Assessing Pluvial Flooding Risk in Urban Areas with High Spatial Heterogeneity Using a Fused Physically-Based and Data-Driven Framework

Climate resilient development must be risk-informed to protect citizens, homes, and infrastructure from climate risks. Especially, urban floods underscore the vulnerability of cities and the complex challenges in managing growth and development.  Here we present two tools to support flood risk assessment and adaptation planning.  HydroFlows  (developed in the UP2030-HE project) provides modular workflows for standardized and reproducible probabilistic flood risk modelling and assessments based on a cascade of climate, hydrological, hydrodynamic and socio-economic impact models. The tool generates flood hazard and risk maps for various climate and urbanization scenarios. First, a rapid first-order flood risk screening can be performed based on global datasets at any given location, which can be refined further with local data where available. FloodAdapt leverages HydroFlows-generated data to bring the power of flood and impact modelling to a wider group of practitioners, such as policymakers and city staff, enabling them to explore different mitigation and adaptation strategies hands-on through a user-friendly graphical interface. This tool supports the economic and social evaluation of measures such as floodwalls, urban greening, water storage, elevating homes, buyouts, and floodproofing under diverse flood events and future conditions.  The Acari River basin in Rio de Janeiro, a densely populated and flood-prone region, has experienced significant floods, including a major event in January 2024 affecting 78,000 people. These floods caused extensive damage to homes, infrastructure, and public services. Despite ongoing efforts to improve drainage and build protective infrastructure, rapid urbanization and climate-related heavy rains continue to pose challenges. While the city has high-quality data, there is a need for comprehensive flood models to assess and predict flood risks. By combining these local datasets with public global data and our tooling, we were able to analyse how our tools can contribute to more informed, effective flood risk management and support climate resilient development.  

Abstract: Climate change and urbanization have increased the occurrence of natural disasters, including floods, tsunamis and hurricanes. Among these disasters, floods occur with high frequency, impact a large number of people, cause high economic losses, and lead to high toll of deaths. Examples include floods in Indonesia in 2021 and Pakistan in 2022. The flood in Indonesia affected about 1 million people. The flood in Pakistan affected 33 million and killed 1,739 people and costed US$15 billion in economic damage. Flood risk assessment and evacuation are effective mitigation measures to create flood-resilient cities. Previous studies have focused on flood modelling and risk assessment, yet it is recently recognized that optimal evacuation routes are necessary and critical for social adaptation to flood risks. To date, there are limited research on evacuation route optimisation problem.There are two approaches for evacuation route optimisation: namely exact methods and meta-heuristic methods. The exact methods such as linear programming, weighted summation, and mixed integer programming have been widely applied. Nevertheless, meta-heuristic algorithms are gaining attention as flexible, non-problem-specific, and computationally efficient optimisation methods. The principle of Meta-heuristic algorithms is based on simulating the optimisations that occur naturally in biological or physicochemical processes. For example, there is a commonality between an animal herd searching for routes and a population searching for routes in a flood disaster. Commonly applied meta-heuristic algorithms are Genetic Algorithms, Ant Colony Algorithms, Particle Swarm Algorithms, and Sparrow’s Algorithms. This is because these algorithms have simple structures and high adaptability, desirable local and global convergence properties and require few parameters.In this study, the flood in Beijing, China, in late July and early August 2023 will be simulated. The flood claimed at least 33 lives, damaged 209,000 homes and more than 15,000 hectares of cropland and caused 127 thousand people to evacuate. The flood extent, water depth and flow velocity will be obtained from a two-dimensional hydrodynamic flood model. The flood risk for pedestrians or vehicles will be estimated with the hydrodynamic model result and a mechanic-based stability method. Optimal evacuation routes will be obtained with Genetic Algorithm, Ant Colony Algorithm, Particle Swarm Algorithm, and Sparrow’s Algorithm. The performance of the optimisation algorithms will be compared and evaluated.  This study contributes to the scientific planning of urban flood evacuation routes and provides insight for urban planners and managers to enhance urban resilience.Keywords: Urban floods, evacuation routes, Meta-heuristic algorithms.

