Flood Risk In Urban Areas Research Articles

URBAN FLOOD RESILIENCE: A MULTI-CRITERIA EVALUATION USING AHP AND TOPSIS

Floods are increasingly recognized as one of the most destructive natural disasters, driven by urban expansion, climate change, and unregulated development. This is particularly true in developing countries, where rapid urbanization has increased impervious surfaces, amplifying flood risks in urban areas. This study focuses on Albania, evaluating urban resilience against floods through the lens of water-related disaster data. Using a multi-criteria decision analysis (MCDA) approach, specifically the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the research assesses flood vulnerability and resilience in rapidly urbanizing regions. Integrating AHP and TOPSIS with mixed methods introduces a novel approach, offering a comprehensive evaluation framework for flood risk management. The findings highlight critical vulnerabilities and suggest that targeted urban planning and disaster mitigation efforts can enhance resilience. Future research could incorporate climate projections and granular urban data, supporting a more adaptive flood management strategy.

A multicriteria decision model to improve emergency preparedness: Locating-allocating urban shelters against floods

The increasing prominence of natural disasters in recent years has made emergency disaster management one of the main challenges for building resilient societies. This way, the warming combination with human-induced urban interventions with climate change reshapes the strategic decision-making that public managers must deal with to promote safety and preparedness. Consequently, it implies complex situations under uncertainty that comprise conflicting dilemmas about which decision-makers (DMs) should establish trade-offs. Given this backdrop, this work proposes a risk-based multidimensional model for assessing urban shelter location against flood risk with Decision Analysis and Multi-Attribute Utility Theory. By using principles of the Queue Theory, the decision model undertakes four criteria that measure in a probabilistic manner the alternatives’ performance when considering urban flood disaster evacuation routes in affected areas. Altogether, it enables the DM to establish his/her preferences with views to rank potential locations for mounting temporary shelters and for planning emergency preparedness in risky areas. Being replicable to any urban context, our proposal has the potential to support and promote the development and implementation of crisis protocols consisting of measures to reduce flood impacts and adapt critical areas to climate change. From a numerical application of the proposed model, the DM is encouraged to explore graphical and tabular visualization tools, as well as sensitivity analysis, to establish in strategic terms how to design and implement emergency shelters to reduce flood risks in urban areas effectively.

A High‐Performance Coupled Human And Natural Systems (CHANS) Model for Flood Risk Assessment and Reduction

AbstractIn recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high‐performance hydrodynamic model with an agent‐based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real‐time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood‐human dynamics and assess flood impact at the household‐level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short‐term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.

Experimental study of the effect of high-strength clogging-resistant permeable pavement (CRP) on the runoff using a rainfall simulator.

Permeable pavements play an effective role in reducing runoff by decreasing the impermeable area. But, conventional permeable pavements suffer disadvantages such as low resistance. To address this, the 'high-strength clogging-resistant permeable pavement (CRP)' has been developed. The present study aimed to evaluate the performance of the CRP model with varying percentages of coverage (A) of 25, 50, and 100%, slopes (S) of 1, 3, and 5%, as well as rainfall intensities (I) of 45, 55, 70, 90, 170, and 200 mm/h. Based on the results, there was an increase in A from 50 to 100% at I = 90 mm/h, decreased runoff coefficient (C) of 18, 15, and 13% at S of 1, 3, and 5%, respectively. At the same I, increasing S from 1 to 5% increased the C coefficient in A of 0, 25, 50, and 100% by 3, 31, 32, and 39%, respectively. Due to the ever-increasing urbanization and the subsequent increase in impervious areas, the risk of severe floods has greatly increased. Therefore, providing solutions such as the CRP model can help reduce flood risks in urban areas. The findings of this research can be used as a guide in the design of high-strength clogging-resistant permeable pavements in urban areas.

