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Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH4) is a crucial indicator for assessing atmospheric CH4 levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH4 concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).

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.

Questions have been raised about the correctness of water quality models with complete mixing assumptions in cross junctions of water distribution systems. Recent developments in the mixing phenomenon within cross junctions of water distribution networks (WDNs) have heightened the need for evaluating the existing incomplete mixing models under real-world conditions. Therefore, in this study, two cross junctions with pipe diameters of 100 Â 100 Â 100 Â 100 mm and 150 Â 150 Â 150 Â 150 mm were employed in laboratory experiments to evaluate six existing incomplete mixing models for 25 flow rate scenarios ranging between 1.5 and 3.0 L/s. It was observed that within the same flow rate scenario, the degree of mixing in a cross junction with a pipe relative roughness of 6.00 Â 10À5 (pipe diameter of 25 mm) was higher than that in a cross junction with a pipe relative roughness of 3.00 Â 10À5 (pipe diameter of 50 mm) and smaller. Considering the real-world size of pipes in evaluating the incomplete mixing models showed that two incomplete mixing models, AZRED and the one by Shao et al., had the best accordance with the results of the laboratory experiments.

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.

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 [...]

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.

Abstract Integrating hydrogeomorphological (HGM) principles into the restoration of degraded rivers can achieve sustainable results and provide various human benefits. HGM principles mainly involve understanding the context and processes that shape a fluvial system before any intervention, in order to support its dynamism and to align with its potential functioning and uses. Despite recent management approaches inspired by HGM principles, most restoration projects carried out in Quebec (Canada) are not process‐based and target specific one‐dimensional objectives. Although there is an overall lack of post‐project monitoring, several projects appear to have failed or had mixed success. This research aims to shed light on the diversity of societal drivers behind river restoration projects and to examine how they influence the integration of HGM principles and human benefits. Four restoration projects were characterized through participant observation and interviews with the organizations running them. Representatives of two ministries involved in river restoration and management were also interviewed. The results show that projects were mainly shaped by public acceptance disregarding HGM principles, which can lead to poorly‐informed action. Project funding and stakeholders' expertise have also challenged project implementation and played a key role in defining their objectives. The addition of these components improve the current analytical frameworks for identifying river restoration objectives. Depending on specific sociocultural, political and legislative contexts, funding programs and stakeholders' expertise may either facilitate or restrict the integration of HGM principles and human benefits in the projects. Recognizing these key drivers reframes river restoration as a fundamentally social activity and enlightens how they could impel innovative approaches towards more sustainable results.

<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>

<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 Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study in Quebec, Canada. FMR provides a better mean FDC estimation when obtained over sites by considering simultaneously all FDC quantiles in the assessment of each given site. However, traditional regression provides a better mean FDC estimation when obtained over given FDC quantiles by considering all sites in the assessment of each quantile separately. Mean daily streamflow estimation is similar; yet FMR provides an improved estimation for most sites. Furthermore, FMR represents a more suitable framework and provides a number of practical advantages, such as insight into descriptor influence on FDC quantiles. Hence, traditional regression may be preferred if only few FDC quantiles are of interest; whereas FMR would be more suitable if a large number of FDC quantiles is of interest, and therefore to estimate daily streamflows.

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.

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.