Urban flood risk assessments are crucial for climate resilient development, as they help understand and map flood risk and are used for adaptation planning. Especially in cities with urban growth and rapid development it is a challange to have up to date information about flood risk and to use this information to identify and evalute adaptation measures.In this study, we introduce and test two innovative tools which aim to addresses these challanges.HydroFlows, developed under the UP2030-HE project, offers modular workflows for standardized and reproducible probabilistic flood risk assessments. This tool integrates climate, hydrological, hydrodynamic, and socio-economic impact models to generate flood hazard and risk maps for various climate and urbanization scenarios. It allows for rapid first-order flood risk screening using global datasets, which can be refined with local data. The automated workflows enable continuous updates to the flood risk assessment as the city evolves.FloodAdapt builds on HydroFlows-generated data, making flood and impact modeling accessible to a broader range of practitioners, including policymakers and city staff. Through a user-friendly graphical interface, FloodAdapt enables users to explore different mitigation and adaptation strategies, such as floodwalls, urban greening, water storage, elevating homes, buyouts, and floodproofing. This tool supports the economic and social evaluation of these measures under diverse flood events and future conditions.The Acari River basin in Rio de Janeiro, a densely populated and flood-prone area, has experienced significant flooding, including a major event in January 2024 that affected 78,000 people. These floods caused extensive damage to homes, infrastructure, and public services. Despite efforts to improve drainage and build protective infrastructure, rapid urbanization and climate-related heavy rains continue to pose challenges. By combining local datasets with public global data and our tools, we demonstrated how these tools can contribute to more informed and effective flood risk management, ultimately supporting climate resilient development.

Urban flood modelling plays a key role in assessment of flood risk in urban areas by providing detailed information of the flooding process (e.g. location, depth and velocity of flooding). Accurate modelling results are the basis of reliable flood risk evaluation. In this paper, modelling of a flood event in a densely urbanized area within the city of Glasgow is presented. Modelling is performed using a new three-dimensional (3D) flooding model, which is an unstructured mesh, finite element model that solves the Navier-Stokes equations, and developed based on Fluidity. The terrain data considered comes from a 2 m Light Detection and Ranging (LiDAR) Digital Terrain Model (DTM) and aerial imagery. The model is validated with flood inundation area and flow features, and sensitivity analyses are conducted to identify the mesh resolution required for accuracy purposes and the effect of the uncertainty in the inflow discharge. Good agreement has been achieved when comparing the results with those published in other 2D shallow water models in ponded areas. However, larger vertical velocity (>0.2 m/s) and larger differences between the 3D and 2D models can be observed in areas with greater topographic gradients (>3 %). Finally, performance of the proposed 3D flooding model has been analysed. Through the modelling of a real flooding event this paper helps illustrate the case that 3D modelling techniques are promising to improve accuracy and obtain more detailed information related to urban flooding dynamics, which is useful in urban flood control planning and risk management. To the best of our knowledge, this is the first paper to apply a 3D unstructured mesh finite-element model (FEM model) to a real urban flooding event. It highlights some of the differences between the 3D and 2D urban flood modelling results.

Diurnal changes within communities can significantly alter the level of impacts during a flood, yet these essential daily variations are not currently catered for within flood risk assessments. This paper develops a flood vulnerability and risk model that captures crucial features of flood vulnerability; integrating physical and socio‐economic vulnerability data, combined with a flood hazard analysis, to give overall flood risk at neighbourhood scale, at two different times of day, for floods of different magnitudes. The flood vulnerability and risk model, the resulting diurnal coastal flood vulnerability and risk indexes, and corresponding maps for the ward of Hilsea (Portsmouth, United Kingdom), presented within this paper, highlight three previously unidentified neighbourhoods in particular in the northwest of the Hilsea ward, which have the highest levels of risk during both time zones and for flood events of different magnitude. Critically, these neighbourhoods lie further inland and not directly on the Hilsea coastline, yet by analysing at this resolution (including diurnal impacts), substantial levels of underlying vulnerability were identified within these areas.

Towards flood risk reduction: Commonalities and differences between urban flood resilience and risk based on a case study in the Pearl River Delta

Despite traditional measures to prevent disasters, climate change and urbanization increase flood risk. Thus, flood resilience has attracted increased global concern. Understanding the commonalities and differences between flood resilience and risk is arguably important for flood risk reduction. However, these factors have been seldom reported in previous studies, and discussions on the role of flood resilience in flood risk analysis, assessment, and management are lacking. In this study, the association between flood resilience and risk is discussed using a case study in the Pearl River Delta. Flood resilience is quantified using a pressure-state-response (PSR) model, while flood risk is assessed based on the hazard-vulnerability framework and the extension catastrophe progression method. The implications of considering flood resilience in flood risk analysis, assessment, and management are proposed. The results suggest that the overall flood resilience (risk) in the study area is greater (lower) than that in the highly urbanized areas, and areas with low (high) flood resilience (risk) are mainly concentrated within the highly urbanized areas. Indices extracted from human society and highly related to human activities have the same attributes in both frameworks, while indices associated with climate and geography contribute to the two con- cepts differently. Flood resilience supplements the concept of flood risk, and can be incorporated into risk assessment as an index. Moreover, pre-disruption (post-disaster) measures should follow flood risk (resilience) assessment, and strategies that foster flood resilience should be included in flood risk management. This study provides references for flood resilience improvement and risk mitigation.

