Urban Flood Research Articles

Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism

ABSTRACT Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency.

Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation

Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation

Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty

Reducing the impact of artificial neural networks (ANN) affected by sources of uncertainty is crucial to improving the reliability of the flood prediction model. This study proposes an ensemble artificial neural network (EANN) model to predict the degree of flooding in coastal cities. Combined methods are used to reduce the model’s uncertainty, heuristic neural pathway strength feature selection is used to select inputs, the coupling method is used to optimize network architecture and parameters, and the integration method which paralleling three ANN models with different predicted lead periods ensemble together is used to capture output uncertainty. The EANN model has successfully predicted flooding in the Chinese coastal city of Macao during a typhoon, with convincing accuracy. The study also analyzed the impacts of both long and short training datasets with appropriate time intervals on ANN modeling performance. It was found that the performance of short training datasets, with appropriate time intervals, was similar to or better than models with long training datasets.

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Monitoring spatiotemporal changes in urban flood vulnerability of Peninsular Malaysia from satellite nighttime light data

Urban flood vulnerability monitoring requires a large amount of socioeconomic and environmental data collected at regular time intervals. However, collecting such a large volume of data poses a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region as floods are the most devastating and recurrent phenomena in the region. The study developed a flood vulnerability index (FVI) based on socioeconomic and environmental data from a single year. This FVI was then linked to NTL data using an Adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000–2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient, and percentage of foreign nationals. The ANFIS technique performed well in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ∼ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

Predicting real-time roadway pluvial flood risk: A hybrid machine learning approach coupling a graph-based flood spreading model, historical vulnerabilities, and Waze data

Urban pluvial flash flooding (PFF), driven by extreme weather and urban expansion, introduces complex challenges that arise from the dynamic interaction of rainfall hazard, road vulnerability, and traffic exposure. These three critical components must be interconnected to provide a comprehensive prediction of roadway PFF risk. Our integrated approach combines historical data and real-time Waze flood alerts using a simplified physics-based PFF model and hybrid machine learning methods to predict flash flooding risk at the road segment scale. In a Dallas case study with four intersections, we trained multiple models with data from 15 storms and tested on 5 storms. The XGBoost method excels in test precision, while a Random Forest model offers better recall, and both methods outperform Support Vector Machines (SVM). The choice between models depends on factors such as negative class (prediction of unflooded areas) uncertainty and false positive cost (i.e., predicting no flooding incorrectly). For the case study, our approach could boost flood awareness, enhance safety, and improve urban flood management by correctly predicting 73% of risk observations during the test storm events.

Green infrastructure layout based on a dynamic operation feature of drainage systems.

Increasingly frequent urban floods strain the traditional grey infrastructure, overwhelming the capacity of drainage networks and causing challenges in managing stormwater. The heavy precipitation leads to flooding and damage to drainage systems. Consequently, efficient mitigation strategies for flooding have been researched deeply. Green infrastructure (GI) has proved to be effective in responding the increasing risk of flood and alleviate pressure on drainage systems. However, as the primary infrastructure of stormwater management, there is still a lack of attention to the dynamic operation feature of urban sewer systems during precipitation events. To fill this gap, we proposed a novel approach that integrates hydraulic characteristics and the topological structure of a sewer network system. This approach aims to identify influential nodes, which contribute to the connectivity of the sewer network amidst dynamic changes in inflow during precipitation events. Furthermore, we adopted rain barrels to serve as exemplars of GI, and 14 GI layout schemes are produced based on the different ranks of influential nodes. Implementing GI measures on both poorly performing and well-performing nodes can yield distinct benefits in mitigating node flooding. This approach provides a new perspective for stormwater management, establishing effective synergy between GI and the drainage system.

Urban flood modeling with a novel coupling method of surface and sewer hydrodynamic processes.

