Flood Risk Assessment Framework Research Articles

Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

Continuously global warming and landscape change have aggravated the damage of flood disasters to ecological safety and sustainable development. If the risk of flood disasters under climate and land-use changes can be predicted and evaluated, it will be conducive to flood control, disaster reduction, and global sustainable development. This study uses bias correction and spatial downscaling (BCSD), patch-generating land-use simulation (PLUS) coupled with multi-objective optimization (MOP), and entropy weighting to construct a 1 km resolution flood risk assessment framework for the Guanzhong Plain under multiple future scenarios. The results of this study show that BCSD can process the 6th Climate Model Intercomparison Project (CMIP6) data well, with a correlation coefficient of up to 0.98, and that the Kappa coefficient is 0.85. Under the SSP126 scenario, the change in land use from cultivated land to forest land, urban land, and water bodies remained unchanged. In 2030, the proportion of high-risk and medium-risk flood disasters in Guanzhong Plain will be 41.5% and 43.5% respectively. From 2030 to 2040, the largest changes in risk areas were in medium- and high-risk areas. The medium-risk area decreased by 1256.448 km2 (6.4%), and the high-risk area increased by 1197.552 km2 (6.1%). The increase mainly came from the transition from the medium-risk area to the high-risk area. The most significant change in the risk area from 2040 to 2050 is the higher-risk area, which increased by 337 km2 (5.7%), while the medium- and high-risk areas decreased by 726.384 km2 (3.7%) and 667.488 km2 (3.4%), respectively. Under the SSP245 scenario, land use changes from other land use to urban land use; the spatial distribution of the overall flood risk and the overall flood risk of the SSP126 and SSP245 scenarios are similar. The central and western regions of the Guanzhong Plain are prone to future floods, and the high-wind areas are mainly distributed along the Weihe River. In general, the flood risk in the Guanzhong Plain increases, and the research results have guiding significance for flood control in Guanzhong and global plain areas.

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

With climate change and rapid urbanization, the prevalent flood disasters and associated risks in urban areas have become increasingly crucial global issues. Risk assessment is a basic step in flood management and mitigation, while flood management is important to achieve risk mitigation. A comprehensive assessment framework considering both pre- and post-disasters that can address flood risk is lacking. Therefore, this study developed an integrated framework by combining the assessments of flood risk and flood management performance. Taking Beijing and Munich as research objects, this study generated flood risk maps at the grid scale for the period 2000–2020 and comparatively assessed the performance of various flood management measures. The urban flood risk in Beijing showed an increasing trend and then a decreasing one from 2000 to 2020, exhibiting high spatial characteristics in the southeast and low spatial characteristics in the northwest. The flood risk in Munich showed an overall decreasing trend, with strong spatial characteristics in the center area but less so in suburban areas. The shift from low to high urbanization levels was the major factor increasing the regional flood risk. Therefore, effective flood management measures in urban areas could potentially mitigate the flood risk, and Munich had a higher flood management performance owing to its well-developed flood management systems. This study provides a new framework for flood risk assessment and resilient flood management and contributes to the development of practical measures for disaster mitigation in rapidly-developing urban areas.

Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area's contexts being investigated.

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

With accelerated urbanization and climate change, urban flooding is becoming more and more serious. Flood risk assessment is an important task for flood management, so it is crucial to map the spatial and temporal distribution of flood risk. This paper proposed an urban flood risk assessment method that takes into account the influences of hazard, vulnerability, and exposure, by constructing a multi-index urban flood risk assessment framework based on Geographic Information System (GIS). To determine the weight values of urban flood risk index factors, we used the analytic hierarchy process (AHP). Also, we plotted the temporal and spatial distribution maps of flood risk in Zhengzhou City in 2000, 2005, 2010, 2015, and 2020. The analysis results showed that, the proportion of very high and high flood risk zone in Zhengzhou City was 1.362%, 5.270%, 4.936%, 12.151%, and 24.236% in 2000, 2005, 2010, 2015, and 2020, respectively. It is observed that the area of high flood risk zones in Zhengzhou City showed a trend of increasing and expanding, of which Dengfeng City, Xinzheng City, Xinmi City, and Zhongmu County had the fastest growth rate and the most obvious increase. The flood risk of Zhengzhou City has been expanding with the development of urbanization. The method is adapted to Zhengzhou City and will have good adaptability in other research areas, and its risk assessment results can provide a scientific reference for urban flood management personnel. In the future, the accuracy of flood risk assessment can be further improved by promoting the accuracy of basic data and reasonably determining the weight values of index factors. The risk zoning map can better reflect the risk distribution and provide a scientific basis for early warning of flood prevention and drainage.

