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Urban flooding is becoming more frequent and severe due to the impact of climate change, underscoring the urgent need for effective flood risk management. This study investigates the dynamics of flood risk through two decades, from 2000 to 2020, in New Orleans, United States—a city historically marked by catastrophic flooding events. This research also explores the spatial patterns of socially vulnerable neighborhoods at the census tract level and patterns that have changed over the two decades. The Modified Normalized Difference Water Index (MNDWI) was used to indirectly evaluate flood risks over time utilizing Landsat 5 and Landsat 8 satellite imagery and geospatial analyses. Thematic mapping and geospatial analysis were used to generate maps revealing neighborhoods at the intersection of high flood risk and social vulnerability in New Orleans. Integrating flood maps derived from satellite observations with Social Vulnerability Index (SVI) calculations provides a comprehensive view of flood dynamics in the context of social vulnerability in an urban setting. The final composite products provide insight into zones where past resilience-building and risk-reduction efforts have reduced vulnerability in New Orleans and identify zones requiring intervention. The findings demonstrate how integrated data-driven analysis can inform urban infrastructure and policy development, thereby promoting discussions on urban resilience and the nuanced understanding of interactions between urban settings and flood risks, potentially aiding in implementing adaptive strategies to build resilience in New Orleans.

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.

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability’s spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers. The analysis also revealed that Socioeconomic Disadvantage, Group Quarters and Unemployment, and Housing Density and Vehicle Access are closely related to flood risk in urban areas, while in rural areas, the relationship between flood risk and factors such as Elderly and Disability and Youth and Mobile Housing is more pronounced. This study underscores the necessity for targeted, region-specific strategies to mitigate flood risks and enhance resilience, particularly in areas where high flood risk and social vulnerability converge. These findings provide critical insights for policymakers and planners aiming to address environmental justice and promote equitable flood risk management across diverse geographic settings.

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

Urbanization and climate change are two major challenges of the 21st century, and the effects of climate change, combined with the urbanization of coastal areas, increase the frequency of coastal flooding and the area exposed to it, resulting in increased risk of flooding and larger numbers of people and properties being vulnerable. An urban growth modeling system was used to simulate future growth scenarios along the coast of the Vendée region in western France, and the potential exposure to flooding with each scenario was evaluated. The model used was an Artificial Neural Network combined with a Markov Chain, using data obtained by the remote sensing and geographic information system techniques to predict three future urban growth scenarios: business as usual, environmental protection, and strategic urban planning. High-risk flood areas and future sea level projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change were then used to assess future flood risk under each growth scenario in the study area. According to the results, the different growth scenarios are associated with different development patterns, and the strategic urban planning scenario significantly reduces the risk of flooding compared to the other two scenarios. However, the rise in sea level considerably expands the areas vulnerable to flooding. Finally, the methodology adopted can be used to prepare for the impact of climate change and develop strategies to mitigate the risk of flooding in the future.

Human safety is paramount in flood disasters. Current research indicates that the majority of fatalities in such disasters are due to people moving in water. Existing studies on human stability in floods have primarily focused on the static resistance of a standing posture against water flow, neglecting the realistic scenario where people need to move and attempt self-rescue in the aftermath of destabilization. This paper introduces an analysis of the stability during the self-rescue process following a fall in floodwaters, providing insights into the baseline risks of human impact in floods. The self-rescue process is defined as the recovery to a standing position after a fall, segmented into four postures: sitting, kneeling, squatting, and standing. Additionally, considering the significant variability of the current method (D×v water depth multiplied by flow velocity) used to assess human stability in floods, this research thoroughly investigates previously undefined parameters, including submerged volume, frontal area, wet friction coefficient, and flow resistance coefficient. This leads to the development of a physically meaningful self-rescue risk assessment formula, which is validated against previous studies for accuracy, with the aim of contributing new insights to flood risk management and public education.

Extreme precipitation events, which are common natural hazards, are expected to increase in frequency due to global warming, leading to various types of floods, including pluvial floods. In this study, we investigated the probabilities of maximum 3-day precipitation amount (Rx3day) occurrences during spring in the Vojvodina region, covering both past (1971–2019) and future (2020–2100) periods. We utilized an ensemble of eight downscaled, bias-corrected regional climate models from the EURO-CORDEX project database, selecting the RCP8.5 scenario to examine future Rx3day amounts. The probabilities of occurrences of Rx3day were modeled using the GEV distribution, while the number of events where Rx3day in spring exceeds specific thresholds was modeled using the Poisson distribution. The results indicate that Rx3day with a ten-year return period during the spring months is expected to increase by 19% to 33%. Additionally, the probabilities of having more than one event where Rx3day exceeds thresholds are projected to rise by 105.6% to 200.0% in the future compared to the historical period. The analysis comparing the design values of Rx3day with future projections for the period 2020–2100 revealed that 51 drainage systems are likely to function without difficulties under future climate conditions. However, for the remaining 235 drainage systems, an increased risk of pluvial flooding was identified, as their design precipitation amounts are lower than the future projections. This study reveals that analyzing extreme rainfall events in the context of climate change yields crucial information that facilitates effective planning and policy making in water management, particularly flood protection.

The abandonment of inland areas has become a major demographical challenge, causing a condition of local fragility in terms of spatial marginalization. To deal with this issue in Italy, a number of policy actions have been implemented over the time, namely the National Strategy for Inland Areas, established a decade ago, and more recently the Next Generation EU (NGEU) to foster local economic recovery and employment. In this context, RI.P.R.O.VA.RE., a project funded by the former Italian Ministry of the Environment and Protection of Land and Sea (MATTM), was aimed at strengthening the resilience features of communities and territories, focusing on areas within the Matese and Ufita in the Campania region and the Medio Agri in the Basilicata region (southern Italy). Besides the ability to respond to different pressure factors (e.g., of demographic and economic origin), the project also addressed issues concerning seismic and flood risks. This paper synthesizes the methods and tools provided for their analysis and mitigation as a prerequisite for regenerating inland areas. After presenting the developed methods and tools, the results obtained for the Matese study area are presented and discussed. While, on the one hand, the retrieved risk zoning maps allow individuating those municipalities requiring priority structural interventions, on the other hand, the latter are properly individuated based on the peculiar characteristics of urban fabrics.

Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.

Flooding is a major environmental problem facing urban cities, causing varying degrees of damage to properties and disruption to socio-economic activities. Nigeria is the most populous African country and Kano metropolis is the second largest urban center in Nigeria, and the most populated in Northern Nigeria. The aim of the paper was to conduct a flood risk assessment of Kano metropolis. The city is divided into two hydrological basins: the Challawa and Jakara basins. Flood frequency analyses for 2 to 100-year return periods were carried out for both the basins using a Log-Pearson Type III distribution and flood inundation and hazard mapping was carried out. The social vulnerability to flooding of both basins was assessed using the method for the improvement of vulnerability assessment in Europe (MOVE) framework. Flood risk was determined as a product of flood hazard and flood vulnerability. The results showed that areas of 50.91 and 40.56 km2 were vulnerable to a 100-year flood. The flood risk map for the two basins showed that 10.50 km2 and 14.23 km2 of land in Challawa and Jakara basins, respectively, was affected by the risk of a 100-year flood, out of which 11.48 km2 covers built-up areas. As the city is densely populated, with a population density of well over 20,000 persons per square kilometer in the highly built-up locations, this means that much more than 230,000 persons will be affected by the flood risk in the two basins.

The concept of “water smart city” is increasingly being recognized as a new approach to managing urban environments (including urban floods), especially in the context of developing countries, such as Indonesia. While Indonesia’s national capital relocation plan is expected to attract significant human migration to two nearby cities, Samarinda City and the port city of Balikpapan, these cities have continuously faced with severe risk of flooding. Therefore, this research proposes a flood management approach by reviewing the local city government’s flood risk management strategies and the smart city plan to enhance flood resilience. The integration of the SETS (Social–Ecological–Technological systems) framework and the Flood Resilience Cycle is undertaken to determine the state of flood management, which is followed by a review of smart city plans and programs in two selected cities (Samarinda and Balikpapan). The research mainly identifies how it can be implemented in the two selected cities based on SETS–FRC distribution. In accordance with the SETS–FRC (Flood Resilience Cycle) framework, it is revealed that both these cities have a higher emphasis on the flood prevention phase, as compared to other resilience phases. Based on the overall results, this study emphasizes the implementation of a water smart city concept for effective and smart flood risk management.

In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the city scale. This study proposes an integrated approach based on remote sensing data using CA, Markov, and simplified hydrodynamic (FloodMap) models to accurately and effectively assess flood risk under urbanization. Taking Chongqing City as a case study, this paper analyzes the temporal and spatial variations in land use/land cover (LULC) in 2010, 2015, and 2018 and predicts the LULC for 2030, based on historic trends. Flood risk is assessed by combining the hazard, exposure, and modified vulnerability. The results suggest that the area of built-up land will increase significantly from 19.56% in 2018 to 25.21% in 2030. From 2010 to 2030, the area of medium and high inundation depths will increase by 10 and 16 times, respectively. Flood damage varies remarkably according to the LULC and return period. The expected annual damage (EAD) has been estimated to increase from USD 68 million in 2010 to USD 200 million in 2030. Flood risk is proportional to population and is significantly inversely proportional to socioeconomic level. The approach used here can provide a comprehensive understanding of flood risk and is significant for land-use policymaking and the management of flood control facilities.

The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms.

The active drainage network (ADN), as a dynamic component of a catchment, plays an important role in a catchment’s functioning. Changes in the ADN are the most noticeable during extreme hydro-meteorological events, and they result from, among others, the incorporation of man-origin incisions into the ADN. Knowledge of the parameters of the “real” ADN is a key element in the field of catchment hydrology because the ADN affects the intensity of hydro-, geomorpho-, and biological processes. The goals of this study are to assess (1) the changes in the ADN during extreme hydro-meteorological events (with special attention paid to the human-induced impact on the ADN transformation) and (2) the consequences of the ADN changes on the hydrological response of a catchment and their impact on the flood hazard/risk management processes. The study was performed in a mountain catchment, prone to flash flood occurrences. The ADN was reconstructed with the use of ALS-LiDAR data using GIS tools, and the hydrological response was evaluated by using SCS-CN and GIUH models. The results revealed that the ADN functioning during heavy rainfalls is three to four times denser than the natural-origin river drainage network (RDN) (11.4 km·km−2 vs. 2.9 km·km−2), and the RDN is significantly modified by human-origin elements (e.g., roads, ditches, furrows, etc.—they constitute ca. 1/3 of the ADN). Moreover, significant structural changes in the ADN have occurred, which were confirmed by the Hortonians’ type of analysis. The changes in the ADN have affected the hydrological response of the catchment (predominantly an increase in the peak flow—up to 7%) and the dimensions of the 1% probable flood hazard zone (increase of ca. 5%). It may be concluded that significant changes in the ADN, in the catchment studied, had a moderate impact on the changes in the flood hazard level. The results give a new insight into the flood hazard/risk assessment processes in a small flysch mountain catchment.

