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Abstract Floods are amongst the most frequent disasters in terms of human and economic impacts. This study provides new insights into the frequency of loss of life at the global scale, mortality fractions of the population exposed to floods, and underlying trends. A dataset is compiled based on the EM-DAT disaster database covering the period 1975 until 2022, extending previous studies on this topic. Flood impact data is analysed over spatial, temporal and economic scales, decomposed in various flood types and compared with other natural disasters. Floods are the most frequent natural disasters up to 1,000 fatalities, and flash floods lead to the highest mortality fractions per event, i.e. the number of deaths in an event relative to the exposed population. Despite population growth and increasing flood hazards, the average number of fatalities per event has declined over time. Mortality fractions per event have decreased over time for middle and high-middle-income countries, but increased for low-income countries. This highlights the importance of continuing and expanding risk reduction and adaptation efforts.

Abstract Disasters worldwide tend to affect the poorest more severely and increase inequality. Brazil is one of the countries with high income‐inequality rates and has unplanned urbanization issues and an extensive disaster risk profile with little knowledge on how those disasters affect people's welfare. Thus, disasters often hit the poorest hardest, increasing the country's income inequality and poverty rates. This study proposes a method to assess the impact of floods on households spatially based on their income levels by conducting flood analysis and income analysis. The method is applied to the Itapocu River basin (IRB) located in Santa Catarina State, Brazil. The flood is assessed by conducting rainfall analysis and hydrological simulation and generating flood inundation maps. The income is evaluated using downloaded 2010 census data and a dasymetric approach. Flood and income information is combined to analyze flood‐impacted households by income level and flood return period. The results confirm the initial assumption that flood events in the IRB are more likely to affect the lowest‐income households rather than the highest‐income levels, thus, increasing the income inequality.

Abstract A timely and cost-effective method of creating inundation maps could assist first responders in allocating resources and personnel in the event of a flood or in preparation of a future disaster. The Height Above Nearest Drainage (HAND) model could be implemented into an on-the-fly flood mapping application for a Canada-wide service. The HAND model requires water level (m) data inputs while many sources of hydrological data in Canada only provide discharge (m 3 /sec) data. Synthetic rating curves (SRCs), created using river geometry/characteristics and the Manning’s formula, could be utilized to provide an approximate water level given a discharge input. A challenge with creating SRCs includes representing how multiple different land covers will slow impact flow due to texture and bulky features (i.e., smooth asphalt versus rocky river channel); this relates to the roughness coefficient ( n ). In our study, two methods of representing multiple n values were experimented with (a weighted method and a minimum-median method) and were compared to using a fixed n method. A custom ArcGIS tool, Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D), was developed to create SRCs using all three methods. Control data were sourced from gauge stations across Canada in the form of rating curves. Results indicate that in areas with medium to medium–high river gradients (S > 0.002 m/m) or with river reaches under 5 km, the CERC-HAND-D tool creates more accurate SRCs (NRMSE = 3.7–8.8%, Percent Bias = −7.8%—9.4%), with the minimum-median method being the preferred n method.

Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.

Abstract In flood frequency analysis (FFA), annual maximum (AM) model is widely adopted in practice due to its straightforward sampling process. However, AM model has been criticized for its limited flexibility. FFA using peaks-over-threshold (POT) model is an alternative to AM model, which offers several theoretical advantages; however, this model is currently underemployed internationally. This study aims to bridge the current knowledge gap by conducting a scoping review covering several aspects of the POT approach including model assumptions, independence criteria, threshold selection, parameter estimation, probability distribution, regionalization and stationarity. We have reviewed the previously published articles on POT model to investigate: (a) possible reasons for underemployment of the POT model in FFA; and (b) challenges in applying the POT model. It is highlighted that the POT model offers a greater flexibility compared to the AM model due to the nature of sampling process associated with the POT model. The POT is more capable of providing less biased flood estimates for frequent floods. The underemployment of POT model in FFA is mainly due to the complexity in selecting a threshold (e.g., physical threshold to satisfy independence criteria and statistical threshold for Generalized Pareto distribution – the most commonly applied distribution in POT modelling). It is also found that the uncertainty due to individual variable and combined effects of the variables are not well assessed in previous research, and there is a lack of established guideline to apply POT model in FFA.

