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While the methods of risk analysis are generally based on objective measurements, the subjective assessment of risk, such as risk perception, is currently considered a crucial aspect in the context of flood risk management. Risk perception is regarded as an assessment of the perceived probability of hazard and the perceived probability of the results (most often—negative consequences). The work attempts to answer the question: What determines flood risk perception? The knowledge of the factors influencing flood risk perception can solve the issue of the society’s underestimation of flood risk. This issue was considered both in terms of the impact of particular factors on flood risk perception and the interrelationship between three characteristics of flood risk perception: preparedness, worry and awareness. The results were developed based on critical analysis of the empirical research. The review shows that the way particular characteristics determine flood risk perception is not clear and many authors show the diverse conclusions from the similar research. Taking into account various research results, the following factors were distinguished: primary (which clearly influence risk perception), secondary (which influence it unclearly and require further research) and intervening (often describing the context). The organization of the results of the research on the flood risk assessment conducted herein aims to improve the understanding of the human perception of flood risk and, as a result, will lead to the decrease in flood risk by improving the communication of the issue and motivating the residents of the endangered areas to take actions that reduce the negative effects of floods.

Abstract Impacts of floods on human society have been drawing increasing human concerns in recent years. In this study, flood observations from EM-DAT (Emergency Events Database) and DFO (Dartmouth Flood Observatory) datasets were analyzed to investigate frequency and intensity of floods, and flood-induced mortality, flood-affected population as well during 1975–2016 across the globe. Results indicated that: (1) occurrence rate of floods, flood-induced mortality and flood-affected population were generally increasing globally. However, flood-induced mortality and flood-affected people per flood event were in slight decrease, indicating that flood-induced mortality and flood-affected people due to increased floods exceeded those by individual flood event; (2) annual variation of mortality per flood event is highly related to floods with higher intensity. Specifically, the flood frequency and flood-induced mortality are the largest in Asia, specifically in China, India, Indonesia and Philippine; while significantly increased flood-affected population and mean annual mortality was detected in China, USA and Australia; (3) tropical cyclones (TC) are closely related to flood-induced mortality in parts of the countries along the western coast of the oceans. The frequency of channel floods in these regions is the largest and large proportion of flood-induced deaths and the highest flood-induced mortality can be attributed to TC-induced flash floods; (4) Population density and GDP per unit area are in significantly positive correlation with the number of flood-related victims per unit area, number of deaths and economic losses with exception of low-income countries. However, the flood-affected population and flood-induced mortality increase with decrease of per capita GDP; while the per capita economic loss increases with the increase of per capita GDP, indicating that the higher the population density and GDP per unit for a region, the higher sensitivity of this area to flood hazards.

A la suite de la tragedie ferroviaire de Lac-Megantic de 2013, des enquetes populationnelles ont permis de dresser un portrait de la sante des adultes vivant sur le territoire de la MRC du Granit, sans toutefois repertorier le vecu specifique des jeunes. Le present document vise a combler cette lacune en offrant un bilan de la realite et des besoins des jeunes âges de 10 a 25 ans demeurant au sein de la communaute de Lac-Megantic. Il presente les resultats d'une etude mixte realisee a l'hiver 2017 aupres d'eleves de second cycle de quatre ecoles primaires (5e et 6e annees), de la polyvalente Montignac, ainsi que d'etudiants frequentant le Centre de formation professionnelle Le Granit, le centre d'etudes collegiales de Lac-Megantic et le Centre d'education des adultes de la Commission scolaire des Hauts-Cantons, secteur Lac-Megantic. Cet ouvrage s'adresse aux chercheurs, aux etudiants ainsi qu'aux professionnels des milieux scolaires et du reseau de la sante et des services sociaux qui s'interessent au vecu des jeunes a la suite d'une catastrophe. Plus precisement, il permet de mieux connaitre les caracteristiques personnelles, familiales, scolaires et sociales des jeunes de la commmunaute de Lac-Megantic, tout en leur donnant la parole sur leurs attentes, leurs desirs et leurs besoins.

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This paper describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981–2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCM-driven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonal-mean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity.

The Saint-Jean River (SRJ) in Eastern Canada is prone to the formation of very large rafts of wood. Managers of the SJR suspected these jams to influence salmon migration and carried out a dismantling operation to remove large wood accumulated in a 1.2 km long wood raft. This operation became a great opportunity to address key issues relating to large wood dynamics in a fluvial system: residence time and flood contribution to wood recruitment and transport. During the dismantling, we systematically sampled 319 trees from which year of death could be estimated from dendrochronology and year of accumulation in the raft could be obtained from satellite and aerial photos. These two dates allowed us to quantify the residence time for 262 datable large wood (LW) within the fluvial system, to examine the peak years of LW recruitment and to correlate the raft growth rate with hydrometeorological conditions since 1993. The results also emphasized four types of LW flood related to wood dynamics: 1) an erosive flood that produces a large amount of wood in river, 2) a mobilizing flood that carries large quantities of wood, 3) a flood mix that both recruits and transports large quantities of wood, and 4) an ice-breakup flood.