Spatial and temporal scaling of extreme rainfall in the United Kingdom

AbstractExtreme rainfall estimates for ungauged areas contribute to improved resilience to flooding. This study applies a sub‐daily rainfall scaling method to annual daily maximum rainfall series from 102 UK weather stations. We analyse resultant parameters by season, homogenous rainfall region, urban area and geographic factors to investigate how scaling varies temporally and spatially. Dummy regression models are built using these variables to predict the sub‐daily scaling parameter for any location in the United Kingdom. Estimated rainfall intensities are validated with observations and yield Mean Absolute Errors of 3.0, 1.9 and 0.9 mm/h for 1‐, 2‐ and 6‐h events, respectively. We also demonstrate intensity‐duration‐frequency curves at a site in Oxfordshire for scaled and observed data and find that 1‐ to 6‐h, 20‐year rainfall intensities are estimated to be within 9.4%. With such unified scaling relationships, it is possible to derive extreme rainfall for specified durations and return periods at ungauged locations. According to our cross‐validation of estimated and observed intensities, more than 88% of sites fall within 10% error bounds. This method offers a means of generating design rainfall series as input to flood simulation models to evaluate pluvial flood risks in urban areas.

Monsoon flood risks in urban areas of Pakistan: A way forward for risk reduction and adaptation planning

Climate change poses a significant threat to sustainable urban development. Heavy rainfall has led to severe urban flooding, disrupting human life and causing widespread damage. This study aims to examine the impacts, preparedness, and adaptation strategies related to monsoon flooding in Lahore, Pakistan's second-most populous metropolitan area. Using Yamane's sampling method, a total of 370 samples were surveyed and analyzed using descriptive analysis and chi-square tests. The results indicate that houses and parks were the most commonly damaged properties, with common impacts including roof collapse, house fires, seepage, and wall dampness. These impacts not only caused physical damage but also disrupted basic amenities and damaged roads, resulting in significant socioeconomic costs. Despite these challenges, residents adopted a variety of adaptation strategies such as the use of temporary tarps, moving household appliances to upper floors, and shifting to tiled floors and wall paneling to mitigate damage. However, the study highlights the need for further measures to reduce flood risks and promote adaptation planning in order to effectively address the ongoing challenges posed by climate change and urban flooding.

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

Climate change has resulted in the increased frequency of extreme storms and associated flood risk uncertainties in urban areas, which brings more challenges to future urban flood prevention. Assessing future trends in probabilistic urban flood risk can improve uncertainty estimation of urban floods under climate change, and thus it is becoming increasingly urgent and important. In this study, a probabilistic risk assessment approach of urban flooding was developed to quantify the probabilistic flood risk in urban areas and assess the impacts of future climate change on flood risk. Results showed probabilistic urban flood risk under the historical condition ranged from 0 to 0.508 with a mean value of 0.104, indicating probabilistic urban flood risk was at the medium risk level. The areas where probabilistic flood risk reached the medium risk level under historical condition accounted for 34.3 % of the study area. The ranges of probabilistic flood risk in the study area were 0–0.588 and 0–0.601 with mean values of 0.126 and 0.131 under SSP2-4.5 and SSP5-8.5 climate change scenarios, respectively. The areas where probabilistic flood risk reached the high risk level respectively accounted for 17.1 % and 32.7 % of the study area for SSP2-4.5 and SSP5-8.5 scenarios. Compared with the historical period, probabilistic risk of urban flooding in the study area was projected to increase by 51.3 % and 67.4 % on average for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The largest increase of probabilistic flood risk was approximately 200 % and 250 % for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, which mainly occurred for forestlands and grasslands. These results can effectively improve the uncertainty estimation of risk trends for urban floods under future climate change, and thus better-informed decision making for urban flood prevention.

An Online Data-Driven Evolutionary Algorithm–Based Optimal Design of Urban Stormwater-Drainage Systems

To reduce flood risk in urban areas, an optimal design of drainage networks for urban areas is essential for flooding control and drainage management of the urban stormwater drainage system (USDS). A conventional design is generally used for drainage networks, resulting in high computational costs and limited flooding reduction effect. In this study, based on an on-line data-driven evolutionary algorithm coupled with the storm water management model (SWMM), a novel approach for USDS drainage network optimization design was developed. A case in Xi’an City, China, was then selected for practical implementation, where the performances of the local planning scheme, the particle swarm optimization algorithm (PSO) and the proposed approach were compared. Results confirmed that our proposed methodological approach is feasibility and highly efficiency, leading to a 32% reduction in total flooding from that resulting from the local planning scheme. In addition, the average computational time was reduced by 57%, while the flooding control effect was better, compared to PSO algorithm optimization. These results suggest that our optimization design approach is reliable and applicable, and can benefit and assist designers in practice.

