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ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.

AbstractThe frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

Combined sewer surcharges in densely urbanized areas have become more frequent due to the expansion of impervious surfaces and intensified precipitation caused by climate change. These surcharges can generate system overflows, causing urban flooding and pollution of urban areas. This paper presents a novel methodology to mitigate sewer system surcharges and control surface water. In this methodology, flow control devices and urban landscape retrofitting are proposed as strategies to reduce water inflow into the sewer network and manage excess water on the surface during extreme rainfall events. For this purpose, a 1D/2D dual drainage model was developed for two case studies located in Montreal, Canada. Applying the proposed methodology to these two sites led to a reduction of the volume of wastewater overflows by 100% and 86%, and a decrease in the number of surface overflows by 100% and 71%, respectively, at the two sites for a 100-year return period 3-h Chicago design rainfall. It also controlled the extent of flooding, reduced the volume of uncontrolled surface floods by 78% and 80% and decreased flooded areas by 68% and 42%, respectively, at the two sites for the same design rainfall.

In recent years, understanding and improving the perception of flood risk has become an important aspect of flood risk management and flood risk reduction policies. The aim of this study was to explore perceptions of flood risk in the Petite Nation River watershed, located in southern Quebec, Canada. A survey was conducted with 130 residents living on a floodplain in this river watershed, which had been affected by floods in the spring of 2017. Participants were asked about different aspects related to flood risk, such as the flood hazard experience, the physical changes occurring in the environment, climate change, information accessibility, flood risk governance, adaptation measures, and finally the perception of losses. An analysis of these factors provided perspectives for improving flood risk communication and increasing the public awareness of flood risk. The results indicated that the analyzed aspects are potentially important in terms of risk perception and showed that the flood risk perceptions varied for each aspect analyzed. In general, the information regarding flood risk management is available and generally understandable, and the level of confidence was good towards most authorities. However, the experiences of flood risk and the consequences of climate change on floods were not clear among the respondents. Regarding the adaptation measures, the majority of participants tended to consider non-structural adaptation measures as being more relevant than structural ones. Moreover, the long-term consequences of flooding on property values are of highest concern. These results provide a snapshot of citizens’ risk perceptions and their opinions on topics that are directly related to such risks.

In Canada, flooding is the most common and costly natural hazard. Flooding events significantly impact communities, damage infrastructures and threaten public security. Communication, as part of a flood risk management strategy, is an essential means of countering these threats. It is therefore important to develop new and innovative tools to communicate the flood risk with citizens. From this perspective, the use of story maps can be very effectively implemented for a broad audience, particularly to stakeholders. This paper details how an interactive web-based story map was set up to communicate current and future flood risks in the Petite-Nation River watershed, Quebec (Canada). This web technology application combines informative texts and interactive maps on current and future flood risks in the Petite-Nation River watershed. Flood risk and climate maps were generated using the GARI tool, implemented using a geographic information system (GIS) supported by ArcGIS Online (Esri). Three climate change scenarios developed by the Hydroclimatic Atlas of Southern Quebec were used to visualize potential future impacts. This study concluded that our story map is an efficient flood hazard communication tool. The assets of this interactive web mapping tool are numerous, namely user-friendly mapping, use and interaction, and customizable displays.

Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

Floods are the most common natural hazard worldwide. GARI is a flood risk management and analysis tool that is being developed by the Environmental and Nordic Remote Sensing Group (TENOR) of INRS in Quebec City (Canada). Beyond mapping the flooded areas and water levels, GARI allows for the estimation, analysis and visualization of flood risks for individuals, residential buildings, and population. Information can therefore be used during the different phases of flood risk management. In the operational phase, GARI can use satellite radar images to map in near real-time the flooded areas and water levels. It uses an innovative approach that combines Radarsat-2 and hydraulic data, specifically flood return period data. Information from the GARI enable municipalities and individuals to anticipate the impacts of a flood in a given area, to mitigate these impacts, to prepare, and to better coordinate their actions during a flood.

Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Dam spillways are susceptible to a range of engineering challenges including structural deficiencies, insufficient discharge capacity, and mechanical failures; however, a particularly significant issue is hydraulic erosion, which poses a significant threat to dam infrastructure. This necessitates a comprehensive assessment of both hydraulic and rock mechanical parameters to ensure structural integrity and operational resilience. In the rock mechanical aspect of hydraulic erosion, the resistive capacity of the material holds great importance, while in the hydraulic aspect, the erosive force of water plays a pivotal role. Hence, neglecting these incidents would increase the risk of overtopping and subsequent downstream flooding, thereby impacting the overall safety and operational reliability of the dam. This study focuses on investigating the hydraulic parameters of a smooth surface unlined open channel spillway. By utilizing both numerical modeling and experimental analysis, we aim to explore how variations in these parameters impact erosion in dams’ spillways. The research centers on the Romaine 4 dam spillway, situated in the northeastern region of Quebec in Canada as a representative case study. The physical model of this spillway was constructed at the Université du Québec à Chicoutimi, where we carried out the experimental analyses. In this research, we also conducted a comprehensive numerical analysis using Finite Volume Method (FVM), enabling a detailed examination of three-dimensional flow behavior within the spillway. This enabled a precise monitoring of the fluid motion patterns. Moreover, an experimental approach was utilized to enhance the accuracy and reliability of the results. This involved conducting detailed tests on the reduced-scale model using a XYZ robotic system capable of movement in X,Y,Z directions and capturing position, velocity and pressure. The results of numerical and experimental analyses reveal that the numerical model effectively captures the overall flow characteristics, closely predicting the average velocity throughout the channel. However, it indicates limitations in accurately predicting extreme velocities, such as maximum and minimum values. The results show that the maximum discrepancies between experimental and numerical data primarily concern extreme velocities, with the numerical model underestimating maximum velocities and overestimating minimum velocities, with errors more pronounced at higher flow rates and upstream. This discrepancy can reach up to 60% in certain areas. Furthermore, the study examined the effects of gates on variability of hydraulic parameters like flow depth and velocity. The analysis of a number of gate configurations revealed that double-gate spillways maintain more consistent flow depths across all significant cross-sections. By explaining the complex interaction between hydraulic behavior and spillway design, this research attempts to advance our understanding of hydraulic-prone erosion areas in dam spillways and ensure the long-term resilience of dam infrastructure. Les évacuateurs de crues des barrages sont sujets à divers défis d'ingénierie, incluant des défaillances structurelles, une capacité d'évacuation insuffisante et des pannes mécaniques; cependant, l'érosion hydraulique constitue une problématique particulièrement importante qui menace l'infrastructure des barrages. Il est donc nécessaire d’évaluer de manière approfondie les paramètres hydrauliques et mécaniques des roches afin d’assurer l’intégrité structurelle et la résilience opérationnelle. Dans l’aspect mécanique des roches concernant l’érosion hydraulique, la capacité de résistance du matériau revêt une grande importance, tandis que dans l’aspect hydraulique, la force érosive de l’eau joue un rôle essentiel. Par conséquent, ignorer ces phénomènes augmenterait le risque de débordement et d’inondation en aval, impactant ainsi la sécurité et la fiabilité opérationnelle globale du barrage. Cette étude se concentre sur l’analyse des paramètres hydrauliques d'un évacuateur de crues à canal ouvert non revêtu et à surface lisse. En utilisant à la fois la modélisation numérique et l’analyse expérimentale, nous visons à explorer comment les variations de ces paramètres influencent l’érosion dans les évacuateurs de crues des barrages. La recherche porte sur l’évacuateur de crues du barrage Romaine 4, situé dans la région nord-est du Québec au Canada, en tant qu’étude de cas représentative. Le modèle physique de cet évacuateur a été construit à l’Université du Québec à Chicoutimi, où nous avons effectué les analyses expérimentales. Dans cette recherche, nous avons également réalisé une analyse numérique complète en utilisant la méthode des volumes finis (FVM), permettant un examen détaillé du comportement tridimensionnel de l’écoulement dans l’évacuateur. Cela a permis un suivi précis des schémas de mouvement du fluide. En outre, une approche expérimentale a été utilisée pour accroître la précision et la fiabilité des résultats, en réalisant des tests détaillés sur le modèle réduit à l’aide d’un système robotisé XYZ qui est capable de se déplacer dans trois directions (X, Y, Z), pour effectuer des prises de mesures de position, vitesse et pression. Les résultats des analyses numériques et expérimentales révèlent que le modèle numérique capture efficacement les caractéristiques générales de l’écoulement, prédisant de manière précise la vitesse moyenne dans le canal. Cependant, il présente des limitations dans la prédiction précise des pression dynamique et statique extrêmes comme les valeurs maximales et minimales. Les résultats montrent que les écarts maximaux entre les données expérimentales et numériques concernent principalement les vitesses extrêmes, le modèle numérique sous-estimant les vitesses maximales et surestimant les minimales, avec des erreurs plus marquées aux débits élevés et en amont. Cet écart peut aller jusqu’aux 60% à certains endroits. Par ailleurs, l’étude a examiné les effets des vannes sur la variabilité des paramètres hydrauliques tels que la profondeur de l’écoulement et la vitesse. L’analyse de plusieurs configurations de vannes a révélé que les évacuateurs à double vanne maintiennent des profondeurs d’écoulement plus constantes à travers toutes les sections transversales significatives. En expliquant l’interaction complexe entre le comportement hydraulique et la conception des évacuateurs de crues, cette recherche vise à améliorer notre compréhension des zones sujettes à l’érosion hydraulique dans les évacuateurs de barrages et à assurer la résilience à long terme de l’infrastructure des barrages.

