Urban Flood Risk Research Articles

The contributions of hazard and exposure to flood risk in Shenzhen City

Flood risk strongly depends on the hazard and the exposure of people and assets. With the continuous growth of the urban population, exposure is contributing more and more to urban flood risk. Meanwhile, climate warming has led to frequent extreme precipitation events, resulting in severe flood hazards in cities. However, the individual contributions of hazard and exposure to flood risk remain unclear, hindering effective risk reduction strategies in metropolitan areas. This study examines the contributions of pluvial flood hazard and population exposure to the population mortality risk, as well as the contributions of pluvial flood hazard and urban built-up area exposure to the economic loss risk in the Shenzhen metropolitan area. The results indicate that the concentration of population and assets in central Shenzhen increases the pluvial flood risk. With almost the same water depth, the Longgang, Longhua, and Luohu districts exhibit higher death tolls and asset losses compared to the Guangming district. Additionally, changes in pluvial flood hazard also significantly contribute to the variation in economic loss risk, which could explain up to 84% of the observed fluctuations. With the existing distribution of population and urban built-up areas, pluvial flood risk in Shenzhen is expected to increase in the future as extreme precipitation intensifies.

A SWMM-based evaluation of the impacts of LID and detention basin retrofits on urban flooding

ABSTRACT Different stormwater management practices have been used to mitigate the impacts of urbanization and attenuate urban flooding risk. However, there is a lack of studies on the comparison between conventional stormwater control measures such as detention basins, their retrofits, and low impact development (LID) practices, especially from the perspective of their impacts on erosive flow attenuation and flood control. The aim of this study is to compare the effects of two detention basins and their outlet retrofits with LID, in an urbanized catchment under designed storm events with different return periods, using a Storm Water Management Model (SWMM) modeling approach. The results show that detention basins are effective in decreasing peak flow and delaying peak time. Detention basin retrofits could significantly reduce the frequency and duration of erosive flows in Sub A detention basin compared with LID, especially in smaller rainfall events. LID outperforms the detention basin retrofits in reducing peak flow from larger storms.

How do social management systems and urbanization influence the spatio-temporal characteristics of urban flood risk? A comparison between Guangzhou and Hong Kong, China

Previous studies have shown that there are some differences in the pattern and evolution of floods within urban agglomerations. The differences reflect the variations in the geographical environment and the level of urban governance. However, in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), which has different social systems, the differences in urban flood between mainland cities and external cities are more significant. Concerning the causes of urban floods, the rainfall amounts are approximately equivalent in each region within the GBA, indicating that the external causes are mostly similar. Consequently, this study examines the effects of urbanization and flood management measures on urban floods under different social systems from the perspective of internal causes, taking Guangzhou and Hong Kong as examples. The results indicate that: (1) The flood degree in Guangzhou continues to increase, with the flood points increasing from 15 to 237, and the high-density areas expanding from a single-core to multi-core. In contrast, flood points in Hong Kong decreased from 136 to 17, and the high-density areas were concentrated in strips and gradually dispersed. (2) The urbanization process of Guangzhou fluctuates considerably, with urban expansion intensity (UEI) of 17.32, 4.15, and 8.82. Hong Kong remains stable, with a UEI of around 4.0 in each period. The percent of landscape (PLAND) and largest patch index (LPI) of impervious surfaces have the highest impact on urban flood and showed a significant positive correlation. However, The effect of patch density (PD) on urban flood changes from a positive to a negative correlation (from 0.151 to −0.169). (3) Hong Kong has formulated systematic flood control measures from the aspects of laws and regulations, territorial planning, engineering design, etc., and has effectively reduced the occurrence of flooding incidents using rainwater interception, underground flood storage, and pipe widening. This study clearly illustrated the spatio-temporal differences of urban flood within urban agglomeration and the key factors of urban management level and reviewed the flood risk management strategies of Guangzhou and Hong Kong, which have far-reaching significance for the mainland cities to learn from their experiences and improve their flood management strategies.