RÉSUMÉ : Pour atténuer les risques d'inondation au Québec mais aussi partout dans le monde, plusieurs organismes gouvernementaux et des organismes privés, qui ont dans leurs attributions la gestion des risques des catastrophes naturelles, continuent d'améliorer ou d'innover en matière d'outils qui peuvent les aider efficacement à la mitigation des risques d'inondation et aider la société à mieux s'adapter aux changements climatiques, ce qui implique des nouvelles technologies pour la conception de ces outils. Après les inondations de 2017, le ministère de l'Environnement et de la Lutte contre les changements climatiques (MELCC) du gouvernement du Québec, en collaboration avec d'autres ministères et organismes et soutenu par Ouranos, a initié le projet INFO-Crue qui vise d'une part, à revoir la cartographie des zones inondables et, d'autre part, à mieux outiller les communautés et les décideurs en leur fournissant une cartographie prévisionnelle des crues de rivières. De ce fait, l'objectif de notre travail de recherche est d'analyser de façon empirique les facteurs qui influencent l'adoption d'un outil prévisionnel des crues. La revue de la littérature couvre les inondations et les prévisions, les théories et les modèles d'acceptation de la technologie de l'information (TI). Pour atteindre l'objectif de recherche, le modèle développé s'est appuyé particulièrement sur le modèle qui combine les concepts de la théorie unifiée de l'acceptation et l'utilisation des technologies (UTAUT) de Venkatesh et al. (2003) avec le concept « risque d'utilisation ». Afin de répondre à notre objectif de recherche, nous avons utilisé une méthodologie de recherche quantitative hypothético-déductive. Une collecte de données à l'aide d'une enquête par questionnaire électronique a été réalisée auprès de 106 citoyens qui habitent dans des zones inondables. L'analyse des résultats concorde avec la littérature. La nouvelle variable « risque d'utilisation » rajoutée au modèle UTAUT a engendré trois variables qui sont : « risque psychologique d'utilisation »; « risque de performance de l'outil » et « perte de confiance ». Pour expliquer l'adoption d'un nouvel outil prévisionnel des crues, notre analyse a révélé que cinq variables à savoir : « l'utilité perçue », « la facilité d'utilisation », « l'influence sociale », « la perte de confiance » et « le risque psychologique » sont des facteurs significatifs pour l'adoption du nouvel outil prévisionnel. -- Mot(s) clé(s) en français : Inondation, Prévision, UTAUT, Adoption de la technologie, Risque perçu d'utilisation, facteurs d'adoption, Projet INFO-Crue. -- ABSTRACT : With the aim of mitigating flood risks in Canada as well as around the world, several government and private organizations that have the responsibility of natural hazard risk management, are working hard to improve or innovate the flood mitigation approaches that can help effectively reducing flood risks and helping people adapt to climate change. After the 2017 floods, the Ministry of the Environment and the Fight against Climate Change (MELCC) of the Government of Quebec, in collaboration with other ministries and organizations and supported by Ouranos, initiated the INFO-Crue project which aims at reviewing the mapping of flood zones and providing communities and decision-makers with a forecast mapping of river floods. In this context, the objective of our research is to analyze the factors that may influence the adoption of a flood forecasting tool. The literature review covers flood and forecasting, as well as technology adoption models. To achieve the goal of our research, a conceptual model that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) of Venkatesh et al. (2003) with perceived use risk was developed. A quantitative research methodology was used, and we administrate an electronic questionnaire survey to 106 citizens who live in flood-plain area. Results analysis show that the new variable "perceived use risk" introduced in the model generates three variables which are: "psychological risk"; "performance risk" and "loss of trust". To explain the adoption of a new forecasting tool, our analysis revealed that the following five variables which are "perceived usefulness", "ease of use­", "social influence", "loss of trust" and "psychological risk" are significant factors for the adoption of the new forecasting tool. -- Mot(s) clé(s) en anglais : Flood, Forecasting, UTAUT, Technology Adoption, perceived Risk of use, adoption factors, INFO-Crue project.