The coastal plains of Tamil Nadu, India, are prone to floods, the most common disaster experienced in this region almost every year. This research aims to identify flood risks in the coastal plain region of Tamil Nadu, delineated through a watershed approach with 5020 micro-administrative units covering an area of about 26,000 sq. km. A comprehensive flood risk assessment covering hazard, vulnerability, and exposure parameters was carried out using multiple datasets derived from field surveys, satellite data, and secondary data sources. The flood hazard layer was prepared on a probability scale (0–1) with the help of Sentinel-1 Synthetic Aperture Radar data coupled with GIS-based water rise modelling using Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) and reports of the District Disaster Management Plans of 13 coastal districts. In addition, the National Resources Conservation Service-Curve Number (NRCS-CN) method was adopted to estimate surface runoff potential for identifying low probability flood-prone regions. The vulnerability and exposure of the population to flood hazards were determined using census and household data-based indicators. The different categories of built-up areas were delineated and intersected with the flood hazard layer to estimate elements at flood risk. An exhaustive field survey was conducted at 514 locations of the study area, targeting deprived communities of all major settlements to validate the flood hazard layer and understand the public perceptions. The amalgamation of results shows that very high flood risk prevails in the northern parts of coastal Tamil Nadu, especially the stretch between Chennai and Cuddalore. In addition, to provide baseline datasets for the first time at micro-administrative units for the entire coastal plains of Tamil Nadu, the study offers a pragmatic methodology for determining location-specific flood risks for policy interventions.

In this study, an analysis of coastal vulnerability and flood risk due to sea-level rise was conducted in the southern margin of the Ría of Arosa, Pontevedra (Spain), which is an area of urban impact and tourist activity. The vulnerability index was calculated using the following parametric maps: lithology, geomorphology, slope, elevation, distance, coastline change, significant wave height, sea level, and extreme tidal range. This vulnerability index was validated through the results obtained from the flood risk analysis, developed for different temporal and extreme scenarios (Xa—present, Xb—100 years, Xc—500 years, Xd—1000 years, Xe—storm, and Xf—tsunami). These analyses were performed using Geographic Information System and remote sensing techniques, spatial analysis, interpolation processes, and geostatistical analysis. The results of the analysis show the vulnerable areas and areas at high risk of coastal flooding, with the urbanized area exposed to a risk of 7.45 km2. Thus, this study contributes to designing appropriate management for the coastline of the southern margin of the Ría of Arosa in the event of a flood.

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

The United Nations Office for Disaster Risk Reduction (UNDRR) resolutely endorses the development of people-centred early warning systems. Moreover, several scientific studies have deprecated the vast technology-driven technocratic approaches to implementing these systems, contending the prioritisation of communities and their contextualised needs for systems that ensure effective risk protection and resilience building. However, both the UNDRR and the scientific literature have failed to define appropriate means of implementation (MoI) for community-based early warning system (CBEWS) development. Floods are a major hazard annually impacting several communities in rural sub-Saharan Africa, and though several opportunities for participatory systems have been identified, investment in developing countries is often lagging, and no defined mechanism for developing these systems exists. Adopting a modified Integrated Emergency Management Framework, this study demonstrates that an impact-based CBEWS can be established by leveraging existing resources, institutions and stakeholders, essentially merging last-mile and first-mile approaches. The study further reveals that directly linking technical capacities with community-based efforts allows communities to define system development parameters, strengthen risk knowledge and response, and build resilience for improved physical, economic and environmental protection, essentially bridging the gap between first and last-mile approaches. The study also highlights the need for governments to appropriately streamline DRR to improve coordination and communication.

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim.

The integration of early warning and evacuation systems (EWES) with estimations for mitigating the loss of life in flood risk assessments marks an advancement towards developing robust emergency action plans for dam breaks. Through the simulation of diverse EWES scenarios, the impact of these systems, coupled with community preparedness, on minimising the potential for loss of life could be calculated. This study was conducted in the theoretical context of a dam break located upstream from a densely populated urban region in Brazil. Hydrodynamic and agent-based models were utilised to estimate potential loss of life across various scenarios and simulations. The Monte Carlo approach, in combination with the LifeSim model, was applied to assess how factors such as warning issuance timing, evacuation strategies and community responses impact the model’s outcomes. Sensitivity analysis was performed considering the overall exposed area and specific areas at risk for different spatiotemporal EWES strategies. The results of simulations highlighted the EWES’ great potential for risk mitigation and displayed optimal times for warning issuance. The warning diffusion and the protective action initiation parameters proved crucial for improving EWES. The spatiotemporal sectorisation of the alert and evacuation was also an effective strategy to optimise EWES. This methodology should allow for further similar tests and incite EWES improvements based on consistent loss of life alleviation simulations.

The present study is part of the context in which Romania adopted the European Parliament Directive 2007/60/EC on flood risk assessment and management. Therefore, the aim of this research is to assess the risk induced by a hydrological hazard, expressed by a financial value estimation, for the Turcu River in the northern sector of the Bran–Dragoslavele transcarpathian corridor (Romania), an important tourist axis where the pressure on land has increased considerably. As a result, the intra-village areas of Moieciu de Sus, Cheia, Moieciu de Jos, Bran and Tohanu Nou have also expanded into areas vulnerable to flooding. There are currently no studies on the areas potentially affected as well as the extent of the possible damage. For this reason, we proceeded to model the water level corresponding to the maximum flow value with a probability exceeding 1%, using HEC-RAS and ArcGIS software. The results of the implementation of the spatial analysis model resulted in the delineation of the floodplain and the assessment of the potential financial loss related to the minimum market value of the land with the related real estate infrastructures. The research reveals that in the 1% band area (78.7841 ha) with water depth > 0.5 m, more than 433 infrastructures are at high risk of flooding, most of them with high real estate value, i.e., 5.61 km of roads for which a cost of EUR 3,402,666.90 was calculated for restoration. A knowledge of financial vulnerability to flooding becomes important for the community; local authorities involved in making decisions for insuring real estate at risk and planning/managing investments work to prevent/combat the effects of flooding.