Rivers inherently show heterogeneous sediment sizes and can also show a strong sediment supply variability in time because of natural episodic events or as a consequence of human activities, which alter the characteristics and dynamics of alluvial bars at the macro-scale. The impact of the combination between sediment size heterogeneity and sediment supply variation, or even with other forcings (i.e. hydrology, channel geometry) remains poorly documented. In this work, a physics-based numerical model is applied on a trained reach of a sandy-gravel bed river to investigate the combination of these parameters on bar morphodynamics. The morphodynamic computations are performed with a two-dimensional depth-averaged hydrodynamic solver, internally coupled to a sediment transport and bed evolution module, which estimate the transport of graded sediment and model bed stratigraphy, respectively. A 1 km long reach of the Loire River at Bréhémont (France) is selected to conduct the numerical investigations. The interaction between several forcing mechanisms induces highly complex bar morphodynamic processes in this area.A comprehensive set of high-definition data is available, which allows to study the river morphodynamics for a succession of three flooding events and a period of low flows. Based on this model, a variety of scenarios is presented with the aim of exploring the implications of sediment gradation, geometrical and boundary forcing effects on in situ bars morphodynamics.

ABSTRACTTrends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These in...

Quantile estimates are generally interpreted in association with the return period concept in practical engineering. To do so with the peaks‐over‐threshold (POT) approach, combined Poisson‐generalized Pareto distributions (referred to as PD‐GPD model) must be considered. In this article, we evaluate the incorporation of non‐stationarity in the generalized Pareto distribution (GPD) and the Poisson distribution (PD) using, respectively, the smoothing‐based B‐spline functions and the logarithmic link function. Two models are proposed, a stationary PD combined to a non‐stationary GPD (referred to as PD0‐GPD1) and a combined non‐stationary PD and GPD (referred to as PD1‐GPD1). The teleconnections between hydro‐climatological variables and a number of large‐scale climate patterns allow using these climate indices as covariates in the development of non‐stationary extreme value models. The case study is made with daily precipitation amount time series from southeastern Canada and two climatic covariates, the Arctic Oscillation (AO) and the Pacific North American (PNA) indices. A comparison of PD0‐GPD1 and PD1‐GPD1 models showed that the incorporation of non‐stationarity in both POT models instead of solely in the GPD has an effect on the estimated quantiles. The use of the B‐spline function as link function between the GPD parameters and the considered climatic covariates provided flexible non‐stationary PD‐GPD models. Indeed, linear and nonlinear conditional quantiles are observed at various stations in the case study, opening an interesting perspective for further research on the physical mechanism behind these simple and complex interactions.

Phosphorus (P) mobilization in agricultural landscapes is regulated by both hydrologic (transport) and biogeochemical (supply) processes interacting within soils; however, the dominance of these controls can vary spatially and temporally. In this study, we analyzed a 5‐yr dataset of stormflow events across nine agricultural fields in the lower Great Lakes region of Ontario, Canada, to determine if edge‐of‐field surface runoff and tile drainage losses (total and dissolved reactive P) were limited by transport mechanisms or P supply. Field sites ranged from clay loam, silt loam, to sandy loam textures. Findings indicate that biogeochemical processes (P supply) were more important for tile drain P loading patterns (i.e., variable flow‐weighted mean concentrations ([ C f ]) across a range of flow regimes) relative to surface runoff, which trended toward a more chemostatic or transport‐limited response. At two sites with the same soil texture, higher tile [ C f ] and greater transport limitations were apparent at the site with higher soil available P (STP); however, STP did not significantly correlate with tile [ C f ] or P loading patterns across the nine sites. This may reflect that the fields were all within a narrow STP range and were not elevated in STP concentrations (Olsen‐P, ≤25 mg kg −1 ). For the study sites where STP was maintained at reasonable concentrations, hydrology was less of a driving factor for tile P loadings, and thus management strategies that limit P supply may be an effective way to reduce P losses from fields (e.g., timing of fertilizer application). Core Ideas We used metrics to evaluate controls on edge‐of‐field phosphorus losses. We examined a 5‐yr database of stormflow events (all seasons, including winter). Tile P runoff trended toward being more supply limited than surface runoff.

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.