Introduction

Over the past decade, urban rainstorm waterlogging disasters have become increasingly frequent in China; hundreds of cities suffer from varying degrees of inundation every year. These events cause substantial losses to human life, private property, and critical urban infrastructure. A paradigmatic case is the “7·20” torrential rain event in Zhengzhou, Henan Province, on 20 July 2021, which affected 1.88 million residents, damaged 2,730 municipal roads, and produced direct economic losses of CNY 53.2 billion. Likewise, on 1 August 2023, Zhuozhou in Hebei Province experienced an extreme rainfall episode (mean: 355.1mm; peak: 435.7mm) that impacted 133,900 people, resulted in 9 fatalities and 6 missing persons, and inflicted direct economic losses exceeding CNY 100 billion. Effectively controlling the surface runoff concentration time of urban underlying surfaces can markedly reduce the peak occurrence of waterlogging, thereby providing residents with additional evacuation and emergency-response time and safeguarding lives, assets, and public infrastructure. Consequently, mitigating or suppressing urban rainstorm waterlogging disasters has emerged as an urgent challenge for sustainable urban development in China.

With the accelerated process of urbanization in China, the intensity of urban development is continually increasing, accompanied by a gradual rise in hardened pavement. Concurrently, issues such as the aging of drainage pipe networks and subsidence of urban pavement have emerged1. These challenges significantly impact basin hydrological processes2, particularly in short-time scale hydrological processes3. Consequently, densely populated and economically intensive cities experience more severe losses due to waterlogging disasters4. Presently, numerous scholars have endeavored to simulate the evolution process of urban waterlogging disasters. Scholars such as Jiao Sheng5,6 and Huang Huabing7,8 have conducted in-depth explorations. They have thoroughly analyzed the spatio-temporal distribution characteristics and causes of urban waterlogging disasters, and systematically proposed a series of countermeasures and suggestions to address these disasters. Meanwhile, scholars like Zhang Lin and Li Haihong have developed waterlogging disaster simulation models9,10,11, providing more precise tools for the study of urban waterlogging issues. These models not only facilitate a deeper understanding of the occurrence mechanisms of waterlogging disasters but also offer strong support for urban waterlogging risk assessment12,13,14,15. Building upon this foundation, scholars have further explored the establishment and improvement of urban waterlogging early warning systems16,17 and emergency management strategies18,19,20. However, the overall effectiveness of these studies in addressing the mitigation of urban rainstorm waterlogging disasters remains limited. The aforementioned research primarily focuses on the study of urban rainstorm waterlogging disasters within the current urban context in China. There is a paucity of detailed studies on the suppression or mitigation of urban rainstorm waterlogging disasters based on different land use types, particularly from the perspectives of vegetation coverage and the implementation of sponge city concepts.

This study proposes the first multi-scale coupled analytical framework for urban waterlogging mitigation, transcending the theoretical and technical constraints of conventional stormwater-management research. By integrating hydrodynamic simulation, distributed hydrological modelling, and drainage-system inversion technology in an interdisciplinary manner, we achieve the first dynamic coupling analysis of the complete process from “surface runoff generation–conveyance–drainage to LID facility regulation.” Contrary to previous single-dimensional investigations, we innovatively uncover the cascading regulation mechanism of vegetation-cover gradients and low-impact development (LID) facilities on rainstorm-induced waterlogging, and develop a multi-land-use-oriented waterlogging-mitigation assessment model. A quantitative mapping system of “land use–hydrological response–disaster intensity” is constructed to systematically elucidate the nonlinear regulatory effects of urban underlying-surface heterogeneity on waterlogging evolution.

Methodologically, the study delivers three major technological breakthroughs:

  1. (1)

    A 3-D urban hydrological model is developed on the basis of high-precision geospatial data (2 m remote-sensing imagery plus 10 m DEM), overcoming the vertical-process representation limitations of traditional 2-D simulations.

  2. (2)

    An enhanced Green–Ampt infiltration model, coupled with a machine-learning-driven drainage-capacity degradation diagnostic algorithm, enables accurate simulation of water–soil–air multiphase coupling.

  3. (3)

    A geographic information system platform incorporating multi-source data-fusion verification mechanisms is established. Through multi-dimensional validation that combines model calculations with field measurements, our findings demonstrate both theoretical innovation and practical guidance value, offering a next-generation technical paradigm for smart urban stormwater management.

