Sensitivity Analysis of the SWMM Model for Runoff Simulation in an Arid Urban Catchment

Document Type : Original Article

Authors

Department of Water and Soil, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran.

Abstract
Hydrological models’ performance is highly dependent on the accurate calibration of multiple parameters, Sensitivity analysis can play a key role in optimizing this process by identifying the most influential parameters. The present study investigates the sensitivity of the Storm Water Management Model (SWMM) in simulating urban runoff in Shahroud, a city located in an arid region of Iran. The sensitivity of the SWMM to changes in seven key calibration parameters including the Width factor of the subcatchment, Curve Number (CN), Manning’s roughness coefficients for pervious (N-Perv) and impervious (N-Imperv) areas, the percentage of impervious areas (Imperv factor), and depression storage depth for both pervious (Des-Perv) and impervious (Des-Imperv) areas was quantified using Sensivity Factor (S) proposed by Morris. The study area was divided into 18 relatively homogeneous sub-catchments, with runoff from these areas drained through two outlets. During the sensitivity analysis process, the responses of peak discharge and runoff volume to these parameters were examined across seven rainfall events characterized by different intensities and durations. The results showed that peak discharge (Qp) and runoff volume (RV) are sensitive to the Imperv factor(S= 0.64 and 0.93, respectively). Qp is generally sensitive to the Width factor (S=0.17) and N-Imperv (S=0.13), while showing no sensitivity to other parameters (S<0.05). Longer rainfall durations increased the sensitivity of Qp to both CN and the Imperv factor, while sensitivity to other parameters decreased. CN significantly affected both RV and Qp only during long-duration events.