Atmospheric reanalysis data provides a numerical description of global and regional water cycles by combining models and observations. These datasets are increasingly valuable as a substitute for observations in regions where these are scarce. They could significantly contribute to reducing losses by feeding flood early warning systems that can inform the population and guide civil security action. We assessed the suitability of two different precipitation and temperature reanalysis products readily available for predicting historic flooding of the La Chaudière River in Quebec: 1) Environment and Climate Change Canada's Regional Deterministic Reanalysis System (RDRS-v2) and 2) ERA5 from the Copernicus Climate Change Service. We exploited a multi-model hydrological ensemble prediction system that considers three sources of uncertainty: initial conditions, model structure, and weather forcing to produce streamflow forecasts up to 5 days into the future with a time step of 3 hours. These results are compared to a provincial reference product based on gauge measurements of the Ministère de l'Environnement et de la Lutte contre les Changements Climatiques. Then, five conceptual hydrological models were calibrated with three different meteorological datasets (RDRS-v2, ERA5, and observational gridded) and fed with two ensemble weather forecast products: 1) the Regional Ensemble Prediction System (REPS) from the Environment and Climate Change Canada and 2) the ensemble forecast issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). Results reveal that the calibration of the model with reanalysis data as input delivered a higher accuracy in the streamflow simulation providing a useful resource for flood modeling where no other data is available. However, although the selection of the reanalysis is a determinant of capturing the flood volumes, selecting weather forecasts is more critical in anticipating discharge threshold exceedances.

<p>Spring floods have generated colossal damages to residential areas in the Province of Quebec, Canada, in 2017 and 2019. Government authorities need accurate modelling of the impact of theoretical floods in order to prioritize pre-disaster mitigation projects to reduce vulnerability. They also need accurate modelling of forecasted floods in order to direct emergency responses. </p><p>We present a governmental-academic collaboration that aims at modelling flood impact for both theoretical and forecasted flooding events over all populated river reaches of meridional Quebec. The project, funded by the ministère de la Sécurité publique du Québec (Quebec ministry in charge of public security), consists in developing a diagnostic tool and methods to assess the risk and impacts of flooding. Tools under development are intended to be used primarily by policy makers. </p><p>The project relies on water level data based on the hydrological regimes of nearly 25,000 km of rivers, on high-precision digital terrain models, and on a detailed database of building footprints and characterizations. It also relies on 24h and 48h forecasts of maximum flow for the subject rivers. The developed tools integrate large data sets and heterogeneous data sources and produce insightful metrics on the physical extent and costs of floods and on their impact on the population. The software also provides precise information about each building affected by rising water, including an estimated cost of the damages and impact on inhabitants.  </p>

Hydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.