Au Québec, les conditions printanières extraordinaires de 2017 et 2019 ont incité le gouvernement provincial à commander une mise à jour des cartes des zones inondables. La plupart des cartes existantes ne reflètent pas adéquatement l’aménagement actuel du territoire, ni l’aléa associé. Généralement, pour la cartographie, les modèles hydrodynamiques tel que HEC-RAS sont utilisés, mais ces outils nécessitent une expertise significative, des données hydrométriques et des relevés bathymétriques à haute résolution. Étant donnée la nécessité de mettre à jour ces cartes tout en réduisant les coûts financiers associés, des méthodes conceptuelles simplifiées ont été développées. Ces approches, y compris l’approche géomatique HAND (Height above the nearest drainage), qui reposent uniquement sur un modèle numérique d’élévation (MNE), sont de plus en plus utilisées. HAND permet de calculer la hauteur d’eau nécessaire pour inonder chaque pixel du MNE selon la différence entre son élévation et celle du pixel du cours d’eau dans lequel il se déverse. Les informations sur la géométrie hydraulique dérivées par HAND ainsi que l’application de l’équation de Manning permettent la construction d’une courbe de tarage synthétique (CTS) pour chaque tronçon de rivière homogène. Dans la littérature, cette méthode a été appliquée pour établir une cartographie de la zone inondable de première instance de grands fleuves aux États-Unis avec un taux de correspondance de 90% par rapport à l’utilisation de HEC-RAS. Elle n’a toutefois pas été appliquée sur de petits bassins versants, car ceux-ci engendrent des défis méthodologiques substantiels. Ce projet s’attaque à ces défis sur deux bassins versants Québécois, ceux des rivières à la Raquette et Delisle. Les conditions frontières des modèles sont dérivées d’un traitement statistique empirique des séries de débits simulés avec le modèle hydrologique HYDROTEL. Étant donnée l’absence de stations météorologiques sur le territoire à l’étude, des chroniques du système Canadien d’Analyse de la précipitation (CaPA) ont été utilisées pour cette modélisation hydrologique. Les résultats de ce projet pointent vers des performances satisfaisantes de l’approche géomatique HAND-CTS en comparaison avec le modèle hydrodynamique HEC-RAS (1D/2D et 2D au complet), avec des taux de correspondance entre les étendues des inondations supérieurs à 60 % pour les bassins versants de Delisle et à la Raquette. Les comparaisons étaient effectuées sur une gamme de débit allant d’un débit de période de retour de 2 ans jusqu’à un débit de plus de 350 ans. On notera que l’application sur la rivière à la Raquette a été développée dans les règles de l’art, incluant un processus de calage développé dans le cadre d’un projet de maitrise en sciences de l’eau connexe à ce mémoire, relativement à la longueur du tronçon, le calage vertical de la CTS en considérant la hauteur d’eau présente dans le cours d’eau lors du relevé LiDAR et sa précision verticale. Les résultats ont montré que le coefficient de précision globale le plus bas était de 98 % pour un débit de 350 ans, avec une précision de plus que 99 % pour les autres périodes de retour, ce qui représente une très bonne performance du modèle. Et par ailleurs, le coefficient de Kappa conditionnel humide variait entre 58 % et 28 %. Alors, que pour la rivière Delisle, l’application se veut naïve, c’est-à-dire sans calage préalable de la méthode HANDCTS. La précision globale a varié entre 83 % et 96 %, ce qui est considéré comme "très approprié" et une variation du coefficient Kappa conditionnel humide de 35,2 à 64,3 %. Alors que pour une différence d’élévations d'eau entre les élévations de référence et simulées, la performance était quantifiée par un RMSE qui variait pour les périodes de retour de 100 ans et de 350 ans respectivement de 4,5 m et de 7,1 m. Enfin, la distribution spatiale des différences d’élévations montre une distribution gaussienne avec une moyenne qui est à peu près égale à 0 où la plupart des erreurs se situent entre -0,34 m et 1,1 m La cartographie des zones inondables dérivée de HAND-CTS présente encore certains défis associés notamment à la présence d’infrastructures urbaines complexes (ex. : ponceaux, ponts et seuils) dont l’influence hydraulique n’est pas considérée. Dans le contexte où l’ensemble du Québec (529 000 km²) dispose d’une couverture LiDAR, les résultats de ce mémoire permettront de mieux comprendre les sources d’incertitude associées à la méthode HAND-CTS tout en démontrant son potentiel pour les bassins versants dépourvus de données bathymétriques et hydrométéorologiques. <br /><br />The 2017 and 2019 extraordinary spring conditions prompted the Quebec government to update flood risk maps, as most of them do not adequately reflect current land use and associated hazard. Generally, hydrodynamic models such as HEC-RAS are used for flood mapping, but they require significant expertise, hydrometric data, and high-resolution bathymetric surveys. Given the need to update these maps while reducing the associated financial costs, simplified conceptual methods have been developed over the last decade. These methods are increasingly used, including HAND (height above the nearest drainage), which relies on a Digital Elevation Model (DEM) to delineate the inundation area given the water height in a river segment. Furthermore, the river geometry derived from HAND data and the application of Manning’s equation allow for the construction of a synthetic rating curve (SRC) for each homogeneous river segment. In the scientific literature, this framework has been applied to produce first-instance floodplain mapping of large rivers. For example, in the Continental United States 90% match rates were achieved when compared to the use of HEC-RAS. However, this framework has not been validated for small watersheds, as substantial methodological challenges are anticipated. This project addresses these underlying challenges in two Quebec watersheds, the à la Raquette and Delisle watersheds. The boundary conditions of the HECRAS models were derived from an empirical statistical treatment of flow time series simulated by HYDROTEL, a hydrological model, using Canadian Precipitation Analysis Product (CaPA) time series. The results of this project point towards satisfactory performances, with match rates greater than 60 % for both watersheds. It should be noted that the application on the Delisle River is naive, that is without prior calibration of the HAND-SRC method. The overall accuracy ranged from 83.4 % to 96.2 % while the water surface elevation difference was quantified by an RMSE that was for the 100-year and 350-year return periods of 4.5 m and 7.1 m respectively and where most errors are between -0.34 m and 1.1 m representing a very good model comparing to similar studies. For à la Raquette, the application showed an overall accuracy coefficient of 98 % for a 350-year flow, with an accuracy of over 99 % for other return periods. The mapping of flood risk areas using HAND-SRC still faces certain challenges, notably the presence of complex urban infrastructures (e.g., culverts, bridges, and weirs) whose hydraulic influences are not considered by this geomatic approach. Given that most of Quebec (529,000 km²) topography has been digitized using LiDAR data, the results conveyed in this MSc thesis will allow for a better understanding of the sources of uncertainty associated with the application of the HAND-SRC method while demonstrating its potential for watersheds lacking hydrometeorological and high-resolution bathymetric data.