Coupling dynamics of urban flood resilience in china from 2012 to 2022: A network-based approach

Urban flooding presents a significant challenge in Chinese cities, necessitating a deeper understanding of the coupling effects of China's urban flood resilience for effective resilience planning. This study introduces a four-component Environment-Institution-Infrastructure-Agent (EIFA) framework and utilizes an updated correlation network approach to analyze the temporal variation of coupling effects of urban flood resilience across 639 Chinese cities from 2012 to 2022. The findings indicate a decline in synergy and increased tradeoffs, primarily due to intensified competition within and between institutional and infrastructural sectors, marginal impacts of infrastructure investments, and socially excessive infrastructure. The study also highlights the agent component's strong internal and inter-component coupling effects, implying the effectiveness of China's people-centered resilience strategies, though risks of decoupling remain. Additionally, it notes a good match between societal urban flood resilience and natural flood risks, while natural vegetation loss due to urban expansion is noteworthy. The study further suggests that refining agent-focused deposit and insurance policies could coordinatively enhance urban flood resilience, as these elements are hubs within the network. The updated network-based framework and its findings offer insights for informing and optimizing urban flood resilience planning in China.

Framework for urban flood risk assessment – a hydrological modelling approach: the case of Nilambur, Kerala

ABSTRACT The study has evolved in response to the challenges posed by climate change and rapid urbanization occurring worldwide, which contribute to an increased frequency of natural disasters. It addresses flood hazards in Kerala’s Nilambur urban area, where climate change and rapid urbanization heighten disaster risk. Kerala's coastal location and steep terrain in the Western Ghats increase vulnerability, particularly as one of India’s most densely populated states. The research identifies flood risk zones applying overlay analysis, using data from the 2018 Kerala Floods, to create a flood risk framework responsive to runoff volume and thereby flood susceptibility. Key factors include climatic variables, terrain characteristics like elevation, slope, soil, land-use land-cover including urban density, and hydrological and ecological elements like stream networks and floodplains. These are integrated in the SWAT tool to model flood risks, creating a resilient flood risk assessment framework that combines hydrological and ecological parameters.

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

This study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Under the context of global warming and rapid urbanization, cities worldwide confront the pressing problem of urban waterlogging, hindering progress towards Sustainable Development Goals. Effective planning and mitigation of urban flooding require a comprehensive understanding of the spatial and temporal patterns of rainfall and risk heterogeneity. However, evaluating urban water-logging risk is challenged by the need for city-scale hydrological simulation and generally lacks comprehensive metrics integrating fine-scale datasets. To address these gaps, we developed a simulation method for urban flood hazards by integrating hydrological models and Random Forest algorithms. We then took Shenzhen, a megacity in China, as a case study, and investigated the spatial patterns of urban flooding risk and its determinants at the block level based on the risk assessment framework represented by Hazards-Exposure-Vulnerability (H-E-V) dimensions. We found that socio-economic indicators exhibited spatial clustering, while hazard-related indicators displayed more dispersed patterns. High-risk areas exhibited a highly heterogeneous spatial pattern, predominantly influenced by vulnerability and exposure factors, as well as the spatial mismatch among the three dimensions. Our results emphasize the importance of integrating spatial heterogeneity of exposure and vulnerability into climate adaptation resource allocation, addressing both current and future demands for effective climate mitigation.

GIS and hybrid multi-criteria decision analysis based urban flood risk assessment in Hanchuan City, Hubei Province, China

GIS and hybrid multi-criteria decision analysis based urban flood risk assessment in Hanchuan City, Hubei Province, China

