## 1 Introduction  
In recent years, urban flood disasters have frequently occurred worldwide, posing serious threats to urban development as well as the safety of lives and property [1, 2]. In China, rapid large-scale urbanization, the overdevelopment of natural landscapes, and increased surface runoff and flow rates have significantly altered major water cycle conditions, thereby exacerbating urban flood disasters [3, 4, 5, 6]. Moreover, global climate extremes, including exceptionally high temperatures, marine heatwaves, and heavy precipitation, are anticipated to intensify extreme rainfall events, further contributing to urban waterlogging disasters. The concept of sponge cities has emerged as an effective approach to mitigate urban waterlogging issues, enabling cities to flexibly adapt to environmental changes [7, 8, 9, 10]. Conducting urban runoff simulations and waterlogging analysis is of great significance for reducing flood disasters. To effectively analyze urban stormwater disasters, it is essential to employ scientific and reliable methods for simulating and assessing these events, thereby uncovering the for mation mechanisms and spatial distribution characteristics of waterlogging. Currently, urban stormwater modeling methods primarily include hydrological methods [11; 12], hydrodynamic methods [13; 14], and integrated hydrological-hydrodynamic approaches [15; 16]. Widely used urban stormwater simulation software includes, among others, the Storm Water Management Model (SWMM), developed by the United States Environmental Protection Agency (EPA) [17]; MIKE URBAN, developed by DHI in Denmark [18]; and InfoWorks ICM, from Wallingford, UK [19]. SWMM is an open-source and widely accessible software known for its robust rainfall-runoff, network hydrodynamic, and low-impact development (LID) modules, earning its positive recognition in both U.S. and international academic circles. SWMM has been effectively applied in various countries, including South Korea [20], the United States [21], and Finland [22]. For example, [PERSON] et al. [23] used a calibrated hydrodynamic SWMM model to examine climate change impacts on urban flooding using projected future rainfall data. They calibrated a hydraulic model for two urban watersheds in Delhi, India, under historical (1990-2016) and future (Representative Concentration Pathway 4.5, 2021-2100) scenarios. [PERSON] et al. [24] further enhanced SWMM by integrating groundwater (GW) feedback into LID simulations (SWMM-LID-GW), achieving improved model calibration and validation. [PERSON] et al. [25] applied EPA-SWMM to simulate and evaluate cumulative overflow in urban manholes within a Samsung-dong drainage basin in Seoul under variable rainfall intensities. Furthermore, [PERSON] et al. [26] employed SWMM to develop hydrological and hydrodynamic models to analyze rainfall and flooding dynamics before and after implementing runoff control measures, studying the five-year and ten-year recurrence intervals in Pingyu County, Henan Province, China. These studies underscore SWMM's critical role in simulating urban waterlogging, with applications in early warning and forecasting, disaster management, drainage planning, and sponge city design, all essential for mitigating urban disasters and promoting socio-economic resilience. In many developing countries, rapid urbanization coupled with inadequate urban planning has led to numerous streets prone to flooding after heavy rains. For instance, Khulna, in Bangladesh, is highly vulnerable to climate change, experiencing frequent flooding during heavy rainfall seasons. To address this issue, [PERSON] et al. [1] developed a hydrological simulation model using MIKE URBAN to assess waterlogging severity in Khulna City. The findings provide guidance for relevant departments in making informed decisions on drainage and water management to mitigate waterlogging. Flooding and waterlogging also affect India and other developing nations. [PERSON] et al.