Urban Pluvial Flooding Research Articles

Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence

Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.

A Systematic Literature Review on the Operationalization of Usability in Urban Pluvial Flooding Climate Services and the Influence of Contextual Factors

ABSTRACTExtreme weather impacts due to climate change, such as urban pluvial flooding, necessitate adaptive measures. To support decision making in climate adaptation, climate services provide information and guidance to policy makers and other stakeholders. Effective use of climate services is prohibited by usability gaps. Some causes for these gaps originate from contextual factors. However, these factors are not well understood within academic literature. Therefore, our systematic review has two research questions: (1) What dimensions of climate service usability are considered in the design and evaluation of their use and how are these operationalized?; and (2) What contextual influences may affect these identified dimensions? To this end, we analyzed 51 articles that cover one or more urban pluvial flooding climate services. Our results show several overarching categories of usability dimensions (functionality, ease of use, reducing time and effort, reliability, matching user needs, and other). However, these dimensions are not always fully operationalized by authors. Furthermore, our results show that most contextual factors originate from the meso level (within or between organizations), and in lesser extent the macro (e.g., national policies) and micro level (individual users). Here especially organizational (e.g., Does the climate service fit organizational work approaches?); and process factors (e.g., Was the climate service co‐produced?) have been shown to affect usability. We conclude that more attention should be given to usability as a concept within the design and evaluation of climate services well as to the influence of contextual influences.

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.

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Ecological Flood Resilience Index (EFRI) to Assess the Urban Pluvial Flood Resilience of Blue-Green infrastructure: A case from a southwestern coastal city of India

Urban pluvial flood resilience planning must account for the ecological capacity to absorb, adapt, and transform with floods. Research on flood resilience indices highlights resilience as a normative condition, characterized by the return to equilibrium or the recovery time. However, the socio-ecological approach, including adaptive, absorptive, and transformative capacities is often overlooked, and the determinants of these capacities are underexplored. The present study addresses this gap by proposing an impact-based composite index to measure the flood resilience of urban Blue-Green Infrastructure (BGI). An eight-step procedure was used for the composite index construction. Statistical correlation and regression analysis established indicator appropriateness, and Principal Component Analysis (PCA) was used for weighing and aggregation. The Ecological Flood Resilience Index (EFRI) was developed and applied to the city of Kochi, India to assess the ability of existing urban BGI to temporarily store, infiltrate, and delay runoff, indicating the capacity of the system to withstand urban flooding. The index was validated using crowd-based data followed by sensitivity analysis. The EFRI Map outlines that the highly urbanized low resilient central areas, marked by poor vegetation and disrupted natural drainage contribute to flooding in various parts of the city. A comprehensive analysis of the 37 % regions of the city that falls within ‘very low’ to ‘extremely low’ resilience is essential to understand the critical factors in flood resilient spatial planning. The proposed composite index is significant for its novelty and theoretical clarity in resilience evaluations, promoting a shift from risk-based to resilience approaches in urban flood management.

Exploring the network structure of coupled green-grey infrastructure to enhance urban pluvial flood resilience: A scenario-based approach focusing on ‘centralized’ and ‘decentralized’ structures

Urban pluvial floods pose a significant risk to cities, occurring when precipitation exceeds the carrying capacity of the urban drainage network. Coupled green-grey infrastructure has emerged as a sustainable solution for mitigating urban pluvial floods. This study aims to explore best practices in the network configuration of urban drainage systems coupled with low-impact development (LID) to enhance flow distribution and stormwater infiltration. To do so, we focused on two competing key concepts in network analysis: (1) Centralization and (2) Decentralization. We integrated a one-dimensional stormwater model with a rapid flood spreading model to assess the flood mitigation performance of various centralized and decentralized network configurations in the Gangnam region of Seoul, South Korea. To further assess the combined effects of green and grey infrastructure, we compared the performance of each drainage network configuration with and without identical mixed LID practices. Here we show that the centralized drainage network scenario performed best in reducing flood volume by 40.3%, the decentralized drainage network scenario performed best in shortening flood duration by 47.8%, and the LID practices scenario performed best in mitigating peak flooding rates by 4.2%, each as independent scenarios. When all three scenarios were coupled together, flood volume could be reduced by 73.5%, flood duration by 54.7%, and peak flooding rates by 19.8% in the study area. This exploratory study underscores the potential of network analysis in urban flood research, particularly the effectiveness of loosely-connected network topology. Our findings contribute to the development of best practices for coupled green-grey infrastructure, facilitating sustainable stormwater management and urban flood resilience.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability

