Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways scenarios

Bagheri Khaneghahi, Marzieh; Hezarjaribi, Abutaleb; Kamali, Mohammad Ismaeil; Zamani, Fatemeh

doi:10.22098/mmws.2025.17682.1615

دانشگاه محقق اردبیلی

تعداد نشریات	31
تعداد شماره‌ها	484
تعداد مقالات	4,286
تعداد مشاهده مقاله	7,298,709
تعداد دریافت فایل اصل مقاله	4,896,218

	Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways scenarios
مدل سازی و مدیریت آب و خاک
مقاله 3، دوره 5، ویژه نامه 1، 1404، صفحه 32-48 اصل مقاله (664.6 K)
نوع مقاله: Special issue on "Climate Change and Effects on Water and Soil"
شناسه دیجیتال (DOI): 10.22098/mmws.2025.17682.1615
نویسندگان
Marzieh Bagheri Khaneghahi¹؛ Abutaleb Hezarjaribi^* ²؛ Mohammad Ismaeil Kamali³؛ Fatemeh Zamani⁴
¹PhD student in irrigation and drainage engineering, Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
²Associate Professor, Department of Water Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
³Assistant Professor, Soil and Water Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, AREEO, Sari, Iran.
⁴Assistant Professor, Artificial Intelligence Department, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
چکیده
The phenomenon of climate change, caused by both anthropogenic and natural factors, makes the forecasting of future climate and its impact on the proper management of agriculture, water, and soil resources, as well as watershed management, the environment, and desertification control, crucial. Accordingly, this study investigates future temperature and solar radiation in arid (Mashhad and Ahvaz) and semi-arid (Kermanshah) climates in Iran. First, daily climatic data for the baseline period (1991-2020) were obtained from synoptic stations in the study areas. Subsequently, temperature and solar radiation projections were generated for the future periods of 2021-2040, 2041-2060, and 2061-2080 using LARS-WG version 8, based on the HadGEM3 climate model under the SSP scenarios. The model’s high accuracy in downscaling and its excellent performance in predicting climatic parameters across all stations were validated by high R² values (99%) and NS efficiency coefficient (99%), alongside low RMSE values (less than 30%). Results indicated that, compared to the 30-year baseline period, the average maximum temperature over the next 60 years is projected to increase by 0.37, 1.48, and 2.73°C (Mashhad); 1.09, 1.47, and 2.3°C (Ahvaz); and 1.3, 1.75, and 2.65°C (Kermanshah) under the SSP126, SSP245, and SSP585 scenarios, respectively. Similarly, the average minimum temperature is expected to rise by 1.01 °C, 1.89 °C, and 2.8 °C (Mashhad); 1.57 °C, 2.17 °C, and 3.24 °C (Ahvaz); and 1.8 °C, 2.43 °C, and 3.33 °C (Kermanshah), respectively. However, changes in mean annual solar radiation did not show a consistent pattern. The monthly trends for temperature and radiation were significant at a 95% confidence level for most months. The results suggest that future temperature increases may lead to a decline in the quantity and quality of agricultural products, reduced water resources, and increased soil erosion. Future changes in solar radiation will also affect photosynthesis, evapotranspiration, energy production, and fossil fuel consumption. Therefore, to mitigate the negative impacts and adapt to future climatic conditions in the study areas, managers and planners should adopt optimal strategies. These strategies include cultivating heat- and light-resistant crops, optimizing irrigation systems, designing watershed management systems to prevent water loss, and promoting sustainable land development.
کلیدواژه‌ها
Climate؛ Downscaling؛ SSP scenarios؛ Water and soil resources؛ HadGEM3

