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Abstract Previous studies have drawn attention to racial and socioeconomic disparities in exposures associated with flood events at varying spatial scales, but most of these studies have not differentiated flood risk. Assessing flood risk without differentiating floods by their characteristics (e.g. duration and intensity of precipitation leading to flooding) may lead to less accurate estimates of the most vulnerable locations and populations. In this study, we compare the spatial patterning of social vulnerability, types of housing, and housing tenure (i.e. rented vs. owned) between two specific flood types used operationally by the National Weather Service—flash floods and slow-rise floods—in the floodplains across the Contiguous United States (CONUS). We synthesized several datasets, including established distributions of flood hazards and flooding characteristics, indicators of socioeconomic status, social vulnerability, and housing characteristics, and used generalized estimating equations to examine the proportion of socially vulnerable populations and housing types and tenure residing in the flash and slow-rise flood extents. Our statistical findings show that the proportion of the slow-rise flooded area in the floodplains is significantly greater in tracts characterized by higher percentages of socially vulnerable. However, the results could not confirm the hypothesis that they are exposed considerably more than less vulnerable in the flash flooded floodplains. Considering housing-occupancy vulnerability, the percentage of renter-occupancies are greater in the flash flood floodplains compared to slow-rise, especially in areas with high rainfall accumulation producing storms (e.g. in the Southeast). This assessment contributes insights into how specific flood types could impact different populations and housing tenure across the CONUS and informs strategies to support urban and rural community resilience and planning at local and state levels.

Based on the Yearbook of Meteorological Disasters in China, we analyzed the spatiotemporal variations in major meteorological disaster (MD) losses at the provincial scale during 2001–2020 to determine the spatiotemporal variations in MDs and vulnerability in China. Our results suggest that the impacts of MDs, including floods, droughts, hail and strong winds (HSs), low temperature and frosts (LTFs), and typhoons, have been substantial in China. MDs in China affect an average of 316.3 million people and 34.3 million hectares of crops each year, causing 1,739 deaths and costing 372.3 billion yuan in direct economic losses (DELs). Floods and droughts affected more of the population in China than the other MDs. Fatalities and DELs were mainly caused by floods, and the affected crop area was mainly impacted by drought. The national average MD losses decreased significantly, except for DELs. The trends in the affected population and crop area were mainly caused by droughts, and the trends in fatalities and DELs were dominated by floods. Floods and typhoons showed increasing influence in the last two decades relative to other disasters. The annual mean and long-term trends in MD losses exhibited regional heterogeneity and were subject to different dominant hazards in different regions. The disaster losses and their trends in southeastern China were mainly attributed to typhoons. The affected population, crop area, and DELs were all significantly and positively correlated with exposure. The vulnerability of the population, crops, and economy tended to decrease. Economic development reduced the vulnerability of the population and economy but showed no significant influence on the vulnerability of crops. Our findings suggest that more focus should be placed on the impacts of floods and typhoons and that socioeconomic development has an important influence on the vulnerability of the population and economy. These results provide a foundation for designing effective disaster prevention and mitigation measures.

Tokyo is located in a lowland area that is vulnerable to flooding. Due to global climate change, the scalability and frequency of flooding is increasing. On the other hand, population aging and family structural changes, as well as the lack of adaptation measures, would accelerate flooding vulnerability. The key factors involved in social vulnerability must be studied to reduce the risk of flooding. In this study, we refer to the MOVE framework (a disaster vulnerability assessment framework) and analyze it from three perspectives: Exposure to social vulnerability, susceptibility, and resilience. We subsequently develop an index system to complete the evaluation using 11 indicators. The collected data will help reveal social vulnerability to floods in the Katsushika Ward, Tokyo, using the information entropy method and GIS. We found that the western region of the Katsushika Ward is at more risk than the eastern region during flooding. Additionally, the possibility of a serious crisis erupting is greater in the southwestern region than in the northwestern region. Consequently, we conclude that the spatial distribution of flooding varies in the region. The results of this study will help in understanding social vulnerability, in selecting and combining adaptation measures suited to the characteristics of the area, and in the effective and efficient implementation of these measures by the local government’s disaster department.

