High Flood Risk Research Articles

Leveraging Spatial Analysis Techniques for Developing a Climate Resilient and Localized Urban Greening Plan for Pasig City

Abstract. A recent study has been conducted to evaluate the current state of green spaces in Pasig City, Philippines, and to propose a Climate Resilient and Localized Urban Greening Plan (CRLUGP) to address urban greening that is essential for creating sustainable cities, especially in the face of climate change and urban population growth. The study utilized Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) to map green spaces, analyze climate risks, and identify necessary corrective measures. The findings revealed that Pasig City currently only has 70.18 hectares of green space, which is significantly below the World Health Organization's recommended minimum of nine (9) square meters of green space per capita. Projections indicate that by 2032, the city will need 925.72 hectares of green space to meet the needs of its growing population. Additionally, the study identified a connection between low canopy cover and increased urban heat island effects, with the top 10 barangays showing a canopy cover of only 27.64%, well below the recommended 40%. Moreover, areas with limited green spaces were found to be at a higher risk of flooding, highlighting the importance of more vegetation for managing stormwater and mitigating climate-related risks. The study recommends a strategic approach to urban greening, leveraging spatial analysis for continuous monitoring and evaluation. By integrating spatial technologies and data-driven decision-making, Pasig City aims to establish a more sustainable and resilient urban environment for its residents.

Strategies for Enhancing Slum Settlement Study Case: Tegal Sari Mandala III, Medan Denai District

Tegal Sari Mandala III in the Medan Denai District is a residential area characterized by poor sanitation and a high risk of flooding, making it a mild slum. To prevent slum conditions from becoming normalized in Indonesian society, it is essential to address and eliminate such settlements. Each location presents unique challenges and requires tailored solutions to tackle slum issues effectively. This research employs both qualitative and quantitative methods, which are suitable for examining slum areas. The analysis reveals that the primary problems include poor environmental maintenance, such as garbage accumulation and improper waste management, leading to flooding, polluted rivers, and foul-smelling surroundings. Solutions to these issues include developing and enhancing the settlement quality by establishing a night market and creating green spaces along the banks of the Sulang Saling River.

The effect of wildfires on flood risk: a multi-hazard flood risk approach for the Ebro River basin, Spain

Abstract. Climate change increases the risk of wildfires and floods in the Mediterranean region. Yet, wildfire hazards are often overlooked in flood risk assessments and treated in isolation, despite their potential to amplify floods. Indeed, by altering the hydrological response of burnt areas, wildfires can lead to increased runoff and amplifying effects. This study aims to comprehensively assess flood risk using a multi-hazard approach, considering the effect of wildfires on flood risk, and integrating diverse socio-economic indicators with hydrological properties. More specifically, this study investigates current and future flood risks in the Ebro River basin in Spain for the year 2100 under the Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6) and SSP5-8.5 scenarios, taking into account projected socio-economic conditions and the effect of wildfires. An analytical hierarchy process (AHP) approach is employed to assign weights to various indicators and components of flood risk based on insights gathered from interviews with seven experts specializing in natural hazards. Results show that the influence of wildfires on the baseline flood risk is not apparent. Under the SSP1-2.6 scenario, regions with high flood risk are expected to experience a slight risk reduction, regardless of the presence of wildfires, due to expected substantial development in adaptive capacity. The highest flood risk, almost double compared to the baseline, is projected to occur in the SSP5-8.5 scenario, especially when considering the effect of wildfires. Therefore, this study highlights the importance of adopting a multi-hazard risk management approach, as reliance solely on single-risk analyses may lead to underestimating the compound and cascading effects of multi-hazards.

Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.

Triple exposure: the geographic correlation between flood risk, climate skepticism, and social vulnerability in the United States