Drainage modeling that accurately captures urban storm inundation serves as the foundation for flood warning and drainage scheduling. In this paper, we proposed a novel coupling ideology that, by integrating 2D-1D and 1D-2D unidirectional processes, overcomes the drawback of the conventional unidirectional coupling approach that fails to properly represent the rainfall surface catchment dynamics, and provides more coherent hydrological implications compared to the bidirectional coupling concept. This paper first referred to a laboratory experimental case from the literature, applied and analyzed the coupling scheme proposed in this paper and the bidirectional coupling scheme that has been widely studied in recent years, compared the two coupling solutions in terms of the resulting accuracy and applicability, and discussed their respective strengths and weaknesses to validate the reliability of the proposed method. The verified proposed coupling scheme was then applied to the modeling of a real drainage system in a region of Nanjing, China, and the results proved that the coupling mechanism proposed in this study is of practical application value.

Sponge City Development from the Perspective of Sustainability: A Case Study of China

Traditional urban construction methods often result in the inability of rainwater to penetrate and store effectively, increasing the risk of urban flooding disasters. At the same time, due to the rapid development of urbanization, the problem of water shortage has become increasingly prominent. The sponge city policy aims to transform the urban construction mode, make full use of natural resources and ecological means, realize the natural accumulation, infiltration and purification of rainwater, and improve the city's absorption, storage and release capacity of rainwater, so as to reduce urban flood disasters and alleviate the shortage of water resources. This paper takes China as an example to explore the construction of a sponge city from the perspective of sustainable development. This paper analyzes the current situation and existing problems of sponge cities development in China, and puts forward the development prospect of PPP model, in order to promote sponge cities construction and sustainable urban development.

Assessment of urban underground spaces inundation during extreme rainfall events.

As urbanization progresses and the impacts of climate change become more pronounced, urban flooding has emerged as a critical challenge for resilient cities, particularly concerning urban underground spaces where flooding can lead to significant loss of life and property. Drawing upon a comprehensive review of global research on underground space flood simulation and evacuation, this paper undertakes the modelling of inundation in a substantial underground area during the extraordinary rainfall event on 7 September 2023, in Shenzhen, China. Specifically, it introduces a two-step method to simulate the coupled surface-underground inundation process with high accuracy. The study simulates the inflow processes in three types of underground spaces: parking lots, metro stations, and underpasses. Utilizing the specific force per unit width evaluation, the research examines how varying flood barrier heights influence evacuation time and inundation risk. Subsequently, the paper proposes corresponding evacuation strategies based on the obtained findings. By highlighting the vulnerability of urban underground spaces to flooding, the study underscores the urgent need for further research in this domain.

Multi-Criteria Analysis for Assessing Flood Susceptibility in the Municipality of Conselheiro Lafaiete (MG)

Objective: To evaluate the susceptibility to flooding in the municipality of Conselheiro Lafaiete – Minas Gerais, using the Hierarchical Analytical Process method. Theoretical Framework: Floods are events that affect several Brazilian cities, where the main related factors are denoted by the morphometric characteristics of the river basin(s) that comprise them, associated with slope, hypsometry, soil typology and use and land occupation. In this sense, multi-criteria analyzes are fundamental for mapping areas of susceptibility to flooding in river basins. Method: The study proceeded by characterizing the study area, integrating the diagnosis of the drainage system, morphometric analysis of the river basin in which Conselheiro Lafaiete is integrated, and the application of the Hierarchical Analysis Method (AHP) to map areas susceptibility to flooding of the river basin. Results and Discussion: The results obtained revealed [synthesize the main results of the research]. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The study indicates gaps in Conselheiro Lafaiete PMSB with regard to urban drainage and points out, through mapping, the areas of the municipality most susceptible to the occurrence of flood events. Originality/Value: The mapping of areas susceptible to flooding carried out for Conselheiro Lafaiete considered a multi-criteria approach, integrating anthropogenic factors. The product can be applied as a basic tool for public management regarding urban drainage and flood risk management in the municipality.