Urban pluvial flood susceptibility mapping aims to identify flood-prone areas in cities. Machine learning methods are increasingly used, but typically face a trade-off between accurate identification of flood-susceptible areas and development of effective local mitigation strategies. To synchronously enhance fitting capability and explainability, this study introduced a novel machine learning model, Residual-Logistic Regression (RES-LR). RES-LR is developed by integrating Artificial Neural Network (ANN) and Logistic Regression (LR). The model adopts ANN to form its explainability module, which increases model complexity to ensuring the model’s fitting ability. Meanwhile, the explainability module was incorporated into the LR structure through residual connections to provide parametric expressions for local feature contributions, enabling the identification of key flood conditioning factor by site. The model was applied to the Beijing Metropolitan Area, achieving an accuracy rate exceeding 80% in identifying flooded sites. Based on its local explainability, the model unveiled the diverse flood formation mechanism in the study area and the complex impact of drainage-related factors. These results were informative for formulating site-specific flood mitigation measure. This study represents a significant advancement in the application of explainable machine learning for systematic flood management.

Multifunctional Porous Pavement Prototype for Urban Pluvial Flood Protection: Preliminarily Findings on Contribution of Attitudes to Acceptance Willingness Toward Proposed Scientific and Engineering Solutions

Rapid urbanization and climate change correlate with an increase in frequency of flood events, globally. For many cities worldwide pluvial floods bring significant risk. Permeable/pervious paving (PePav), as an essential sustainable urban drainage technique, is one of the key environmental solutions for urban flooding. Waste and recycled materials in the construction industry for PePav production comply with the principles of circular economy and sustainable development. The influence of the human factor is well recognized in efforts to apply those solutions and make them self-sustainable. The preliminary results show that younger female students and students with lower family monthly income are prone to express more positive attitudes toward PePav generally. Students from different study groups (Departments of Psychology, Hydraulic and Environmental Engineering, and Construction Project Management) show significantly different attitudes toward PePav. Statistical models indicate that the PePav acceptance willingness can be predicted with moderate accuracy by knowing attitudes toward PePav and personal experience in construction. The contributions of the fact that someone's close person has been affected by a flood may vary depending on the type of PePav scientific and engineering solutions.

Alleviating urban pluvial floods via dual-use water plazas orchestrated by predictive algorithms

Old urban areas, characterized by complex land use and inadequate infrastructure, exhibit high vulnerability to precipitation extremes. The integration of dual-use public infrastructure stands at the forefront of planning initiatives to enhance urban flood resilience. This study introduces a pioneering Smart Water Plaza (SWP) system, orchestrated by predictive algorithms, to mitigate pluvial floods. A high-risk old neighborhood (4.5 km2) in Changsha, China, is selected as a case for empirical analysis. Two types of SWPs—in-situ detention, and runoff-receiving units—are initially established based on spatial mapping and potential analysis. The core feature of the SWP system is a multi-horizon 2D-1D model predictive control (MPC), which directly verifies the suitability of terrain configurations and evaluates temporal efficiency. The study plots the performance curves of the SWPs under various design rainfalls and demonstrates flood dynamics using an actual torrential event. The findings indicate that SWPs’ resilience contribution index for the stormwater system ranges from 9 % to 38 %. Furthermore, SWPs covering only 5 % of the site lead to an obvious decrease in flooded areas, with reductions ranging from 46 % to 48 %. These outcomes highlight the disaster mitigation potential inherent in multifunctional public spaces for Sponge City transformation and urban renewal efforts.