<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

Abstract Flood quantile estimation at sites with little or no data is important for the adequate planning and management of water resources. Regional Hydrological Frequency Analysis (RFA) deals with the estimation of hydrological variables at ungauged sites. Random Forest (RF) is an ensemble learning technique which uses multiple Classification and Regression Trees (CART) for classification, regression, and other tasks. The RF technique is gaining popularity in a number of fields because of its powerful non-linear and non-parametric nature. In the present study, we investigate the use of Random Forest Regression (RFR) in the estimation step of RFA based on a case study represented by data collected from 151 hydrometric stations from the province of Quebec, Canada. RFR is applied to the whole data set and to homogeneous regions of stations delineated by canonical correlation analysis (CCA). Using the Out-of-bag error rate feature of RF, the optimal number of trees for the dataset is calculated. The results of the application of the CCA based RFR model (CCA-RFR) are compared to results obtained with a number of other linear and non-linear RFA models. CCA-RFR leads to the best performance in terms of root mean squared error. The use of CCA to delineate neighborhoods improves considerably the performance of RFR. RFR is found to be simple to apply and more efficient than more complex models such as Artificial Neural Network-based models.

Over the last decades, different methods have been used by hydrologists to extend observed hydro-climatic time series, based on other data sources, such as tree rings or sedimentological datasets. For example, tree ring multi-proxies have been studied for the Caniapiscau Reservoir in northern Quebec (Canada), leading to the reconstruction of flow time series for the last 150 years. In this paper, we applied a new hydro-climatic reconstruction method on the Caniapiscau Reservoir and compare the obtained streamflow time series against time series derived from dendrohydrology by other authors on the same catchment and study the natural streamflow variability over the 1881–2011 period in that region. This new reconstruction is based not on natural proxies but on a historical reanalysis of global geopotential height fields, and aims firstly to produce daily climatic time series, which are then used as inputs to a rainfall–runoff model in order to obtain daily streamflow time series. The performances of the hydro-climatic reconstruction were quantified over the observed period, and showed good performances, in terms of both monthly regimes and interannual variability. The streamflow reconstructions were then compared to two different reconstructions performed on the same catchment by using tree ring data series, one being focused on mean annual flows and the other on spring floods. In terms of mean annual flows, the interannual variability in the reconstructed flows was similar (except for the 1930–1940 decade), with noteworthy changes seen in wetter and drier years. For spring floods, the reconstructed interannual variabilities were quite similar for the 1955–2011 period, but strongly different between 1880 and 1940. The results emphasize the need to apply different reconstruction methods on the same catchments. Indeed, comparisons such as those above highlight potential differences between available reconstructions and, finally, allow a retrospective analysis of the proposed reconstructions of past hydro-climatological variabilities.

In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.

