Water and Irrigation Management

Water and Irrigation Management

Impact of climate change in design discharge and sediment yield in urban streams (Case study: Soulghan hydrometric station)

Document Type : Research Paper

Authors
Mehdi Karami
MohammadReza Majdzadeh Tabatabai
Reza Shams
Department of Civil, Water and Environmental, Shahid Beheshti University, Tehran, Iran.
10.22059/jwim.2026.414072.1302
Abstract
In this study, the impact of climate change on the design discharge and sediment loading in the Kan River was studied using data from the Soulghan hydrometric station. The effective discharge, the full-flow discharge, and the discharges with return periods of 1.58 and 2.33 years were calculated for current and future conditions under three climate scenarios SSP126, SSP245, and SSP585. The results showed that the effective discharge under current conditions is about 5.05 m3 / s, which transports more than 69.4 percent of the sediment, but in the future, with the decrease in the amplitude of the flows, the effective discharge will decrease to about 2.76 to 2.86 m3/s, carrying more than 91 percent of the accumulated sediment. The sensitivity index of sediment transport to discharge also decreased from 1.32 in the past to about 0.45 to 0.49 in the future, indicating a decrease in the sharp jump in sediment with an increase in discharge. The LARS-WG model performed well in generating future climate data (r=0.97), (RMSE=2.57), and the LSTM and SVM machine learning models performed well with a correlation coefficient of about 0.90 and an NSE efficiency coefficient higher than 0.79 were able to predict discharge and sediment with acceptable accuracy. The results indicate increased flow fluctuations, increased frequency of extreme events, and redistribution of sediment load towards flows with lower amplitude due to climate change, which requires a review of the hydraulic design and management of urban waterways.
Keywords
Bankfull discharge
Downscaling
Effective discharge
Machine learning
Sediment rating curve
Subjects
  1. Wang, B., & Xu, Y. J. (2020). Estimating bed material fluxes upstream and downstream of a controlled large bifurcation‐the Mississippi‐Atchafalaya River diversion. Hydrological Processes, 34(13), 2864-2877.
  2. Braithwaite, N. S. (2023). Regime Channel Bedload Transport Equation. Journal of Hydraulic Engineering, 149(3), 06022020.
  3. Wolman, M. G., & Miller, J. P. (1960). Magnitude and frequency of forces in geomorphic processes. The Journal of Geology, 68(1), 54-74.
  4. Sarker, S. (2023). Separation of Floodplain Flow and Bankfull Discharge: Application of 1D Momentum Equation Solver and MIKE 21C. CivilEng 2023, 4, 933-948.
  5. Li, D., Wang, G., Qin, C., & Wu, B. (2021). River extraction under bankfull discharge conditions based on sentinel-2 imagery and DEM data. Remote Sensing, 13(14), 2650.
  6. Eaton, B. C., & Davidson, S. L. (2022). Hydraulic geometry: Empirical investigations and theoretical approaches.
  7. Tranmer, A. W., Caamaño, D., Clayton, S. R., Giglou, A. N., Goodwin, P., Buffington, J. M., & Tonina, D. (2022). Testing the effective-discharge paradigm in gravel-bed river restoration. Geomorphology, 403, 108139.
  8. Pirani, A., Fuglestvedt, J. S., Byers, E., O’Neill, B., Riahi, K., Lee, J. Y., ... & Tebaldi, C. (2024). Scenarios in IPCC assessments: lessons from AR6 and opportunities for AR7. npj Climate Action, 3(1), 1.‏
  9. Kikstra, J. S., Nicholls, Z. R., Smith, C. J., Lewis, J., Lamboll, R. D., Byers, E., ... & Riahi, K. (2022). The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures. Geoscientific Model Development, 15(24), 9075-9109.