Interaction between formal and informal actors in the shadow of policymaking: Case studies of community-based urban pluvial flood risk management in Pearl River Delta cities

Policymakers increasingly integrate urban pluvial flood risk management into multiple strategies, acknowledging that local contexts and the actors involved are crucial. However, the literature on the decision-making processes of community-based pluvial flood risk management sheds little light on how local formal and informal actors relate to each other. This paper contributes to filling this research gap by exploring the interdependency between local authorities and community residents from a multilevel governance perspective. Two cases, based in Pearl River Delta Cities, are analysed to explore actors' interactions locally in the Chinese Sponge City Program, a national programme for urban pluvial flood risk management. The comparative study of the two cases leads to four conclusions. First, the Sponge City Program at the local level can be viewed as multilevel governance. Second, triggered by the goal set by the national government and the local contexts, local authorities see the benefit of locally integrating the Sponge City Program into an integrated and area-specific plan, emphasizing the importance of institutional assertiveness. Third, tensions and synergies exist in the interaction process between local authorities and residents that will have to be recognized and embraced and, where necessary, converted from constraining to enabling factors. Fourth, institutional barriers still dominate locally in the Sponge City Program because of a lack of support for cross-boundary cooperation and public participation. To span these boundaries, policymakers should be more open to changing the perspective from ‘planning for people’ to ‘planning with people’.

Smart roofs featuring predictive control: An upgrade for mitigating precipitation extreme-induced pluvial floods

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%–50% and eliminated 60%–100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies.

Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era

The recent advancement in digital twin technology, which creates virtual replicas of real-world processes, offers an interactive testbed for understanding and predicting environmental changes. As pluvial flood damage escalates globally in urban areas, there remains a gap in understanding the most effective collaboration between governments and local residents for sustainable flood risk management. To address this gap, we develop a participatory framework for urban pluvial flood modeling, incorporating open source software, virtual reality, minimum viable product, and gamification components. This framework engages citizens in every phase of the participatory modeling process, from input data preparation, through hydrological model construction, to model verification, and experiments. We present a case study on the recurring pluvial flood damages in South Korea's Gangnam region, demonstrating the practical implications of an interactive, web-based crowdsourcing platform to leverage community engagement and local knowledge. The results underscore the evidence of a proactive role for citizens, not merely as recipients of disaster information but as key contributors collaborating with a range of stakeholders in stormwater management and modeling. Combining digital twin technology with citizen participation can empower informed decision-making and collective actions in the evolving digital era, leading to a disaster-resilient community.

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Exploring optimal deep tunnel sewer systems to enhance urban pluvial flood resilience in the gangnam region, South Korea

Urban pluvial flooding is becoming a global concern, exacerbated by urbanization and climate change, especially in rapidly developing areas where existing sewer systems lag behind growth. In order to minimize a system's functional failures during extreme rainfalls, localized engineering solutions are required for urban areas chronically suffering from pluvial floods. This study critically evaluates the Deep Tunnel Sewer System (DTSS) as a robust grey infrastructure solution for enhancing urban flood resilience, with a case study in the Gangnam region of Seoul, South Korea. To do so, we integrated a one-dimensional sewer model with a rapid flood spreading model to identify optimal routes and conduit diameters for the DTSS, focusing on four flood-related metrics: the total flood volume, the flood duration, the peak flooding rate, and the number of flooded nodes. Results indicate that, had the DTSS been in place, it could have reduced historical flood volumes over the last decade by 50.1–99.3%, depending on the DTSS route. Regarding the conduit diameter, an 8 m diameter was found to be optimal for minimizing all flood-related metrics. Our research also developed the Intensity-Duration-Frequency (IDF) surfaces in three dimensions, providing a correlation between simulated flood-related metrics and design rainfall characteristics to distinguish the effect of DTSS on flood risk reduction. Our findings demonstrate how highly engineered solutions can enhance urban flood resilience, but they may still face challenges during extreme heavy rainfalls with a 80-year frequency or above. This study contributes to rational decision-making and emergency management in the face of increasing urban pluvial flood risks.

