CCCAM and MPI Barley in Simulation of Climatic Parameters (Case Study: Khorasan Razavi Province), National Congress of Irrigation and Drainage of Iran. 2015.
      17. Racsko, P. Szeidl, L. Semenov, M, 1991, A Serial Approach to Local Stochastic Weather Nodels. Ecol Model, 57:27–41.
      18. Reddy.K.S. Kumar. M. Maruthi.V. Umesha.B. Vijayalaxmi. & Nageswar Rao.c.v.k, 2014, Climate Change Analysis Southern Telangana Region, a Pradesh Using LARS- WG Model. Journal of Current Science. 107(1): 54- 62.
      19. Rietveld, M.R, 1978, “A New Method for the Estimating the Regression Coefficients in the Formula Relating Solar Radiation to Sunshine”, Agricultural Meteorology 19, 243-252.
      20. Sadat Ashofte, P. Messahboani, A, 2012, Investigating the Effect of Uncertainty of General Atmosphere and Ocean Cycle Models and Greenhouse Gas Emission Scenarios on Basin Runoff under the Influence of Climate Change; Case Study of Qarnaqo Basin, East Azerbaijan. Iranian Journal of Water Resources Research, 2: 36-47.
      21. Saadatfar A. Barani, H. Bahremand, A. Massah Bavani A. Sepehri A. Abedi A, 2013, Statistical Downscaling HadCM3 Model for Detection and Perdiction of Seasonal Climatic Variations (Case Study: Khabr Rangeland, Kerman, Iran). Journal of Rangeland Science. 3(3).
      22. Sarkar, J. and Chicholikar, JR, 2015, Climate Change Scenario in the Gujarat Region. Analyses Based on LARS-WG Model. Asian Journal of Water, Environment and Pollution, 31-41:12.
      23. Semenov, M. A. Stratonovitch, P, 2010, “Use of Multi-Model Ensembles from Global Climate Models for Assessment of Climate Change Impacts,” CLIMATE RESEARCH, Number 4, (Pp. 1–14). https://climatology.ir.
      24. Semenov, M.A. & Barrow, E. M, 2002, “LARS-WG A Stochastic Weather Generator for Use in Climate Impact Studies”, User Manual, Version, 3.0: 28.
      25. Wang, X. Yang, T. Shao, Q. Kumud, A. Wang, W. Yu, Z, 2011, “Statistical Downscaling of Extremes of Precipitation and Temperature and Construction of Their Future Scenarios in An Elevated and Cold Zone,” Stoch. Environ. Res. Risk. Asses, 26, 405-418.
      26. Wilby, R. L. Charles, S. P. Zorita, E. Timbal, B. Whetton, P. & Mearns, L.O, 2004, Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis.
      27. Xu, C.H. and Xu, Y, 2012, The Projection of Temperature and Precipitation over China under RCP Scenarios Using a CMIP5 Multi-Model Ensemble, Atmospheric and Oceanic Science Letters, 5(6): 527-533.
      28. Zarakani, F. Kamali, GH, & Chizari, A, 2014, The Effect of Climate Change on Rainfed Wheat Economy (Case study of North Khorasan).
      29. Zahraei, A. Asaad, Hosseini, 2020, Climate Change and its Effects on Water Resources. Ilam. Hawar Publications.
      30. Zahraei, A & Nazaripour, H, 2013,  Assessing the Rate of Climate Change in the Southeast of the Country Using Three-dimensional Models of General Atmospheric Circulation (Case Study: Zahedan Station), the Second International Conference on Plant, Water, Soil and Air Modeling, Kerman.