Research area and research methods

Study area

Erdao District, located in Changchun City, Jilin Province, China, is situated in the eastern part of the central city of Changchun (Fig. 1). It is adjacent to Wanchang Town, Yongji County, Jilin City, to the east, and borders on Jingyue Development Zone and Shuangyang District to the south. The district is bounded by the Yitong River to the west and connects to Kuancheng District and Jiutai District Donghu Street to the north. With a resident population of 522,453 people, Erdao District covers a total area of 465.11 km2. The district falls within the continental temperate sub-humid monsoon climate zone, with an average annual temperature of 4.8 °C and an average annual precipitation of 571 mm. The dry and wet seasons are distinct, with precipitation mostly concentrated in summer, being both abundant and intensive. Currently, Erdao District has implemented sponge city road construction on nine roads in the main urban area, totaling approximately 16.59 km in length. However, due to the aging drainage pipe network and relatively low terrain in Erdao District, localized flooding still occurs during short-duration heavy rainfall, posing a severe threat to urban infrastructure, residents’ safety, and property. Taking into account the climate, urban development, data availability, and the feasibility of field sampling, Erdao District is selected as the research area for this paper.

Fig. 1
figure 1

Location and land use of the study area. The map was created via ArcGIS V. 10.8 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources) based on the standard map No.

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Research methods

Research data

The research data in this study can be categorized into three types. The first type is raster data. The remote sensing imagery of the study area was sourced from the Ziyuan-2 satellite. Specifically, imagery captured during the summer of 2022 with cloud cover less than 10% and a spatial resolution of 2 m was selected. The 10-m resolution DEM data (source: http://www.resdc.cn). Basedon the DEM data, watershed data were extracted using ArcGIS software. Second, vector data, primarily comprising land use data obtained through visual interpretation using ArcGIS software (Fig. 2). The interpretation process involved a comparative analysis of the region’s topographic, geomorphic, and vegetation characteristics to select representative areas as sample regions. Based on these sample regions, visual interpretation criteria applicable to the entire study area were established, including direct indicators such as shape, size, and tone. These criteria were then applied to comprehensively interpret the remote sensing images and classify land use types. The accuracy of the interpretation results was validated through field surveys. Grid data were generated using the fishnet tool in ArcGIS software, with a grid size of 400*400 m, totaling 5,536 grids (Fig. 3). Third, attribute data, including rainfall data sourced from meteorological station records and historical rainfall data of the study area (source: http://www.resdc.cn). Drainage capacity data were obtained through model inversion, while rainfall infiltration data for different land cover types were calculated using models and combined with actual measurements. Detailed information is provided in Table 1.

Fig. 2
figure 2

Construction Process of Numerical Model for Urban Waterlogging. The map was created via ArcGIS V. 10.8 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources) based on the standard map No.

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Fig. 3
figure 3

Grid Division and Attributes of the Study Area. The map was created via ArcGIS V. 10.8 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources) based on the standard map No.

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Table 1 study data and sources.
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Waterlogging model

The numerical model for urban rainstorm waterlogging is built upon a two-dimensional unsteady flow as the fundamental framework. It utilizes regular grids to generalize ground features, incorporating a consideration of one-dimensional unsteady flow in the drainage network (Fig. 2)21,22. This model comprises four major modules: the runoff model, confluence model, drainage model, and waterlogging model. The runoff is treated using hydrological models, confluence is achieved through hydrodynamic simulations, drainage is addressed through generalization, and waterlogging is the synthesis of the previous models. The confluence model forms the backbone of the entire model, employing the two-dimensional unsteady flow equation. The simulation area is divided into regular grids based on topographical and feature characteristics. Using these grids as basic units, numerical calculations are performed using the finite volume method to determine the waterlogging range and water depth in the study area23,24. Net precipitation refers to the difference obtained by deducting the amounts of rainfall intercepted by vegetation, filled in depressions, evaporated, and infiltrated into the soil from the total rainfall. The infiltration amount is obtained through calculations in this paper, and the infiltration value for each land cover type is assigned to the corresponding grids using ArcGIS attribute tools. Considering the complexity and diversity of vegetation types, and the fact that vegetation cover in cities is mostly grassland, the infiltration rate of grassland types will be introduced into each scenario for subsequent model runs.To elucidate the mitigation mechanism of urban rainstorm waterlogging in different regions, four scenarios are set(Table 2).

