Year: 2026 | Month: October | Volume: 13 | Issue: 10 | Pages: 434-441
DOI: https://doi.org/10.52403/ijrr.20261038
Stormwater Quantity and Quality Modeling in the Urban Business District of Suzhou City Using SWMM Model
K. Senanou GBAFA1,2, Adoh KPANDJA2,3
1Laboratory of Structure and Materials Mechanics, 2,3Laboratory of Geosciences Water and Environnement: Université of Lome Togo, 3College of Environmental Engineering, Hohai University, Nanjing 210098, China
Corresponding Author: K. Senanou GBAFA
ABSTRACT
Urbanization has altered the sources, processes, and sinks of nonpoint source (NPS) pollution in cities, substantially increasing the pollution load carried by urban runoff into receiving waters. Urban runoff pollution is therefore a focal point of current NPS research. This paper analyzes stormwater quantity and quality in the urban business district of Suzhou using the Storm Water Management Model (SWMM), a software platform valued for its graphical capabilities and rapid convergence toward optimal design parameters. The model was driven by rainfall data from three events — May 5, June 20, and June 26, 2024 — and hydraulic calibration, together with runoff pollution calculations (TSS, COD), was performed through an iterative process of parameter and error adjustment.
The results demonstrate the strong predictive capability of the SWMM model once properly calibrated against measured data. Hydraulic validation showed a close match in both the timing and magnitude of flow variations, with simulated total runoff volumes differing from measured volumes by only 2.12% and 7.9%. For water-quality validation, performed using the rainfall event of June 26, 2024, the agreement between simulated and measured pollutant loads reached 98% for TSS and 90% for COD, confirming the model's strong predictive capacity. Modeling the quantity and quality of urban runoff pollution enables urban planning professionals to monitor environmental conditions in support of urban sustainability.
Keywords: runoff pollution; stormwater management; SWMM model; Suzhou
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