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ABSTRACT Flood risk management (FRM) involves planning proactively for flooding in high‐risk areas to reduce its impacts on people and property. A key challenge for governments pursuing FRM is to pinpoint assets that are highly economically exposed and vulnerable to flood hazards in order to prioritize them in policy and planning. This paper presents a novel flood risk assessment, making use of a dataset that identifies the location, dwelling type, property characteristics, and potential economic losses of Canadian residential properties. The findings reveal that the average annual costs are $1.4B, but most of the risks are concentrated in high‐risk areas. Data gaps are uncovered that justify replication through local validation studies. The results provide a novel evidence base for specific reforms in Canada's approach to FRM, with a focus on insurance that improves both implementation and effectiveness.

Flood resilience (resilient flood risk management), which has been repeatedly demanded, can be achieved through the phases of the risk management cycle. There is a vast body of literature on adaptation, disaster risk reduction measures, and effectiveness of prevention, seen through the lens of postdisaster recovery, but oftentimes the existing literature seems to underestimate the impact of financial flood recovery schemes on resilient recovery of individual households in particular. This contribution focuses on how financial schemes for flood damage compensations—their sources, design, and timing—shape the resilience of recovery of individual households. It discusses the dilemma of recovery of whether recovery schemes should be used strategically to increase resilience, or rather serve early restoration needs, equality access issues, and so on. This contribution seeks to unify the current fragmented academic debate on household resilient recovery by focusing on the ambiguous role of financial recovery schemes. This article is categorized under: Engineering Water > Planning Water Human Water > Value of Water.

Abstract The exposure of urban populations to flooding is highly heterogeneous, with the negative impacts of flooding experienced disproportionately by the poor. In developing countries experiencing rapid urbanization and population growth a key distinction in the urban landscape is between planned development and unplanned, informal development, which often occurs on marginal, flood‐prone land. Flood risk management in the context of informality is challenging, and may exacerbate existing social inequalities and entrench poverty. Here, we adapt an existing socio‐hydrological model of human‐flood interactions to account for a stratified urban society consisting of planned and informal settlements. In the first instance, we use the model to construct four system archetypes based on idealized scenarios of risk reduction and disaster recovery. We then perform a sensitivity analysis to examine the relative importance of the differential values of vulnerability, risk‐aversion, and flood awareness in determining the relationship between flood risk management and social inequality. The model results suggest that reducing the vulnerability of informal communities to flooding plays an important role in reducing social inequality and enabling sustainable economic growth, even when the exposure to the flood hazard remains high. Conversely, our model shows that increasing risk aversion may accelerate the decline of informal communities by suppressing economic growth. On this basis, we argue for urban flood risk management which is rooted in pro‐poor urban governance and planning agendas which recognize the legitimacy and permanence of informal communities in cities. , Key Points The distribution of flood risk in urban areas is uneven, with the negative impacts experienced disproportionately by the urban poor Our model shows that reducing the vulnerability of informal residents to flooding can reduce inequality, even when their exposure is high Based on the model results, we argue that urban flood risk management should be rooted in pro‐poor urban governance and planning agendas

Does salient information on social media influence individuals’ economic decisions and beliefs? Using aggregated data from Facebook and a difference-in-differences strategy, I show that individuals who are socially connected to someone affected by Hurricane Harvey are more likely to purchase flood insurance policies after the event. This effect is stronger in areas at higher risk of flooding. Being socially connected to someone affected by Hurricane Harvey also influences individuals’ perceptions of global warming.

Abstract We present a new open source dataset FLODIS that links estimates of flood-induced human displacements, fatalities, and economic damages to flooded areas observed through remote sensing. The dataset connects displacement data from the Internal Displacement Monitoring Centre (IDMC), as well as data on fatalities and damages from the Emergency Events Database (EM-DAT), with the Global Flood Database (GFD), a satellite-based inventory of historic flood footprints. It thereby provides a spatially explicit estimate of the flood hazard underlying each individual disaster event. FLODIS contains two datasets with event-specific information for 335 human displacement events and 695 mortality/damage events that occurred around the world between 2000 and 2018. Additionally, we provide estimates of affected population, GDP, and critical infrastructure, as well as socio-economic indicators; and we provide geocoding for displacement events ascribed to other types of disasters, such as tropical cyclones, so that they may be linked to corresponding hazard estimates in future work. FLODIS facilitates integrated flood risk analysis, allowing, for example, for detailed assessments of local flood-damage and displacement vulnerability.

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.

There is increasing evidence that exposure to weather-related hazards like storms and floods adversely affects mental health. However, evidence of treated and untreated mental disorders based on diagnostic criteria for the general population is limited. We analysed the Adult Psychiatric Morbidity Survey, a large probability sample survey of adults in England (n = 7525), that provides the only national data on the prevalence of mental disorders assessed to diagnostic criteria. The most recent survey (2014–2015) asked participants if they had experienced damage to their home (due to wind, rain, snow or flood) in the six months prior to interview, a period that included months of unprecedented population exposure to flooding, particularly in Southern England. One in twenty (4.5%) reported living in a storm- or flood-damaged home in the previous six months. Social advantage (home ownership, higher household income) increased the odds of exposure to storm or flood damage. Exposure predicted having a common mental disorder over and above the effects of other known predictors of poor mental health. With climate change increasing the frequency and severity of storms and flooding, improving community resilience and disaster preparedness is a priority. Evidence on the mental health of exposed populations is key to building this capacity.

Abstract Objective To examine mental health and community cohesion in women living in Calgary after a natural disaster considering previously collected mental health data. Methods Data from an ongoing longitudinal cohort, the All Our Families study, were used to examine mental health and community cohesion 5 months after a major flood in Calgary, Canada. Participants who had completed a baseline questionnaire before the flood were eligible for inclusion in this study (N=923). Four multivariable logistic regression models were built to examine predictors of post-traumatic stress, depression, anxiety, and community cohesion. Results Elevated anxiety before the flood was associated with 2.49 (95% CI: 1.17, 5.26) increased odds of experiencing high levels of post-traumatic stress, regardless of whether respondents lived in a flood-risk community or not. Women who experienced damage to property, or who provided help to others, were more likely to perceive an increased sense of community cohesion (adjusted ods ratio (AOR): 1.67; 95% CI: 1.09, 2.54 and AOR: 1.68; 95% CI: 1.13, 2.52, respectively). Conclusions Women with underlying mental health conditions may be more vulnerable to the psychological impacts of a natural disaster regardless of their level of exposure. Natural disasters may bring communities together, especially those who were more tangibly impacted. ( Disaster Med Public Health Preparedness . 2018;12:470–477)