The impacts of flooding are expected to rise due to population increases, economic growth and climate change. Hence, understanding the physical and spatiotemporal characteristics of risk drivers (hazard, exposure and vulnerability) is required to develop effective flood mitigation measures. Here, the long-term trend in flood vulnerability was analysed globally, calculated from the ratio of the reported flood loss or damage to the modelled flood exposure using a global river and inundation model. A previous study showed decreasing global flood vulnerability over a shorter period using different disaster data. The long-term analysis demonstrated for the first time that flood vulnerability to economic losses in upper-middle, lower-middle and low-income countries shows an inverted U-shape, as a result of the balance between economic growth and various historical socioeconomic efforts to reduce damage, leading to non-significant upward or downward trends. We also show that the flood-exposed population is affected by historical changes in population distribution, with changes in flood vulnerability of up to 48.9%. Both increasing and decreasing trends in flood vulnerability were observed in different countries, implying that population growth scenarios considering spatial distribution changes could affect flood risk projections.

Although numerous studies have been conducted on the vulnerability of marginalized groups in the environmental justice (EJ) and hazards fields, analysts have tended to lump people together in broad racial/ethnic categories without regard for substantial within-group heterogeneity. This paper addresses that limitation by examining whether Hispanic immigrants are disproportionately exposed to risks from flood hazards relative to other racial/ethnic groups (including US-born Hispanics), adjusting for relevant covariates. Survey data were collected for 1283 adult householders in the Houston and Miami Metropolitan Statistical Areas (MSAs) and flood risk was estimated using their residential presence/absence within federally-designated 100-year flood zones. Generalized estimating equations (GEE) with binary logistic specifications that adjust for county-level clustering were used to analyze (separately) and compare the Houston (N = 546) and Miami (N = 560) MSAs in order to clarify determinants of household exposure to flood risk. GEE results in Houston indicate that Hispanic immigrants have the greatest likelihood, and non-Hispanic Whites the least likelihood, of residing in a 100-year flood zone. Miami GEE results contrastingly reveal that non-Hispanic Whites have a significantly greater likelihood of residing in a flood zone when compared to Hispanic immigrants. These divergent results suggest that human-flood hazard relationships have been structured differently between the two MSAs, possibly due to the contrasting role that water-based amenities have played in urbanization within the two study areas. Future EJ research and practice should differentiate between Hispanic subgroups based on nativity status and attend to contextual factors influencing environmental risk disparities.