Abstract : Flood forecasting plays a pivotal role in effective water resource management and flood risk mitigation. Despite recent advancements, existing ensemble forecasting systems often grapple with issues of unreliability and under-dispersion. The uncertainty in ensemble forecasts can emanate from various sources, including inputs like precipitation, temperature, and streamflow, as well as initial conditions, model structure and parameters, and boundary conditions. Over the past two decades, numerous endeavors have been made to address bias and under-dispersion through the introduction of various statistical post-processing methods in meteorological and hydrological forecasts. However, a significant challenge lies in selecting the appropriate method and strategy in the forecast chain. Limited research has addressed the integration of pre-processing and post-processing in streamflow ensemble forecasting. Moreover, existing studies have generally focused on lumped hydrological models, while the performance of this integration in distributed hydrological models remains scarcely examined. On the other hand, streamflow data, often indirectly measured through rating curves, are particularly susceptible to errors. While various methods have been proposed to estimate rating curve uncertainty (RCU), the impact of RCU on streamflow forecasting system performance remains underexplored. In addition, current post-processing methods utilized in streamflow forecasting systems have disregarded the inherent uncertainty in observational streamflow data. Thus, this thesis delves into the potential and hurdles associated with considering different sources of uncertainty in flood forecasting systems and their impact on system performance. The primary objectives entail 1) assessing and identifying optimal scenarios from pre-processing of both temperature and precipitation and post-processing of streamflow forecasts approaches, or a combination thereof, and 2) evaluating the consideration of rating curve uncertainty on flood forecasting system performance through incorporating into targeted post-processing methodologies. The thesis focuses on a short-range ensemble streamflow forecasting framework spanning lead times of 1 to 5 days for the au Saumon watershed in southern Quebec, Canada. This watershed, being flood-prone, encountered significant challenges during the 2019 and 2017 flood events, including widespread inundation, road closures, and damage to property and infrastructure. Enhancing streamflow ensemble forecasts and upgrading flood forecasting systems holds the potential to greatly benefit decision-makers and the local populace. Chapter 4 (Results part 1), presented as a scientific article, thus delves into assessing and employing various pre- and post-processing strategy scenarios within the flood forecasting framework employing a spatially distributed hydrological model. By applying different statistical processing and bias correction techniques, the performance and quality of ensemble streamflow forecasts were evaluated across different scenarios. The findings highlight biases and under-dispersion as significant factors affecting raw ensemble forecasts. Pre-processing partially improves raw forecasts but doesn't fully address bias in under-dispersed forecasts. Combining pre- and post-processing enhances forecast skill and reliability, albeit with some variations compared to post-processing alone. Integrating flood events into the training dataset and optimizing its length improves the effectiveness of processing methods, underscoring the critical role of data management strategies in enhancing streamflow forecasting systems. Subsequently, Chapter 5 (Results part 2) , evaluates the consideration of rating curve uncertainty, derived from the Voting Point Method (VPM) and Bayesian Rating curve (BaRatin) estimation methods, on the performance of streamflow ensemble forecasts by integrating into a targeted post-processing approach using Weighted Ensemble Dressing (WED) and Cumulative Distribution Function Matching (CDFM) post-processing methods. Post-processing without RCU enhances forecasting system skill and reliability but overlooks the inherent uncertainty in observational data, posing concerns about its effectiveness. Conversely, integrating RCU improves forecast skill, reliability, and effectively addresses observational data uncertainty, offering a notable advantage over traditional post-processing methods. Comparing WED and CDFM post-processing methods highlighted nuanced differences in forecast outcomes, influenced by uncertainty estimation techniques. Both methods showed favorable accuracy metrics, with RCU integration, especially from VPM, notably enhancing forecast quality. However, variations in RCU from different estimation methods may lead to forecast underestimation or overestimation, warranting careful consideration. These findings collectively shed light on the potential and challenges associated with incorporating different sources of uncertainty into flood forecasting systems.