Flood risk assessment is an important tool for urban planning, land development, and hydrological analysis. The flood risks are very high in arid countries due to the nature of the rainfall resulting from thunderstorms and the land cover, which comprises mostly very dry arid soil. Several methods have been used to assess the flood risk, depending on various factors that affect the likelihood of occurrence. However, the selection of these factors and the weight assigned to them remain rather arbitrary. This study assesses the risk of flood occurrence in arid regions based on land cover, soil type, precipitation, elevation, and flow accumulation. Thematic maps of the aforementioned factors for the study area were prepared using GIS. The Fuzzy Analytic Hierarchy Process (F-AHP) was used to calculate the likelihood of the flood occurrence, and land use was used to assess the exposure impact. Using the likelihood map (i.e., probability) from the Fuzzy-AHP and an exposure map, the flood risk was assessed. This method was applied to Qatar as a case study. Results were compared with those produced by fuzzy logic. To explore the pairwise importance of the F-AHP, equal weight analysis was performed. The resulting risk map shows that the majority of urbanized areas in Qatar are within the high-risk zone, with some smaller parts within the very high flood-risk area. The majority of the country is within the low-risk zone. Some areas, especially land depressions, are located within the intermediate-risk category. Comparison of Fuzzy logic and the F-AHP showed that both have similarities in the low-risk and differences in the high-risk zones. This reveals that the F-AHP is probably more accurate than other methods as it accounts for higher variability.

Flood risk has increased distressingly, and the incidence of waterborne diseases, such as diarrhoeal diseases from bacteria, has been reported to be high in flood-prone areas. This study aimed to evaluate the flood risk patterns and the plausible application of flow cytometry (FCM) as a method of assessment to understand the relationship between flooding and waterborne diseases in Malaysia. Thirty years of secondary hydrological data were analysed using chemometrics to determine the flood risk patterns. Water samples collected at Kuantan River were analysed using FCM for bacterial detection and live/dead discrimination. The water level variable had the strongest factor loading (0.98) and was selected for the Flood Risk Index (FRI) model, which revealed that 29.23% of the plotted data were high-risk, and 70.77% were moderate-risk. The viability pattern of live bacterial cells was more prominent during the monsoon season compared to the non-monsoon season. The live bacterial population concentration was significantly higher in the midstream (p < 0.05) during the monsoon season (p < 0.01). The flood risk patterns were successfully established based on the water level control limit. The viability of waterborne bacteria associated with the monsoon season was precisely determined using FCM. Effective flood risk management is mandatory to prevent outbreaks of waterborne diseases.

The 2021 flooding events in The Netherlands, Belgium and Germany have sensitized many municipalities across Europe and especially in Germany, the federal states of North Rhine-Westphalia (NRW) and Rhineland-Palatinate, having been particularly affected, have formulated more extensive requirements for the municipalities to be able to manage these drastic situations. Flood risks and heavy rainfall maps are crucial instruments in this context because they serve as a basis for identifying risks and also for selecting suitable measures to minimize them and, in 2021, in Germany, the Federal Agency for Cartography and Geodesy (BKG) made available for the first time a state-wide, cross-municipal heavy rainfall information map for the state of North Rhine-Westphalia, in which heavy rainfall and flooding risks are recognizable for every citizen. Therefore, municipalities that already had their own heavy rainfall maps had a comparative possibility of plausibility checks, while those that did not yet have their own heavy rainfall map, had the possibility to benefit from these resources. Especially in this second scenario, the municipalities and the wastewater utilities need reliable practical advice on how to classify the information from the maps regarding drainage consequences, and this paper aims to fill this void, summarizing the experience of the 80 municipalities represented in the KomNetAbwasser in dealing with heavy rainfall maps and discussing requirements for their informative value. Furthermore, measures for risk minimization and their prioritization are also proposed, focusing on risk identification, organizational tasks, action planning and implementation, analysis of the hazard maps, risk assessment and the selection of emergency measures.

The advantages derived from the use of Uncrewed Aerial Vehicles (UAVs) are well-established: they are cost-effective and easy to use. There are numerous environmental applications, particularly when monitoring contexts characterized by rapid morphological changes and high rates of sediment transport, such as coastal areas. In this paper, three different case studies of survey and monitoring with high resolution and accuracy obtained through the use of UAVs are presented; these concern transgressive coastal sites. Results allow for the definition and quantification of coastal landforms and processes, including: (i) The anatomy of a parabolic dune and the rate of landward migration that could interfere with a tourist settlement; (ii) The mode and timing of morphological recovery and realignment of a barrier island overwashed by storm surge episodes; and (iii) The potential flood risk of a progradational spit that is a nesting site of a species of migratory breeding birds of conservation concern. The results demonstrate and confirm that, through a good coupling of drone-sensed quality data and accurate topographic control, quantitative estimates that are useful in assessing the impacts of natural processes involving both human and natural assets can be obtained.

Floods are natural phenomena, inextricably related to river regimes, which can threaten human health and life, the environment, cultural heritage, economic activity and infrastructure. The aim of the research is to assess the connection between rainfall and river flood risk. The proposed methodology is presented on the example of the upper Nysa Kłodzka River (NKR) catchment and Kłodzko town located on NKR, which are two of the most flood-prone areas in the Odra River basin. The methodology is based on the well-established methods of potential flood losses (PFL) estimation and the copula-based model, allowing an assessment of connections between rainfall and flood losses in a probabilistic way. The results are presented using the ‘synchronicity’ measure. Seventeen significant summer (rainfall-driven) flood waves were selected, for which PFL were estimated and cumulative rainfall was calculated for 24, 48, 72, 96 and 120 h preceding the flood peak. It was found that the synchronicity of PFL and the 24 h rainfall was the lowest among the analyzed variants, while for the 48 to 120 h rainfall the highest synchronicity was identified at precipitation gauge Podzamek.

This study evaluates how the characteristics of daily rainfall and extreme events in Côte d’Ivoire changed during 1961–2015 using the rain gauge observation network of the National Meteorological Service (SODEXAM). The results indicate that the northern and southern parts of Cote d’Ivoire experienced a change from a wet to a dry period, with cut-offs in 1982 and 1983, respectively. In the northern part, this dry period was marked by a decrease in rainfall intensity, the length of wet spells, and the contribution of heavy and extreme rainfall, as well as an increase in the number of rainy days and a decrease in the length of dry spells. Over the southern part, this dry period was marked by an increase in the maximum length of dry spells associated with an increase in the maximum 1-day and 5-day precipitation events. The western part of Côte d’Ivoire experienced a late cut-off from the wet to dry period in 2000; the dry period was associated with a decrease in the number of rainy days, rainfall intensities, and maximum length of wet spells. Changes in the central part of Cote d’Ivoire presented high variability, and trends were less marked, even though a cut-off from a wet to dry period was detected in 1991. This study shows that Côte d’Ivoire, which is located in a subhumid and humid region and has an economy dependent on agriculture (especially cash crops, which comprise 60% of the GDP), is experiencing dry spells that are increasing in frequency and length. Combined with deforestation to increase production, this situation could lead to desertification and compromise the sustainable development goals of the country. The contribution of heavy rainfall was found to increase during the last 15 years, increasing the overall risk of floods, especially in urban areas where city authorities and populations are not prepared, thereby threatening infrastructure and human security.

