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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.

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.

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.

Introduction: Over the past years, the Outaouais region (Quebec, Canada) and their residents have had to endure no less than five natural disasters (floods, tornadoes). These disasters are likely to have a variety of consequences on the physical and mental health of adolescents, as well as on their personal, family, school and social lives. The experiences of teenagers are also likely to vary depending on whether they live in rural or urban areas. Method: Data were collected via a self-administered questionnaire in February 2022. A total of 1307 teenagers from two high schools participated in the study by completing an online survey. The questionnaire measured various aspects of the youth's mental health using validated tests, such as manifestations of post-traumatic stress, anxiety and depression, as well as the presence of suicidal thoughts and self-harm. Other aspects of the youth's experience were measured, including their level of social support, school engagement, alcohol and drug use, and coping strategies. Results: One third of young students (n=1307) were experiencing depressive symptoms and suicidal thoughts, as well as significant daily stress. More than 25% of the students had moderate or severe anxiety and thoughts of self-harm. These problems were significantly more prevalent among youths with prior exposure to a natural disaster. The study data also revealed that youths living in rural areas had a more worrying profile than those living in urban areas. Conclusion: Similar to other studies (Ran et al., 2015; Stratta et al., 2014), our research data revealed that youths living in rural areas presented a more concerning profile than those residing in urban areas. It therefore seems important, in future studies and services, to focus more specifically on these teenagers to better understand their needs and to develop adapted services more likely to meet them.

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.