Probabilistic Assessment of Pluvial Flood Risk Across 20 European Cities: A Demonstrator of the Copernicus Disaster Risk Reduction Service for Pluvial Flood Risk in Urban Areas

Pluvial flooding is a major concern in urban environments with limited or temporarily reduced efficiency of surface drainage. Climate change, land use change, and increased exposure in hazard prone areas play a role in increasing pluvial flood risks. We describe a methodology for rapid probabilistic pluvial flood hazard mapping and risk assessments in urban environments developed for the Copernicus Climate Change Service “Pluvial Flood Risk Assessment in Urban Areas” demonstrator project for large computational domains. The methodology is designed to be flexible and robust enough to be adapted to other cities in Europe and elsewhere, and is composed of three main steps: (i) computation of the intensity–frequency relationship to express the depth of extreme hourly rainfall events at the urban scale; (ii) characterization of the pluvial flood hazard by means of raster-based flood modeling, and; (iii) estimation of the direct tangible damages at the building level for residential, commercial, and industrial buildings. Methods are tested for 20 cities across Europe and considering two infiltration rate scenarios. Spatial-explicit results at the urban-scale highlight the neighborhoods and sub-urban areas more adversely affected in terms of direct tangible damage to buildings and assets. Probabilistic risk assessment results indicate that Brussels, Antwerp, and London are the cities with higher expected annual damages per built-up area among the 20 cities considered in this study. These results can be useful for supporting the implementation of the Sendai Framework for DRR 2015–2030, for developing multi-hazard risk assessments, and for supporting the development of national climate change adaptation plans.

Rainwater Harvesting for Metro Rail Projects in India – Scope for Efficiency Improvement

Abstract Rainwater Harvesting (RWH), although an ancient technique of artificial recharge to groundwater and reuse of stored rainwater, has been improved with the developing technology. RWH systems have many benefits in rural as well as in urban areas of arid and semi-arid regions. Almost all metro cities of India are facing problems of water logging and flooding even after a short heavy rainfall. In metro cities RWH could be helpful for artificially recharging groundwater in depleted water level areas; reduction of flood risk in urban areas; assured source for an effective alternative water supply solution and use of harvested water during water scarcity period. Nevertheless, most of the studies in literature focused on the potential of RWH for buildings, institutional areas for reducing water consumption, whereas few examples were examined for their efficient use. Almost all big cities in India are planning metro rails which have huge potential for RWH with its elevated stations, viaduct (elevated corridor) and depot areas. This work aimed to investigate and realize the RWH potential especially for metro rails which are in the implementation stage for different cities in India. This paper also highlighted the legislations of RWH of different states where metro rails were implemented. It also emphasized on the root cause of inefficiency of the RWH system and how to increase the efficiency.

Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research.

Exploring global sensitivity analysis on a risk-based MCDM/A model to support urban adaptation policies against floods

Strategies for reducing flood risks and adapting urban systems involve estimating parameters and conducting difficult trade-offs among human, financial, and environmental issues, which are usually conflicting with each other. This way, multicriteria models are useful as they can aid risk-based decision-making by dealing with all these aspects simultaneously, while the decision-maker (DM) exerts a great influence when establishing his/her preferences. However, this problem is usually associated with uncertainties about defining the variables required, and these certainly affect the credibility of the decision. Hence, sensitivity analysis (SA) is a powerful tool for assessing how changes in these variables lead to robust results. In this context, this paper compiles a SA protocol and this includes using a Monte Carlo Simulation in a multicriteria decision model. It aims to prioritize flood risks in urban areas under climate effects. The model quantifies the risk by using Multi-Attribute Utility Theory and aggregates five criteria: accessibility to public services, economic, human, sanitary conditions, and the need for social assistance. By undertaking a critical analysis, the SA links risk and uncertainty so as to deal with climate risks adequately. It simulates the behavior of three groups of input data: climatic variability, exposure to risk, and the DM's preference statements. Our findings explore graphical and statistical tools to provide the DM with a broad range of evidence with the potential to increase confidence in his/her own decisions. Also, innovative insights emerged from conducting this study which leads us to making suggestions for new improvements in the multicriteria model.