The present work is aimed at assessing the change in time of flood risk as a consequence of landscape modifications. The town of San Donà di Piave (Italy) is taken as a representative case study because, as most parts of the North Italy floodplains, it was strongly urbanized and anthropized in the last several decades. As a proxy for flood risk, we use flood damage to residential buildings. The analysis is carried out at the local scale, accounting for changes to single buildings; GIS data such as high-resolution topography, technical maps, and aerial images taken over time are used to track how the landscape evolves over time, both in terms of urbanized areas and of hydraulically relevant structures (e.g., embankments). Flood hazard is determined using a physics-based, finite element hydrodynamic code that models in a coupled way the flood routing within the Piave River, the formation of levee failures, and the flooding of adjacent areas. The expected flood damage to residential buildings is estimated using an innovative method, recently proposed in the literature, which allows estimating how the damage evolves during a single flood event. The decade-scale change in the expected flood damage reveals the detrimental effect of urbanization, with flood risk growing at the pace of a fraction of urbanized areas. The within-event time evolution of the flood damage, i.e., how it progresses in the course of past or recent flood events, reflects changes in the hydrodynamic process of flooding. The general methodology used in the present work can be viewed as a promising technique to analyze the effects on the flood risk of past landscape evolution and, more importantly, a valuable tool toward an improved, well-informed, and sustainable land planning.

We developed a novel iterative classifier optimizer (ICO) with alternating decision tree (ADT), naïve Bayes (NB), artificial neural network (ANN), and deep learning neural network (DLNN) ensemble algorithms to build novel ensemble computational models (ADT-ICO, NB-ICO, ANN-ICO, and DLNN-ICO) for flood susceptibility (FS) mapping in the Padma River basin, Bangladesh. The models consist of environmental, topographical, hydrological, and tectonic circumstances, and the final result was chosen based on the causative attributes using multicollinearity analysis. Statistical techniques were utilized to assess the model’s performance. The results revealed that rainfall, elevation, and distance from the river are the most influencing variables for the occurrence of floods in the basin. The ensemble model of DLNN-ICO has optimal predictive performance (AUC = 0.93, and 0.91, sensitivity = 0.93 and 0.92, specificity = 0.90 and 0.80, F score = 0.91 and 0086 in the training and validation stages, respectively) followed by ADT-ICO, NB-ICO, and ANN-ICO, and might be a viable technique for precisely predicting and visualizing flood events.

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta.

Accelerated coastal erosion and elevated risks of flooding due to global warming put enormous burden on the ecosystems and economic health of coastal communities. Optimal policies to lessen these negative impacts require an estimation of their costs and benefits. The aim of this paper is to calculate the costs of beach erosion and flood risk through the valuation of property prices in Hilton Head Island, a barrier island located in South Carolina, USA. Spatial lag hedonic pricing was introduced in order to account for spatial autocorrelation in the dataset. The results show that properties that are located within the zone of high, or very high, flood risk experience a 15.6% reduction in value. The implicit price of being located close to an eroded beach is approximately 26% of the price of an oceanfront property. However, this negative impact on property value diminishes with distance from the shoreline.

Floods are frequent natural hazards, triggering significant negative consequences for the economy every year. Their impact is expected to increase in the near future due to socio-economic development and climate change. In order to minimize the probability and magnitude of expected economic losses and compensation costs, it is essential that flood risk managers are properly informed about potential damage related to hazard features and exposure. In this paper, a flood damage estimation method was proposed for the assessment of flood risk in the Drammen River basin by using a hydraulic model, GIS, and a flood loss estimation model. Hazard variables such as flood depth, flood extent, and flood velocity were computed for the current and future climatic scenarios using the hydraulic model for flood damage assessment. To visualize the flood extent, velocity, depth, and their impact, the results of modelling are illustrated in the form of flood inundation maps produced in GIS. A flood loss estimate included buildings and other infrastructure that are major exposures in flood-prone areas. The flood damage model is formulated based on stage–damage relationships between different flood depths and land-use categories. It calculates the economic loss related to different land-use features based on the simulated flood parameter obtained from the hydraulic model from 100- to 1000-year return periods. For the case study, the results show that the highest proportion of the total damage in each repetition interval (approximately 90–92%) is expected to occur in buildings. In addition, results showed that the effects of climate change will raise the total damage from floods by 20.26%.

The functional relationship between detention dam inflows and outflows was derived in a closed form in a recent work, which led to a theoretically derived probability distribution (TDD) of the peak outflows from in-line detention dams. This TDD is tested using the generalized extreme value (GEV) as a reference distribution for floods.

Pluvial flooding causes significant damage in urban areas worldwide. The most common approaches to mitigating these impacts at regional scales include structural measures such as dams, levees and floodways. More recently, the use of nature-based solutions (NBS) is receiving increasing attention, as such approaches are more adaptive than structural measures and have a number of potential co-benefits (e.g., improvements in water quality and amenity). As NBSs are generally applied at house or block scales in urban areas, their potential for reducing the impacts of urban flooding at the regional scale are unknown. We introduce an approach that enables the potential of using portfolios of NBSs to reduce the impact of urban flooding to be assessed at the regional scale. This approach enables the most suitable locations for such portfolios of NBSs to be identified, as well as their effectiveness to be modeled at spatial resolutions that are commonly used for regional planning studies. The approach is applied to a case study area to the north of Adelaide, South Australia, with results obtained suggesting that there is significant potential for using strategically placed portfolios of NBSs to reduce the impact of pluvial flooding in urban areas at the regional scale.