The performance of two urban flood regulation models using different input data

Increasing climate change has led to an increase in urban flood events. Events with a return period of twenty years become events with a period of two to three years. The primary objectives of this study are to evaluate the performance of flood regulation models using different input data and compare their performance for flood regulation supply (FRS) assessment. The assessment of the ecosystem services of urban FRS was performed using two models: Urban Flood Risk Mitigation (UFRM) by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and IMECOGIP Waterflow Regulation (Retention Model) (WRRM). They rely on relatively accessible open-source data, require a short time to model each scenario and provide opportunities to interpret results in different spatial and measurement units. We performed several improvements to the input data, including updating the national ecosystem-type assessment for the case study area by connecting it with the Urban Atlas typology and proposing an approach for soil data refinement. The outputs of the two models show significant differences in FRS values for each land-use/land-cover (LULC) class. The UFRM assesses different impervious urban classes with different values, varying from very low to medium. In contrast, the WRRM assesses the FRS in densely sealed areas with one fixed value for low supply (where nearly all rainfall is transformed into runoff).

Environmental drivers of sediment accumulation in urban sewer systems: A case study of Kyoto City, Japan

Sediment accumulation can reduce the drainage capacity of sewer systems, and thus increase the risk of urban flooding and jeopardize the safety of urban environments. The interaction between multiple environmental factors drives sedimentation in sewer systems, but this interaction is poorly understood. To fill this gap, this study examined the interaction between five categories of environmental factors (population, traffic, climate, air quality, and wastewater quality: 25 factors overall) and the effects of these factors on sediment accumulation in different sewer system structures (open channels (OCs), circular channels (CCs), junctions) in Kyoto, Japan, from 2014 to 2023. The results showed that population and traffic contributed to sediment accumulation through pollutant emission, whereas climate factors promoted sediment accumulation by enhancing wash-off and the deposition of air pollutants. Most sediment was deposited in OCs and junctions under the combined effects of climate and air quality. Air quality has a greater influence on sedimentation in the OCs than climate, and the opposite is found for Junction, whereas sedimentation in CCs was mainly influenced by the combined effects of climate, air quality, and wastewater quality. Photochemical oxidants, humidity, temperature, and vapor pressure had the greatest impact on sediment deposition in OCs and junctions, while wastewater alkalinity, NH4-N and PO4-P concentration, and water volume had a non-negligible effect on CCs sedimentation. These results emphasize the key role of climate-driven wash-off and dry/wet deposition, and the localized role of wastewater quality (in CCs), in sewer sedimentation. These insights can inform the predictive modeling of sewer sedimentation during climate change.

Coastal urban flood risk management: Challenges and opportunities − A systematic review

Generational mechanisms and spatio-temporal evolution patterns of coastal urban flood risk involve complex interactions between climate change, sea level rise and human-induced factors, necessitating integrated adaptive flood management strategies to mitigate evolving vulnerabilities. This systematic review offers a thorough assessment of the challenges and strategic opportunities for sustainable adaptation in managing flood risk in coastal urban areas. It integrates emerging innovative technologies and financial solutions to identify promising approaches to implement mitigation strategies and improve coastal urban flood resilience. Enhancing governance and policy frameworks is crucial for the successful implementation of coastal urban flood risk management (CUFRM) plans. An innovative participatory planning framework is developed to promote flood management practices which are socially inclusive and equitable. Funding for green infrastructure and nature-based solutions and the strategic use of public-private partnerships are effective methods for advancing sustainable flood risk management (FRM). The advancements in emerging technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), social media and digital twin technologies, provide dynamic and collaborative platforms for simulating flood scenarios and have potential to significantly improve CUFRM practices. In the end, a cross-country comparison of current practices in Australia, China, the Netherlands, the UK and the USA reveals a diverse range of approaches and valuable insights derived from regional experiences. The review provides a comprehensive analysis for researchers, policymakers and practitioners aiming to improve flood resilience in coastal metropolitan regions by learning from effective UFRM approaches that enhance governance structures, infrastructure resilience and funding mechanisms.

