پایش و مدل‌سازی بارش دشت اردبیل در دهه‌های آینده بر اساس خروجی برخی GCMها

صلاحی, برومند; فروتن, مهدی

doi:10.22034/hyd.2024.60175.1724

نوع مقاله : کاربردی

نویسندگان

¹ استاد آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل.

² دانشجوی دکتری آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل.

https://doi.org/10.22034/hyd.2024.60175.1724

چکیده

پایش تغییرات و نوسانات بارش مناطق جغرافیایی می‌تواند دید بهتری از رفتار این پدیده در سال‌های آینده داشته باشد. هدف این پژوهش، بررسی وضعیت بارش دشت اردبیل (ایستگاههای اردبیل، بیله‌درق و کلور) و پیش‌نگری آن در سال‌های آینده بر اساس برونداد مدل‌های CMIP6 توسط مدل مقیاس‌کاهی CMhyd می‌باشد. سپس با استفاده از سنجه‌های آماری R²، MAE، MSE، RMSE و دیاگرام تیلور، به مقایسه داده‌های مشاهداتی دوره پایه با داده‌های تاریخی 5 مدل GCM از CMIP6 پرداخته شد و برای هر ایستگاه مورد مطالعه، مدل برتر انتخاب گردید. خروجی مدل‌های برتر با روش linier scaling تصحیح اریبی گردیدند و بر اساس سناریو‌های SSP126، SSP245 و SSP585، بارش سال‌های 2023-2050 برای هر ایستگاه، پیش‌نگری و روند آن با آماره من‌-‌کندال ترسیم شد. نتایج نشان داد در نواحی شرقی و غربی دشت اردبیل (منتهی به ارتفاعات کوه‌های تالش و سبلان)، تغییرات بارش افزایشی بوده است (80/2 میلی‌متر). در ایستگاه اردبیل، مدل MIROC6 با ضریب همبستگی 94/0 درصد و در ایستگاه‌های بیله‌درق و کلور، مدل MPI-ESM1-2-HR با ضریب همبستگی به ترتیب 88/0 و 92/0 درصد، بیش‌ترین دقت را در شبیه‌سازی بارش داشته‌اند. همچنین نتایج سناریو‌ها نشان دادند که تغییرات بارش در ایستگاه اردبیل در دوره آینده نسبت به دوره پایه تحت سناریو‌های SSP126، SSP245 و SSP585، به ترتیب 24/0، 36/6- و 2- درصد خواهد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Monitoring and Modeling the Precipitation of Ardabil Plain in the Coming Decades based on the Output of some GCMs

نویسندگان [English]

Bromand Salahi ¹
Mahdi Foroutan ²

¹ Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

² Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.

چکیده [English]

Monitoring the changes and fluctuations of precipitation in geographical areas can give a better view of the behavior of this phenomenon in the coming years. The purpose of this research is to investigate the precipitation situation in Ardabil Plain (Ardabil, Bileh-Daragh, and Kolour stations) and forecast it in the coming years based on the output of CMIP6 models by the CMhyd downscaling model. Then, using R2, MAE, MSE, RMSE, and Taylor diagram, the observational data of the base period were compared with the historical data of 5 GCM models from CMIP6, and the best model was selected for each studied station. The output of the top models was corrected for skewness by linear scaling method and based on SSP126, SSP245, and SSP585 scenarios, the precipitation of 2050-2023 for each station, forecast, and its trend were drawn with the Mann-Kendall statistic. The results showed that in the eastern and western areas of Ardabil Plain (leading to the heights of Talesh and Sablan mountains), the rainfall changes were increasing (2.80 mm). In the Ardabil station, the MIROC6 model with a correlation coefficient of 0.94%, and in Bileh-Daragh and Kolour stations, the MPI-ESM1-2-HR model with a correlation coefficient of 0.88% and 0.92%, respectively, have the highest accuracy in simulating the precipitation. Also, the results of the scenarios showed that the precipitation changes in Ardabil station in the future period compared to the base period under the SSP126, SSP245, and SSP585 scenarios will be 0.24, -6.36, and -2%, respectively.

کلیدواژه‌ها [English]

Modeling
GCMs
Precipitation
Ardabil Plain

مراجع

Abdi Dezfouli, A., Ghahreman, N. (2021). Assessment of the CHIRPS precipitation estimates in north of Karkhe river basin. Amphibious Science and Technology: 2(2), 54-65. (In Persian).

doi: 10.22034/jamst.2021.246257.