Extreme flood events continue to be one of the most threatening natural disasters around the world due to their pronounced social, environmental and economic impacts. Changes in the magnitude and frequency of floods have been documented during the last years, and it is expected that a changing climate will continue to affect their occurrence. Therefore, understanding the impacts of climate change through hydroclimatic simulations has become essential to prepare adaptation strategies for the future. However, the confidence in flood projections is still low due to the considerable uncertainties associated with their simulations, and the complexity of local features influencing these events. The main objective of this doctoral thesis is thus to improve our understanding of the modelling uncertainties associated with the generation of flood projections as well as evaluating strategies to reduce these uncertainties to increase our confidence in flood simulations. To address the main objective, this project aimed at (1) quantifying the uncertainty contributions of different elements involved in the modelling chain used to produce flood projections and, (2) evaluating the effects of different strategies to reduce the uncertainties associated with climate and hydrological models in regions with diverse hydroclimatic conditions. A total of 96 basins located in Quebec (basins dominated by snow-related processes) and Mexico (basins dominated by rain-related processes), covering a wide range of climatic and hydrological regimes were included in the study. The first stage consisted in decomposing the uncertainty contributions of four main uncertainty sources involved in the generation of flood projections: (1) climate models, (2) post-processing methods, (3) hydrological models, and (4) probability distributions used in flood frequency analyses. A variance decomposition method allowed quantifying and ranking the influence of each uncertainty source on floods over the two regions studied and by seasons. The results showed that the uncertainty contributions of each source vary over the different regions and seasons. Regions and seasons dominated by rain showed climate models as the main uncertainty source, while those dominated by snowmelt showed hydrological models as the main uncertainty contributor. These findings not only show the dangers of relying on single climate and hydrological models, but also underline the importance of regional uncertainty analyses. The second stage of this research project focused in evaluating strategies to reduce the uncertainties arising from hydrological models on flood projections. This stage includes two steps: (1) the analysis of the reliability of hydrological model’s calibration under a changing climate and (2) the evaluation of the effects of weighting hydrological simulations on flood projections. To address the first part, different calibration strategies were tested and evaluated using five conceptual lumped hydrological models under contrasting climate conditions with datasets lengths varying from 2 up to 21 years. The results revealed that the climatic conditions of the calibration data have larger impacts on hydrological model’s performance than the lengths of the climate time series. Moreover, changes on precipitation generally showed greater impacts than changes in temperature across all the different basins. These results suggest that shorter calibration and validation periods that are more representative of possible changes in climatic conditions could be more appropriate for climate change impact studies. Following these findings, the effects of different weighting strategies based on the robustness of hydrological models (in contrasting climatic conditions) were assessed on flood projections of the different studied basins. Weighting the five hydrological models based on their robustness showed some improvements over the traditional equal-weighting approach, particularly over warmer and drier conditions. Moreover, the results showed that the difference between these approaches was more pronounced over flood projections, as contrasting flood magnitudes and climate change signals were observed between both approaches. Additional analyses performed over four selected basins using a semi-distributed and more physically-based hydrological model suggested that this type of models might have an added value when simulating low-flows, and high flows on small basins (of about 500 km2). These results highlight once again the importance of working with ensembles of hydrological models and presents the potential impacts of weighting hydrological models on climate change impact studies. The final stage of this study focused on evaluating the impacts of weighting climate simulations on flood projections. The different weighting strategies tested showed that weighting climate simulations can improve the mean hydrograph representation compared to the traditional model “democracy” approach. This improvement was mainly observed with a weighting approach proposed in this thesis that evaluates the skill of the seasonal simulated streamflow against observations. The results also revealed that weighting climate simulations based on their performance can: (1) impact the floods magnitudes, (2) impact the climate change signals, and (3) reduce the uncertainty spreads of the resulting flood projection. These effects were particularly clear over rain-dominated basins, where climate modelling uncertainty plays a main role. These finding emphasize the need to reconsider the traditional climate model democracy approach, especially when studying processes with higher levels of climatic uncertainty. Finally, the implications of the obtained results were discussed. This section puts the main findings into perspective and identifies different ways forward to keep improving the understanding of climate change impacts in hydrology and increasing our confidence on flood projections that are essential to guide adaptation strategies for the future.