Abstract This study investigates the geographic correlation between flood risk, climate skepticism, and social vulnerability across the United States. Our results reveal a systematic underestimation of flood risk in the Federal Emergency Management Agency’s (FEMA) Flood Insurance Rate Maps, especially in Appalachia, parts of New England, and the Northwest. These three regions face two additional risks: high levels of social vulnerability and skepticism about climate change. Nationally, there is a statisically significant correlation (0.19, p < 0.005) between flood risk and climate change skepticism, which increases (0.28, p < 0.005) in regions with high FEMA undercounts and elevated flood risk. Climate change skepticism manifests as distrust in science, an underestimation of property and community risk, and a resistance to mitigation and adaptation efforts. Indicators of social vulnerability, such as poverty rates, physical disabilities, unemployment, households in mobile homes, and lack of vehicle access, are especially pronounced in Appalachia. Addressing this geographically-embedded triple exposure—flood risk, social vulnerability, climate change skepticism— requires strategies to enhance local resilience. These include revising the 100-year floodplain categorization in FEMA’s National Flood Insurance Program to better reflect climate change, conducting public education campaigns in vulnerable communities, and scaling-up financial assistance for flood mitigation and adaptation projects.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Does flood risk affect property prices? Evidence from a property-level flood score

One in six properties in England is exposed to flood risk and around half of those affected properties can be characterised as high risk. In this paper we examine whether the probability of flooding is capitalised in England's property market prices. We use unique property-level data from Rightmove, UK's no.1 property website and the property-level FloodScoreTMby Twinn, Royal HaskoningDHV. The latter is a metric of objective flood risk based on the likelihood of an individual property being flooded due to rainfall, overflowing rivers and tidal surges and is commonly used by UK lenders. By comparing the unconditional averages of our data, we find that properties at risk are sold at an 8.14 % discount compared to non-exposed properties, and the price discount increases to 32.2 % for properties with very high flood risk. By 2080 the flood events are expected to become more frequent and the average flood risk is projected to increase by 8 %. Our empirical model suggests that one percentage point increase in properties' flood risk is associated with a decline of 0.07 % to 0.11 % in both sold and asking property prices. The impact is higher for properties of which flood risk is expected to increase or for regions that have recently experienced a flood event.

Storytelling and good relations: Indigenous youth capabilities in climate futures

AbstractHow can we support young citizens facing chaotic climate futures? This question is urgent, particularly for Indigenous communities who face disproportionate risks and impacts of climate change. For the past three decades, climate‐related education has focused largely on the acquisition of scientific knowledge in instrumental ways, while encouraging individual behaviour change. This approach centres the problem rather than human capabilities to generate solutions, which is especially misaligned with the increasing practice and significance of Indigenous communities’ regenerating self‐determining capabilities. This article reports on a pilot study that uses intergenerational storytelling methods or pūrākau to support leadership capabilities among Indigenous Māori and Pacific young people aged 10 to 14 years in communities at high risk of flooding in Ōtautahi/Christchurch, Aotearoa/New Zealand. The study showed how storytelling locates and scaffolds Indigenous young people into positions of individual and collective responsibility for grappling with “wicked problems” such as climate and injustice and climate‐related challenges as part of the future they will inherit and shape within a broader intergenerational journey of resilience and reclamation.

GIS Application for Creating Potential Flood Map Using AHP: A Case Study in Chaq-Chaq Valley, Sulaymaniyah City, Kurdistan Region, Iraq

This study aims to analyze and classify the hazards of flooding within the Chaq-Chaq sub-basin, the Sharazoor-Piramagroon basin, Sulaymaniyah City, Iraqi Kurdistan Region. The selected area is exposed to flooding from time to time. The study adopted many thematic maps, namely the topographic wetness index (TWI), elevation, slope, precipitation, land use–land cover (LULC), normalized difference vegetation index (NDVI), distance from the river, distance from the road, and drainage density. Modern technologies like remote sensing, GIS, ENVI, and AHP programs are used to obtain a digital map showing the classification of the flooding hazard within the sub-basin area. The results showed that large parts, about 50% of the basin area, are exposed to high and very high classes of flood risk. In comparison, areas of medium risk constituted about 27% of the total area of the sub-basin. Parts with low and very low risk recorded a percentage estimated at 22% of the total area. Large parts of the city of Sulaymaniyah face a high and very high risk of flooding, significantly impacting the tourist areas and economic facilities situated in the valley basin.

