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Abstract Resilience has become a cornerstone for risk management and disaster reduction. However, it has evolved extensively both etymologically and conceptually in time and across scientific disciplines. The concept has been (re)shaped by the evolution of research and practice efforts. Considered the opposite of vulnerability for a long time, resilience was first defined as the ability to resist, bounce back, cope with, and recover quickly from the impacts of hazards. To avoid the possible return to conditions of vulnerability and exposure to hazards, the notions of post-disaster development, transformation, and adaptation (build back better) and anticipation, innovation, and proactivity (bounce forward) were then integrated. Today, resilience is characterized by a multitude of components and several classifications. We present a selection of 25 components used to define resilience, and an interesting linkage emerges between these components and the dimensions of risk management (prevention, preparedness, response, and recovery), offering a perspective to strengthen resilience through the development of capacities. Despite its potential, resilience is subject to challenges regarding its operationalization, effectiveness, measurement, credibility, equity, and even its nature. Nevertheless, it offers applicability and opportunities for local communities as well as an interdisciplinary look at global challenges.

Due to limitations in traditional concrete gravity dam (CGD) design, a new approach is necessary. In this study, the lean analysis as a novel approach for CGD design, considering the interaction between dam and reservoir was considered. Maximum and minimum stresses at the heel and displacement of the crest were obtained as crucial input values of bubble sorting based on seismic analysis using Finite element analysis (FEA), and the Fuzzy Analytic Hierarchy Process (FAHP). The fuzzy bubble sorting analytic process, aimed at developing a novel method for selecting the best CGD configuration, was developed. Required Criteria, Sub-Criteria and developed models were applied to optimize the body of CGD. The weight of each sub-criterion and models were calculated based on pairwise comparison matrices. The novel approach was designed in MATLAB with the OPT-CGD code to select the best CGD model. The best weight of the Criteria, for selecting the best CGD model, based on the lean construction principles was selected from 60 developed models under implicit dynamic analysis. Statistical analysis reveals a 20% reduction in the concrete mass of the case study’s optimal body compared to the traditionally designed dam.

Droughts are increasingly recognized as a significant global challenge, with severe impacts observed in Canada's Prairie provinces. While less frequent in Eastern Canada, prolonged precipitation deficits, particularly during summer, can lead to severe drought conditions. This study investigates the causes and consequences of droughts in New Brunswick (NB) by employing two drought indices: the Palmer Drought Severity Index (PDSI) and Standardized Evapotranspiration Deficit Index (SEDI)– at ten weather stations across NB from 1971 to 2020. Additionally, the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) dataset (1979–2014) was utilized to examine spatial and temporal drought variability and its alignment with station-based observations. Statistical analyses, including the Mann–Kendall test and Sen's slope estimator, were applied to assess trends in drought indices on annual and seasonal timescales using both station and gridded data. The results identified the most drought-vulnerable regions in NB and revealed significant spatial and temporal variability in drought severity over the 1971–2020 period. Trend analyses further highlighted the intensification of extreme drought events during specific years. Coastal areas in southern NB were found to be particularly susceptible to severe drought conditions compared to inland regions, consistent with observed declines in both the frequency of rainy days and daily precipitation amounts in these areas. These findings underscore the need for targeted drought mitigation strategies particularly in NB’s coastal zones, to address the region’s increasing vulnerability to extreme drought events.

En 2017 et en 2019, le Québec a vécu des inondations ayant provoqué d’importants dommages dans plus de 300 municipalités. Ces inondations ont mobilisé un grand nombre d’intervenantes et d’intervenants sociaux et municipaux afin d’assurer la sécurité et le bien-être des personnes sinistrées. Cet article présente le point de vue de ces personnes en lien avec les interventions psychosociales mises en place s’étant avérées efficaces pour atténuer ou prévenir l’apparition de problèmes de santé chez les individus sinistrés, ainsi que les facteurs organisationnels qui ont favorisé leur bon déroulement. Plusieurs types d’intervention psychosociale semblent avoir le potentiel de prévenir la détérioration de l’état de santé et le fonctionnement social des personnes sinistrées, dont l’adoption de l’approche « reaching out » et la mise en place d’équipes dédiées au rétablissement. , The floods that hit the province of Quebec in 2017 and 2019 resulted in significant damage to over 300 municipalities. Many social and municipal stakeholders were mobilized to ensure the safety and well-being of those affected by these floods. This article presents their point of view regarding the psychosocial interventions implemented. Interventions such as these have proven successful in mitigating or preventing health problems among disaster victims, as well as facilitating smooth operations. Psychosocial interventions, such as the “Reaching Out” approach and the creation of dedicated recovery teams, appear to be effective in preventing deterioration in the health status and social functioning of disaster victims.

Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

Abstract Background Posttraumatic stress disorder (PTSD) has been hailed by some as the emblematic mental disorder of the COVID-19 pandemic, assuming that PTSD’s life-threat criterion was met de facto. More plausible outcomes like adjustment disorder (AD) have been overlooked. Methods An online cross-sectional survey was launched in the initial stage of the pandemic using a convenience sample of 5 913 adults to compare the prevalence of COVID-related probable PTSD versus probable AD. The abridged Impact of Event Scale – Revised (IES-6) assessed the severity of trauma- and stressor-related symptoms over the previous week. Demographic and pandemic-related data (e.g., receiving a formal diagnosis of COVID-19, job loss, loss of loved one, confinement, material hardship) were collected. A Classification and Regression Tree analysis was conducted to uncover the pandemic experiences leading to clinical ‘caseness’. Caseness was defined by a score > 9 on the IES-6 symptom measure and further characterized as PTSD or AD depending on whether the Peritraumatic Distress Inventory’s life-threat item was endorsed or not. Results The participants were predominantly Caucasian (72.8%), women (79.2%), with a university degree (85%), and a mean age of 42.22 ( SD  = 15.24) years; 3 647 participants (61.7%; 95%CI [60.4, 63.0]) met the threshold for caseness. However, when perceived life-threat was accounted for, only 6.7% (95%CI [6.1, 7.4]) were classified as PTSD cases, and 55% (95%CI [53.7, 56.2]) as AD cases. Among the AD cases, three distinct profiles emerged marked by the following: (i) a worst personal pandemic experience eliciting intense fear, helplessness or horror (in the absence, however, of any life-threat), (ii) a pandemic experience eliciting sadness/grief, and (iii) worrying intensely about the safety of significant others. Conclusions Studies considering the life-threat criterion as met de facto during the pandemic are confusing PTSD for AD on most counts. This misconception is obscuring the various AD-related idioms of distress that have emerged during the pandemic and the actual treatment needs.

Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

In recent years, the utility of earlywood vessels anatomical characteristics in identifying and reconstructing hydrological conditions has been fully recognized. In riparian ring-porous species, flood rings have been used to identify discrete flood events, and chronologies developed from cross-sectional lumen areas of earlywood vessels have been used to successfully reconstruct seasonal discharge. In contrast, the utility of the earlywood vessel chronologies in non-riparian habitats has been less compelling. No studies have contrasted within species their earlywood vessel anatomical characteristics, specifically from trees that are inversely exposed to flooding. In this study, earlywood vessel and ring-width chronologies were compared between flooded and non-flooded control Fraxinus nigra trees. The association between chronologies and hydroclimate variables was also assessed. Fraxinus nigra trees from both settings shared similar mean tree-ring width but floodplain trees did produce, on average, thicker earlywood. Vessel chronologies from the floodplain trees generally recorded higher mean sensitivity (standard deviation) and lower autocorrelation than corresponding control chronologies indicating higher year-to-year variations. Principal components analysis (PCA) revealed that control and floodplain chronologies shared little variance indicating habitat-specific signals. At the habitat level, the PCA indicated that vessel characteristics were strongly associated with tree-ring width descriptors in control trees whereas, in floodplain trees, they were decoupled from the width. The most striking difference found between flood exposures related to the chronologies' associations with hydroclimatic variables. Floodplain vessel chronologies were strongly associated with climate variables modulating spring-flood conditions as well as with spring discharge whereas control ones showed weaker and few consistent correlations. Our results illustrated how spring flood conditions modulate earlywood vessel plasticity. In floodplain F. nigra trees, the use of earlywood vessel characteristics could potentially be extended to assess and/or mitigate anthropogenic modifications of hydrological regimes. In absence of major recurring environmental stressors like spring flooding, our results support the idea that the production of continuous earlywood vessel chronologies may be of limited utility in dendroclimatology.

Abstract Autism spectrum disorder prevalence more than quadrupled in the United States between 2000 and 2020. Ice storm-related prenatal maternal stress (PNMS) predicts autistic-like trait severity in children exposed early in gestation. The objective was to determine the extent to which PNMS influences the severity and trajectory of autistic-like traits in prenatally flood-exposed children at ages 4–7 years and to test moderation by sex and gestational timing. Soon after the June 2008 floods in Iowa, USA, 268 women pregnant during the disaster were assessed for objective hardship, subjective distress, and cognitive appraisal of the experience. When their children were 4, 5½, and 7 years old, mothers completed the Social Communication Questionnaire (SCQ) to assess their children’s autistic-like traits; 137 mothers completed the SCQ for at least one age. The final longitudinal multilevel model showed that the greater the maternal subjective distress, the more severe the child’s autistic-like traits, controlling for objective hardship. The effect of PNMS on rate of change was not significant, and there were no significant main effects or interactions involving sex or timing. Prenatal maternal subjective distress, but not objective hardship or cognitive appraisal, predicted more severe autistic-like traits at age 4, and this effect remained stable through age 7.