Floods, intensified by climate change, pose major challenges for flood zone management in Quebec. This report addresses these issues through two complementary aspects: a historical analysis of the evolution of flood zone management in Quebec and the projected impact of the cartographic and regulatory overhaul, as well as an exploration of the imaginary surrounding the flood-prone territory of the city of Lachute, which has faced recurrent floods for decades and yet continues to be inhabited. The historical analysis reveals that the major floods of 1974, 1976, 2017, and 2019 marked significant turning points in Quebec’s risk management, particularly by highlighting gaps in the regulatory framework and flood zone mapping. The adoption of the Act Respecting Land Use Planning and Development (LAU) in 1979 and the Policy for the Protection of Shorelines, Littorals, and Floodplains (PPRLPI) in 1987 represented a shift toward a preventive approach. However, inconsistencies, insufficient updates to maps, and uneven enforcement of standards have hindered their effectiveness. The catastrophic floods of 2017 and 2019 triggered a regulatory overhaul, a modernization of mapping, and measures to strengthen community resilience. In 2022, a transitional regime came into effect to tighten the regulation of activities in flood zones, pending the adoption of a risk-based management framework. However, to this day, the regulatory perimeters proposed in the modernization project fail to account for the adaptive capacities deployed by communities to live with water, thus providing a biased interpretation of flood risk. The second part explores the social and cultural representations associated with Lachute’s flood-prone territory. It highlights the complex relationships that have developed between residents and the Rivière du Nord through successive flooding episodes and the adaptation strategies implemented to cope, particularly by those who have repeatedly experienced flooding. These residents have come to live with overflow events and to (co)exist with water, challenging the persistent notion that flood-prone areas are inherently dangerous. While local strategies are sometimes innovative, they remain constrained by a regulatory framework that disregards the human experience of the territory and the specific ways in which people inhabit exposed areas to learn to manage flood risks. In summary, this report underscores the urgency of a territorialized, risk-based approach to modernizing flood zone management. It also highlights the need to look beyond cartographic boundaries and better integrate human and cultural dimensions into planning policies, as illustrated in the case of Lachute, to more accurately reflect the true level of risk. These reflections aim to promote more coherent, sustainable, and acceptable management, planning, and development of exposed territories in response to the growing challenges posed by climate change.

Abstract Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.

RÉSUMÉ : Les relocalisations de populations et les démolitions de bâtiments sont des moyens pour réduire les risques associés aux inondations, dont ceux pour la santé humaine. Au Québec, l’usage de ces mesures pourrait s’accroître avec les changements climatiques. En Chaudière-Appalaches, au moins 404 bâtiments ont été démolis à Sainte-Marie et 88 à Scott après les inondations de 2019. L’expérience de démolition de domiciles post-inondation est toutefois peu documentée au Québec et encore moins selon le point de vue des personnes touchées, particulièrement chez les hommes. Ce mémoire présente les résultats d’une étude ayant documenté cette expérience auprès de treize hommes propriétaires d’un domicile dans la MRC Nouvelle-Beauce à partir d'entretiens semi-dirigés (méthode photo-élicitation) et d’un groupe de discussion. Cette étude repose sur l’expérience clinique de l’étudiante-chercheuse qui a constaté la présence de détresse chez la population masculine touchée par ce phénomène et sur la littérature scientifique qui démontre une plus faible propension à l’usage de services psychosociaux et de santé chez les hommes. À partir d’une analyse thématique inspirée du cadre théorique « Psychological Processes That Influence Adaptation to and Coping With Climate Change » de Reser et Swim et d’une perspective écosystémique, quatre nouvelles phases ont été dégagées soient : l’inondation, les démarches administratives, la démolition et la relocalisation. Chacune est caractérisée par des besoins et des impacts psychosociaux systémiques, l’usage de stratégies adaptatives spécifiques et des moments critiques pour la santé et le bien-être des hommes. Les résultats suggèrent que des impacts et besoins individuels et collectifs peuvent se cumuler et se prolonger dans le temps comme des manifestations anxio-dépressives ou traumatiques, de la détresse, une désaffiliation sociale ainsi qu’une modification de projets de vie. Une réduction de l’exposition aux inondations et une augmentation du bien-être et de la sécurité ressortent également. La proactivité, les pensées axées sur l’autonomie et le recours au soutien informel sont apparues comme des stratégies aidantes comparativement au repli sur soi et au surinvestissement dans le travail. Les résultats permettent d’exposer des pistes de réflexion et d’action favorisant le bien-être des hommes et d’autres pertinentes pour le travail social. Parmi celles-ci se trouvent d’encourager les hommes touchés par la démolition de leur domicile post-inondation à s’investir dans leur nouveau milieu de vie pour favoriser son appropriation et sa personnalisation ainsi que des recommandations pour le travail social de prendre en compte le genre dans la compréhension des problèmes socioenvironnementaux. -- Mot(s) clé(s) en français : Inondation, chez-soi, hommes, changements climatiques, travail social, désastre, besoins psychosociaux, adaptation, mesures d’atténuation du risque, événements météorologiques extrêmes. -- ABSTRACT : Population relocation and building demolition are ways of reducing the risks associated with flooding, including those to human health. In Quebec, the use of these measures could increase with climate change. In Chaudière-Appalaches, at least 404 buildings were demolished in Sainte-Marie and 88 in Scott after the 2019 floods. However, the experience of post-flood home demolition is poorly documented in Quebec, and even less so from the perspective of those affected, specifically men. This memoir presents the results of a study that documented this experience with thirteen male homeowners in the Nouvelle-Beauce MRC using semi-directed interviews (photo-elicitation method) and a focus group. This study is based on the student-researcher's clinical experience of distress among the male population affected by this phenomenon, and on scientific literature demonstrating a lower propensity to use psychosocial and health services among men. Based on a thematic analysis inspired by the Reser and Swim’s theoretical framework, the Psychological Processes That Influence Adaptation to and Coping With Climate Change, and an ecosystem perspective, four new phases were identified: flooding, administrative procedures, demolition and relocation. Each is characterized by systemic psychosocial needs and impacts, the use of specific adaptive strategies and critical moments for men's health and well-being. The results suggest that individual and collective needs and impacts can accumulate and extend over time, such as anxio-depressive or traumatic manifestations, distress, social disaffiliation and changes in life plans. A reduction in exposure to flooding and an increase in well-being and safety also stand out. Proactivity, autonomy-oriented thinking and reliance on informal support emerged as helpful strategies compared to withdrawal and over-investment in work. The results provide food for thought and action to promote men's well-being, and others relevant to social work. These include encouraging men affected by the demolition of their post-flood home to get involved in their new living environment to promote its appropriation and personalization and taking gender into account in understanding socioenvironmental problems. -- Mot(s) clé(s) en anglais : Flooding, home, men, climate change, social work, disaster, psychosocial needs, adaptation, risk mitigation measures, extreme weather events.