XBEACH IMPLEMENTATION IN THE NEW NATIONAL COASTAL FLOOD RISK ASSESSMENT FRAMEWORK FOR THE DUTCH COAST

Dunes and beaches protect large stretches of the Dutch coast against flooding from the sea. As prescribed by the Dutch Water Law, the Dutch dune coast is periodically assessed to ensure an acceptable level of flood risk in the hinterland. This safety assessment methodology is currently being renewed within the framework of the BOI program (Assessment and Design Instruments for flood defenses) to utilize the process-based storm impact model XBeach as replacement of the empirical dune erosion model DUROS+. The project consists of six steps: 1. XBeach model formulation improvements and model robustness, 2. XBeach calibration, 3. XBeach validation, 4. derivation of semi-probabilistic model input for the Dutch coast, 5. derivation of a failure definition and 6. implementing XBeach in a renewed flood risk assessment framework (Wilmink et al., 2020). The main goal of the project is to increase the accuracy of the flood risk assessment of dunes in the Netherlands by implementing XBeach as dune erosion model and develop a user-friendly GUI and guideline for the assessment framework.

Increasing flood risk under climate change and social development in the Second Songhua River basin in Northeast China

Study regionSecond Songhua River basin (SSRB), Northeast China. Study focusAccurately assessing future flood risks is the premise and foundation of flood control and water resources management. At present, studies are still lacking in assessing future watershed flood risks that consider climate change and social development in Northeast China and the relative flood risk assessment framework. This study proposed an integrated flood assessment framework containing the AHP, entropy weight, and GCM downscaling methods, which was implemented in the SSRB, a typical watershed in Northeast China, to estimate future basin flood risks under climate change and social development. New hydrological insights for the regionThe flood risk in the SSRB will increase significantly in the mid-term (2051–2075) and long-term (2076–2100) future. The areas under the four scenarios with greater than high-risk levels will expand by 28.9 %, 105.6 %, 165.6 %, and 156.7 %, respectively, compared to the historical scenario. The newly added areas mainly located in the lower reaches. Precipitation changes are the primary reason for the future flood risks. The increased GDP under a higher forcing degree will also significantly increase the flood risk in the long-term future in the lower reaches. Based on these above findings, extreme precipitation forecasting and flood management in downstream areas in the basin need to be enhanced. Energy-saving and emission-reduction measures should be simultaneously implemented.

Probabilistic modeling framework for flood risk assessment: A case study of Poldokhtar city

Study regionPoldokhtar City is located on the bank of the Kashkan river in Iran. Study focusThis study presents a probabilistic modeling framework for flood risk assessment using Monte Carlo simulations. We developed a Machine Learning (ML)-based flood depth prediction model for Poldokhtar city using the Least Squares Support Vector Machine. HEC-RAS was utilized for 2D flood modeling, and its performance was evaluated by comparing simulated and remote sensing-derived flood extent maps. The simulated results were used to develop a surrogate ML-based model that predicts flood depth maps. Finally, we used this model to estimate the flood risk of Poldokhtar city as a combination of hazard, exposure, and vulnerability for 10000 flood scenarios. New hydrological insights for the regionThe mean annual flood damage of the city based on the proposed framework is US$ 1177034, which is about three times lower than that calculated using the simplified method used in the classical risk analysis (US$ 3455400). Buildings near the floodwalls of the river and in the southwestern parts of the city have higher mean flood losses than those in other areas. Risk index frequencies of buildings reveal the most at-risk zones in the city, where there have been building constructions without considering flood hazards. The proposed framework would be of use to stakeholders such as policymakers to develop effective flood management strategies

Integrating resilience into an urban flood risk assessment framework: a case study of the Minzhi region, Shenzhen City

Integrating resilience into an urban flood risk assessment framework: a case study of the Minzhi region, Shenzhen City

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process.

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC=0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC=0.918). The final flood risk map shows 5075ha (0.63%) in extremely high risk, 47,955ha (5.95%) in high-risk, 40,460ha (5.02%) in medium risk, 431,908ha (53.55%) in low risk areas, and 281,127ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.

