Urban Flood Risk Research Articles

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

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

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

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

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

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

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

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

Navigating the definition of urban flooding: a conceptual and systematic review of the literature

ABSTRACT Urban flooding is a significant global challenge, posing risks to urban planners, policymakers, and communities. This paper reviews the literature to analyze how urban flooding is defined across scientific disciplines. Our objectives are to uncover the elements used to define urban flooding and evaluate how these elements can impact future research and practice. A key difficulty is the lack of a consistent, comprehensive definition that captures both physical and social dimensions of urban flooding. Current definitions often focus solely on physical aspects (e.g., rainfall, infrastructure) or social impacts, rarely integrating both. This fragmentation hinders effective flood risk management and interdisciplinary collaboration. Our contribution is a multifaceted definition incorporating spatial and social concerns, including water origins, built environment characteristics, and local community aspects. We introduce the "Urban Water Transect" concept to illustrate the continuum of flood risk across urban zones, addressing a gap in the literature. The analysis reveals that many papers discuss flooding causes without providing an explicit definition. Urban flooding is predominantly defined based on water source, imperviousness, and drainage infrastructure. Future research should adopt an interdisciplinary perspective considering both physical and social aspects, potentially transforming urban flood risk management.

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

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

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

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

Interregional watershed services: Valuation of the benefits of controlled embankment breaches to manage urban flood risk

The Lower Mekong delta of Vietnam, particularly Can Tho city, is highly susceptible to flooding which urgently calls for significant efforts to mitigate urban flood risks. A possible measure to reduce flood risk is the application of controlled embankment breaches (CEB) in a rural province upstream. Utilising a Protection Motivation Theory approach, this paper uses a contingent valuation to assess the welfare effects of a reduction of flood risks in the city resulting from CEB in the upstream region of the delta. The findings revealed that households residing in the downstream areas of Vietnamese Mekong Delta were willing to financially support the implementation of the CEB upstream in An Giang city, as a measure to alleviate urban flood threats in Can Tho city. These results suggest that interregional cooperation, involving the concept of payments for watershed services between regional jurisdictions, may help to effectively mitigate flood risk in a low-lying population centre in a major river delta. As such, these findings can inform policies for urban flood risk mitigation regarding the development of a more comprehensive flood risk reduction strategy in similar geographical contexts.

A novel urban flood risk assessment framework based on refined numerical simulation technology

Due to urbanization and climate change, urban areas are becoming more susceptible to flooding, posing significant risks to human life and property. Numerical simulation techniques can provide valuable support in mitigating urban flood risks. The objective of this study is to establish a novel framework for flood risk assessment based on refined urban flood simulations. To achieve refined urban flood simulations, the Torricelli’s law is incorporated into a two-dimensional fluid dynamics model, and a numerical analysis model is developed to estimate the inundation within buildings and accurately simulate the entire inundation process. The model was used to reproduce a real flood event in Omihachiman City, Japan, with the Nash-Sutcliffe Efficiency (NSE) values of 0.6488 and 0.7182, demonstrating the credibility of the model. Based on the simulation results, the hydrological causes of the flood event were analyzed, and flood prevention measures were proposed. After implementing these measures, the maximum water depths at the study area were reduced to 0.14 m, 0.62 m, and 0.12 m, respectively, proving the effectiveness of the flood prevention measures. To address the issue of inundation depth not fully reflecting flood risk, the instability of human bodies in floodwaters is conducted to evaluate flood risk for adults and children in the target area. In response to increasingly frequent extreme rainfall events, the extreme precipitation events of three different return periods (10-year, 50-year, and 100-year) are used to assess the flood risk and the effectiveness of flood prevention measures. Based on the assessment results, the flood prevention measures not only reduced the flood risk levels but also decreased the flood risk area, further confirming their effectiveness.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

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

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

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.

Urban flooding damage prediction in matrix scenarios of extreme rainfall using a convolutional neural network

The pivotal role of quantitative risk assessment in managing urban stormwater is underscored by the high spatial heterogeneity and complex non-stationarity complex of urban flooding. Conventional methods, reliant on measurable data, often fall short in accurately mapping spatial variations and gauging the full impacts of urban flooding. Addressing this gap, this study proposed a robust convolutional neural networks (CNN)-based tool, specifically designed for urban flooding risk and economic losses estimation under extreme design rainfall scenarios. Furthermore, the study validates the transferability of CNN models trained in data-abundant regions to similar but data-scarce regions, using Guangzhou as a case study for urban flooding damage prediction. The findings reveal that the most affected areas, particularly the old, densely built-up urban areas in the south-central Guangzhou, are susceptible to significant economic losses during extreme rainfall events. Notably, under the most severe scenario (Scenario 5), estimated economic losses amount to approximately $6359.91 million, with industrial and residential sectors bearing the brunt, accounting for 28.29 % and 39.94 % of the total losses, respectively. These insights are crucial for prioritizing mitigation efforts and formulating effective evacuation strategies in high-risk areas, ultimately aiding in the reduction of economic losses.