Esmaeilzadeh, Z., Salahi, B., Saber, M. (2023). Revealing the relationship between the changes of some relative humidity indices of the southern coasts of Iran with Indian Ocean teleconnection patterns. Amphibious Science and Technology, 4(3): 59-78. (In Persian).

doi: 10.22034/jamst.2023.544224.1119.

Homsi, R., Sanusi Shiru, M., Shahid, Sh., Ismail, T., Harun, S.B., Al-Ansari, N. Chau, K.W., Yaseen, Z.M. (2019). Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria, Engineering Applications of Computational Fluid Mechanics, 14: 90-106.

https://doi.org/10.1080/19942060.2019.1683076.

Jahanbakhsk Asl, S., Asakereh, H., Ashrafi. S. (2019). Downscaling the Relationship between the Precipitation and Runoff of the Rood Zard Basin in the Climate Change Context, Hydrogeomorphology, 6(21): 109-132. (In Persian).

https://dorl.net/dor/20.1001.1.23833254.1398.6.21.6.1.

Jahanbakhsh Asl, S., Khorshiddoust, A., Alinejad, M. H., Pourasghr, F. (2016). Impact of Climate Change on Precipitation and Temperature by Taking the Uncertainty of Models and Climate Scenarios (Case Study: Shahrchay Basin in Urmia). Hydrogeomorphology, 3(7): 107-122. (In Persian).

doi: 20.1001.1.23833254.1395.3.7.6.2.

Kamari, H., Zeinali, E., Soltani, A., Ghaderifar, F. (2020). An assessment of LARS-WG’s model to forecast meteorological parameters for climatic zones of cotton-harvested areas over Iran, Crop Production (Electronic Journal of Crop Production), 13(4): 27-40. (In Persian).

https://doi.org/10.22069/ejcp.2021.17645.2300.

Kamyar, A., Mohebbi, S., Yazdanpanah, H. (2017). Projection of Minimum and Maximum Air Temperatures in Isfahan Province during 2050-2017, Climatology Research, 8(29-30): 37-54. (In Persian).

https://clima.irimo.ir/article_62689.html.

Kavwenje, Sh., Zhao, L., Chen, L., Chaima, E. (2021). Projected temperature and precipitation changes using the LARS-WG statistical downscaling model in the Shire River Basin, Malawi, International Journal of Climatology, 42(1): 400-415.

https://doi.org/10.1002/joc.7250.

Khajeh amiri, C., Khosravi, M., Tavousi, T., Hamidianpour, M., & kiani moghadam, M. (2022). An analysis of the output performance of CMIP6 climate models in comparison with the observational data of Makran coast, Journal of Meteorology and Atmospheric Science, 5(1): 22-41. (In Persian).

doi: 10.22034/jmas.2023.379448.1193.

Khan, N., Shahid, Sh., Ahmed, A., Ismail, T., Nawaz, N., Son, M. (2018). Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets, Water, 10(12): 1793.

https://doi.org/10.3390/w10121793.

Luo, M., Liu, T., Meng, F., Duan, Y., Frankl, A., Bao, A., De Maeyer, Ph. (2018). Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China, Water, 10(8): 1046.

https://doi.org/10.3390/w10081046.

Mesgari, E., Hosseini, S.A., Houshyar, M., Kaseri, K., Safarpour, F. (2023). Future projection of early fall and late spring frosts based on EC-earth models and shared socioeconomic pathways (SSPs) scenarios over Iran plateau, Natural Hazards, 119: 1421-1435.

https://doi.org/10.1007/s11069-023-06155-y.

Mohmedraffi Shaikh, M., Lodha, P., Lalwani, P., Mehta, D. (2022). Climatic projections of Western India using global and regional climate models. Water Practice and Technology, 17 (9): 1818–1825.

https://doi.org/10.2166/wpt.2022.090.

Noor, M., Ismail, T.B., Ullah, Sh., Iqbal, Z., Nawaz, N., Ahmed, K. (2020). A non-local model output statistics approach for the downscaling of CMIP5 GCMs for the projection of rainfall in Peninsular Malaysia. Journal of Water and Climate Change, 11 (4): 944–955.

https://doi.org/10.2166/wcc.2019.041.