Table 2 Scenario Simulation Conditions Table.
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Calculation of underlying surface water permeability

  1. (1)

    Sponge City Concept

    A sponge city is a new generation urban rainwater management concept characterized by the city’s ability to exhibit resilience similar to a sponge. It refers to the city’s capacity to adapt to environmental changes and effectively respond to natural disasters caused by rainfall, often termed a "water-resilient city." The fundamental principle of a sponge city is to achieve sustainable urban development and efficient water resource management by emulating the water circulation processes of natural systems. The core idea involves implementing strategies such as green belts, rain gardens, ecological ditches, and resurfaced pavements to collect, infiltrate, and store rainwater. This approach aims to reduce urban rainwater runoff and mitigate waterlogging disasters. The increase in vegetation coverage serves a dual purpose: enhancing rainwater interception (e.g., coniferous and broad-leaved forests) and augmenting the rainwater infiltration capacity of the surface (e.g., grasslands, shrubs). Additionally, the use of features such as storage layers, planting layers, permeable geotextiles, graded crushed stone layers, and drainage systems in the underlying surface (Fig. 4) facilitates water storage, rapid infiltration, drainage, and the deceleration of water flow. This helps eliminate or mitigate the severity of road water accumulation.

    Fig. 4
    figure 4

    The Principle of Urban Vegetation and Sponge City in Mitigating Waterlogging (Image created using Visio).

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  2. (2)

    Calculation of Permeability Coefficient

    In 1931, Richards conducted experiments demonstrating that the infiltration in unsaturated soil also follows Darcy’s law, meaning that water flow is proportional to the gradient of soil and water potential25,26. The calculation method for the soil permeability coefficient (K) is given by:

    $$k = C \cdot \frac{{e^{3} }}{1 + e} \cdot \frac{{d_{H}^{2} }}{\tau } \cdot \frac{\gamma }{\mu }$$
    (1)

    where \(C = \,c \cdot k_{j} \cdot k_{s} \cdot k_{w} ;\;d_{H} = 1 \cdot \sin \theta ,\;d_{H}\) representing the average effective particle size of soil particles; \(\tau\) for tortuosity; \(\gamma\) for groundwater gravity; \(\mu\) for fluid dynamic viscosity; \(k_{j} \cdot k_{s} \cdot k_{w}\), are coefficients representing the influences of soil gradation, saturation, and soil matrix suction, respectively. The permeability coefficient K is determined through the above calculation, and the Infiltration flow Q is calculated using Darcy’s law:

    $$Q = KA{ }\frac{{{\text{H}}_{1} - {\text{H}}_{2} }}{L} = KAI$$
    (2)

    where \({\text{H}}_{1}-{\text{H}}_{2}\) represents the head difference between upstream and downstream; \(\text{A}\) is the cross-sectional area perpendicular to the direction of water flow;\(L\) is the penetration length; \(K\) is the permeability coefficient; Is the hydraulic gradient. The soil infiltration rate can be calculated from the Infiltration flow:

    $$v = Q/\left( {6{ }A \cdot T} \right)$$
    (3)

    where \(\upsilon\) is the infiltration rate of green soil; \(Q\) is the amount of water infiltration; \(\text{A}\) is the cross-sectional area perpendicular to the direction of water flow;\(\text{T}\) is time.

    Based on the measurements in this paper, the soil permeability rate with turf is approximately 20% higher than that of bare soil under the same conditions. Additionally, planting certain shrubs in the lawn increases the permeability coefficient by about 15% compared to simply planting grass, with a permeability rate reaching 10.5 \(\text{m}/\text{s}\). Refer to Table 3 for specific measurement results. For the spatial distribution, please refer to Fig. 5.

    Table 3 water permeability of different types.
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    Fig. 5
    figure 5

    Map of deep infiltration rates for different land use types. The map was created via ArcGIS V. 10.8 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources) based on the standard map No.

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  3. (3)

    Inversion of Drainage Capacity in the Study Area

    This paper employs an inversion method to estimate the drainage capacity data of the study area. According to the standard of the "Code for Design of Outdoor Sewerage" (GB 50014–2018), the design of rainwater drainage should consider the rainfall intensity of different areas. The inversion is based on the common drainage capacity range of 20–85 mm/h in urban municipal designs in China. Using data from existing water accumulation monitoring stations, the drainage volume is calculated through model inversion. Subsequently, the drainage capacities of different regions are estimated according to the inversion results and are associated with the corresponding grid attributes27. Taking a short-term heavy rainfall event on July 7, 2022, as an exampleas, the Erdao District of Changchun City has low-lying and flat terrain, with inherently poor drainage conditions. Additionally, floodwater from surrounding higher areas easily backflows into the district, further increasing the drainage pressure. During heavy rainfall, the existing drainage system is unable to quickly drain the accumulated water. the event lasted for 2 h, with a total rainfall of 67 mm and a water depth of 1.5 m at the monitoring point. Through model inversion, a drainage capacity of 28 mm/h was set, and the simulation results were most consistent with the water depth at the monitoring point28,29,30. Therefore, this paper adopts the corresponding method to correct the drainage capacity of the study area.:

    $$Q_{{{\text{pipe}}}}^{\prime } = \frac{{Q_{{{\text{pipe}}}} }}{{V_{p} }} \times V_{i}$$
    (4)

    where \(Q_{{{\text{pipe}}}}^{\prime }\) is the corrected drainage capacity; \(Q_{{{\text{pipe}}}}\) is the drainage capacity of the area; \({V}_{p}\) is the model-calculated drainage amount, with 28 mm/h taken as the design drainage amount for the area where the pixel to be corrected is located \(V_{i}\). Since this paper lacks segmented drainage capacity data for the drainage network in the study area, a uniform drainage capacity of 28 mm/h is assigned to each drainage grid, and the drainage process in the study area is estimated based on this unified drainage capacity31,32,33,34,35.

  4. (4)

    Model Validation

    For each type of land use, the sample data consists of five soil monoliths. The field measurement method used is the double-ring infiltrometer method (double-ring permeameter)1. The double-ring structure controls lateral flow to measure the vertical infiltration rate. Specifically, metal rings of different diameters (with the inner ring approximately 20–30 cm and the outer ring approximately 40–50 cm) are vertically pressed into the soil surface to ensure that the inner and outer rings are concentric. Water is added to the outer ring to maintain a constant water head (5–10 cm) to suppress lateral flow, while water is added to the inner ring to measure vertical infiltration. Continuous monitoring is conducted until the infiltration rate stabilizes. The time intervals of the water level drop and the changes in water volume in the inner ring are then recorded. Finally, the infiltration rate is calculated and compared with the measured infiltration rate as follows (Table 4) :

    Table 4 Validation of Infiltration Coefficient Comparison.
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Mitigation effects of urban vegetation cover on waterlogging

The design of urban vegetation cover is a crucial component of sponge cities. Based on local climate conditions, soil types, and greening objectives, selecting plant species with strong adaptability and scientifically arranging them can maximize the functions of plants, including rainwater collection, flood detention, water purification, and landscaping. Simultaneously, it can help slow down the occurrence of waterlogging disasters. To compare the design scenarios in this paper, Scenario 1 is initially set with rainfall data referencing July 7, 2022. The average rainfall across the region is 67mm over approximately 2 h36,37. The initial rainfall for the waterlogging model in this study is set at 67mm. Using the urban rainstorm waterlogging model, Fig. 6a illustrates the thematic map of rainstorm waterlogging. Results indicate that there are 1218 grids with accumulated water in the study area, with the maximum submerged depth being 1.80m. The main distribution is in the eastern, western, and south-central regions of the study area, while the minimum submerged depth is 1.00m, mainly found in the northeast.

Fig. 6
figure 6

Simulation results of urban rainstorm waterlogging(a scenario 1; b scenario 2; c Scenario 3; d Scenario 4). The map was created via ArcGIS V. 10.8 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources) based on the standard map No.

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To examine the mitigating effect of vegetation cover on urban rainstorm waterlogging, Scenario 2 is established with underlying land types. ArcGIS spatial correlation methods are employed to assign vegetation types and their permeability values to corresponding grids. Running the model again yields Fig. 6b, which shows the rainstorm waterlogging thematic map. The results indicate that, overall, the location of accumulated water in the study area has not changed significantly. The number of grids with accumulated water is 1156, representing a 5.09% reduction compared to Scenario 1. Both the minimum and maximum submerged depths have decreased by 0.50m and 0.30m, respectively. This suggests that vegetation cover has a certain alleviating effect on urban rainstorm waterlogging. However, the existing vegetation in the study area is primarily concentrated in non-urban residential areas, resulting in suboptimal mitigation of urban rainstorm waterlogging.