Flooding, a major natural calamity, severely threatens communities and infrastructures in areas susceptible to floods. Consequently, implementing an Internet of Things (IoT)-based flood monitoring system becomes crucial. Existing flood monitoring systems lack a comprehensive and scalable IoT platform to collect real-time data from diverse sensors efficiently, visualize flood information, and provide accurate water level forecasts. This thesis proposes a complete system designed to address the challenges associated with efficient data collection and flood monitoring from diverse IoT sensors. Our proposition involves creating and deploying a centralized system known as HYDROSIGHT, which facilitates the real-time gathering, monitoring, and visualization of flooding-related sensor data. HYDROSIGHT system also provides a log monitoring feature for effective debugging and troubleshooting. The IoT environment for flood monitoring and prediction system was designed to promote sustainability and autonomy by preferring sensors with minimal footprints and compatibility with solar panels. The system architecture leverages a 4G network for seamless data transmission. To validate the practical applicability of the proposed design,HYDROSIGHT system was tested at two municipalities of Quebec, namely Terrebonne, and Lac-Supérieur. In addition, the platform was also deployed at the Ericsson facility in Montreal to test the 5G capabilities. The deployment in these locations allowed us to evaluate the performance and effectiveness of the HYDROSIGHT system in a real flood monitoring environment. In addition to implementing the IoT testbed, a preliminary machine learning tool was developed on water level forecasting. In this experiment, we opted for an online machine-learning approach, recognizing the significance of real-time updates and low computational resources of IoT devices. Leveraging the constantly updating data from HYDROSIGHT, we trained and tested our online machine-learning model, enhancing its forecasting capabilities. We conducted a comparative analysis to understand the advantages of online machine learning over traditional batch learning. This analysis involved examining the water level forecasting results obtained from both methods using time series data from the HYDROSIGHT system deployed at Lac-Supérieur in Quebec.