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This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

Abstract The potential effects of climate change on the hydrodynamic and sediment transport regime of the lower Athabasca River (LAR) in Alberta, Canada, is investigated. Future climate projections for the region suggest a potential increase in mean air temperature and precipitation by about 2.8–7.1 °C and 8–25%, respectively, by the end of this century. Implications of these climatic changes on the hydrologic regime of the LAR are found to be significant with spring flows expected to increase by about 11–62% and 26–71% by the end of the century for a moderate and high emissions scenarios respectively with corresponding decreases in summer flows. The effects of such changes are examined using the MIKE‐11 hydrodynamic and sediment transport modelling system with inflow boundary conditions corresponding to the changing hydro‐climatic regime. The results suggest that there will be an overall increase in flow velocity, water level, and suspended sediment concentration and transport for most seasons except in the summer months when there may be some decreases. The projected changes in suspended sediment concentration will result in an overall increase in mean annual sediment load in the LAR and to the Peace Athabasca Delta by over 50% towards the latter part of this century (2080s) compared with the 1980s base‐line period. Implications of such potential changes in the transport characteristics of the river system to the mobilization and transport of various chemical constituents and their effects on the region's aquatic ecosystems are subjects of other ongoing investigations.

The objective of this study is to use two hydrological indices (coefficients of variation and immoderation) to analyze the impacts of dam management methods on seasonal daily flow rate change downstream of three dams: Manouane (diversion-type management method), Ouareau (natural-type management method) and Matawin (inversion-type management method). The results show that this change is far greater downstream of the Matawin dam (characterized by an inversion-type management method) than downstream of the two other dams. Moreover, downstream of the Matawin dam, this daily flow rate change increases significantly over time, while decreasing downstream of the two other dams and in natural rivers. Lastly, this change is better correlated with climate downstream of the Ouareau dam than downstream of the two other dams. It is positively correlated with winter and spring temperatures as well as summer and fall rain. Contrary commonly accepted hypothesis, this study shows that the impacts of dams generally result in an increase of the seasonal flow rate change in Quebec.

Background: Canadian public safety personnel (PSP; e.g., correctional workers, dispatchers, firefighters, paramedics, police officers) are exposed to potentially traumatic events as a function of their work. Such exposures contribute to the risk of developing clinically significant symptoms related to mental disorders. The current study was designed to provide estimates of mental disorder symptom frequencies and severities for Canadian PSP. Methods: An online survey was made available in English or French from September 2016 to January 2017. The survey assessed current symptoms, and participation was solicited from national PSP agencies and advocacy groups. Estimates were derived using well-validated screening measures. Results: There were 5813 participants (32.5% women) who were grouped into 6 categories (i.e., call center operators/dispatchers, correctional workers, firefighters, municipal/provincial police, paramedics, Royal Canadian Mounted Police). Substantial proportions of participants reported current symptoms consistent with 1 (i.e., 15.1%) or more (i.e., 26.7%) mental disorders based on the screening measures. There were significant differences across PSP categories with respect to proportions screening positive based on each measure. Interpretation: The estimated proportion of PSP reporting current symptom clusters consistent with 1 or more mental disorders appears higher than previously published estimates for the general population; however, direct comparisons are impossible because of methodological differences. The available data suggest that Canadian PSP experience substantial and heterogeneous difficulties with mental health and underscore the need for a rigorous epidemiologic study and category-specific solutions.

Abstract Recent flood events in Canada have led to speculation that changes in flood behaviour are occurring; these changes have often been attributed to climate change. This paper examines flood data for a collection of 132 gauging stations in Canada. All of these watersheds are part of the Canadian Reference Hydrometric Basin Network (RHBN), a group of gauging stations specifically assembled to assist in the identification of the impacts of climate change. The RHBN stations are considered to have good quality data and were screened to avoid the influences of regulation, diversions, or land use change. Daily flow data for each watershed are used to derive a peaks over threshold (POT) dataset. Several measures of flood behaviour are examined based on the POT data, which afford a more in‐depth analysis of flood behaviour than can be obtained using annual maxima data. Analysis is conducted for four time periods ranging from 50 to 80 years in duration; the latter period results in a much smaller number of watersheds that have data for the period. The changes in flood responses of the watersheds are summarized by grouping the watersheds by size (small, medium, and large) and also by hydrologic regime (nival, mixed, and pluvial). The results provide important insights into the nature of the changes that are occurring in flood regimes of Canadian rivers, which include more flood exceedances, reduced maximum flood exceedance magnitudes for snowmelt events, and earlier flood events. Copyright © 2016 John Wiley & Sons, Ltd.

The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired windthrow data for bF-stands affected by the Christmas Mountains windthrow event of November 7, 1994. In general, the prediction of windthrow in the area agreed fairly well with the windthrow sites identified by photogrammetry. The occurrence of windthrow in the Christmas Mountains was prominent in areas with shallow soils and prone to localised accelerations in mean and turbulent airflow. The windthrow function for bF was subsequently used to examine the future impact of windthrow under two climate scenarios (RCP’s 4.5 and 8.5) and species response to local changes anticipated with global climate change, particularly with respect to growing degree-days and soil moisture. Under climate change, future windthrow in bF stands (2006–2100) is projected to be modified as the species withdraws from the high-elevation areas and NB as a whole, as the climate progressively warms and precipitation increases, causing the growing environment of bF to deteriorate.

Abstract Water table depth in peatlands is strongly linked to physical properties of the peat, such as density (ρ dry ), peat composition and humification, hydraulic conductivity (K), and specific yield (S y ). Dry bulk density and peat depth are commonly used as indicators of K in ecohydrological models. However, no mathematical relationship exists to quantify S y based on K and ρ dry . As a result, ecohydrological models cannot explicitly reproduce the strong buffering capacity of peatlands. The objectives of this study were to analyse the literature‐reported mathematical link between all the physical properties to develop new mathematical relationships between these parameters and to evaluate whether variations in the physical properties of the peat control water table depth in peatlands. Seven peatlands located in the St. Lawrence Lowlands (Québec, Canada) were sampled, and 1 m long peat cores were collected from up‐gradient, mid‐gradient, and down‐gradient zones. All cores were used to measure ρ dry , K, S y , and to estimate peat composition and humification. Statistically significant correlations were found between (a) K and S y (log–log model), (b) K and depth (log–log model), (c) S y and depth (log–log model), (d) ρ dry and S y (log model), and (e) ρ dry and K (log model). No significant difference was found in either K or S y between sites. However, significant differences were found in water table depths. Because they provide a fuller description of the peat properties that control water table depths, these newly developed functions have the potential to improve the capacity of ecohydrological models to simulate time‐varying hydrological conditions.

With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.

Abstract This study confronts the new concept of ‘surface storage’ with the old concept of ‘sponge effect’ to explain the spatio-temporal variability of the annual daily maximum flows measured in 17 watersheds of southern Quebec during the period 1930–2019. The new concept takes into account the hydrological impacts of wetlands and other topographic components of the landscape (lakes, depressions, ditches, etc.) while that of the sponge effect only takes into account the hydrological impacts of wetlands. With regard to spatial variability, the area of wetlands and other water bodies is the variable best correlated negatively with the magnitude but positively with the duration of flows. As for the temporal variability, the application of the long-term trend tests revealed a significant increase in the magnitude and, to a lesser extent, the duration of the flows occurring in the watersheds of the north shore characterized by a greater area of wetlands and other water bodies (>5%). This increase is explained by the fact that the storage capacity of these land types, which remains unchanged over time, does not make it possible to store the surplus runoff water brought by the increase in rainfall during the snowmelt season.