Mitigation effects of sponge cities on rainstorm waterlogging

The construction of sponge cities represents a pivotal strategy for mitigating urban waterlogging disasters. It effectively manages heavy rainfall events within the recurrence interval specified for waterlogging prevention and control designs, thereby enhancing urban resilience to climate change and storm disasters. In Scenario 3 designed in this study, the focus is solely on the mitigating effect of artificial pavement on waterlogging. By replacing roads and bare soil areas with sponge city pavement, the stormwater flooding model was rerun to generate the thematic map shown in Fig. 6c. The results indicate that the study area contains 482 water storage grids, representing a reduction of 60.43% and 58.30% compared to Scenario 1 and Scenario 2, respectively. The minimum and maximum water storage depths decreased by 0.85 m and 1.35 m compared to Scenario 1, and by 0.35 m and 1.05 m compared to Scenario 2. These findings demonstrate that the underlying surface created by sponge city pavement significantly alleviates urban stormwater flooding.However, the uniform application of artificial pavement across urban roads and bare soil areas in the study may introduce discrepancies between the research results and actual conditions. Future research will involve on-site investigations to obtain actual data, such as pavement types and materials, to replace the current hypothetical data. This will facilitate a re-evaluation of the actual infiltration capacity of sponge cities and their mitigation effects on urban stormwater flooding.

Mitigation effects of urban vegetation and sponge city on rainstorm waterlogging

In Scenario 4, the underlying land use types in the study area are comprehensively configured to reflect the actual urban land use and sponge city construction. The land use types are reset, linking the infiltration capacity of each type to grid attributes. The stormwater flooding model is then rerun to generate the thematic map shown in Fig. 6a–d. Results indicate a significant reduction in severely waterlogged grids within the study area. Specifically, compared to the previous scenarios, the number of waterlogged grids drops to 108, representing a decrease of 91.13% (Scenario1), 90.66% (Scenario2), and 77.59% (Scenario3). Notably, water accumulation depth is eliminated in some grids, reducing to zero.Further analysis shows that compared to Scenario 1, the minimum and maximum water accumulation depths decrease by 1.00 m and 1.55 m, respectively; compared to Scenario 2, they decrease by 0.50 m and 1.25 m; and compared to Scenario 3, both decrease by 0.15 m. These findings demonstrate that comprehensive consideration of urban land use types significantly mitigates stormwater flooding. This is primarily reflected in the transformation of previously contiguous severely waterlogged areas into isolated accumulation points. The rapid decline in water accumulation depth also indicates that current flooding conditions no longer pose a threat to traffic and resident safety, as shown in Fig. 7.

Fig. 7
figure 7

Comparison of water depth under different situations.

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Discussion and conclusion

Conclusion

This study, based on the urban rainstorm waterlogging model, explores the relationship between urban land use and rainwater infiltration capacity, elucidating the impact of urban vegetation and sponge city construction on urban rainstorm waterlogging. The main research conclusions are as follows:

  1. (1)

    In Scenario 1, without considering vegetation and sponge city construction, the proportion of urban impervious surfaces increased. This disrupted the natural water cycle and limited the capacity for rainwater storage and infiltration. As a result, rapid runoff and extensive flooding occurred within the study area, with a maximum water depth of 1.80 m. This posed a serious threat to the safety of residents’ lives and property.

  2. (2)

    Scenario 2 introduced vegetation types based on Scenario 1, altering the structure of the urban underlying surface. Compared to Scenario 1, the number of flooded grids decreased by 5.09%, demonstrating the mitigating effect of vegetation cover. The increase in vegetation coverage and the improvement of soil conditions enhanced the permeability of the watershed and the water retention capacity of the region. This, in turn, slowed the formation of surface runoff and reduced the severity of urban flooding during heavy rainfall. However, due to insufficient vegetation coverage in the study area, the overall effectiveness of this measure was limited. While the number of flooded grids and the minimum and maximum flooding depths were reduced to some extent compared to Scenario 1, the threat of flooding was not completely eliminated.

  3. (3)

    Scenario 3 incorporated sponge city land use types on the basis of Scenario 2. Compared to Scenario 1, the number of flooded grids decreased by 60.43%. This measure aimed to restore the normal water cycle between nature and the city. It significantly improved rainwater infiltration and drainage capabilities, further reducing the severity of flooding disasters caused by heavy rainfall. Compared to Scenario 1, the number of flooded grids decreased significantly, and the minimum and maximum flooding depths were also notably reduced. This indicates that sponge city construction plays a crucial role in mitigating flooding disasters caused by heavy rainfall.

