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The concept of urban resilience focuses on understanding the process and mechanisms of disaster occurrence, providing innovative approaches to address stormwater flooding. However, existing studies primarily concentrate on enhancing overall system resilience, with limited research examining the temporal progression from stormwater disturbance to flood generation. To fill this gap, this study categorizes the development process of stormwater flooding into three periods: disturbance resistance (DR), adjustment and adaptation (AA), and rapid recovery (RR). Using the SWMM (Storm Water Management Model) software, 27 representative parcels in the Beijing-Tianjin-Hebei region of China were simulated. By sequentially considering single-indicator control variables, resilience indicators that significantly impact the three periods were identified through the construction of a stormwater flooding resilience indicator library. Subsequently, resilience models for each disaster phase were constructed using the BP (Back Propagation) neural network, and genetic algorithms were employed to optimize the models and determine the optimal values of resilience indicators for each period. Finally, the research findings were summarized into a resilience design method for the built environment to address stormwater flooding, accompanied by a guide for improving stormwater flooding resilience.The study reveals the following key findings: (1) the influence of physical and spatial elements in the built environment on stormwater flooding formation varies across different stages of the disaster process; (2) distinct resilience indicators operate at different times and in different ways throughout the entire stormwater flooding resilience process; (3) enhancing stormwater flooding resilience in the built environment does not necessarily require setting specific threshold values for each influencing indicator; instead, an optimal single value emerges when multiple indicators interact. Moreover, when multiple indicators interact, an optimal combination module with the best value for a single indicator exists. This study investigates the complete cycle from storm disturbance to flood disaster formation, offering both solutions for cities to mitigate storm flood disasters and advancing theoretical research on urban storm flood resilience while fostering interdisciplinary integration.

Surface water flooding poses significant threats to communities and environments. This threat has historically been managed through sewers and combined sewer overflows; however, it is now well recognised that, alone, these legacy systems are insufficient to manage the growing pressures from climate change, population growth and urbanisation. This realisation has led to research and practice developing a broad range of new technologies to enhance the coping capacities of existing sewer systems through capturing and attenuating or reusing surface water across catchments using sustainable drainage systems (SuDS). However, despite technical understanding, industry champions and significant best practice, SuDS remain underutilised, particularly at a synergistic catchment scale where they can be most effective. In this paper we respond to this challenge by developing preliminary screening tools to enhance the visibility of SuDS among the multidisciplinary decision-makers responsible for directing strategic surface water management. We achieve this through upscaling a regional decision support model and demonstrating implementation across a case study in South West England. We find that it is possible to use easily accessible and open-source data to provide initial indications of SuDS opportunities and that this early visibility in the decision-making process can be used to support the consideration of novel and effective surface water management strategies.

• A library of built environment resilience indicators for stormwater flooding is proposed based on the timeline. • Key resilience indicators at different stages of time are identified by using SWMM model to simulate control variables. • BP neural networks and genetic algorithms are integrated to determine the optimal values under stormwater flooding scenarios. • A resilience enhancement method for urban built environment is proposed based on the results of the study. The concept of urban resilience focuses on understanding the process and mechanisms of disaster occurrence, providing innovative approaches to address stormwater flooding. However, existing studies primarily concentrate on enhancing overall system resilience, with limited research examining the temporal progression from stormwater disturbance to flood generation. To fill this gap, this study categorizes the development process of stormwater flooding into three periods: disturbance resistance (DR), adjustment and adaptation (AA), and rapid recovery (RR). Using the SWMM (Storm Water Management Model) software, 27 representative parcels in the Beijing-Tianjin-Hebei region of China were simulated. By sequentially considering single-indicator control variables, resilience indicators that significantly impact the three periods were identified through the construction of a stormwater flooding resilience indicator library. Subsequently, resilience models for each disaster phase were constructed using the BP (Back Propagation) neural network, and genetic algorithms were employed to optimize the models and determine the optimal values of resilience indicators for each period. Finally, the research findings were summarized into a resilience design method for the built environment to address stormwater flooding, accompanied by a guide for improving stormwater flooding resilience. The study reveals the following key findings: (1) the influence of physical and spatial elements in the built environment on stormwater flooding formation varies across different stages of the disaster process; (2) distinct resilience indicators operate at different times and in different ways throughout the entire stormwater flooding resilience process; (3) enhancing stormwater flooding resilience in the built environment does not necessarily require setting specific threshold values for each influencing indicator; instead, an optimal single value emerges when multiple indicators interact. Moreover, when multiple indicators interact, an optimal combination module with the best value for a single indicator exists. This study investigates the complete cycle from storm disturbance to flood disaster formation, offering both solutions for cities to mitigate storm flood disasters and advancing theoretical research on urban storm flood resilience while fostering interdisciplinary integration. [ABSTRACT FROM AUTHOR]