Keywords


Arvand S., Delghandi M., Ganji Z., Alipour A. 2020. Evaluation of Storm Water Management Model (SWMM5.0) in simulation of urban runoff (case study: urban catchment of Neyshabur). Irrigation and Water Engineering, 10(3): 68-81. https://doi.org/10.22125/iwe.2020.107092.
Arvand, S., Ganji Noroozi, Z., Delghandi, M., Alipour, A. 2023. Evaluating the impact of LID-BMPs on urban runoff reduction in an urban sub-catchment. Urban Water Journal, 20(5): 604-615. https://doi.org/10.1080/1573062X.2023.2207083.
Badizadegan, R., Khodashenas, S.R., Esmaili, K. 2021. Sensitivity analysis of the affecting parameters on the urban runoff results in the SWMM model (case study: north catchments of Tehran City). Modares Civil Engineering Journal, 21(5): 57-63.
Bajracharya, A., Awoye, H., Stadnyk, T., Asadzadeh, M. 2020. Time variant sensitivity analysis of hydrological model parameters in a cold region using flow signatures. Water, 12(4): 961. https://doi.org/10.3390/w12040961.
Behrouz, M.S., Zhu, Z., Matott, L.S., Rabideau, A.J. 2020. A new tool for automatic calibration of the Storm Water Management Model (SWMM). Journal of Hydrology, 581: 124436. https://doi.org/10.1016/j.jhydrol.2019.124436.
D’Ambrosio, R., Balbo, A., Longobardi, A., Rizzo, A. 2022. Re-think urban drainage following a SuDS retrofitting approach against urban flooding: a modelling investigation for an Italian case study. Urban Forestry & Urban Greening, 127518. https://doi.org/10.1016/j.ufug.2022.127518.
D’Ambrosio, R., Longobardi, A., Balbo, A., Rizzo, A. 2021. Hybrid approach for excess stormwater management: combining decentralized and centralized strategies for the enhancement of urban flooding resilience. Water, 13(24): 3635. https://doi.org/10.3390/w13243635.
Francos, A., Elorza, F.J., Bouraoui, F., Bidoglio, G., Galbiati, L. 2003. Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale. Reliability Engineering & System Safety, 79(2): 205-218. https://doi.org/10.1016/S0951-8320(02)00231-4.
Gu, X., Liao, Z., Zhang, G., Xie, J., Zhang, J. 2017. Modelling the effects of water diversion and combined sewer overflow on urban inland river quality. Environmental Science and Pollution Research, 24: 21038-21049. https://doi.org/10.1007/s11356-017-9686-x.
Höllering, S., Wienhöfer, J., Ihringer, J., Samaniego, L., Zehe, E. 2018. Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints. Hydrology and Earth System Sciences, 22(1): 203-220. https://doi.org/10.5194/hess-22-203-2018.
Imani, T., Delghandi, M., Emamgholizadeh, S., Ganji Noroozi, Z. 2023. Evaluating uncertainty in climate change impacts on peak discharge and flood volume in the Qaran Talar watershed, Iran. Journal of Water and Climate Change, 14(4): 1300-1313. https://doi.org/10.2166/wcc.2023.453.
Jiang, Y., Li, J., Xia, J., Gao, J. 2024. Sensitivity identification of SWMM parameters and response patterns of runoff pollution on hydrological and water quality parameters. Ecohydrology & Hydrobiology (in press).
Kheshti Azar, M., Giudicianni, C., Creaco, E. 2025. Sensitivity analysis-aided calibration of urban drainage modeling. Water, 17(5): 612. https://doi.org/10.3390/w17050612.
Lai, Y., Lu, Y., Ding, T., Sun, H., Li, X., Ge, X. 2022. Effects of low-impact development facilities (water systems of the park) on stormwater runoff in shallow mountainous areas based on dual-model (SWMM and MIKE21) simulations. International Journal of Environmental Research and Public Health, 19(21): 14349. https://doi.org/10.3390/ijerph192114349.
Ma, B., Wu, Z., Hu, C., Wang, H., Xu, H., Yan, D. 2022. Process-oriented SWMM real-time correction and urban flood dynamic simulation. Journal of Hydrology, 605: 127269. https://doi.org/10.1016/j.jhydrol.2021.127269.
Madrazo-Uribeetxebarria, E., Antin, M.G., Berrondo, J.A., Andrés-Doménech, I. 2021. Sensitivity analysis of permeable pavement hydrological modelling in the Storm Water Management Model. Journal of Hydrology, 600: 126525. https://doi.org/10.1016/j.jhydrol.2021.126525.
Mai, J., Craig, J.R., Tolson, B.A., Arsenault, R. 2022. The sensitivity of simulated streamflow to individual hydrologic processes across North America. Nature Communications, 13(1): 455. https://doi.org/10.1038/s41467-022-28010-7.
Omidi Arjenaki, M., Zarif Sanayei, H.R., Heidarzadeh, H. 2021. Investigation of performance and determination of optimal dimensions of surface runoff collection network using SWMM model. https://doi.org/10.1038/s41467-022-28010-7.
Peng, J., Zhao, H., Li, R., Xue, R. 2024. Parameter sensitivity analysis of SWMM: a case study of airport airfield area. Natural Hazards, 120(7): 6551-6568. https://doi.org/10.1007/s11069-024-06453-z.
Rahimi, M.A., Emamgholizadeh, S., Karami, G.H., Delghandi, M., Ganji Norouzi, Z. 2023. Evaluation urban drainage network using SWMM model (case study: Shahrood City). Irrigation and Water Engineering, 14(2): 146-163.
Ranatunga, T., Tong, S.T., Yang, Y.J. 2017. An approach to measure parameter sensitivity in watershed hydrological modelling. Hydrological Sciences Journal, 62(1): 76-92. https://doi.org/10.1080/02626667.2016.1151983.
Rezvani, F.S., Ghorbani, K., Salarijazi, M., Yazarloo, B., Rezaei Ghaleh, L. 2024. Comparative assessment of IHACRES, AWBM, and Tank models for daily runoff simulation in wet and dry periods. Journal of Water and Soil Conservation, 31(1): 51-72. https://doi.org/10.22069/jwsc.2024.21968.3697.
Rossman, L.A., Huber, W.C. 2016. Storm Water Management Model Reference Manual Volume I–Hydrology (Revised). US Environmental Protection Agency, Cincinnati, OH, USA.
Samiei, M., Seyedian, M., Fathabadi, A., Jahantig, N. 2024. Modeling and investigation of the most important factors affecting the occurrence of floods in the Gorganrood watershed. Journal of New Approaches in Water Engineering and Environment, 2(2): 123-136. https://doi.org/10.22034/nawee.2024.433344.1059.
Taghizadeh, S., Khani, S., Rajaee, T. 2021. Hybrid SWMM and particle swarm optimization model for urban runoff water quality control by using green infrastructures (LID-BMPs). Urban Forestry & Urban Greening, 60: 127032. https://doi.org/10.1016/j.ufug.2021.127032.
Van Zelm, R., Huijbregts, M.A. 2013. Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment. Environmental Science & Technology, 47(16): 9274-9280. https://doi.org/10.1021/es305107s.
Xu, Z., Xiong, L., Li, H., Xu, J., Cai, X., Chen, K., Wu, J. 2019. Runoff simulation of two typical urban green land types with the Stormwater Management Model (SWMM): sensitivity analysis and calibration of runoff parameters. Environmental Monitoring and Assessment, 191: 1-16. https://doi.org/10.1007/s10661-019-7445-9.
Yang, Z., Liu, J., Feng, Y., Wang, J., Wang, H., Li, C. 2025. An intelligent SWMM calibration method and identification of urban runoff generation patterns. Frontiers in Environmental Science, 13: 1582306. https://doi.org/10.3389/fenvs.2025.1582306.
Zahiri, A., Mostakhdemin, F. 2024. Multi-branched river flood routing by multiple-reach Muskingum model. Journal of New Approaches in Water Engineering and Environment, 3(1): 111-123. https://doi.org/10.22034/nawee.2024.456184.1077.
Zakizadeh, F., Moghaddam Nia, A., Salajegheh, A., Sañudo-Fontaneda, L.A., Alamdari, N. 2022. Efficient urban runoff quantity and quality modelling using SWMM model and field data in an urban watershed of Tehran metropolis. Sustainability, 14(3): 1086. https://doi.org/10.3390/su14031086.
Zhuang, Q., Li, M., Lu, Z. 2023. Assessing runoff control of low impact development in Hong Kong's dense community with reliable SWMM setup and calibration. Journal of Environmental Management, 345: 118599. https://doi.org/10.1016/j.jenvman.2023.118599.
Volume 4, Issue 2
Special Issue: Guest Editor: Prof. Ozgur Kisi
September 2025
Pages 213-226