Risk assessment for people and vehicles in an extreme urban flood: Case study of the “7.20” flood event in Zhengzhou, China

Extreme flood events in urban environments have become a major source of threat to human life and property, and therefore have attracted widespread concerns. In this study, a hydrodynamic modeling and flood risk assessment framework was utilized to replicate the “7.20” extreme urban flood process in Zhengzhou, China, with precise assessments of corresponding hazard degrees for people and vehicles being provided. Model predictions indicated that the study area was seriously flooded during the “7.20” urban flood event, with 28.9% of the buildings having an inundation water depth of more than 0.5 m. Due to the low-lying nature, roads were the vulnerable areas during the flood event, with the maximum water depth and flow velocity up to 1.2 m and 1.0 m/s, respectively. In addition, the response between rainfall intensity and flood risk was also discussed. The overall hazard degrees for people and vehicles sharply increased during the peak rainfall period, and however, the hazard degree of people declined after this period, while the hazard degree for vehicles remained almost unchanged. The flood risk in the Jingguang Road North Tunnel (JRT) was extremely high after the tunnel was inundated. The cascaded inflow from the entrance of the tunnel would reduce the evacuation speed of trapped people, or even lead to a loss of human stability and cause consequent drowning. The results obtained in this study can facilitate the awareness of urban flood risk among the public as well as decision-makers, and can therefore help to improve urban resilience.

National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system.

Dealing with Multisource Information for Estuarine Flood Risk Appraisal in Two Western European Coastal Areas

Estuaries are usually affected by compound flooding triggers that cause diverse territorial damages. While fluvial flood risk assessment frameworks are well established in the literature, integrated management instruments that deal with estuarine flood risk remain incomplete and often lacking. This research presents a methodology to extract relevant information from multiple sources post-event and a database building process that is applied to two contrasting estuaries (the Tagus River estuary in Portugal, and the Shannon River estuary in Ireland) in the Western European coastal area. Overall, a total of 274 documents were analyzed and the information was stored in two databases. Multiple correspondence analysis was applied to extract the most informative and relevant estuarine flood indicators. An integrated estuarine flood risk assessment framework is presented and discussed based on the extracted indicators. The framework is driven by two distinct dimensions (oceanic and hydrographic) and revealed the transversal position of triggers of estuarine floods, reflecting the compounding effects usually present in these areas. The results also highlight two levels of flood risk mostly based on damage typology.

Development of flood vulnerability curves for Sri Lankan hospitals

PurposeThe government-led public healthcare services in Sri Lanka became a major strength in managing the COVID-19 comparatively well. However, natural hazards are a major threat to this healthcare system, as they cause severe damages, especially to curative healthcare infrastructures such as hospitals. Floods have been the major contributor to the economic loss of the Sri Lankan healthcare system. Therefore, the purpose of this study is to develop a proper flood risk assessment framework for Sri Lankan hospitals.Design/methodology/approachThis research study has attempted to develop a flood vulnerability assessment tool for hospitals using the concept of Depth Damage Functions (DDFs). Flood vulnerability curves have been developed for identified critical units of hospitals considering the damage caused to building contents which are predominantly expensive medical equipment. The damage caused only by wetting was considered in generating vulnerability curves. Structured interviews were conducted with government officials in the healthcare sector to gather details on the cost and damages of medical equipment. Pilot studies were carried out in two hospitals identified as located in flood-prone areas and have previous experiences of flooding, to acquire data regarding building contents of the critical units.FindingsThe developed vulnerability curves indicate that no major damage would occur to building contents in critical units (other than the labor room) until the inundation depth reaches a value of 0.6–0.9 m (varies for each type of unit). It is also noteworthy that after a certain range in the inundation depth, the damage increases drastically, and building contents would incur total damage if the inundation depth passes a value of 1.2–1.5 m.Originality/valueThis study explains the initial phase of developing a flood vulnerability assessment framework for Sri Lankan hospitals. Not many studies had been carried out to assess the vulnerability of hospitals specifically for floods using vulnerability curves. The study recommends a zoning system with pre-defined vulnerability levels for critical units during a flood, which can be associated with evacuation planning as well. Further studies must be carried out to verify this system for hospitals in Sri Lanka.

An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis

Accurately quantifying and assessing the flood risks is critical for flood hazard mitigation and prevention. This study proposed a new integrated flood risk assessment framework to map flood risk, hazard and vulnerability by coupling the HEC-HMS hydrological model, the HEC-RAS 1D and 2D hydraulic models, and a bottom-up hazard vulnerability analysis. The Old Brahmaputra River floodplain of Bangladesh, a flood-prone region, was chosen as a case study. The coupled hydrological-hydraulic model shows a comparable robust performance in both calibration and validation periods with Nash-Sutcliffe efficiency coefficient = 0.93 (0.81), coefficient of determination = 0.95 (0.89), and percent bias = −1.17% (2.40%) for the calibration (validation) period. Our results indicate that the assessed risk levels are roughly consistent with the overall property distribution and flood-hazard potential in the study area. The proposed framework and associated findings are valuable for developing adaptation strategies and early-warning systems to reduce flood impacts in the future.