  10. Goodarzi, M. R., Abedi, M. J., & Niazkar, M. (2024). Effects of climate change on streamflow in the Dez Basin of Iran using the IHACRES model based on the CMIP6 model. Journal of Water and Climate Change, jwc2024571.
  11. Di Virgilio, G., Ji, F., Tam, E., Nishant, N., Evans, J. P., Thomas, C., ... & Delage, F. (2022). Selecting CMIP6 GCMs for CORDEX dynamical downscaling: Model performance, independence, and climate change signals. Earth's Future, 10(4), e2021EF002625.‏
  12. Tabari, H., Paz, S. M., Buekenhout, D., & Willems, P. (2021). Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought. Hydrology and Earth System Sciences, 25(6), 3493-3517.
  13. Hariri, H., Hosseini, S. A., & Khosrojerdy, A. (2023). [Title in Persian: The effect of climate change and gabion check dams on sediment yield; case study: Dehbar]. Watershed Engineering and Management, 15(4), 535-553. https://doi.org/10.22092/ijwmse.2023.357033.1939. (In Persian).
  14. Yousefi, H., Ahani, A., Moridi, A., & Razavi, S. (2024). The future of droughts in Iran according to CMIP6 projections. Hydrological Sciences Journal, 69(7), 951-970.
  15. Nda, M., Adnan, M. S., Yusoff, M. A. B. M., & Nda, R. M. (2023). An overview of machine learning techniques for sediment prediction. Engineering Proceedings, 56(1), 204.
  16. Heidary, H., Musavi Baygi, M., Esmaili, K., & Golkarian, A. (2020). The impact of climate change on watershed runoff and sedimentation using SWAT and WEPP models (Case study: Dehbar Basin). Iranian Journal of Soil and Water Research, 51(4), 1027-1040. https://doi.org/10.22059/ijswr.2020.287152.668293. (In Persian).
  17. Hameed, M. M., Masood, A., Hamid, A., Elbeltagi, A., Razali, S. F. M., & Salem, A. (2025). Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. PLoS One, 20(5), e0321008.
  18. Bezak, N., Lebar, K., Bai, Y., & Rusjan, S. (2025). Using machine learning to predict suspended sediment transport under climate change. Water Resources Management, 1-16.
  19. Hao, R., & Bai, Z. (2023). Comparative study for daily streamflow simulation with different machine learning methods, Water, 15, 1179.
  20. Shakya, D., Deshpande, V., Kumar, B., & Agarwal, M. (2023). Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models. Artificial intelligence review, 56(9), 10067-10098.
  21. Azad, N., & Ahmadi, A. (2024). Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran. Scientific Reports, 14(1), 24165.‏
  22. Hassani, M.R., Niksokhan, M.H., Ardestani, M., & Mousavi Janbehsarayi, S.F. (2023). Evaluating the effects of climate change on urban runoff based on CMIP6 models (case study: district 10 of Tehran municipality). Water and Soil Management and Modeling, 3(2), 269-285. (In Persian).
  23. Bigdeli, Z., Majnooni-Heris, A., Delearhasannia, R., & Karimi, S. (2024). Rainfall-runoff modeling of khormazard and bonab hydrometric stations using support vector machine and random forest algorithms. Water and Soil, 37(6), 971-989.‏ (In Persian).
  24. Nasiri, A., Salimi, E., Delfan Azari, M., Yazdi, J., & Shahsawandi, M. (2025). Urban Flood Zoning Using an Integrated Hydrological-Hydraulic Watershed Modeling Approach, Case Study: Districts 21 and 22 of Tehran. Disaster Prevention and Management Knowledge (quarterly), 14(4), 392-405.‏
  25. Gholami, M., Ganavati, E., & Ahmadabadi, A. (2020). Simulation of floodplain zones in Tehran's metropolitan watershed (case study: Kaan basin). Journal of Spatial Analysis Environmental Hazards,6 (4), 95-108. (In Persian).
  26. Moriasi, D. N., Gitau, M. W., Pai, N., & Daggupati, P. (2015). Hydrologic and water quality models: Performance measures and evaluation criteria. Transactions of the ASABE, 58(6), 1763-1785.
  27. Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829-844.
Water and Irrigation Management
Volume 16, Issue 2
Summer 2026
Pages 325-347