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Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

Floods account for a large part of global economic losses from natural disasters. As a result, the private insurance sector is increasingly participating in the financial risk sharing, thus expanding the role of actuaries to flood risk management. In this article, we investigate pricing and spatial segmentation of flood risk in the context of private insurance, meaning that individual risk assessment should minimize adverse selection. As such, we design a hierarchical flood risk model that allows an assessment at the individual level. Our model relies on a chain of physics-based climate, hydrological, and hydraulics modules combined with civil engineering methods to map the distribution of individual flood losses at high resolution. Building on such approach, we design pricing and segmentation methods tailored for flood risk management. We then apply the methods to study flood risk in a small city in the province of Quebec. We calculate premiums, analyze the impacts of risk sharing, set pricing territories consistent with the spatial flood risk, and finally, quantify the impact of greenhouse gas emission scenarios on individual and aggregate losses, premiums, and tail risk measures.

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.

In gravelly floodplains, streamflood events induce groundwater floodwaves that propagate through the alluvial aquifer. Understanding groundwater floodwave dynamics can contribute to groundwater flood risk management. This study documents groundwater floodwaves on a flood event basis to fully assess environmental factors that control their propagation velocity, their amplitude and their extension in the floodplain, and examines the expression of groundwater flooding in the Matane River floodplain (Quebec, Canada). An array of 15 piezometers equipped with automated level sensors and a river stage gauge monitoring at 15-minute intervals from September 2011 to September 2014 were installed within a 0.04-km2 area of the floodplain. Cross-correlation analyses were performed between piezometric and river-level time series for 54 flood events. The results reveal that groundwater floodwave propagation occurs at all flood magnitudes. The smaller floods produced a clear groundwater floodwave through the floodplain, while the largest floods affected local groundwater flow orientation by generating an inversion of the hydraulic gradient. Propagation velocities ranging from 8 to 13 m/h, which are two to three orders of magnitude higher than groundwater velocity, were documented while the induced pulse propagated across the floodplain to more than 230 m from the channel. Propagation velocity and amplitude attenuation of the groundwater floodwaves depend both on flood event characteristics and the aquifer characteristics. Groundwater flooding events are documented at discharge below bankfull (< 0.5 Qbf). This study highlights the role of flood event hydrographs and environmental variables on groundwater floodwave properties and the complex relationship between flood event discharge and groundwater flooding. The role that groundwater floodwaves play in flood mapping and the ability of analytical solutions to reproduce them are also discussed.

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.

Modelling techniques now allow flood risk to be mapped down to street or even building level in a study showing that floods disproportionately affect disadvantaged communities and particular ethnic groups.

Floods are among natural disasters that increasingly threaten society, especially with current and future climate change trends. Several tools have been developed to help planners manage the risks associated to flooding, including the mapping of flood-prone areas, but one of the major challenges is still the availability of detailed data, particularly bathymetry. This manuscript compares two modeling approaches to produce flood maps. An innovative large-scale approach that, without bathymetric data, estimates by inverse modeling the bed section for a given flow and a given roughness coefficient through 1 D/2D hydraulic modeling (LISFLOOD-FP). And a local approach, with a detailed coupled 1 D/2D hydraulic model (HEC-RAS) that uses all available information at the bed and floodplain (LiDAR and bathymetry). Both implementations revealed good performance values for flood peak levels as well as excellent fit indices in describing the areal extent of flooding. As expected, the local approach is more accurate, but the results of the large-scale approach are very promising especially for areas lacking bathymetric data and for large-scale governmental programs.

The summer 1993 flooding of the upper Mississippi River valley reminds us that floods are the most globally pervasive, environmentally diverse and continually destructive of all natural hazards. The fact that flood damages continue to rise raises commonsense questions about conventional flood science. Like much modern environmental science, conventional flood science has followed the model of theoretical physics. It advanced from early emphasis on streamflow measurement to the use of simple formulae, and finally to the abstract theoretical sophistication of modern modeling studies. Two approaches are now used to “predict” flood phenomena: (1) beginning with the conventional database of measured properties of small common floods, a conceptual generalization is made to the idealized properties of the large, rare floods from which society is assumed to be at risk, and (2) explanation of detailed, specific flood phenomena is achieved through theoretical generalization (models) based on “first principles”, which are assumed to apply to the entire class of phenomena. Unfortunately, both approaches devote almost all their attention to methodology, increasingly mathematical, without questioning basic underlying assumptions. Increasingly it is the assumptions, often unstated, that serve to embody the understanding of floods as real-world particular phenomena, rather than as conceptual generalities. Such trends lead to an unease that it is not floods that are being researched by much of conventional flood science. Rather, such flood “science” is increasingly becoming the mathematical manipulation of idealized parameters that are assumed to have flood-like properties. These idealizations of flood attributes are generalized, and the resulting predicted consequences are imposed upon society through engineering designs, flood-hazard zonations, and the like. Geomorphological understanding of floods derives a from along geological tradition of studying indices of real processes operating in the past. In contrast to the conceptual, theoretical treatment of floods as classes or generalizations, geomorphologists study particular floods revealed as a natural experience that is recorded in the sediments, landforms, and erosional scars of past floods. The strength of this approach is in its affinity to the commonsense perceptional basis that underpins human action. Geomorphological flood studies, including recent advances in paleoflood hydrology, are needed as a complement to conventional hydrological approaches. The resulting complementarity will allow the predictions of the conventional approach to be grounded in the concrete particulars of experience. Without such grounding, flood science risks continuing as an empty quest for universal ideals while humanity, paralyzed by inaction, continues to suffer from the reality of particular floods.

Background Exposure to adverse experiences during pregnancy, such as a natural disaster, can modify development of the child with potential long-term consequences. Elemental hair analysis may provide useful indicators of cellular homeostasis and child health. The present study investigated (1) if flood-induced prenatal maternal stress is associated with altered hair elemental profiles in 4-year-old children, and (2) if hair elemental profiles are associated with behavioural outcomes in children. Methods Participants were 75 children (39 boys; 36 girls) whose mothers were exposed to varying levels of stress due to a natural disaster (2011 Queensland Flood, Australia) during pregnancy. At 4 years of age, language development, attention and internalizing and externalizing problems were assessed and scalp hair was collected. Hair was analyzed by inductively coupled plasma mass spectrometry (ICP-MS) for 28 chemical elements. Results A significant curvilinear association was found between maternal objective hardship and copper levels in boys, as low and high maternal objective hardship levels were associated with the highest hair copper levels. Mediation analysis revealed that low levels of maternal objective hardship and high levels of copper were associated with lower vocabulary scores. Higher levels of maternal objective hardship were associated with higher magnesium levels, which in turn were associated with attention problems and aggression in boys. In girls, high and low maternal objective hardship levels were associated with high calcium/potassium ratios. Conclusion Elemental hair analysis may provide a sensitive biomonitoring tool for early identification of health risks in vulnerable children.

Abstract Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.