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The increasing threats of global flood risk mandate rapid and accurate high-resolution flood modeling strategies over large scales. In the United States, the National Oceanic and Atmospheric Administration (NOAA) Office of Water Prediction (OWP) has operationalised a Flood Inundation Mapping (FIM) framework utilising the Height Above Nearest Drainage (HAND)-Synthetic Rating Curve (SRC) approach. It translates streamflow into stage and subsequently maps the inundation over the floodplain. It is a low-fidelity FIM framework, suitable for large-scale applications with much less computational effort. The SRCs are calculated for each river segment using Manning's equation; however, uncertainty in Manning's parameters and missing bathymetry impart bias in SRC calculation, and thus in FIM. An SRC adjustment factor (λsrc), introduced by OWP, calibrates SRCs against USGS rating curves, HEC-RAS 1D rating curves, and National Weather Service (NWS)-Categorical Flood Inundation Mapping (CatFIM) locations. Adjusted SRCs improve the FIM predictions but are limited to locations with the above data sources. In this paper, we develop machine learning models to predict the λsrc over the entire United States river network. Results show that the eXtreme Gradient Boosting model yielded the strongest predictability, with an R2 of 0.70. The impact of λsrc on FIM predictions is evaluated for Hurricane Matthew in North Carolina and synthetic flood events in 15 watersheds. For Hurricane Matthew flooding, the mean percentage improvements in Critical Success Index (CSI), Probability of Detection (POD), and F1 Score are 17.5%, 20% and 12.5%, while for synthetic events, the improvements are 2.59%, 4.93%, and 3.03%, respectively. © 2025 The Author(s)

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km2) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. © 2025 by the authors.

Les événements météorologiques extrêmes (EME) et les désastres qu’ils entrainent provoquent des conséquences psychosociales qui sont modulées en fonction de différents facteurs sociaux. On constate aussi que les récits médiatiques et culturels qui circulent au sujet des EME ne sont pas représentatifs de l’ensemble des expériences de personnes sinistrées : celles qui en subissent les conséquences les plus sévères tendent aussi à être celles qu’on « entend » le moins dans l’espace public. Ces personnes sont ainsi susceptibles de vivre de l’injustice épistémique, ce qui a des effets délétères sur le soutien qu’elles reçoivent. Face à ces constats s’impose la nécessité de mieux comprendre la diversité des expériences d’EME et d’explorer des stratégies pour soutenir l’ensemble des personnes sinistrées dans leur rétablissement psychosocial. Cet article soutient que la recherche narrative peut contribuer à répondre à ces objectifs. En dépeignant des réalités multiples, la recherche narrative centrée sur les récits de personnes sinistrées présente aussi un intérêt significatif pour l’amélioration des pratiques d’intervention en contexte de désastre. , Extreme weather events (EWE) and their resulting disasters cause psychosocial consequences that are moderated by different social factors. Media and cultural accounts of EWEs do not represent the full range of disaster survivor experiences, that is, those who experienced the most severe consequences also tend to be those least “heard” in the public arena. These people are therefore most likely to experience forms of epistemic injustice that negatively impact the support offered to cope with disaster. Considering these findings, there is a need to better understand the diversity of EWE experiences and explore strategies for supporting all disaster survivors in their psychosocial recovery. This article argues that narrative research can help meet these needs. By portraying the multiple realities of people affected by EWEs, narrative research focusing on the stories of disaster survivors is also of significant interest for improving intervention practices in this context.

AbstractThe frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

Combined sewer surcharges in densely urbanized areas have become more frequent due to the expansion of impervious surfaces and intensified precipitation caused by climate change. These surcharges can generate system overflows, causing urban flooding and pollution of urban areas. This paper presents a novel methodology to mitigate sewer system surcharges and control surface water. In this methodology, flow control devices and urban landscape retrofitting are proposed as strategies to reduce water inflow into the sewer network and manage excess water on the surface during extreme rainfall events. For this purpose, a 1D/2D dual drainage model was developed for two case studies located in Montreal, Canada. Applying the proposed methodology to these two sites led to a reduction of the volume of wastewater overflows by 100% and 86%, and a decrease in the number of surface overflows by 100% and 71%, respectively, at the two sites for a 100-year return period 3-h Chicago design rainfall. It also controlled the extent of flooding, reduced the volume of uncontrolled surface floods by 78% and 80% and decreased flooded areas by 68% and 42%, respectively, at the two sites for the same design rainfall.