Ostad-Ali-Askari. K., Ghorbanizadeh Kharazi. H., Shayannejad. M., Javad Zareian. M. (2020). Effect of Climate Change on Precipitation Patterns in an Arid Region Using GCM Models: Case Study of Isfahan-Borkhar Plain. Natural Hazards Review, 21(2).

https://doi.org/10.1061/(ASCE)NH.1527-6996.0000367.

Salamati Hormozi, V., mazidi, A., Balali Komizi, A. A. (2021). Investigation of the effects of climate change scenarios on temperature and precipitation forecasting using LARS-WG model (Case study: Bandar Abbas). Amphibious Science and Technology, 2(2): 1-16. (In Persian).

doi: 10.22034/jamst.2021.245333.

Salman, S.A., Nashwan, M.S., Ismail, T., Shahid, Sh. (2020). Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Hydrology Research, 51 (4): 781–798.

https://doi.org/10.2166/nh.2020.154.

Sari sarraf, B., Jalali Ansaroodi, T. (2019). The Investigation of the Impact of Climate Change on Water Balance Caused by Precipitation in Tasuj Aquifer for the Period of 2017-2030. Hydrogeomorphology, 6(19): 163-185. (In Persian).

doi: 20.1001.1.23833254.1398.6.19.9.0.

Sobhani. B, nasiri. F. (2022). Agro-ecological zonation of canola cultivation in Ardebil Plain. Jgs, 22 (65): 61-78. (In Persian).

http://dx.doi.org/10.52547/jgs.22.65.61.

Sha, J., Li, X., Liang Wang, Z. (2019). Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios. Theoretical and Applied Climatology, 137: 3027–3039.

https://link.springer.com/article/10.1007/s00704-019-02781-4.

Shabanpour, F., Bazrafshan, J., Araghinejad, Sh. (2021). Evaluation of the Effect of Bias Correction Methods on the Skill of Seasonal Precipitation Forecasts of CFSv2 Climate Model, Iranian Journal of Soil and Water Research, 51(12): 3017-3032. (In Persian).

https://doi.org/10.22059/ijswr.2020.306717.668680.

Trigo. R.M., Palutikof. J.P. (2001). Precipitation Scenarios over Iberia: A Comparison between Direct GCM Output and Different Downscaling Techniques. Journal of Climate, 14(23): 4422–4446.

https://doi.org/10.1175/1520-0442(2001)014%3C4422:PSOIAC%3E2.0.CO;2.

Zadmehr, H., Farrokhian Firouzi, A. (2022). Investigation of Soil Temperature Using the Modified Mann-Kendall Non- Prametric Test in Selected Regions of Khuzestan Province, Soil research, 35(4): 429-443. (In Persian).

https://doi.org/10.22092/ijsr.2022.355507.622.

Zarrin, A., Dadashi roudbari, A. (2021). Projected changes in temperature over Iran by 2040 based on CMIP6 multi-model ensemble, Physical Geography Research, 53(1): 75-90. (In Persian).

https://doi.org/10.22059/jphgr.2021.308361.1007551.

Zarrin, A., Dadashi roudbari, A. (2021). Projected consecutive dry and wet days in Iran based on CMIP6 bias‐corrected multi‐model ensemble, Journal of Earth and Space Physics, 47(3): 561-578. (In Persian).

https://doi.org/10.22059/jesphys.2021.319270.1007295.

Ziyaye, S., Esmaeili, A., Mostafazadeh, R., Gorbani, A. (2021). Effective Factors in Ground Water Variations and Water Table Decrease in Ardabil Plain, Hydrogeomorphology, 8(28): 127-143.

https://doi.org/10.22034/hyd.2021.46333.1590.

هیدروژئومورفولوژی

پایش و مدل‌سازی بارش دشت اردبیل در دهه‌های آینده بر اساس خروجی برخی GCMها

مراجع

مراجع

دوره 11، شماره 39 - شماره پیاپی 39
تیر 1403
صفحه 99-81

پایش و مدل‌سازی بارش دشت اردبیل در دهه‌های آینده بر اساس خروجی برخی GCMها

مراجع

مراجع

دوره 11، شماره 39 - شماره پیاپی 39تیر 1403صفحه 99-81

دوره 11، شماره 39 - شماره پیاپی 39
تیر 1403
صفحه 99-81