Comparison between different Intensities of Rainfall to identify overflow points in a combined sewer system using Storm Water Management Model

<abstract> <p>Increasing urbanization related to land pressure and the soil arising from it are aggravating factors of flood risk in urban areas, including storm water runoff. Therefore, urban sanitation networks face an excess of water that exceeds their absorption capacity. This article deals with the effect of impermeability in densely populated urban areas and the function of a combined sewer system under higher rainfall intensities using the Storm Water Management Model (SWMM). The objective is to simulate a possible increase in rainfall intensities for reducing overflow points in the combined sewage system at the study area, which was the city of Ahmed Rachdi, Mila in Algeria. The excessive rainfall intensities were modeled using the SWMM software program to estimate maximum water volumes inside the combined sewage system of the study area. To evaluate the model's performance, a comparison process was used between the values of the flow rates of the pipelines of the sewerage system combined with the design flow rates in the current state and the flow rates of a single modeling of future events available during the study interval. The comparison results showed a good and convergent performance for these models. The results of the flooding volumes using different values of rainfall intensities and different return periods, which were 2, 5, 10 and 25 years, in the modeling of the combined sewage system are 3626, 6888, 8636 and 12676m<sup>3</sup>, respectively. The suggested scenario included increasing diameters of some pipes in the combined sewage system pipelines. The results using this scenario showed reductions in the total percentage of overflow points from the integrated sewage system of 52.42%, 40.63%, 31.83% and 20.51% using the rainfall intensities for the return periods of 2, 5, 10 and 25 years, respectively. The present study can provide technical support for using software in the planning, controlling and tests of the sewer systems, which contribute to solving the sewer systems' problems.</p> </abstract>

A novel spatiotemporal multi-attribute method for assessing flood risks in urban spaces under climate change and demographic scenarios

The combined effects of global warming and population growth induce policymakers to enhance flood risk management practices in order to reduce substantially the causes of floods and to mitigate their future and multiple impacts simultaneously. This way, this paper puts forward a new multi-attribute and non-stationary decision model for managing flood risks in urban areas under climate demographic changes, which we call the Non-Stationary Multi-Attribute Utility Theory (NSMAUT). Under expected utilities, our model inserts time-dependency into the decision-maker's (DM's) preference statements based on his/her trade-offs regarding psychological distance induced by delayed prospects. The NSMAUT analyzes flood risks under five attributes, linked to environmental, financial, human, mobility, and social concerns of society. By combining climate and demographic scenarios for this century, our findings make use of statistical, graphic, and risk performance measures that increase the DM's perception of risk in order to control and monitor the dynamism of circumstances of risk and thus to facilitate on what he/she should base future decisions on adapting cities for future adverse events arising from flooding. A numerical application in a Brazilian municipality from 2021 – 2100 is used to validate our approach. Moreover, the model can be replicated in other urban contexts.

Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning

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.

Bare‐Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models

AbstractAccurate terrain representation is critical to estimating flood risk in urban areas. However, all current global elevation data sets can be regarded as digital surface models in urban areas as they contain building artifacts that cause artificial blocking of flow pathways. By taking surveyed terrain and LIDAR data as “truth,” the vertical error in three popular global DEMs (SRTM 1″, MERIT DEM, and TDM90) was analyzed in six European cities and an Asian city, with RMSE found to be 2.32–5.98 m. To increase the utility of global DEM data for flood modeling, a Random Forest model was developed to correct building artifacts in the MERIT DEM using factors from widely available public datasets, including satellite night‐time lights, global population density, and OpenStreetMap buildings. The proposed correction reduced the vertical errors of MERIT by 15%–67%, despite not using data samples from the target city in training the model. When training data from the target city was included error reduction improved by between 57 and 76 percentage points. The resulting Urban Corrected MERIT DEM improved simulated inundation depth by 18% over original MERIT in a hydrodynamic model of flooding in the UK city of Carlisle, although it did not outperform TDM90 at this site. We conclude that the proposed method has the potential to generate a bare‐earth global DEM in urban areas with improved terrain representation, although in data scarce regions this requires more complete OpenStreetMap building information. In the future, the method should be applied to TDM90.