This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion (SfM) technology. To this end, the Flood-Level Marks (FLMs) were created from high-resolution UAV ortho-images and compared to the flood inundated areas simulated using the HEC-RAS hydraulic model. The flood quantiles for 25, 50, 100, and 200 return periods were then estimated by synthetic hydrographs using the Natural Resources Conservation Service (NRCS). The proposed method was applied to UAV image data collected from the Khosban village, in Taleghan County, Iran, in the ungauged sub-basin of the Khosban River. The study area is located along one kilometre of the river in the middle of the village. The results showed that the flood inundation areas modeled by the HEC-RAS were 33%, 19%, and 8% less than those estimated from the UAV’s FLMs for 25, 50, and 100 years return periods, respectively. For return periods of 200 years, this difference was overestimated by more than 6%, compared to the UAV’s FLM. The maximum flood depth in our four proposed scenarios of hydraulic models varied between 2.33 to 2.83 meters. These analyses showed that this method, based on the UAV imagery, is well suited to improve the hydraulic modeling for seasonal inundation in ungauged rivers, thus providing reliable support to flood mitigation strategies

Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Au Québec, les conditions printanières extraordinaires de 2017 et 2019 ont incité le gouvernement provincial à commander une mise à jour des cartes des zones inondables. La plupart des cartes existantes ne reflètent pas adéquatement l’aménagement actuel du territoire, ni l’aléa associé. Généralement, pour la cartographie, les modèles hydrodynamiques tel que HEC-RAS sont utilisés, mais ces outils nécessitent une expertise significative, des données hydrométriques et des relevés bathymétriques à haute résolution. Étant donnée la nécessité de mettre à jour ces cartes tout en réduisant les coûts financiers associés, des méthodes conceptuelles simplifiées ont été développées. Ces approches, y compris l’approche géomatique HAND (Height above the nearest drainage), qui reposent uniquement sur un modèle numérique d’élévation (MNE), sont de plus en plus utilisées. HAND permet de calculer la hauteur d’eau nécessaire pour inonder chaque pixel du MNE selon la différence entre son élévation et celle du pixel du cours d’eau dans lequel il se déverse. Les informations sur la géométrie hydraulique dérivées par HAND ainsi que l’application de l’équation de Manning permettent la construction d’une courbe de tarage synthétique (CTS) pour chaque tronçon de rivière homogène. Dans la littérature, cette méthode a été appliquée pour établir une cartographie de la zone inondable de première instance de grands fleuves aux États-Unis avec un taux de correspondance de 90% par rapport à l’utilisation de HEC-RAS. Elle n’a toutefois pas été appliquée sur de petits bassins versants, car ceux-ci engendrent des défis méthodologiques substantiels. Ce projet s’attaque à ces défis sur deux bassins versants Québécois, ceux des rivières à la Raquette et Delisle. Les conditions frontières des modèles sont dérivées d’un traitement statistique empirique des séries de débits simulés avec le modèle hydrologique HYDROTEL. Étant donnée l’absence de stations météorologiques sur le territoire à l’étude, des chroniques du système Canadien d’Analyse de la précipitation (CaPA) ont été utilisées pour cette modélisation hydrologique. Les résultats de ce projet pointent vers des performances satisfaisantes de l’approche géomatique HAND-CTS en comparaison avec le modèle hydrodynamique HEC-RAS (1D/2D et 2D au complet), avec des taux de correspondance entre les étendues des inondations supérieurs à 60 % pour les bassins versants de Delisle et à la Raquette. Les comparaisons étaient effectuées sur une gamme de débit allant d’un débit de période de retour de 2 ans jusqu’à un débit de plus de 350 ans. On notera que l’application sur la rivière à la Raquette a été développée dans les règles de l’art, incluant un processus de calage développé dans le cadre d’un projet de maitrise en sciences de l’eau connexe à ce mémoire, relativement à la longueur du tronçon, le calage vertical de la CTS en considérant la hauteur d’eau présente dans le cours d’eau lors du relevé LiDAR et sa précision verticale. Les résultats ont montré que le coefficient de précision globale le plus bas était de 98 % pour un débit de 350 ans, avec une précision de