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

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

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

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

Exploring the role of pedological and climatic aspects in the medium-term decline of green roofs hydrological performance: an experimental study in a Mediterranean environment

ABSTRACT In the realm of sustainable strategies for urban flooding risk mitigation, green roofs (GRs) emerge as a key solution. The complex relationship between hydrological, pedological, and climatic aspects poses several challenges in the definition of GRs’ medium-term behaviour, emphasizing the imperative for further research. Embedding pedological and climatological evidence, this study focuses on relevant observed changes in the hydrological performance and behaviour of two extensive GR test beds located in southern Italy over a 7-year monitoring period. Experimental rainfall and runoff data, at the event scale, point to a reduction of approximately 12% in the stormwater retention capacity (RC) of monitored GRs. Additionally, a comparative analysis of RC values in two specific time windows revealed how, in an early stage, it was controlled by soil moisture content whereas it is currently (aged state) mainly related to rainfall characteristics. After excluding climate variability as a potential driver for observed RC changes, a pedological experimental campaign highlights variations in the physical and hydraulic parameters of the peat substrate, which, in turn, is addressed to affect the retention and detention capabilities of the GRs.

Urban Flood Risk Assessment and Mapping Using GIS-DEMATEL Method: Case of the Serafa River Watershed, Poland

This paper develops a method integrating Geographic Information Systems (GIS) and the Decision-Making Trials and Evaluation Laboratory (DEMATEL) for the analysis of factors influencing urban flood risk and the identification of flood-prone areas. The method is based on nine selected factors: land use/land cover (LULC: the ratio of built-up areas, the ratio of greenery areas), elevation, slope, population density, distance from the river, soil, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The DEMATEL method is used to determine the cause–effect relationship between selected factors, allowing for key criteria and their weights to be determined. LULC and population density were identified as the most important risk factors for urban floods. The method was applied to a case study—the Serafa River watershed (Poland), an urbanized catchment covering housing estates of cities of Kraków and Wieliczka frequently affected by flooding. GIS analysis based on publicly available data using QGIS with weights obtained from DEMATEL identified the vulnerable areas. 45% of the total catchment area was classified as areas with a very high or high level of flood risk. The results match the actual data on inundation incidents that occurred in recent years in this area. The study shows the potential and possibility of using the DEMATEL-GIS method to determine the significance of factors and to designate flood-prone areas.

Understanding drivers of changing flood dynamics for enhancing coastal community resilience: a participatory approach

In recent decades, coastal communities globally have experienced increased frequency and intensity of flood hazards, especially in developing nations. An increase in flooding has often been attributed to population growth, rising sea levels, extreme weather events, rapid urbanization, and poor land use, often exacerbated by insufficient urban flood risk management policies. However, flood risk management is complex and necessitates an in-depth look at factors that drive changing flood dynamics in coastal cities. This study used a participatory approach to identify, categorize, and analyze drivers of change in the flood-prone city of Limbe, Cameroon, a major tourism hub and contributor to Cameroon’s GDP. The study engaged key stakeholders, including community members, government authorities, academic institutions, and non-governmental organizations. The study led to the identification of 46 major drivers classified into six clusters. The study findings emphasized four key priority areas to enhance policy and community resilience: restoring natural buffer zones like wetlands, increasing local involvement in flood risk planning, implementing risk-informed land use regulations, and investing in flood infrastructures. To ensure effective flood risk management in Limbe, a collaborative bottom-up approach involving all stakeholders, especially marginalized community members, is necessary to tailor solutions that meet their needs.

Performance of low impact development techniques in flood hazard mitigation in a closed urbanised catchment for extreme precipitation events

ABSTRACT Sealing of surfaces alters the natural hydrological conditions, thereby increasing the urban flood risk. Rainwater management in urban environments has traditionally been performed using conventional urban drainage systems (UDS). However, low impact development (LID) solutions have been recognised in recent decades as effective in controlling runoff. The present work aims to evaluate the efficiency of LID techniques in flood control in a closed basin characterised by high urbanisation for three extreme precipitation events in the city of Natal, Brazil, using 1D/2D modelling. Two techniques were tested, namely bioretention cells (BCs) and rain barrels (RBs) alone or combined. The existing UDS in the area proved unable to control flooding of the basin. Water depths exceeded 0.5 m in all events. The efficiency of the LID solutions evaluated was extremely low, with reductions of less than 1% for highest hazard class. Combining the techniques produced better performance than implementing isolated techniques.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.