A novel expert opinion-based approach to compute estimations of flood damage to property in dense urban environments. Barcelona case study

A certain acceptable level of risk in major drainage system must be established since urban areas cannot be made entirely free from pluvial flooding. Among the diversity of flood risks in urban areas, direct damage to property has been extensively studied. A novel model approach (SFLOOD) to estimate flood damage to property in urban areas has been developed and presented herein. The model was conceptualised according to the knowledge of an insurance surveyor, acquired over many years on flood economic losses appraisals. It is a micro scale-, depth-damage- and GIS-based model where water depth is the only hydrodynamic variable considered as a damage driver. The model testing has been conducted through the direct comparison of computed damage and damage appraisals provided by the Spanish public insurance company, Consorcio de Compensación de Seguros (CCS), for three actual flood events occurred in Barcelona (Spain). Although a variety of uncertainties related to the flood damage estimates have been revealed here, the model is able to predict the order of magnitude of the actual damages according to the results obtained.

Urban Stormwater Management, a Tool for Adapting to Climate Change: From Risk to Resource

The effects of climate change on rainfall in the Mediterranean region are manifested in an overall decreasing trend, and greater irregularity in annual volumes and the city of Alicante is no exception. In addition, there has also been a spread of the urbanised area, which has led to an increase in the flood risk in urban areas (due to a greater runoff and the occupation of flood hazard areas) and drought events due to an increase in the water demand. In light of these new scenarios, the Mediterranean cities should design adaptation systems based on rainwater harvesting within the framework of a circular economy. This study analyses the integration of rainwater in flood and water demand management in the city of Alicante (Southern Spain). In recent years, this city has developed infrastructures in order to use these resources. To do this, different databases have been analysed (rainfall and volume of water collected in the green infrastructure systems). The results reveal that stormwater has become highly important in urban water management in Alicante as the city is now using a resource that previously went to waste and created problems (flooding and pollution). By way of conclusion, it is worth mentioning that the incorporation of rainwater for urban use in Alicante has reduced the pressure on traditional resources in satisfying water demand and has also acted as a measure for adapting to climate change.

Recent Advances in the Assessment of Flood Risk in Urban Areas

The adverse effects of flood disasters in urban areas have been increasing in severity and extent over the past years [...]

Evaluation and communication of pluvial flood risks in urban areas

AbstractThe increase of pluvial flooding has long been discussed to be a most probable outcome of climate change. This has raised the question of necessary consequences in the design of urban drainage systems in order to secure adequate flood protection and resilience. Due to the uncertainties in future trends of heavy rainfall events, the awareness of remaining risks of extreme pluvial flooding needs to be roused at responsible decision makers and the public as well leading to the implementation of pluvial flood risk management (PFRM) concepts. The state of two core elements of PFRM in Germany are described here: flood hazard and risk evaluation and risk communication. In 2016 the guideline DWA‐M 119 has been published to establish city‐based PFRM concepts in specification of the European Flood Risk Management Directive (EU 2007). As core elements, the guidelines recommend a site‐specific analysis and evaluation of flood hazards and potentials of flood damages to create flood hazard and flood risk maps. In the long run, PFRM needs to be established as a joint community effort and a requirement for more flood resilience. The risk communication within the administration and in the public requires a comprehensible characterization and classification of heavy rainfall to illustrate event extremity. The concept of a rainstorm severity index (RSI) instead of statistical rainfall parameters appears to be promising to gain a better perception by affected citizens and non‐hydrology‐experts as well. A methodical approach is described to specify and assign site‐specific rainfall depths within the severity index scheme RSI12.This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water Engineering Water > Methods