plus que 99 % pour les autres périodes de retour, ce qui représente une très bonne performance du modèle. Et par ailleurs, le coefficient de Kappa conditionnel humide variait entre 58 % et 28 %. Alors, que pour la rivière Delisle, l’application se veut naïve, c’est-à-dire sans calage préalable de la méthode HANDCTS. La précision globale a varié entre 83 % et 96 %, ce qui est considéré comme "très approprié" et une variation du coefficient Kappa conditionnel humide de 35,2 à 64,3 %. Alors que pour une différence d’élévations d'eau entre les élévations de référence et simulées, la performance était quantifiée par un RMSE qui variait pour les périodes de retour de 100 ans et de 350 ans respectivement de 4,5 m et de 7,1 m. Enfin, la distribution spatiale des différences d’élévations montre une distribution gaussienne avec une moyenne qui est à peu près égale à 0 où la plupart des erreurs se situent entre -0,34 m et 1,1 m La cartographie des zones inondables dérivée de HAND-CTS présente encore certains défis associés notamment à la présence d’infrastructures urbaines complexes (ex. : ponceaux, ponts et seuils) dont l’influence hydraulique n’est pas considérée. Dans le contexte où l’ensemble du Québec (529 000 km²) dispose d’une couverture LiDAR, les résultats de ce mémoire permettront de mieux comprendre les sources d’incertitude associées à la méthode HAND-CTS tout en démontrant son potentiel pour les bassins versants dépourvus de données bathymétriques et hydrométéorologiques. <br /><br />The 2017 and 2019 extraordinary spring conditions prompted the Quebec government to update flood risk maps, as most of them do not adequately reflect current land use and associated hazard. Generally, hydrodynamic models such as HEC-RAS are used for flood mapping, but they require significant expertise, hydrometric data, and high-resolution bathymetric surveys. Given the need to update these maps while reducing the associated financial costs, simplified conceptual methods have been developed over the last decade. These methods are increasingly used, including HAND (height above the nearest drainage), which relies on a Digital Elevation Model (DEM) to delineate the inundation area given the water height in a river segment. Furthermore, the river geometry derived from HAND data and the application of Manning’s equation allow for the construction of a synthetic rating curve (SRC) for each homogeneous river segment. In the scientific literature, this framework has been applied to produce first-instance floodplain mapping of large rivers. For example, in the Continental United States 90% match rates were achieved when compared to the use of HEC-RAS. However, this framework has not been validated for small watersheds, as substantial methodological challenges are anticipated. This project addresses these underlying challenges in two Quebec watersheds, the à la Raquette and Delisle watersheds. The boundary conditions of the HECRAS models were derived from an empirical statistical treatment of flow time series simulated by HYDROTEL, a hydrological model, using Canadian Precipitation Analysis Product (CaPA) time series. The results of this project point towards satisfactory performances, with match rates greater than 60 % for both watersheds. It should be noted that the application on the Delisle River is naive, that is without prior calibration of the HAND-SRC method. The overall accuracy ranged from 83.4 % to 96.2 % while the water surface elevation difference was quantified by an RMSE that was for the 100-year and 350-year return periods of 4.5 m and 7.1 m respectively and where most errors are between -0.34 m and 1.1 m representing a very good model comparing to similar studies. For à la Raquette, the application showed an overall accuracy coefficient of 98 % for a 350-year flow, with an accuracy of over 99 % for other return periods. The mapping of flood risk areas using HAND-SRC still faces certain challenges, notably the presence of complex urban infrastructures (e.g., culverts, bridges, and weirs) whose hydraulic influences are not considered by this geomatic approach. Given that most of Quebec (529,000 km²) topography has been digitized using LiDAR data, the results conveyed in this MSc thesis will allow for a better understanding of the sources of uncertainty associated with the application of the HAND-SRC method while demonstrating its potential for watersheds lacking hydrometeorological and high-resolution bathymetric data.