In the planning and design of natural and urban environments, visualization plays an increasingly important role. It has become a core component of communication and dissemination within the various formats of project representation, environmental assessment, workshops, and stakeholder involvement in general. However, as a practical technology, the outputs of our visualizations are, too often, regarded as a static product rather than as a living and evolving tool in and of itself, often due to the inherent restrictions present in both the analog and digital technologies used in data curation and visualization creation. In this paper, we argue that with the increasing complexity and usability of digital technologies, we are now capable of bringing a heightened level of interactive dynamism to planning and design, improving the communicative power of landscape visualization. We introduce a theoretical adaptive visualization (AV) framework, designed to support project meetings and stakeholder interactions with iterative planning and design elements. To demonstrate the merits of the framework, we develop an augmented reality application following AV principles; we discuss the novel design interactions afforded by integrating alongside traditional analog and digital data sources, in an interactive and dynamic application. This is highlighted by a case study from the Pearl River Delta region, with a focus on planning and design for flood risk mitigation.

The article presents an analysis of the potential consequences for Cracow of failure of dams located in the Vistula catchment area upstream of the city. They have been compared with the effects of a flood with a probability of 0.2%. The estimation of losses and damages was made on the basis of the results of model studies and flood hazard and flood risk assessments carried out as part of the analyses of the second planning cycle of the implementation of the Floods Directive in Poland. The MIKE 11 model was used to simulate flows in riverbeds and its Dambreak module is used to simulate dam failure. An adjustment of the loss index for Cracow’s residential areas was proposed. Some elements of the described methodology can be used to estimate flood losses in large cities. They can also be applied to cities that are not at risk of dam disasters.

The aim of this study is to analyse the effects of reservoir operating scenarios, for flood damage evaluation downstream of a dam, using a Monte Carlo bivariate modelling chain. The proposed methodology involves a stochastic procedure to calculate flood hydrographs and the evaluation of the consequent flood inundation area by applying a 2D hydraulic model. These results are used to estimate the inundation risk and, as consequence, the relative damage evaluation under different water level conditions in an upstream reservoir. The modelling chain can be summarized as follows: single synthetic stochastic rainfall event generation by using a Monte Carlo procedure through a bivariate copulas analysis; synthetic bivariate stochastic inflow hydrograph derivation by using a conceptual fully distributed model starting from synthetic hyetographs above the derived; flood hydrographs routing through the reservoir taking in an account of the initial level in the reservoir; flood inundation mapping by applying a 2D hydraulic simulation and damage evaluation through the use of appropriate depth-damage curves. This allowed for the evaluation of the influence of initial water level on flood risk scenarios. The procedure was applied to the case study of the floodplain downstream from the Castello reservoir, within the Magazzolo river catchment, located in the southwestern part of Sicily (Italy).

Extreme rainfall associated with mid-tropospheric cut-off low (COL) pressure systems affected the entire east coast of South Africa during April 2022, leading to flooding and destruction of homes, electricity power lines, and road infrastructure, and leaving 448 people confirmed dead. Therefore, this study investigated the evolution of the two COLs and their impacts, including the occurrence of extreme rainfall and cold weather over the southeast coast of the country. We analysed observed and reanalysis meteorological data and mapped areas at risk to impacts of flood hazards on the east coast of South Africa. Extreme rainfall (>500 mm) accumulated over 16 days was observed along the east coast, with the amount of rainfall progressively decreasing inland. We found that the rainfall associated with the first COL was significantly enhanced by the interactions between a strong low-level onshore airflow across the Agulhas Current and the coastal escarpment, resulting in deep convection and lifting. An unusual surface cyclone with tropical characteristics developed over the subtropical southwest Indian Ocean, driving onshore southeasterly winds which enhanced low-level convergence. Moreover, the flood risk results revealed that, amongst others, land cover/use (52.8%), elevation (16.8%) and lithology (15.5%) were the most important flood predictor variables in this study. Much of the study area was found to have very low (28.33%), low (31.82%), and moderate (21.66%) flood risk, whilst the high- and very-high-risk areas accounted for only 17.5% of the total land area. Nonetheless, the derived flood risk map achieved an acceptable level of accuracy of about 89.9% (Area Under Curve = 0.899). The findings of this study contribute to understanding extreme rainfall events and the vulnerability of settlements on South Africa’s east coast to flood risk, which can be used towards natural disaster risk reduction.

We pose the following research question: “what is the effect of flood protection works on flood risk?” To answer this question, we developed a flood risk assessment method that combines the typical hazard assessment via integrated hydrological and hydrodynamic calculations using HEC-HMS and 1D/2D HEC-RAS, respectively, and an original procedure for vulnerability assessment at the building level, which we applied in the town of Mandra in Attica, Greece. We performed calculations for 15 scenarios—combinations of return periods (T = 20, 50, 100, 150, and 200 y) and rain durations (t = 6, 12, and 18 h)—for the conditions of the year 2017, when there were no flood protection works, and today with these works in place. We identified the regions with high flood risk and concluded that the presence of the works caused a decrease in the inundation areas by 53–89%, along with reductions in the maximum water depths, the maximum flow velocities, and the average flood risk in Koropouli Street—the main street of Mandra, which suffered severe damage during the 2017 flood—by 38–62%, 18–52%, and 27–74%, respectively. The effect of the flood protection works increased with the increases in the return period and rain duration, while for the same return period the effect of the rain duration was more pronounced for the smaller return periods.