Les changements climatiques anthropogéniques posent des défis énormes pour toutes les sociétés humaines. Ces défis majeurs mettront à l’épreuve les capacités d’adaptation des États et de ses institutions et des communautés partout dans le monde et devront se résoudre par un élan de solidarité humaine afin d’en atténuer les conséquences. Le Canada connaît déjà un réchauffement climatique important. Le pays a d’ailleurs récemment été touché par des événements climatiques extrêmes : des canicules, des feux de forêt, une sécheresse anormale et des inondations dont l’intensité est prévue d’augmenter avec les changements climatiques anthropogéniques. La province du Québec a quant à elle été touchée par de fortes inondations entre 2017 et 2019. L’objectif principal de la présente étude vise à discuter la manière dont le paradigme écosocial peut faire évoluer le travail social en tant que champ de savoir et d’intervention dans un contexte de changements climatiques. Cette étude s’est appuyée sur des données issues de groupes focus réalisés avec des intervenants suite aux inondations survenues au Québec (2017-2019). Notre analyse vise les interventions réalisées en contexte d’inondations, dans le sud de la province, mise en œuvre par le système de santé. Les données ont été collectées lors d’entrevues de groupe réalisées avec des intervenants psychosociaux et des gestionnaires de CI(U)SSS au courant des mois d’octobre et de novembre 2019. Les thèmes suivants ont émergé des analyses: les caractéristiques des inondations de 2019, les divergences d’opinions vis-à-vis des changements climatiques, l’aide et le soutien apportés durant les inondations et la participation citoyenne. J’insisterai également sur l’exacerbation possible des inégalités sociales dans ce contexte. D’autres thèmes se sont également révélés importants : l’engagement des intervenants psychosociaux, la participation et la décentralisation des décisions politiques. Enfin, mes réflexions porteront sur les conséquences sociales qu’entrainent les inondations et sur les types de pratiques sociales qui s’avèrent pertinentes à l’ère des changements climatiques et dans un contexte d’urgence.