In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Petite Nation River Watershed, located in southern Quebec (Canada). The methodology follows a modelling approach, in which climate projections are derived from the Hydroclimatic Atlas of Southern Quebec following two representative concentration pathways (RCPs) scenarios, i.e., RCP 4.5 and RCP 8.5. These projections are used to predict future river flows. A frequency analysis was carried out with historical data of the peak flow (period 1969–2018) to derive different return periods (2, 20, and 100 years), which were then fed into the GARI tool (Gestion et Analyse du Risque d’Inondation). This tool is used to simulate flood hazard maps and to quantify future flood risk changes. Projected flood hazard (extent and depth) and damage maps were produced for the two municipalities under current and for future scenarios. The results indicate that the flood frequencies are expected to show a minor decrease in peak flows in the basin at the time horizons, 2050 and 2080. In addition, the depth and inundation areas will not significantly change for two time horizons, but instead show a minor decrease. Similarly, the projected flood damage changes in monetary losses are projected to decrease in the future. The results of this study allow one to identify present and future flood hazards and vulnerabilities, and should help decision-makers and the public to better understand the significance of climate change on flood risk in the Petite Nation River watershed.

Abstract Flood risk may differ across income levels. In this paper, I employ unique survey data from more than 8000 households in Germany to derive an integrated flood risk indicator that accounts for local flood exposure, assets-at-risk, housing characteristics, and household coping behavior. The results suggest that low-income households, due to their smaller homes and less valuable assets, face lower monetary flood risks than wealthier households despite the former’s limited capacity to implement protection measures and purchase insurance. Relative to the available financial budget, however, expected flood damage weighs higher for low-income households.

Empirical evidence points out that urban form adaptation to climate-induced flooding events—through interventions in land uses and town plans (i. e., street networks, building footprints, and urban blocks)—might exacerbate vulnerabilities and exposures, engendering risk inequalities and climate injustice. We develop a multicriteria model that draws on distributive justice's interconnections with the risk drivers of social vulnerabilities, flood hazard exposures, and the adaptive capacity of urban form (through land uses and town plans). The model assesses “who” is unequally at-risk to flooding events, hence, should be prioritized in adaptation responses; “where” are the high-risk priority areas located; and “how” can urban form adaptive interventions advance climate justice in the priority areas. We test the model in Toronto, Ontario, Canada, where there are indications of increased rainfall events and disparities in social vulnerabilities. Our methodology started with surveying Toronto-based flooding experts who assigned weights to the risk drivers based on their importance. Using ArcGIS, we then mapped and overlayed the risk drivers' values in all the neighborhoods across the city based on the experts' assigned weights. Accordingly, we identified four high-risk tower communities with old infrastructure and vulnerable populations as the priority neighborhoods for adaptation interventions within the urban form. These four neighborhoods are typical of inner-city tower blocks built in the 20 th century across North America, Europe, and Asia based on modern architectural ideas. Considering the lifespan of these blocks, this study calls for future studies to investigate how these types of neighborhoods can be adapted to climate change to advance climate justice.

Data include sample replication (N) and flood-ring frequencies (F1, F2) derived from black ash (Fraxinus nigra Marsh.) trees growing in the floodplain of the Driftwood River in northwestern Ontario reported in "Flood ring production modulated by river regulation in eastern boreal Canada" published in "Frontiers in Plant Science - Quantitative Wood Anatomy to Explore Tree Responses to Global Change" by Nolin et al. in 2021c. DriftwoodFR.csv, as in Fig. 4, F1 and F2 flood-rings chronologies per sites and distance class with sample replication (N) to reproduce the flood-ring frequencies. Harricana River F1 and F2 flood ring chronologies from Nolin et al., 2021b are also provided. DriftwoodRW.csv, as in Fig. 5, the mean site chronologies of total ring width with sample replication (N). LAT_LON_Driftwood.kml, the coordinate data for each F. nigra stand sampled on the Driftwood River, including Monteith dam location, in Google Earth format (.kml) meatadatas.txt, a set of self-explanatory instructions and descriptions for data files. All other data are available upon request to the corresponding author at alexandreflorent.nolin@uqat.ca (institutional email), alexandreflorent.nolin@gmail.com (permanent email).

Abstract It is undeniable that coastal regions worldwide are facing unprecedented damages from catastrophic floods attributable to storm-tide (tidal) and extreme rainfall (pluvial). For flood-risk assessment, although recognizing compound impact of these drivers is a conventional practice, the marginal/individual impacts cannot be overlooked. In this letter, we propose a new measure, Tide-Rainfall Flood Quotient (TRFQ), to quantify the driver-specific flood potential of a coastal region arising from storm-tide or rainfall. A set of inundation and hazard maps are derived through a series of numerical and hydrodynamic flood model simulations comprising of design rainfall and design storm-tide. These experiments are demonstrated on three different geographically diverse flood-affected coastal regions in India. The new measure throws light on existing knowledge gaps on the propensity of coastal flooding induced by the marginal/individual contribution of storm-tide and rainfall. It shall prove useful in rationalizing long-term flood management strategies customizable for storm-tide and pluvial dominated global coastal regions.