Adapting a systems perspective for sectoral coordination: approaching flood resilience in Houston and Accra

ABSTRACT Increasing resilience to flooding is a complex process that requires horizontal and vertical coordination between institutions in policy making and implementation. This paper explores the effect of institutional coordination on managing flood risk in two cities plagued by flooding. Our results show that efforts on building urban flood resilience can be undermined by lack of proper coordination between urban development, water management and land use planning. We find that this complexity is magnified by the emergence of the concept of resilience as an urban development goal that is increasingly pursued by various authorities, but that is inherently contested in practice.

Characteristics and risk management of urban surface flooding in Guangzhou, China: Insights from 2022 ground monitoring

Study RegionGuangzhou, ChinaStudy FocusOur research, based on the meticulous collection and rigorous statistical analysis of ground monitoring records for the entire year of 2022 in Guangzhou, delves into the spatio-temporal distribution patterns, evolution processes, flood formation mechanisms, and corresponding disaster mitigation strategies of urban surface flooding related to the capacity of drainage system in the region. New Hydrological InsightsThe urban surface flooding (USF) in Guangzhou in 2022 provides key insights for flood risk management. The overall spatial distribution of USF risk in Guangzhou is stable, but specific flood locations during rainstorms vary due to factors such as rainfall conditions and drainage efficiency. This complexity highlights the diverse nature of USF challenges. The frequency of USF events varies significantly across months and dates, with a higher occurrence during the flood season. However, there is a noticeable extension of flood risk into the non-flood season, challenging traditional flood season concepts. USFs peak within 50 minutes, demanding urgent action within 25 minutes, underscoring the need for an efficient emergency response. Certain areas, times, and processes contribute more to the overall flood risk, emphasizing the importance of focusing on these high-risk factors for effective management and resource allocation. The study also reveals the prevalence of Surface Runoff-type Floods, challenging existing USF simulation methods and necessitating a reevaluation and improvement in the maintenance and design of urban rainwater inlets. The study suggests vigorously promoting private proactive flood mitigation behavior and proposes a hierarchical, classification-based approach to urban flood risk control. Comprehensive intervention in the urban runoff formation process is emphasized for effective flood risk management.

(Re)envisioning inclusive futures: Applying narrative foresight to deconstruct the problem of urban flooding in the slums of Bengaluru, India

AbstractMotivationRecurring urban flooding in Bengaluru, India, has brought multiple intersecting development challenges to the forefront. While climate change is a catalyst for flooding, rapid urbanisation has aggravated the problem by neglecting its ecological history. Repeated floods have particularly affected migrants living in the slums, further worsening their already vulnerable conditions. Currently, only about 40% of slums are formally recognized by city authorities, leaving most slum dwellers with limited access to public benefits and basic infrastructure. Although the city offers piecemeal solutions, it currently lacks foresight for long‐term planning that includes marginalized voices.PurposeWe explore the multiple and intertwined development challenges faced by Bengaluru city, attempting to frame them from the perspective of migrant slum dwellers experiencing flooding. We try to bring to the forefront the everyday risks and vulnerabilities of the marginalized populations in Indian cities, which have received limited attention both in research and policy.The results of this exercise are intended to create sustainable collaborative processes to inform future decisions, particularly addressing the problem of urban flooding.Methods and approachOur proposed methodology integrates climate risk assessment—urban flood modelling and exposure mapping of slums across the city—with vulnerability assessments at the household level including analysis of life histories to capture the relative vulnerabilities of slum dwellers and the slums in which they live.FindingsWe deconstruct urban flooding, particularly from the perspective of migrant slum dwellers to identify some critical challenges, especially that of recognition, to foresight thinking. By incorporating marginalized voices, our methods aim to be inclusive and contextually relevant, while considering intersectional variations among those marginalized.A mixed‐methods approach allows climate risk assessment to be augmented by life histories of vulnerable slum populations to collaboratively reimagine a more inclusive future.Policy implicationsTo make policy more inclusive, more participatory processes are needed. The proposed methods will contextualize everyday vulnerabilities and risks of migrant slum dwellers to bring these perspectives into conventional climate risk assessment. Thus, a more inclusive future with lower impacts from urban floods can be envisaged.