Increasing urbanization, impervious space, and the impact of climate change are threatening the future of cities. Nature-based solutions, specifically urban green infrastructures, are seen as a sustainable strategy to increase resilience against extreme weather events, including the escalating occurrence of stormwater runoff flooding. Consequently, urban planners and decision-makers have pushed their efforts toward implementing green infrastructure solutions to reduce the impact of stormwater floods. Among others, green roofs help store water and decrease stormwater runoff impacts on a local scale. This research aims to investigate the effect of surface permeability and green roof implementation on reducing stormwater flooding and subsequently provide urban planners with evidence-based geospatial planning recommendations to improve urban resilience in Helsinki. First, we modeled the current impact of stormwater flooding using the Arc-Malstrom model in Helsinki. The model was used to identify districts under high stormwater flood risk. Then, we zoomed in to a focus area and tested a combination of scenarios representing four levels of green roof implementation, two levels of green roof infiltration rates under 40-, 60-, 80-, 100 mm precipitation events on the available rooftops. We utilized open geographic data and geospatial data science principles implemented in the GIS environment to conduct this study. Our results showed that low-level implementation of green roofs with low retention rates reduces the average flood depth by only 1 %. In contrast, the maximum green roof scenario decreased most of the average flood depth (13 %) and reduced the number of vulnerable sites. The proposed methodology can be used for other cities to develop evidence-based plans for green roof implementations. • Investigated the benefits of green roof implementation on stormwater flooding. • The methodology is based on geospatial data analysis and planning principles. • The methodology is applied on a case study from Helsinki. • The results reveal the maximum green roof implementation reduces vulnerabilities. • The methodology develops evidence-based plans for green roof implementations. [ABSTRACT FROM AUTHOR]

This work presents a methodology for analyzing the influence of urbanization in a long-term approach on the number of floods in an urban catchment. The mathematical model presented for predicting the multiannual number of stormwater floods accounts for dynamic changes in the urban catchment in the subsequent years covered by the simulations. Logistic regression was applied to predict flooding occurring during rainfall events. The model may be applied to catchments with different characteristics. The assumed solution allows the development of early warning systems by modeling the occurrence of stormwater flooding in a studied catchment area based on the identification of the rainfall origin. To verify the simulations with a mathematical model, an innovative concept based on a hydrodynamic model is used; this concept includes the changes in the impervious area that occur during the simulation period. • Universal model to identify stormwater flooding in an urban catchment. • Simulation of the influence of rainfall origins on stormwater flooding. • Predict of the number of flooding considering the dynamics of land use changes. [ABSTRACT FROM AUTHOR]

• Detailed and feasible retrofit design avoid to overestimate SuDS performance. • Potential SuDS retrofitting reach up to over 70 % CSO total volume reduction. • In catchment with lower imperviousness, SuDS are more effective. • SuDS improve drainage network efficiency, reducing the number of its flooded nodes. • Above an optimal SuDS retrofitting percentage extra benefits are undetectable. In the last decades, urban sprawl and soil sealing led to an increase of urban flooding phenomena. Sustainable Drainage Systems (SuDS) seem able to weaken stormwater-related criticalities, enhancing sustainability and city resilience. Relying on a detailed and feasible preparatory study of SuDS feasible retrofit design, based on a punctual identification of the areas suitable for retrofitting and the most appropriate combination of SuDS technologies, the reported research aimed to assess the effectiveness of a sustainable drainage approach in Sesto Ulteriano (Italy), an urban catchment suffering from stormwater management concerns. In particular, using the approach of a typical scenario analysis, this comparative modeling analysis involved SWMM5 for the assessment of the differences in the catchment hydrological behaviour between the mentioned specific and feasible SuDS retrofitting scenarios and a potential one, where non-specificity is considered for SuDS retrofitting location. Besides, the analyses focused on investigating how rainfall severity, areal extension and land use typical feature could influence the effectiveness of the sustainable redevelopment of the urban area. Results indicate that SuDS projects based on potential designs, which does not account for the feasible suds location, might result in a significant overestimation of the hydrological benefit. They showed, indeed, an improved hydrological performance, with average total volume reductions of the Combined Sewer Overflows up to over 70 % (retrofitting the 8.3 % of the catchment area), that is about 40 % higher than those obtained under the same areal extension by the feasible scenarios. Moreover, it was found that there could be an optimal SuDS retrofitting percentage above which additional hydrological benefits are undetectable. Land use, resulting in the variability in the degree of imperviousness necessarily associated to a variability in the retrofitting potential, also seemed to affect SuDS hydrological performance and for this reason should be included in an overall assessment. SuDS also proved to act successfully on the actual maximum percentage of nodes of the drainage network above a 0.7 filling degree threshold (about 52 % under 10-year return period rainfall) reducing it to 24 % with a feasible retrofitting involving the 8.3 % of the study area. [ABSTRACT FROM AUTHOR]

Surface water flooding poses significant threats to communities and environments. This threat has historically been managed through sewers and combined sewer overflows; however, it is now well recognised that, alone, these legacy systems are insufficient to manage the growing pressures from climate change, population growth and urbanisation. This realisation has led to research and practice developing a broad range of new technologies to enhance the coping capacities of existing sewer systems through capturing and attenuating or reusing surface water across catchments using sustainable drainage systems (SuDS). However, despite technical understanding, industry champions and significant best practice, SuDS remain underutilised, particularly at a synergistic catchment scale where they can be most effective. In this paper we respond to this challenge by developing preliminary screening tools to enhance the visibility of SuDS among the multidisciplinary decision-makers responsible for directing strategic surface water management. We achieve this through upscaling a regional decision support model and demonstrating implementation across a case study in South West England. We find that it is possible to use easily accessible and open-source data to provide initial indications of SuDS opportunities and that this early visibility in the decision-making process can be used to support the consideration of novel and effective surface water management strategies. [ABSTRACT FROM AUTHOR]