Converting floodwater into power without increasing flood risk is critical for energy-stressed regions. Over the past decades, numerous methods have been proposed to solve this problem. However, few studies have investigated the theoretical explanation of the trade-offs between power generation and flood risk. This study establishes an analytical framework to derive optimal hedging rules (OHR) and explains the economic insights into flood risk reduction and power generation improvement. A two-stage model based on the concept of dynamic control of carryover storage (DCCS) was developed as part of the framework, considering forecast uncertainty and risk tolerance. The results illustrated that hedging and trade-offs between power generation and flood risk during DCCS only occurs when the forecasted inflow and forecast uncertainty are within certain ranges, beyond which there is no hedging and trade-offs analysis; either power generation or flood risk becomes the dominant objective. The OHR was divided into three cases under different levels of forecast uncertainty and risk tolerance. Compared to forecast uncertainty, downstream risk tolerance plays a more important role in determining which case of the OHR is adopted in real-world operations. The analysis revealed what and how intense trade-offs are between power generation and flood risk under different scenarios of forecasted inflow, forecast uncertainty, and risk tolerance. The framework serves as a guideline for less abundant water resources or energy-stressed areas of operational policy. Nierji Reservoir (located in northeast China) was taken as a case study to illustrate the analysis, and the application results showed that OHR increases the average annual power generation by 4.09% without extra flood risk compared to current operation rules.

Floods have been among the costliest hydrometeorological hazards across the globe for decades, and are expected to become even more frequent and cause larger devastating impacts in cities due to climate change. Digital twin technologies can provide decisionmakers with effective tools to rapidly evaluate city resilience under projected floods. However, the development of city digital twins for flood predictions is challenging due to the time-consuming, uncertain processes of developing, calibrating, and coupling physics-based hydrologic and hydraulic models. In this study, a flood prediction methodology (FPM) that integrates synchronization analysis and deep-learning is developed to directly simulate the complex relationships between rainfall and flood characteristics, bypassing the computationally expensive hydrologic-hydraulic models, with the City of Calgary being used for demonstration. The developed FPM presents the core of data-driven digital twins that, with real-time sensor data, can rapidly provide early warnings before flood realization, as well as information about vulnerable areas—enabling city resilience planning considering different climate change scenarios.

Flood risk management is crucial for climate change resilience. The Hadejia River basin is known for severe and frequent floods, which have destroyed houses and farmlands and claimed many lives. This study developed a GIS-based flood risk and vulnerability mapping assessment using the Analytical Hierarchical Process (AHP) to outline scenarios that reduce risk and vulnerability associated with floods in the Hadejia River basin. The risk mapping of the basin integrated seven hydro-geomorphological indicators influencing extreme events (elevation, mean annual rainfall, slope, distance from rivers, soil type, and drainage density) and six socio-economic vulnerability indicators (population density, female population density, literacy rate, land use, employment rate, and road network) using a multi-criterion analysis. The average annual rainfall data of 36 years (1982–2018) were used for flood plain mapping in this study. Combining the flood hazard and socio-economic vulnerability indices of the basin revealed high-to-very high flood risk in the downstream and central upstream portions of the basin, which cover about 43.4% of the basin area. The local areas of Auyo, Guri, Hadejia, Ringim, Kafin Hausa, and Jahun were identified as zones at a very high flood risk. The study also revealed that flood hazard and vulnerability indicators have different influences on flood risk. The validated results resonate with the records of previous flood distribution studies of the basin. This research study is significantly important for developing strategic measures and policy revision through which the government and relief agencies may reduce the negative impact of floods in the Hadejia River basin.

To provide quantitative elements to the expected rate of change of flood quantiles in Alpine basins due to global warming, a systematical assessment of a river network is undertaken in this paper through the implementation of a geomorphoclimatic approach. The model, called “FloodAlp” produces the flood frequency curve of mountainous catchments based on the stochastic interaction of precipitation and temperature. In view of a widespread application, FloodAlp was revamped here and applied to all the sections of the river network of the Chisone basin (Northwest Italy) identified through a 50 m resolution digital elevation model (DEM). We show that, based on the decrease in snow-affected contributing areas, flood frequency can increase up to 8 times compared to the current frequency, with amplifications depending on the local elevation characteristics of sub-basins.

The design of hydraulic structures needs to account for a trade-off between implementation costs and flood damages, as well as for the impacts on basins hydrological responses over a wide spectrum of events. In this work, a new methodology for dimensioning an in-line detention dam that integrates geomorphic, probabilistic and economic modeling is proposed. It is formulated as an economic optimization problem aimed at minimizing the sum of the construction cost and the cost of the residual flood risk on residential buildings. The optimization procedure was applied to a hypothetical in-line detention dam located upstream of the urban area of Castellammare di Stabia (Naples, Italy).

Floods are one of the most frequent natural disasters today. Hence, it is highly important to explore the effect of flood risk on residential land prices to promote the rational allocation of land resources and incorporate climate change risk control into territorial spatial planning. This paper takes the primary urban area of Hangzhou as an example, based upon data from 424 residential land plots. With spatial autocorrelation analysis and the Spatial Durbin Model (SDM) approach, the spatial effect of flood risk on residential transaction land price was investigated. The results show that, ceteris paribus, plots with high risk of flooding suffer a price discount of 8.62%. The unique mechanism of the way flood risk affects land prices was discussed further from the perspectives of land ownership and land price systems in China. Furthermore, when the land price in surrounding areas increases one percent, the land price in the area will increase 14.32%. The spatial spillover effects of land price were analyzed with the flood information disclosure system and the stakeholders’ considerations in land price comparison. The effect of flooding on residential land prices in Hangzhou is the result of government regulations and market allocations, which are fundamentally different from those of the free market allocations in many western countries. Interestingly, the risk of flooding is capitalized into the price, whether it is determined by government or market pricing. Integrating flood risk into land price determination can help promote the optimal allocation of land resources and minimize depreciation attributable to flood disasters.