مراجع
References: Al-Kakey, O., Al-Mukhtar, M., Berhanu, S., & Dunger, V. (2023). Assessing CFSR climate data for rainfall-runoff modeling over an ungauged basin between Iraq and Iran. Kuwait Journal of Science, 50(3), 405–414. doi: 10.1016/j.kjs.2022.12.004 Araya, A., Prasad, P. V. V., Gowda, P. H., Djanaguiramana, M., & Gebretsadkan, Y. (2021). Modeling the effects of crop management on food barley production under a midcentury changing climate in northern Ethiopia. Climate Risk Management, 32, 100308. doi: 10.1016/j.crm.2021.100308 Bayatvarkeshi, M., & Fasihi, R. (2018). The analysis of downscaling results of weather parameters for Iran's future. Geography and Environmental Sustainability, 8(26), 87–73. https://ges.razi.ac.ir/article_898.html?lang=e [In Persian]. Chen, H., Sun, J., Lin, W., & Xu, H. (2020). Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Science Bulletin, 65(17), 1415–1418. doi: 10.1016/j.scib.2020.05.015 Clarke, B., Otto, F., Stuart-Smith, R., & Harrington, L. (2022). Extreme weather impacts of climate change: An attribution perspective. Environmental Research: Climate, 1, 012001. doi: 10.1088/2752-5295/ac6e7d Dai, A. (2011). Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 45–65. doi: 10.1002/wcc.81 Dai, A. (2013). Increasing drought under global warming in observations and models. Nature Climate Change, 3, 52–58. doi: 10.1038/nclimate1633 Dantas, L. G., dos Santos, C. A. C., Santos, C. A. G., Martins, E. S. P. R., & Alves, L. M. (2022). Future changes in temperature and precipitation over Northeastern Brazil by the CMIP6 model. Water, 14, 4118. doi: 10.3390/w14244118 Haider, S., Masood, M. U., Rashid, M., Alshehri, F., Pande, C. B., Katipoğlu, O. M., & Costache, R. (2023). Simulation of the potential effects of projected climate and land use change on runoff under CMIP6 scenarios. Water, 15(19), 3421. doi: 10.3390/w15193421 Hajivand Paydari, S., Yazdanpanah, H., & Andarzian, S. B. (2022). Investigation of regional effects of climate change phenomenon in the north of Khuzestan province using the HadCM3 model under LARS-WG exponential comparison in the statistical period of 2030–2010 and 2050–2030. Journal of Geography and Human Relations, 5(1), 299–314. doi: 10.22034/gahr.2022.330821.1669 IPCC. (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. (C. B. Field et al., Eds.). Cambridge University Press. https://www.ipcc.ch/report/managing-the-risks-of-extreme-events-and-disasters-to-advance-climate-change-adaptation/ IPCC. (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 (T. F. Stocker et al., Eds.). Cambridge University Press. https://www.ipcc.ch/report/ar5/wg1/ Iranshahi, M., Ebrahimi, B., Yousefi, H., & Moridi, A. (2022). Investigating the effects of climate change on temperature and precipitation using neural network and CMIP6 (Case Study: Aleshtar and Khorramabad stations). Journal of Water and Irrigation Management, 12(4), 821–845. doi: 10.22059/jwim.2022.346796.1009 Lionello, P., & Scarascia, L. (2018). The relation between climate change in the Mediterranean region and global warming. Regional Environmental Change, 18(5), 1481–1493. doi: 10.1007/s10113-018-1290-1 Mahmoudi, P., & Rigi Chahi, A. (2019). Climate change's impact on the spatial and temporal distribution of precipitation in Iran. In 6th International Regional Conference on Climate Change (pp. 1–12), Tehran, Iran. https://www.researchgate.net/publication/337427637 Moghadas, P., Mahjoobi, E., & Gharechelou, S. (2024). Prioritization of the CMIP6 general circulation models using multi-criteria decision-making methods in the Nekarood watershed. Iranian Journal of Irrigation and Drainage, 18(1), 15–25. https://idj.iaid.ir/article_183799.html?lang=fa [In Persian]. Mosayyebi, M. (1996). Climate change and its effects on the ecosystems of arid and semi-arid areas. Quarterly Scientific-Research Journal of Geographic Information, 5(16), 42–46. https://www.sepehr.org/article_29309.html Muhaisen, N. K. H., Khayyun, T. S., Al Mukhtar, M., & Hassan, W. H. (2024). Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model. Open Engineering, 14, 20220567. doi: 10.1515/eng-2022-0567 Munawar, S., Rahman, G., Moazzam, M. F. U., Miandad, M., Ullah, K., Al-Ansari, N., & Linh, N. T. T. (2022). Future climate projections using SDSM and LARS-WG downscaling methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region. Atmosphere, 13, 898. doi: 10.3390/atmos13060898 Navarro-Racines, C. (2020). High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Scientific Data, 7, 7. doi: 10.1038/s41597-019-0343-8 Osman, Y., Al-Ansari, N., Abdellatif, M., Aljawad, S. B., & Knutsson, S. (2014). Expected future precipitation in central Iraq using LARS-WG stochastic weather generator. Engineering, 6(13), 948–959. doi: 10.4236/eng.2014.613086 Qin, P., Xu, H., Liu, M., Liu, L., Xiao, C., Mallakpour, I., & Sorooshian, S. (2022). Projected effects of climate change on major dams in the Upper Yangtze River Basin. Climatic Change, 170(1–2), 8. doi: 10.1007/s10584-021-03303-w Sarabi, M., Dastorani, M. T., & Zarrin, A. (2020). Investigating impact of future climate changes on temperature and precipitation condition (Case Study: Torogh Dam Watershed, Mashhad). Journal of Meteorology and Atmospheric Sciences, 3(1), 63–83. doi: 10.22034/jmas.2021.278862.1129 [In Persian]. Semenov, M. A., & Barrow, E. M. (2002). LARS-WG: A stochastic weather generator for use in climate impact studies (Version 3.2) [User’s manual]. https://www.researchgate.net/publication/268304865 Semenov, M. A., & Stratonovitch, P. (2009). The use of multi-model ensembles from global climate models for impact assessments of climate change. Climate Research, 41, 1–14. Shoja, F., & Hamidianpour, M. (2024). Projection influences of climate change on tourism development on the southern coast (Kish Island). Journal of Tourism and Development, 12(37), 237–255. doi: 10.22034/jtd.2023.380876.2725 [In Persian]. Tang, J., Niu, X., Wang, S., Gao, H., Wang, X., & Wu, J. (2016). Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections. Journal of Geophysical Research: Atmospheres, 121(5), 2110–2129. doi: 10.1002/2015JD023977 Trenberth, K. E., Dai, A., van der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., & Sheffield, J. (2014). Global warming and changes in drought. Nature Climate Change, 4, 17–22. doi: 10.1038/nclimate2067 Zamani, Y., Hashemi Monfared, S. A., Azhdari Moghaddam, M., & Hamidianpour, M. (2020). A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: The case of northeastern Iran. Theoretical and Applied Climatology, 142, 1613–1623. doi: 10.1007/s00704-020-03406-x Zohrevandi, H., Khorshiddost, A. M., & Sari Saraf, B. (2020). Prediction of climate change in Western Iran using downscaling of HadCM3 model under different scenarios. Journal of Spatial Analysis Environmental Hazards, 7(1), 49–64. http://jsaeh.khu.ac.ir/article-1-2741-fa.html [In Persian].
آمار تعداد مشاهده مقاله: 886 تعداد دریافت فایل اصل مقاله: 801

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب

پیوندهای مفید

آمار

Projection of temperature and radiation in arid and semi-arid climates under shared socioeconomic pathways scenarios