Irrigation and Water Engineering

Irrigation and Water Engineering

Detection of Climate Change using Trend Analysis of Hydro-Climatic Extremes in the Tajan River Basin

Document Type : Original Article

Authors
Elnaz Aghaee Kahlik Bolaghi 1
Ramin Fazloula 1
Mohsen Masoudian 1
Mehdi Yasi 2
Mehdi Nadi 1
1 Water Engineering Department, Agricultural Engineering College, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
2 Department of Irrigation and Reclamation Engineering, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
10.22125/iwe.2026.553384.1901
Abstract
Climate change, as one of the most challenging issues of the twenty-first century, poses a serious threat to the sustainability of water resources. The present study was conducted to investigate the effects of climate change on temperature regimes, precipitation patterns and the occurrence of hydro-climatic extreme events within the Tajan River basin, northern Iran. For this purpose the SDSM downscaling model was employed under the Sixth Assessment Report (AR6) and the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios for two future periods (2025–2055 and 2065–2095) at the Soleyman-Tangeh and Kiassar stations. Design precipitation values were also estimated using statistical analysis and the EasyFit distribution-fitting model. The results indicate that monthly minimum and maximum temperatures increase in all scenarios, with stronger warming under high-emission scenarios. Moreover, the precipitation regime shifts in the future, with increases in winter and autumn precipitation and a noticeable decrease in spring precipitation. During the 2025–2055 period, the intensity of extreme precipitation events rises, increasing the likelihood of destructive floods; however, in the 2065–2095 period, a substantial decline in design precipitation is observed across all return periods, which may reduce the potential for intense rainfall events but increase the risk of widespread droughts. The decline in design precipitation during the late-century period reflects a shift in climate conditions toward fewer extreme rainfall events and generally drier conditions. These findings highlight intensified climatic variability, greater concentration of precipitation in short-duration extreme events, and increased surface runoff, all of which impose additional pressure on water-resources management.
Keywords
Climate Change
Tajan River Basin
Return Period
Downscaling
Subjects
رحیم‌زاده، ف. و عسگری، ا. 1383، نگرشی بر تفاوت نرخ افزایش دمای حداقل و حداکثر و کاهش دامنه شبانه روزی دما در کشور. تحقیقات جغرافیایی، 73(19)، 155-171.
مطالعات شناسایی توجیهی طرح آبخیزداری تجن (حوضه آبریز سد شهید رجائی)، 1332، شرکت خدمات مهندسی جهاد، مهندسین مشاور خزر، جلد10.
موجرلو، ف.، فضل‌اولی، ر. و عمادی، ع. 1398، کاربرد مدل IHACRES برای ارزیابی اثرات تغییر اقلیم بر دبی حوضه آبریز تجن. نشریه آبیاری و زهکشی ایران، 13(1)، 129-141.
Akbari, M., Malekinezhad, H., & Hajibigloo, M. (2025). Analysis of Climate Change and Future Trends of Precipitation and Temperature in the Karde Dam Basin. Desert Management, 12(4), 87-106.‏
Ba, W., Du, P., Liu, T., Bao, A., Luo, M., Hassan, M., & Qin, C. (2018). Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China. Journal of Arid Land, 10, 905-920.‏
Benestad, R. E., Chen, D. & Hanssen-Bauer I. (2008). Empirical-statistical downscaling. World Climate Research Programme, WCRP Report No. 2008-1.
Bolin, B. R. (1986). The greenhouse effect, climate change and ecosystems. SCOPE Report No. 29.
Busuioc A., Giorgi F. & Bi X. (2006). Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania. Theoretical and Applied Climatology, 86(1–4): 101–123.
 Challa, V., & Renganathan, M. (2025). Assessment of climate change impact on meteorological variables of Indravati River Basin using SDSM and CMIP6 models. Environmental Monitoring and Assessment, 197(1), 1-22.‏
Chen, J., Brissette, F. P., Poulin, A., & Leconte, R. (2011). Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resources Research, 47(12). https://doi.org/10.1029/2011WR010602
Christensen J H & Christensen O B. (2007). A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change, 81: 7–30.
Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007). Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27(12), 1547-1578. https://doi.org/10.1002/joc.1556
Giorgi, F., & Mearns, L. O. (1999). Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research: Atmospheres, 104(D6), 6335–6352. https://doi.org/10.1029/98JD02072
Hashmi, M. Z., Shamseldin, A. Y., & Melville, B. W. (2011). Statistical downscaling of rainfall using SDSM for a climate change impact assessment in the upper Indus Basin. International Journal of Climatology, 31(11), 1579-1593. https://doi.org/10.1002/joc.2179
Hewitson, B. C., & Crane, R. G. (2006). Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. International Journal of Climatology, 26(10), 1315-1337. https://doi.org/10.1002/joc.1314
IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Lana, M. A., Eulenstein, F., Schlindwein, S., Guevara, E., Meira, S., Wurbs, A., ... & Bonatti, M. (2016). Regionalization of climate scenarios impacts on maize production and the role of cultivar and planting date as an adaptation strategy. Regional Environmental Change, 16(5), 1319-1331.‏
Liu, Y., Zhang, L., & Wang, Z. (2022). Comparison of statistical and machine learning models for temperature downscaling in the Tibetan Plateau. Journal of Climate, 35(5), 1234-1247.
Maslin, M. (2014). Climate change: a very short introduction. OUP Oxford.‏
Mearns L O, Bogardi I & Giorgi F. (1999). Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. Journal of Geophysical Research Atmospheres, 104(D6): 6603–6621.
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Rogelj, J. (2020). The SSP greenhouse gas concentrations and radiative forcing geoscientific model. Geoscientific Model Development, 13(8), 3571–3605. https://doi.org/10.5194/gmd-13-3571-2020
Mupenzi J D L P & Li L. (2011). Impacts of global warming perturbation on water resources in arid zone: Case study of Kaidu River Basin in Northwest China. Journal of Mountain Science, 8: 704–710.
Nakamura, R., & Shimatani, Y. (2021). Extreme-flood control operation of dams in Japan. Journal of Hydrology: Regional Studies, 35, 100821.‏
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., & van Vuuren, D. P. (2016). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180. https://doi.org/10.1016/j.gloenvcha.2015.01.004
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153-168.
Sachindra D. A., Huang F. & Barton A. F. (2014). Multi-model ensemble approach for statistically downscaling general circulation model outputs to precipitation. Quarterly Journal of the Royal Meteorological Society, 140: 1161–1178.
Schittkowski, K. (2002). EASY-FIT: a software system for data fitting in dynamical systems. Structural and Multidisciplinary Optimization, 23, 153-169.
‏Xu C. C., Zhao J. & Deng H. J. (2016). Scenario-based runoff prediction for the Kaidu River basin of the
Tianshan Mountains, Northwest China. Environmental Earth Sciences, 75: 1126.