A Study on Flood Hazard Zonation Mapping Based on GIS-driven Approach using Remote Sensing Data and Weighted Overlay Analysis (Woa) Model

Flood hazard mapping is crucial for identifying flood-prone areas and developing effective mitigation strategies. This study utilized a Weighted Overlay Analysis, which is one of the subjective model derived from Multi-Criteria Decision Support System (MCDA), to assess flood hazards in the Auranga watershed, India. Six key flood-inducing factors were selected, including topographic data such as elevation, slope, river distance, and flow length derived from remote sensing data of Digital Elevation Model and classified Land use/Land cover data, along with hydro-meteorological data of precipitation. Each dataset was standardized to a common scale to facilitate comparison and integration. Weights were assigned to the layers like elevation, slope, river distance, flow length, Land use/Land cover, and precipitation according to their significance in contributing to flood risk. These weighted layers were then combined using GIS tools like ArcGIS, resulting in a composite map that delineates areas with varying flood hazard zones. The final flood hazard map identified that approximately 914 sq. km (60.0%) of the study area was at high to very high flood risk, particularly in regions close to rivers, while around 97 sq. km (6.4%) exhibited very low flood hazard. The GIS-Weighted Overlay Analysis method proved effective in flood hazard zone mapping, especially when incorporating a greater number of parameters. This approach is widely recognized in environmental planning and risk assessment, especially in areas vulnerable to natural disasters like floods.

Leveraging Greenspace to Manage Urban Flooding: An Investigation of Nature-Based Solutions Implementation in U.S. Public Parks

Many cities are looking to adopt nature-based solutions (NBS) in greenspace to manage urban flooding and provide diverse co-benefits. Yet little research exists to inform the planning and design of park NBS. This study investigated NBS adoption in 58 public parks across major U.S. cities, using a 2022 survey by the Trust for Public Lands and other secondary datasets. We developed a typology to conceptualize a wide range of park NBS into five high-level categories by size/capacity, location of the gray–green spectrum, and design objectives. We then employed this typology to explore how a park’s adopted NBS types may relate to its landscape and sociodemographic contexts. We found that the most used type of NBS in the studied parks was ECO (a typology we defined as conserving, restoring, or creating ecosystems to mitigate flooding through ecological processes and functions), while the least used NBS type was ENG (a typology we defined as imitating natural infiltration processes but having no living elements). Further, parks that adopted ECO had significantly higher percentages of greenspace in the surrounding, as well as higher flood risks. We also found notable—though not statistically significant—evidence of potential associations between the type of NBS implementation in a park and its nearby neighborhoods’ income level, poverty, and population racial and age compositions. Moreover, our findings indicated that park visitors were more privileged compared to residents living near a park. We concluded that park contextual factors deserve more explicit consideration in the planning and design of NBS and discussed key implications of this study for practice and future research around park NBS for urban flooding.

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness

The role of Nature-Based Solutions for the water flow management in a Mediterranean urban area

Due to climate change and rapid urban sprawl, central Mediterranean regions are subjected to short but intensive storm events resulting in a significant increase of flow rates and runoff volumes in low-lying coastal urban areas. Within the framework of green urban infrastructures (GUIs), a suitable combination of Nature-Based Solutions (NBS) with traditional grey infrastructures should be adopted to mitigate flood risk effects in urban and sub-urban areas contributing at the same time to achieve multiple benefits for both the environment and the community. The aim of this study is (i) to evaluate flood hazard maps for identifying the areas within a Sicilian hydrological river basin (Toscano catchment) with a high risk of flooding by implementing the HEC-RAS model at catchment scale; (ii) to quantify the peak flow and floodplain area reduction; (iii) to assess the effectiveness of small-scale NBS (green roofs, porous pavements, rain gardens and rain barrel), in terms of peak flow and runoff volume reductions, at urban block scale by using the EPA SWMM model Model simulations are performed integrating the hydraulic (HEC-RAS) and hydrological (EPA SWMM) models for return periods of 10, 50 and 200 years. The results showed that the estimated peak flow (m3 s−1) obtained from the simulations performed at catchment scale for each T in the current scenario (without NBS) using HEC-RAS and EPA SWMM models are very close to each other (R2 = 0.99). In addition, the utopian scenario simulation showed the HEC-RAS model sensitivity to CN calibration achieving a peak flow reduction up to about 60% with the floodplain area decreasing up to about 17%. Finally, the model EPA SWMM shows its sensitivity to NBS implementation at urban block scale with a peak flow reduction up to about 16% and a runoff volume (mm) reduction up to about 24%. These reductions decrease as T increases with a higher NBS mitigation effects for the lowest T. The results highlighted that the integration of NBS in urban areas could have hydrological and hydraulic positive effects, particularly in terms of peak flow and runoff volume reduction. Furthermore, the results suggest that small-scale NBS have a potential to be effective to smaller rainfall events, but a combination with large-scale NBS is necessary to cope with extreme events.