Fast urban inundation simulation with RIM2D for flood risk assessment and forecasting

IntroductionUrban pluvial flooding is a growing concern worldwide as consequence of rising urban population and climate change induced increases in heavy rainfall. Easy-to-implement and fast simulation tools are needed to cope with this challenge.MethodsThis study describes the development of the parsimonious, GPU-accelerated hydraulic model RIM2D for urban pluvial flood simulations. This is achieved by considering the built-up urban area as flow obstacles, and by introducing capacity-based approaches to consider urban drainage by infiltration on pervious surfaces and sewer drainage from roofs and sealed surfaces. The model performance was analyzed by simulating 8 heavy rainfall events in a test area in the city of Dresden, Germany. For these events detailed discharge measurements of sewer discharge are available, providing a unique dataset for evaluating the sewer drainage simulation, which is of high importance for realistic pluvial inundation simulations in urban areas.Results and discussionWe show that the model simulates the temporal dynamics of the sewer discharge and the sewer volume within acceptable ranges. Moreover, the erratic variation of the simulated to measured sewer discharge suggests that the deviations from the measurements are caused by the precipitation input rather than the model simplifications. We conclude that RIM2D is a valid tool for urban inundation simulation. Its short simulation runtimes allow probabilistic flood risk assessments and operational flood forecasts.

Urban pluvial flood modelling in the absence of sewer drainage network data: A physics-based approach

1D/2D dual drainage models have become one of the most useful tools in the study of urban pluvial floods. However, such models require information about the sewer network that is not always readily available. In this study we present a physics-based approach for assessing urban pluvial floods, with the aim of overcoming the common situation of having limited information on the sewer system. The method proposed involves the use of available open-access information within a virtual sewer network generation tool. This realistic approach allows for the implementation of 1D/2D dual drainage models, such as the recently developed Iber-SWMM model. Results obtained from four storm events using a virtual sewer network in Iber-SWMM were compared with those derived using data from the actual sewer network in a coastal town located in NW Spain. These results revealed that the proposed approach can reasonably represent the sewer network's drainage capacity during pluvial floods, especially compared to other simplified approaches, like the rainfall reduction method. The proposed approach also accounts for the sewer network transport capacity and the impact of overflows when a sewer’s capacity is exceeded. Hence the study confirms the significant effect of these processes on the magnitude of pluvial flooding in urban areas. The methodology is shown to be robust, and can be applied to any urban settlement in which no proper record of the sewer network is available.

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

Compounding effects of changing sea level and rainfall regimes on pluvial flooding in New York City

AbstractCoastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC’s assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC’s drainage models focusing on rainfall-induced flooding. We showed that the city’s stormwater system is highly vulnerable to high-intensity short-duration “cloudburst” events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC’s residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city.

Analyzing urban form influence on pluvial flooding via numerical experiments using random slices of actual city data

Diversified urban forms resulting from urbanization is a pivotal factor influencing urban pluvial flooding. A systematic analysis of the influence of urban forms on the surface flow of urban pluvial flooding was conducted. We obtained 1,000 sets of 1 km × 1 km urban form data that met quality control requirements using a random slicing method, based on actual urban underlying surface data, with nine spatial characteristic parameters used to tabulate the urban forms. A two-dimensional hydrodynamic model was employed to simulate the surface flow of the urban flood inundation. Scenario simulations and analyses were conducted for three slopes (1, 2, and 3 %) and three rainfall scenarios (with return periods of 5, 20, and 100 yr). The results showcased that, in different scenarios, the urban forms had some impact on urban flow variables and their processes, although the impact patterns were relatively complex. Regarding the process of urban flow variables, the urban forms affected the rate of decline in flood variables during the descending stage. The correlation analysis highlighted that road density, road area proportion, spread contact among various patch types, and uniformity across patches have important impacts on the maximum flood volume and maximum inundated area. The research results could help deepen our understanding of the formation mechanisms of urban pluvial flooding, flood-sensitive urban design, and flood risk management.