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

Sustainable Rainwater Management With the Use of Compensatory Urban Drainage Techniques: Case Study on The Maracanã Campus of the University of the State of Rio De Janeiro (UERJ)

Objective: The objective of this study is to evaluate the hydrological impact of using different compensatory techniques on the conventional urban drainage system in an intramural contribution basin to the Maracanã Campus of the State University of Rio de Janeiro (UERJ), located in the Maracanã neighborhood, in the city from Rio de Janeiro-RJ. Theoretical Framework: A bibliographical review was prepared with an approach to the urbanization process associated with urban drainage infrastructures, as well as the use of compensatory techniques to control urban flooding. Method: Rainfall and hydrological data, demand and consumption of non-potable water in the building, sizing of the microdrainage network and compensatory techniques and calculation of the runoff buffered by the techniques were obtained. Results and Discussion: The results obtained revealed that the hydrological impact of using compensatory techniques designed on the drainage network designed in the Maracanã Campus basin, which discharges a flow of 2,041.66 Lts/s, would have a reduction in the flow of around 19%, using only around 14% of the Campus area is a contribution area for techniques. Research Implications: The research brings a scientific contribution based on the analysis of the implementation of compensatory techniques on the microdrainage network as an alternative to be evaluated by municipal managers as a solution to be integrated into the infrastructure development of cities, in order to make them more resilient in terms of to intense rainfall events. Originality/Value: The study showed, based on the results obtained, the efficiency in the use of compensatory urban drainage techniques, with discussion in the scientific community to develop further studies in the context of water resources management.

Urban flood drifters (UFD): Identification, classification and characterisation

Urban flood drifters (UFD): Identification, classification and characterisation

Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation

Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation

Mitigating urban flood Hazards: Hybrid strategy of structural measures

Effective reduction of urban flood risk can be accomplished through structural mitigation measures such as underground drainage tunnels and implementing drainage practices, such as green infrastructure (GI) techniques. In this study, we employed an urban runoff model to quantitatively evaluate the impact of these flood mitigation measures and GI technology in reducing urban flood vulnerability (UFV). Specifically, we assessed the flood control capabilities of underground drainage tunnels and detention reservoirs, among others, exploring various combinations to formulate a comprehensive flood prevention plan (CFPP) for the studied basin. Additionally, our study analyzes the effectiveness of GI technologies, such as impervious area reduction (IAR) and ground retention (GR) and introduces a method for assessing their impact on flood-prone areas. To enhance flood mitigation strategies, we propose a hybrid approach that combines CFPP with GI techniques. The effectiveness of this hybrid strategy was assessed through comparisons with a basic scenario and multiple GI technology scenarios. As a result of applying the hybrid strategy proposed in this study, the reduction rates of peak flow (PF), flood volume (FV), and flooded area (FA) in this basin were up to 37.3 %, 71.5 %, and 93.6 %, respectively. These findings offer valuable insights for designing strategies to enhance urban flood resilience in the face of a changing climate.

Resilient Urban Flood Management: A Multi-Objective Assessment of Mitigation Strategies

This study employs a comprehensive multi-objective efficiency index (EI) to assess urban flood mitigation strategies. The EI enables the simple interpretation of a mitigation strategy’s efficiency with a value range between −1 (low efficiency) and 1 (high efficiency), which represents a practical communication tool for decision makers, engineers, and researchers. This was tested at the study site of Feldbach (Austria) with an integrated 1D–2D urban flood model and a distributed hydrological model. A total of 112 scenarios were analysed for six mitigation strategies, which were built from three future challenge scenarios, two observed heavy storm events, and two hydrological pre-conditions. For the given study site, the analysis identifies mitigation strategies implemented in rural boundary areas as the most effective. A novel aspect of this study is the consideration of the urban water balance change, highlighting its impact on the EI. The analysis highlights the importance of analysing each relevant process separately to determine the EI in order to understand why a mitigation strategy is more or less efficient.