Flood Exposure, Vulnerability, and Risk Distribution in Urban Areas: Application of Geospatial Data Analytics and Index Development

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

The role of large reservoirs in drought and flood disaster risk mitigation: A case of the Yellow River Basin

The acceleration of water cycle processes in the context of global warming will exacerbate the frequency and intensity of extreme events and predispose to drought and flood disasters (DFD). The Yellow River Basin (YRB) is one of the basins with significant and sensitive impacts of climate change, comprehensive assessment and prediction of its DFD risk are of great significance for ecological protection and high-quality development. This study first constructed an evaluation index system for drought disaster risk and flood disaster risk based on hazard, vulnerability, exposure and the role of large reservoirs. Secondly, the weights of each evaluation index are established by the analytic hierarchy process. Finally, based on the four-factor theory of disasters, an evaluation model of DFD risk indicators is established. The impact of large reservoirs on DFD risk in the YRB is analyzed with emphasis. The results show that from 1990 to 2020, the drought disaster risk in the YRB is mainly distributed in the source area of the Yellow River and the northwest region (11.26–15.79 %), and the flood disaster risk is mainly distributed in the middle and lower reaches (30.04–31.29 %). Compared to scenarios without considering large reservoirs, the area at risk of high drought and high flood is reduced by 45.45 %, 44.22 % and 31.29 % in 2000, 2010 and 2020, respectively. Large reservoirs in the YRB play an important role in mitigating DFD risk, but their role is weakened with the enhancement of the emission scenario. Under the influence of different scenario models, the DFD risk in the YRB in 2030 and 2060 will increase, and the area of high drought and high flood risk in the middle and upper reaches of the basin will increase by 0.26–25.15 %. Therefore, the YRB should play the role of large reservoirs in DFD risk defense in its actions to cope with future climate change, while improving non-engineering measures such as early warning and emergency management systems to mitigate the impacts of disasters.

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Flood susceptibility maps provide invaluable information for assessing and managing flood-prone areas, aiding in proactive planning, risk reduction strategies, and safeguarding vulnerable communities. The current research concentrates on advancing sustainable development practices by undertaking a comprehensive assessment of flood susceptibility in the Upper Vellar basin, with a projection for 2050. Employing an integrative methodology, this study utilizes an Analytical Hierarchy Process (AHP) and Bivariate Analysis. Nine critical parameters were used: elevation, distance from the river, distance from the road, drainage density, predicted LULC, projected precipitation, slope, soil type, and Topographic Wetness Index (TWI). The Modules of Land Use Change Evaluation (MOLUSE) plugin, which uses Cellular Automata-Artificial Neural Network (CA-ANN), was employed to predict the LULC map for the year 2050. Furthermore, bias-corrected Coupled Model Intercomparison Project 6 (CMIP 6) EC EARTH 3 Model (GCM) RCP 4.5 and 8.5 projected precipitation data were employed. The resulting flood susceptibility zones are classified into three categories: low, moderate, and high, with proportions of 32.64%, 55.52%, and 11.84% for RCP 4.5, and 34.63%, 53.46%, and 11.91% for RCP 8.5, respectively, concerning the total area. In both scenarios, nearly 38% of the settlement area is at high flood risk. This study provides essential insights for policymakers and stakeholders, facilitating the formulation of sustainable strategies to address projected changes in land use, precipitation patterns, and flood susceptibility in the Upper Vellar region up to 2050.