Abstract The flood-prone Saint John River (SJR, Wolastoq), which lies within a drainage basin of 55 110 km 2 , flows a length of 673 km from its source in northern Maine, United States, to its mouth in southern New Brunswick, Canada. Major industries in the basin include forestry, agriculture, and hydroelectric power. During the 1991–2020 reference period, the SJR basin (SJRB) experienced major spring flood events in 2008, 2018, and 2019. As part of the Saint John River Experiment on Cold Season Storms, the objective of this research is to characterize and contrast these three major spring flood events. Given that the floods all occurred during spring, the hypothesis being tested is that rapid snowmelt alone is the dominant driver of flooding in the SJRB. There were commonalities and differences regarding the contributing factors of the three flood years. When averaged across the upper basin, they showed consistency in terms of positive winter and spring total precipitation anomalies, positive snow water equivalent anomalies, and steep increases in April cumulative runoff. Rain-on-snow events were a prominent feature of all three flood years. However, differences between flood years were also evident, including inconsistencies with respect to ice jams and high tides. Certain factors were present in only one or two of the three flood years, including positive total precipitation anomalies in spring, positive heavy liquid precipitation anomalies in spring, positive heavy solid precipitation anomalies in winter, and positive temperature anomalies in spring. The dominant factor contributing to peak water levels was rapid snowmelt.

Variability and nonstationarity in flood regimes of cold regions are examined using data from hydrometric reference streamflow gauging stations from 27 natural watersheds in Canada and adjacent areas of the United States. Choosing stations from reference networks with nearly 100 years of data allows for the investigation of changes that span several phases of some of the atmospheric drivers that may influence flood behavior. The reference hydrologic networks include only stations considered to have good quality data and were screened to avoid the influences of regulation, diversions, or land use change. Changes and variations in flood regimes are complex and require a multifaceted approach to properly characterize the types of changes that have occurred and are likely to occur in the future. Peaks over threshold (POT) data are extracted from daily flow data for each watershed, and changes to the magnitude, timing, frequency, volume, and duration of threshold exceedences are investigated. Seasonal statistics are used to explore changes in the nature of the flood regime based on changes in the timing of flood threshold exceedences. A variety of measures are developed to infer flood regime shifts including from a nival regime to a mixed regime and a mixed regime to a more pluvial-dominated regime. The flood regime at many of the watersheds demonstrates increased prominence of rainfall floods and decreased prevalence of snowmelt contributions to flood responses. While some individual stations show a relationship between flood variables and climate indices, these relationships are generally weak.

Abstract In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.

Abstract During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state‐of‐the‐art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up‐to‐date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods. , Plain Language Summary In order to study the 2011 Richelieu flood and prepare a tool capable of estimating the effects of climate change on the recurrence of floods, a hydrological model is applied over the Richelieu basin. The application of a distributed hydrological model is useful to simulate the flow of all the tributaries of the Richelieu basin. This new model setup stands out from past models due to its distribution in several hydrological units, its high‐resolution river network, the calibration technique, and the high‐resolution weather forcing data set used to drive the model. The model successfully reproduced the 2011 Richelieu River flood and the annual hydrograph. The simulation of the Richelieu flow was challenging due to the contrasted elevation of the Richelieu basin and the presence of the large Lake Champlain that acts as a reservoir and attenuates short‐term fluctuations. Overall, the application was deemed satisfactory, and the tool is ready to assess the impacts of climate change on the recurrence of Richelieu River floods. , Key Points An advanced high‐resolution distributed hydrological model is applied over a U.S.‐Canada transboundary basin The simulated net basin supply of Lake Champlain and the Richelieu River discharge are in good agreement with observations of the 2011 flood The flow simulation is challenging due to the topographic and meteorological complexities of the basin and uncertainties in the observations

Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.

Abstract Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-year flood) plus a freeboard. This recommendation neglects many uncertainties. Here we analyze a case-study of riverine flood risk management using a multi-objective robust decision-making framework in the face of deep uncertainties. While the quantitative results are location-specific, the approach and overall insights are generalizable. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for expanding on previous integrated analyses to further understand the nature and strength of these connections. Considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility can increase the economically optimal house elevation to values well above FEMA’s recommendation.

The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whereas the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is still lacking. Due to this knowledge gap, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. We show for the first time (to our knowledge) that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic high-resolution modeling of flood hazard and exposure. We find that rising per-capita income coincided with a global decline in vulnerability between 1980 and 2010, which is reflected in decreasing mortality and losses as a share of the people and gross domestic product exposed to inundation. The results also demonstrate that vulnerability levels in low- and high-income countries have been converging, due to a relatively strong trend of vulnerability reduction in developing countries. Finally, we present projections of flood losses and fatalities under 100 individual scenario and model combinations, and three possible global vulnerability scenarios. The projections emphasize that materialized flood risk largely results from human behavior and that future risk increases can be largely contained using effective disaster risk reduction strategies.