Flood vulnerability is the key to understanding and assessing flood risk. However, analyzing flood vulnerability requires sophisticated data, which is usually not available in reality. With the widespread use of big data in cities today, it is possible to quickly obtain building parameters in cities on a large scale, thus offering the possibility to study the risk flooding poses to urban buildings. To fill this research gap, taking Shanghai as an example, this study developed a new research framework to assess urban vulnerability based on vulnerability curves and online data of residential buildings. First, detailed information about residential buildings was prepared via web crawlers. Second, the cleaned residential building information fed a support vector machine (SVM) algorithm to classify the buildings into four flood vulnerability levels that represented the vulnerability curves of the four building types. Third, the buildings of different levels were given vulnerability scores by accumulating the depth–damage ratios across the possible range of flood depth. Further, combined with the unit price of houses, flood risk was assessed for residential buildings. The results showed that the F1-score for the classification of buildings was about 80%. The flood vulnerability scores were higher in both the urban center and the surrounding areas and lower between them. Since 1990, the majority of residential buildings in Shanghai have switched from masonry–concrete structures to steel–concrete structures, greatly reducing the vulnerability to floods. The risk assessment showed decreasing risk trend from the center outward, with the highest risk at the junction of the Huangpu, Jing’an and Xuhui districts. Therefore, this framework can not only identify the flood vulnerability patterns but also provide a clue for revealing the flood risk of residential buildings. With real estate data becoming increasingly accessible, this method can be widely applied to other cities to facilitate flood vulnerability and risk assessment.

Post-disaster flood risk assessment is extremely difficult owing to the great uncertainties involved in all parts of the assessment exercise, e.g., the uncertainty of hydrologic–hydraulic models and depth–damage curves. In the present study, a robust and fast data-driven tool for residential flood risk assessment is introduced. The proposed tool can be used by scientists, practitioners and/or stakeholders as a first step for better understanding and quantifying flood risk in monetary terms. Another contribution of the present study is the fitting of an equation through depth–damage points provided by the Joint Research Center (JRC). The approach is based on hydrologic simulations for different return periods, employing a free and widely used software, HEC-HMS. Moreover, flood depths for the study area are estimated based on hydrodynamic simulations employing the HEC-RAS software and the Inverse Distance Weighting (IDW) interpolation method. Finally, flood risk, in monetary terms, is determined based on the flood depths derived by the coupling of hydrodynamic simulations and the IDW method, depth–damage curves reported in the literature, vulnerability of residential areas and the residential exposure derived by employing GIS tools. The proposed tool is applied in a highly urbanized and flood-prone area, Mandra city, in the Attica region of Greece. The results are maps of flood depths and flood risk maps for specific return periods. Overall, the results derived from the application of the proposed approach reveal that the tool can be highly effective for post-disaster flood risk management. However, it must be noted that additional information and post-disaster data are needed for the verification of the damages from floods. Additional information can result in better calibration, validation and overall performance of the proposed flood risk assessment tool.

The interplay of multi-reservoirs is critical in reservoir joint disposal and water conservancy projects. As the flood risk of upstream hydrological stations could be transferred and unevenly distributed to downstream tributary stations, flood risk transfer through multi-reservoirs warrants further investigation. This study proposed a copula simulation approach to develop a joint flood risk distribution of multi-reservoirs (spanning Xianyang, Huaxian County, and Zhangjiashan) in a drainage tributary of the Weihe River. Pair-copulas of each reservoir pair were constructed to analyse the correlations between the reservoir sites. The approach was then used to create a joint flood risk distribution for the reservoirs. The flood risk and corresponding flood volume of Zhangjiashan were calculated based on the flood risk levels of Xianyang and Huaxian County. The results indicate that the flood risks of Huaxian County would be transferred to Xianyang and Zhangjiashan to some extent, and Xianyang could mitigate more flood risks from Huaxian County than from Zhangjiashan. The findings have significance for informed decision-making regarding the Zhangjiashan reservoir construction project.

The sensitivity of simulated flood depth and area to DEM resolution are acknowledged, but their effects on flood damage and risk estimates are less well understood. This study sought to analyse the relative benefits of using global DEMs of different resolution sizes, 5 m AW3D Standard, 12.5 m ALOS PALSAR and 30 m SRTM, to simulate flood inundation, damage and risk. The HEC-RAS 2D model was adopted for flood simulations, and the Toba River in the Klang River Basin in Malaysia was chosen for the case study. Simulated inundation areas from AW3D coincide the most with reported flooded areas, but the coarser-resolution DEMs did capture some of the reported flooded areas. The inundation area increased as the resolution got finer. As a result, AW3D returned almost double flood damage and risk estimates compared to ALOS PALSAR, and almost quadruple compared to SRTM for building-level damage and risk analysis. The findings indicate that a finer-resolution DEM improves inundation modelling and could provide greater flood damage and risk estimates compared to a coarser DEM. However, DEMs of coarser resolution remain useful in data-scarce regions or for large-scale assessments in efforts to manage flood risk.

Quantifying flood inundation and hazards over large regions is paramount for gaining critical information on flood risk over the vulnerable population and environment. Readily available global data and enhancement in computational simulations have made it easier to simulate flooding at a large scale. This study explores the usability of publicly available datasets in flood inundation and hazard mapping, and ensures the flood-related information reaches the end-users efficiently. Runoff from the North American Regional Reanalysis and other relevant inputs are fed to the CaMa-Flood model to generate flooding patterns for 1 in 100 and 1 in 200-year return period events over Canada. The simulated floodplain maps are overlaid on the property footprints of 34 cities (falling within the top 100 populated cities of Canada) to determine the degree of exposure during 1991, 2001 and 2011. Lastly, Flood Map Viewer—a web-based public tool, is developed to disseminate extensive flood-related information. The development of the tool is motivated by the commitment of the Canadian government to contribute $63 M over the next three years for the development of flood maps, especially in high-flood risk areas. The results from the study indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-year event, which is roughly 4 percent more than observed during the 100-year event. We notice an increase in the properties exposed to flooding during the last three decades, with a signature rise in Toronto, Montreal and Edmonton. The flood-related information derived from the study can be used along with vulnerability and exposure components to quantify flood risk. This will help develop appropriate pathways for resilience building for long-term sustainable benefits.