INTRODUCTION A substantial body of research has focused on the vulnerability of racial/ethnic minorities to hazards and disasters. This work has lumped people with diverse characteristics into general groups, such as "Hispanic" or "Latino/a" (Bolin 2007). Today, Hispanic immigrants represent an important group in U.S. society due to their large and increasing population. According to American Community Survey estimates, as of 2013 there were 21 million foreign-born Hispanics in the U.S., representing 52.5 percent of the total foreign-born population and 6 percent of the U.S. population. Hispanic immigrants are distinguishable from U.S.--born Hispanics due to their concerns about immigration status as well as cultural and linguistic differences. Treating Hispanics as a homogenous group may mask important differences between foreign-born and U.S.--born Hispanics and lead to erroneous conclusions about their disaster vulnerabilities. In order to address the particular risks experienced by foreign-born Hispanics in the U.S., more research characterizing salient dimensions of their vulnerability to hazards and disasters is needed. This study highlights particular vulnerabilities of foreign-born Hispanics living at risk to flooding and hurricanes in the Houston, Texas, and Miami, Florida, Metropolitan Statistical Areas (MSAs) by examining their self-protective actions, and their perceptions of and knowledge about flood risks, in comparison to both U.S.--born non-Hispanic whites and U.S.--born Hispanics. It addresses two research questions: what differences exist in self-protective actions and perceptions of risk between Hispanic immigrants, U.S.--born Hispanics, and U.S.--born white residents who live at high risk to flooding and hurricanes; and why do differences in self-protective actions and perceptions of risk exist between Hispanic immigrants, U.S.--born Hispanics, and U.S.--born white residents who live at high risk to flooding and hurricanes? Approaching these questions, we analyze primary structured survey and semistructured interview data using a mixed-method analysis approach, which enables us to clarify particular factors that place Hispanic immigrants at increased risk to flood and hurricane disasters. LITERATURE REVIEW The last three decades have marked the emergence of a social-vulnerability perspective on hazards and disasters, which emphasizes the influence of inequalities on differential risks (Hewitt 1983, 1997; Peacock and others 1997; Wisner and others 2004; Tierney 2006; Thomas and others 2013). From this perspective, risk is determined partly by human exposure to a hazard and partly by people's social vulnerability. While there is debate about the meaning and measurement of social vulnerability, the following definition is useful: "the characteristics of a person or group and their situation that influence their capacity to anticipate, cope with, resist and recover from the impact of a natural hazard" (Wisner and others 2004, 11). In this study, we analyze the social vulnerability of Hispanic immigrants in terms of self-protection from flood/hurricane hazards, and perceptions of and knowledge about flood/hurricane risks. Here, self-protection is defined as any structural or nonstructural strategy used by households to minimize loss and enable recovery from the impacts of flood or hurricane hazard exposures (NRC 2006). Self-protection strategies in the context of flood and hurricane hazards include home structural as well as nonstructural actions. Structural mitigation actions include elevating home structures, flood-proofing homes, and installing hurricane shutters (FEMA 2014). They also include nonstructural actions, such as maintaining flood insurance. In terms of nonstructural self-protection strategies, in the U.S., flood insurance plays an important protective role, since it provides compensation for property losses. Disaster preparedness is another dimension of nonstructural self-protection that has been examined extensively (Mulilis and Lippa 1990; Faupel and others 1992; Norris and others 1999; Sattler and others 2000; Miceli and others 2008; Borque and others 2013), and can include evacuation planning, maintaining basic supplies (for example, a first aid kit) and being alert (for example, being attentive to hazard reports). …

Abstract This paper demonstrates the importance of disaggregating population data aggregated by census tracts or other units, for more realistic population distribution/location. A newly developed mapping method, the Cadastral-based Expert Dasymetric System (CEDS), calculates population in hyper-heterogeneous urban areas better than traditional mapping techniques. A case study estimating population potentially impacted by flood hazard in New York City compares the impacted population determined by CEDS with that derived by centroid-containment method and filtered areal-weighting interpolation. Compared to CEDS, 37% and 72% fewer people are estimated to be at risk from floods city-wide, using conventional areal weighting of census data, and centroid-containment selection, respectively. Undercounting of impacted population could have serious implications for emergency management and disaster planning. Ethnic/racial populations are also spatially disaggregated to determine any environmental justice impacts with flood risk. Minorities are disproportionately undercounted using traditional methods. Underestimating more vulnerable sub-populations impairs preparedness and relief efforts.

Abstract Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.

Abstract High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

This paper explores the risk approach, considering both the physical and human dimensions of the phenomenon in order to produce a more realistic and spatial analysis of risk. Exposure and vulnerability were combined and evaluated multidimensionally, considering individual, socio-economic, and structural (building-related) aspects. These risk factors were then integrated in a multi-criteria analysis in order to produce a comprehensive risk index that could be visualized at the building scale. The relative importance of the indicators was determined through a participatory process involving local and national experts on civil security and flooding. Particular attention was paid to individual vulnerability, including perception and preparedness for flood risk, which were explored directly with local people using a questionnaire. Qualitative and quantitative analyses of the responses allowed for a better understanding of the perception and preparedness of populations exposed to flooding. These data should help to improve risk communication between the authorities concerned and the populations at risk, as well as encouraging implementation of appropriate measures and a bottom-up participatory management approach. The integration of data in a geographic information system enables the visualization and spatialization of risk, but also each of its components.