This study is concerned with flood risk that can be assessed by integrating GIS, hydraulic modelling and required field information. A critical point in flood risk assessment is that while flood hazard is the same for a given area in terms of intensity, the risk could be different depending on a set of conditions (flood vulnerability). Clearly, risk is a function of hazard and vulnerability. This study aims to introducing a new approach of assessing flood risk, which successfully addresses this above-mentioned critical issue. The flood risk was assessed from flood hazard and vulnerability indices. Two-dimensional flood flow simulation was performed with Delft3D model to compute floodplain inundation depths for hazard assessment. For the purpose of flood vulnerability assessment, elements at risk and flood damage functions were identified and assessed, respectively. Then, finally flood risk was assessed first by combining replacement values assessed for the elements and then using the depth–damage function. Applying this approach, the study finds that areas with different levels of flood risk do not always increase with the increase in return period of flood. However, inundated areas with different levels of flood depth always increase with the increase in return period of flood. The approach for flood risk assessment adopted in this study successfully addresses the critical point in flood risk study, where flood risk can be varied even after there is no change in flood hazard intensity.

Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management.

Floods driven by extreme precipitation events pose significant risks to communities, infrastructure, and ecosystems, particularly in regions with complex topography, such as Nepal. Probable Maximum Precipitation (PMP) is a critical parameter for assessing flood risk and designing resilient infrastructure. This review examines the methodologies and findings of a study focused on flood risk assessment in the West Rapti River Basin, Nepal, with emphasis on PMP and Probable Maximum Flood (PMF). This study employs hydrometeorological and statistical methods to estimate PMP, utilizes Snyder’s unit hydrograph for PMF calculation, and applies HEC-RAS 2D modeling to map flood hazards, vulnerabilities, and risks. Key findings include PMP estimates of 507 mm and 575 mm, a PMF of 11,211.1 m³/s, and detailed flood risk maps highlighting significant inundation risks in Deukhuri Valley. This review synthesizes the contributions of this study, compares its approaches to global practices, and discusses its implications for flood risk management in Nepal.The study on flood risk assessment in the West Rapti River Basin, Nepal, employs a comprehensive approach to evaluate and map flood hazards, vulnerabilities, and risks. By combining hydrometeorological and statistical methods to estimate Probable Maximum Precipitation (PMP), the research provides crucial insights into extreme precipitation events in the region. The use of Snyder's unit hydrograph for Probable Maximum Flood (PMF) calculation and HEC-RAS 2D modeling for flood hazard mapping demonstrates a robust methodology for assessing flood risks in complex topographical areas. The findings of this study have significant implications for flood risk management in Nepal and similar regions. The PMP estimates of 507 mm and 575 mm, along with the calculated PMF of 11,211.1 m³/s, provide valuable data for designing resilient infrastructure and implementing effective flood mitigation strategies. The detailed flood risk maps highlighting substantial inundation risks in Deukhuri Valley offer critical information for local authorities, urban planners, and policymakers to develop targeted interventions and improve community preparedness. This research contributes to the broader understanding of flood risk assessment methodologies and their application in regions with challenging topography, potentially informing similar studies in other flood-prone areas worldwide. Keywords: Flood risk assessment, West Rapti River Basin, Nepal, Probable Maximum Precipitation (PMP), Snyder's unit hydrograph, Probable Maximum Flood (PMF), HEC-RAS 2D modeling, Flood hazard mapping, Deukhuri Valley, Flood mitigation strategies, Hydrometeorological methods, Statistical analysis, Extreme precipitation events, Flood risk management, Inundation risks

A probabilistic assessment of urban flood risk and impacts of future climate change

A comparative analysis on flood risk assessment and management performances between Beijing and Munich

A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood

Urban flood risk assessment under rapid urbanization in Zhengzhou City, China

In urban environments, urban flooding can lead to significant economic losses due to high population density and valuable economic properties. The complexity of urban flood disasters and the diverse entities affected present substantial challenges for accurate flood risk assessment. In response to this critical need, we have developed a comprehensive urban flood risk assessment method that evaluates the flood risk for primary affected entities, including residents' lives, ground buildings, and underground spaces. This proposed assessment method is based on both scenario analysis and the index system method. Initially, it predicts the disaster-causing hydraulic characteristics, such as water depth, flow velocity, and building inundation, using a high-performance 1D/2D coupled urban flood hydrodynamic model. Subsequently, it assesses the flood risk of disaster-affected objects, such as people, ground buildings, and underground spaces, based on hazard, vulnerability, and exposure indices. We successfully applied this comprehensive urban flood risk assessment method to a highly developed urban area in Wuhan City, China. To address the challenge of data acquisition, we utilized web crawling to gather information on industrial distribution, property prices, and shop rents to support flood risk analysis. The flooding process and corresponding risk levels of primarily affected objects under different rainfall return period scenarios were comprehensively evaluated. The established model can serve as a reference for disaster prevention and reduction technologies for other cities threatened by urban flood disasters.