  4. (4)

    In Scenario 4 (a combination of vegetation and sponge facilities), the number of flooded grids decreased by 91.13% compared to Scenario 1 (the traditional impervious underlying surface). This validated the effectiveness of sponge cities in mitigating flooding caused by heavy rainfall. Through the comprehensive application of vegetation coverage and sponge city construction, the flooding situation in the study area was significantly improved. The number of flooded grids decreased substantially, and the minimum and maximum flooding depths were reduced to levels that pose minimal threats to the safety of the study area. This demonstrates that the combined effect of vegetation coverage and sponge city construction is the most effective in mitigating flooding caused by heavy rainfall.

In summary, this paper quantifies rainwater infiltration in different land use types, elucidates the mitigation effects of vegetation cover and sponge city construction on waterlogging, analyzes the impact mechanisms of various land use types on waterlogging, and discusses the mitigation effects of different land use type combinations in various scenarios on urban waterlogging disasters. The research findings not only provide a scientific basis for urban waterlogging disaster prevention and mitigation but also offer technical support for urban sustainable development.

Discussion

Urban waterlogging disasters have become a common issue in Chinese cities, primarily due to the rapid urbanization process and the increasing intensity of extreme weather events. As emphasized in the introduction, numerous scholars have explored the causes of urban waterlogging, early warning systems, and emergency management strategies[^5^–^8^]. However, most existing studies mainly focus on the current urban environment, with less attention to the role of different land use types (especially vegetation coverage and sponge city construction) in mitigating waterlogging disasters. This study fills this research gap by quantitatively analyzing the impact of different land use configurations on urban rainstorm waterlogging. Our research results are consistent with previous studies that emphasized the importance of vegetation and permeable surfaces in reducing surface runoff and enhancing rainwater infiltration9,10,11. Scenario 2, which introduced vegetation, demonstrated the mitigating effect of vegetation cover. However, due to limited vegetation coverage in residential areas, the overall effectiveness was constrained, echoing the findings of Huang et al., who pointed out that urban vegetation distribution is often uneven and insufficient to fully address waterlogging issues.

The introduction of sponge city concepts in Scenarios 3 and 4 further validated the effectiveness of permeable surfaces in reducing waterlogging. This is consistent with the research of Zhang et al., who highlighted the role of sponge city infrastructure in enhancing urban resilience to heavy rainfall. However, our study also found that the uniform application of sponge city pavement across all roads and bare soil may introduce some errors, as real-world conditions often vary significantly. This limitation highlights the need for more detailed and site-specific data in future research.

Through multi-scenario simulation analysis, this study revealed a synergistic amplification effect between vegetation coverage and sponge city infrastructure. This finding is very close to the ecological engineering synergy theory proposed by Sun et al. It is worth noting that this quantitative result has been fully validated in actual sponge city construction: During Typhoon “Muifa” in 2022, the Lingang New Area of Shanghai achieved a remarkable 30% improvement in drainage efficiency through its "gray-green integration" model. Meanwhile, the Tianfu New Area of Chengdu increased its annual runoff control rate to over 70% through its "green–blue network" ecological sponge system, demonstrating excellent flood prevention capabilities during multiple heavy rainfall events.

The research indicates that the current application of sponge city construction in Erdao District still has certain limitations. For instance, the uniform distribution of permeable pavements may not align with actual conditions, resulting in discrepancies between simulation outcomes and real-world situations. In Scenario 4, the comprehensive configuration of vegetation and sponge city facilities significantly reduced the depth of waterlogging, virtually eliminating the threat of flooding to traffic and resident safety. This suggests that in future sponge city construction in Erdao District, greater emphasis should be placed on the organic combination of vegetation cover and permeable facilities. To further enhance the effectiveness of sponge city construction in Erdao District, the research recommends that sponge city construction should clarify zoning standards, ensure that new and retrofit projects meet design requirements, and systematically plan facilities for rainwater collection, infiltration, storage, and utilization to enhance the city’s capacity to cope with rainfall. A dedicated leadership group should be established to coordinate policies, funding, and technical support, and regular cross-departmental meetings should be convened to address issues arising during implementation. In urban renewal and new construction projects, the mandatory implementation of green infrastructure should be enforced, integrating vegetation cover with sponge city facilities to create a synergistic effect. In Scenario 4, the integrated configuration of vegetation and sponge city facilities significantly reduced the depth of waterlogging, virtually eliminating the threat of flooding to traffic and resident safety. This suggests that in future sponge city construction in Erdao District, greater emphasis should be placed on the organic combination of vegetation cover and permeable facilities, and further improvements in rainwater infiltration and drainage capabilities should be made, taking into account costs and feasibility.