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Integrated risk analysis for urban flooding under changing climates

Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding under changing climates, this study coupled the downscaled general circulation model (GCM) projection with hydrologic-hydraulic modeling, with consideration of both the low-greenhouse gas (GHG) emission scenario of SSP1–2.6 and the high-GHG emission scenario of SSP5–8.5. Results demonstrated that the original GCM projection effectively captured the changing trend of rainfall patterns in the given area, with an overestimation of rainfall peaks. The GCM downscaling through the k-nearest neighbors (kNN)-based analog method significantly improved the model accuracy. Scenario analysis indicated that climate change significantly affected the regional hydrology, with the precipitation, surface runoff, and floods increasing by a maximum value of 17.10%, 12.66%, and 63.26%, respectively. The interannual comparison demonstrated that the temporal variability in precipitation and flood intensified with the increase in GHG emissions during 2025–2100, suggesting the uncertainty of long-term climate forecasts. According to flood risk analysis, long-term and short-term floods exhibit varied changing trends across climate scenarios, despite the strong correlations between precipitation and runoff, implying the complexity of flood generation mechanisms. The methods and findings herein provide valuable insights for policymakers and practitioners, to cope with the increasing urban flood risk within the context of evolving environment.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

An integrated approach for urban flood risk prediction using AHP-TOPSIS model: a case study of Jaipur region

An integrated approach for urban flood risk prediction using AHP-TOPSIS model: a case study of Jaipur region

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO11Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

Spatiotemporal evolution and influencing factors of flood resilience in Beibu Gulf Urban Agglomeration

In the context of global climate change and rapid urbanization, enhancing flood resilience is essential for mitigating urban flood risk. However, few studies have conducted long-term, cross-scale dynamic evaluations of flood resilience and analyzed its influencing factors and mechanisms. Taking the Beibu Gulf urban agglomeration as the study area, a long-term, cross-scale dynamic evaluation index system based on the “Robustness-Resistance-Recovery” (3Rs) framework was developed to assess the spatiotemporal evolution of flood resilience from 2000 to 2020. Furthermore, the optimal parameters-based geographical detector model is employed to identify the key influencing factors and mechanisms. The results reveal that pre-flood robustness is lower in coastal areas and higher in inland areas. In the during-flood stage, cities with greater comprehensive power exhibit stronger resistance. Post-flood recovery is higher in city centers and marginal mountainous areas, while coastal and inland low-lying areas show lower recovery. The flood resilience of urban agglomerations has improved in recent years, largely due to the enhancement of urban flood control infrastructure and healthcare capacity. However, disparities between cities persist. From 2000 to 2020, economic factors have been the primary drivers of improved flood resilience, while ecological factors have gained increasing importance over the past decade. These findings provide valuable insights for flood prevention, mitigation, and resilience management in urban agglomerations. The developed dynamic evaluation index system offers a reference framework for evaluating flood resilience in other regions.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Integrated urban and riverine flood risk management in the Fujiang River Basin-Mianyang city

China’s monsoon climate, characterized by hot summers and substantial rainfall, leads to significant seasonal water accumulation in river basins. This often results in short-term heavy rainfall events, causing rapid water flow into river basins and subsequent flooding. River embankments, which narrow channels, exacerbate downstream flooding and pose severe safety threats. This study explores integrated urban and riverine flood risk management strategies within the Fujiang River Basin, focusing on Mianyang City. The research addresses the growing challenges of global climate change and rapid urbanization, which have intensified flood risks. Utilizing the one-dimensional Storm Water Management Model (SWMM) and two-dimensional hydrodynamic Rainfall-Runoff-Inundation (RRI) model, the study simulates flood events and urban flooding in Mianyang City and the Fujiang River Basin. These models were validated with data from hydrological stations and satellite imagery. The study evaluates extreme flood events in Mianyang City between 2015 and 2021. The findings highlight the significant role of sponge city initiatives and low-impact development (LID) facilities in mitigating flood impacts. Despite their benefits, these measures have limitations under extreme flood conditions. This study contributes to understanding the interactions between urban and riverine systems and provides a basis for improving flood risk management and resilience in rapidly urbanizing river basins. It supports ongoing efforts to implement sponge city concepts in China, offering crucial insights into effective strategies for managing flood risks in similar geographic and climatic contexts.