Using Climate Factors to Estimate Flood Economic Loss Risk

Estimation of economic loss is essential for stakeholders to manage flood risk. Most flooding events are closely related to extreme precipitation, which is influenced by large-scale climate factors. Considering the lagged influence of climate factors, we developed a flood-risk assessment framework and used Hunan Province in China as an example to illustrate the risk assessment process. The main patterns of precipitation—as a connection between climate factors and flood economic losses—were extracted by the empirical orthogonal function (EOF) analysis. We identified the correlative climate factors through cross-correlation analysis and established a multiple stepwise linear regression model to forecast future precipitation patterns. Risk assessment was done based on the main precipitation patterns. Because the economic dataset is limited, a Monte Carlo simulation was applied to simulate 1000-year flood loss events under each precipitation regime (rainy, dry, normal years) to obtain aggregate exceedance probability (AEP) and occurrence exceedance probability (OEP) curves. We found that precipitation has a strong influence on economic loss risk, with the highest risk in rainy years. Regional economic development imbalances are the potential reason for the varying economic loss risks in different regions of Hunan Province. As the climate indices with at least several months prediction lead time are strong indicators in predicting precipitation, the framework we developed can estimate economic loss risk several months in advance.

Climate Justice Planning in Global South: Applying a Coupled Nature–Human Flood Risk Assessment Framework in a Case for Ho Chi Minh City, Vietnam

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods.

Assessing flood vulnerability on livelihood of the local community: a case from Southern Bagmati corridor of Nepal

Flood is one of the prominent hazards in terai region of Nepal. The people living at the bank of the river are more vulnerable to floods. The study was objectively conducted to assess vulnerability from floods on livelihood of riverine households & communities living at up-stream, mid-stream and down-stream at Bagmati river of Sarlahi and Rautahat district of Nepal. About 25% sample size, focus group discussion & key informant's interview were surveyed & carried out through random sampling. A pre-tested semi-questionnaire and check list was prepared & used to collect the socio-demographic profile, livelihood strategies, social networks, financial aspects, physical structure and facilities, health, food, water, forest and natural disasters and climate variability. The duration of study was from July 2019 to December 2020. Collected data were analyzed to calculate the indexed value and vulnerability index using Haln et al.,1 and IPCC2 for comparing the components of flood vulnerability from livelihood aspects. A multi-dimensional integrated flood risk assessment framework, Livelihood Vulnerability Index (LVI) developed by Haln et al.1 and LVI-IPCC, was adopted to quantify household-level flood vulnerability. Results showed that flood vulnerability was higher in downstream/lower belt (with highest LVI value 0.528), followed by Middle belt with medium LVI value 0.506. The least vulnerable is upper belt which has low LVI value 0.323. A significant correlation was observed between contributing factors: vulnerability, contributing factors-exposure, sensitivity and adaptive capacity. Gradation of flood vulnerability found as significantly different. The multivariate analysis recommended that households’ having better preparedness and mitigation measures was influenced by their ability to responds to floods vulnerability. The empirical approach and analysis of this study could be used to reduce vulnerability, enhance adaptive capacity and lower the risk of sensitivity in another region.

New conceptual framework for flood risk assessment in Sheffield, UK

AbstractThis study explores a new framework of flood risk assessment that considers four components, namely hazard, exposure, vulnerability, and community resilience. The key variables and coefficients were selected using principal component analysis and geographically weighted regression (GWR) in a geographic information system (GIS). GWR was applied to present a spatial relation between flood risks and regeneration areas for dependent and independent variables in the four components. Then the new conceptual framework was overlaid as a flood risk map using risk indicators of the four components. The first three components were included in the GIS framework to map flood risk indices, whereas the community resilience component was subtracted from the total indices. Finally, the total number of indices of the flood risk map were divided into nine levels, from 0 to 8. Overall, the study shows the utility of the flood risk map, which is based on the spatial coincidence of mapping of the four components in a case study of Sheffield in the United Kingdom. Areas with an index value of 8 on the flood risk map had the highest flood risks, which points to where there is a need for regeneration to improve flood defences and resilience.