Combining environmental-socio-economic data with volunteer geographic information for mapping flood risk zones in Zhengzhou, Henan Province, China

Due to the occurrence of extreme weather phenomena, flooding caused by precipitation has become a frequent, widespread, and highly destructive natural disaster. For densely populated urban areas, efficient identification of people's emergency rescue needs and mapping of high flood risk areas are particularly important for decision makers to formulate disaster relief strategies. In this study, we employed a machine learning approach along with volunteer geographic information (VGI)1 data and environmental-socio-economic indicators to map the flood risk area in Zhengzhou. Firstly, the sentiment analysis tool based on artificial intelligence platform initially identified the public's emergency rescue needs. Secondly, the crowd-sourcing approach and geographical approach were used to evaluate the VGI data quality and the variable importance measures (VIMs)2 method to optimize the evaluation indicators. Lastly, the flood risk area was classified in four categories: flood environmental susceptibility, flood-induced risk, flood exposure risk, and flood mitigation capacity, subsequently resulting in a comprehensive risk map of Zhengzhou. The results showed that the accuracy of flood risk assessment increased from 85.7 % to 92.4 % after VGI quality evaluation. The most important indicators affecting flood risk assessment were population density, river system and nighttime light intensity. The comprehensive flood risk map better reflected the spatial distribution of high-risk areas in Zhengzhou. It allows emergency responders to quickly identify areas that require urgent attention.

Profound interdecadal variability of the summer precipitation over the upper reaches of the Yangtze River Basin

AbstractThe upper reaches of the Yangtze River Basin (UYRB) are famous for their hydropower generation and water resources in China, relying greatly on precipitation. The UYRB summer precipitation has decreased since the early 2000s and has been exposed to a particularly dry period in the most recent two decades. By analyzing the long‐term variability from the beginning of the 20th Century, we found that the precipitation exhibits a profound periodic interdecadal variation, with a significant cycle of 30–50 years. The interdecadal variability of precipitation is shown to be significantly modulated by both the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), exerting their impact through related circulation anomalies. Moreover, the periodical nature of the UYRB precipitation variation and its correlation with AMO/PDO suggest that the UYRB might enter a wet period in the forthcoming decade, i.e. rich in hydroelectric resources but a high risk of flood.

Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors

Annually, Kogi State in Nigeria experiences significant flooding events, leading to serious fatalities, the destruction of livelihoods, and damage to vital infrastructure. This study presents a multi-faceted approach and methodology to generate a state-wide flood risk map by analyzing both flood vulnerability and flood hazard factors. Seven flood hazard factors (drainage length, distance to river, elevation, slope, rainfall, distance from confluence/dam area, and geomorphology) and vulnerability factors (population density, female population, land cover, road length, distance from hospitals, literacy rate, and employment rate) were ranked and weighted based on their contributions to flooding within the state using the Fuzzy Analytical Hierarchy Process (FAHP). From these, the Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were derived. Results showed that Kabba, Idah, Olamabor, Kotonkar, and the southern part of Ajaokuta LGAs exhibit high flood vulnerability due to dense populations, remoteness from roads and critical infrastructure, and considerable distances from healthcare facilities. Likewise, LGAs exhibiting a very high FHI occur along the geographic zones bounded by the confluence of the Niger and Benue rivers, specifically along the Lokoja area, and the Kotonkar, Kogi, Bassa, and Ibaji LGAs. Five flood risk classes—very low, low, moderate, high, and very high FRI classes—occupy 26.82, 31.12, 22.07, 15.26, and 4.71% of the area, respectively. Out of 295 villages, 65 villages are spread across the high flood risk zone. The safest LGAs include Ankpa, Omala, Dekina, Ijumu, Kabba, and Mopa-Muro LGAs.

Projected urban flood risk assessment under climate change and urbanization based on an optimized multi-scale geographically weighted regression

Previous research has primarily focused on evaluating flood risks influenced by single factors, often neglecting the combined impacts of climate change and urbanization on historical and future flood risks. This study utilized 12 assessment indicators across three criteria layers. It employed the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and an optimized Multi-scale Geographically Weighted Regression (MGWR) to develop a comprehensive assessment framework for historical and projected urban flood risks under combined scenarios of RCPs and SSPs. The risk of future extreme precipitation events increased under 4 RCPs. Compared with 2010, Beijing's urban land area is expected to increase by an average of 53.64 % in 2060 under 5 SSPs, accompanied by rapid socioeconomic development. MGWR based on K-means optimization improves operational speed by reducing the number of patches. The areas classified as high and highest flood risk levels increased by 38.87 % and 60.17 % from the 2010s to the 2060s, indicating an upward trend in future flood risk. Higher flood risk is observed under the high emissions combination scenario. Structural equation modeling (SEM) revealed that road network density (RND), runoff depth (RD), and distance to road (DRO) were the main influencing factors contributing to flood risks in Beijing.