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<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 Climate change is affecting freshwater systems, leading to increased water temperatures, which is posing a threat to freshwater ecological communities. In the Nechako River, a water management program has been in place since the 1980s to maintain water temperatures at 20°C during the migration of Sockeye salmon. However, the program's effectiveness in mitigating the impacts of climate change on resident species like Chinook salmon's thermal exposure is uncertain. In this study, we utilised the CEQUEAU hydrological model and life stage-specific physiological data to evaluate the consequences of the current program on Chinook salmon's thermal exposure under two contrasting climate change and socio-economic scenarios (SSP2-4.5 and SSP5-8.5). The results indicate that the thermal exposure risk is projected to be above the optimal threshold for parr and adult life stages under both scenarios relative to the 1980s. These life stages could face an increase in thermal exposure ranging from up to 2 and 5 times by 2090s relative to the 1980s during the months they occurred under the SSP5-8.5 scenario, including when the program is active (July 20th to August 20th). Additionally, our study shows that climate change will result in a substantial rise in cumulative heat degree days, ranging from 1.9 to 5.8 times (2050s) and 2.9 to 12.9 times (2090s) in comparison to the 1980s under SSP5-8.5. Our study highlights the need for a holistic approach to review the current Nechako management plan and consider all species in the Nechako River system in the face of climate change.

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

As Earth's atmospheric temperatures and human populations increase, more people are becoming vulnerable to natural and human-induced disasters. This is particularly true in Central America, where the growing human population is experiencing climate extremes (droughts and floods), and the region is susceptible to geological hazards, such as earthquakes and volcanic eruptions, and environmental deterioration in many forms (soil erosion, lake eutrophication, heavy metal contamination, etc.). Instrumental and historical data from the region are insufficient to understand and document past hazards, a necessary first step for mitigating future risks. Long, continuous, well-resolved geological records can, however, provide a window into past climate and environmental changes that can be used to better predict future conditions in the region. The Lake Izabal Basin (LIB), in eastern Guatemala, contains the longest known continental records of tectonics, climate, and environmental change in the northern Neotropics. The basin is a pull-apart depression that developed along the North American and Caribbean plate boundary ∼ 12 Myr ago and contains > 4 km of sediment. The sedimentological archive in the LIB records the interplay among several Earth System processes. Consequently, exploration of sediments in the basin can provide key information concerning: (1) tectonic deformation and earthquake history along the plate boundary; (2) the timing and causes of volcanism from the Central American Volcanic Arc; and (3) hydroclimatic, ecologic, and geomicrobiological responses to different climate and environmental states. To evaluate the LIB as a potential site for scientific drilling, 65 scientists from 13 countries and 33 institutions met in Antigua, Guatemala, in August 2022 under the auspices of the International Continental Scientific Drilling Program (ICDP) and the US National Science Foundation (NSF). Several working groups developed scientific questions and overarching hypotheses that could be addressed by drilling the LIB and identified optimal coring sites and instrumentation needed to achieve the project goals. The group also discussed logistical challenges and outreach opportunities. The project is not only an outstanding opportunity to improve our scientific understanding of seismotectonic, volcanic, paleoclimatic, paleoecologic, and paleobiologic processes that operate in the tropics of Central America, but it is also an opportunity to improve understanding of multiple geological hazards and communicate that knowledge to help increase the resilience of at-risk Central American communities.

Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long-term variability of hydrological variables: as an overall trend, shifting mean, or as a long-term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD-NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain-River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD-NSOR model can be an appropriate option by reproducing long-term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long-term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water-related risks.

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

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.

Natural calamities like floods and droughts pose a significant threat to humanity, impacting millions of people each year and incurring substantial economic losses to society. In response to this challenge, this thesis focuses on developing advanced machine learning techniques to improve water height prediction accuracy that can aid municipalities in effective flood mitigation. The primary objective of this study is to evaluate an innovative architecture that leverages Long Short Term Networks - neural networks to predict water height accurately in three different environmental scenarios, i.e., frazil, droughts and floods due to snow spring melt. A distinguishing feature of our approach is the incorporation of meteorological forecast as an input parameter into the prediction model. By modeling the intricate relationships between water level data, historical meteorological data and meteorological forecasts, we seek to evaluate the impact of meteorological forecasts and if any inaccuracies could impact water-level prediction. We compare the outcomes obtained by incorporating next-hour, next-day and next-week meteorological data into our novel LSTM model. Our results indicate a comprehensive comparison of the usage of various parameters as input and our findings suggest that accurate weather forecasts are crucial in achieving reliable water height predictions. Additionally, this study focuses on the utilization of IoT sensor data in combination with ML models to enhance the effectiveness of flood prediction and management. We present an online machine learning approach that performs online training of the model using real-time data from IoT sensors. The integration of live sensor data provides a dynamic and adaptive system that demonstrates superior predictive capabilities compared to traditional static models. By adopting these advanced techniques, we can mitigate the adverse impacts of natural catastrophes and work towards building more resilient and disaster-resistant communities.

Les changements climatiques sont un défi mondial imminent, dont les conséquences sont déjà observées. On sait que ces effets s’intensifieront, entraînant une augmentation de la fréquence et de la gravité des événements météorologiques extrêmes, une perturbation substantielle de la production alimentaire et le déplacement de dizaines de millions de personnes en raison de vagues de chaleur mortelles et de sécheresses. La question est donc : que peut-on y faire ? Dans cette thèse, nous faisons des changements climatiques notre objet central et explorons les voies par lesquelles la recherche en apprentissage profond peut contribuer à son atténuation. Un obstacle connu à des politiques climatiques ambitieuses est le manque de soutien et de demande populaires. Cela peut être attribué, en partie, aux causes et conséquences extrêmement complexes et imbriquées des changements climatiques. Une mauvaise conception courante est qu'ils affecteront principalement d’autres personnes que soi-même, des personnes éloignées dans le temps ou l’espace. Pour y remédier, la recherche a montré que présenter aux gens des \textit{images} authentiques, locales et pertinentes d'un concept les aide à mieux comprendre et appréhender ce qui est en jeu. Dans notre première contribution, nous explorons donc comment les récentes avancées en apprentissage profond pour la vision par ordinateur et les réseaux antagonistes génératifs peuvent être utilisées pour générer des images \textit{personnalisées} représentant les impacts du changement climatique. Notre objectif avec \textit{ClimateGAN} est de visualiser à quoi pourrait ressembler une inondation d’un mètre à n’importe quelle adresse, indépendamment de son risque réel d’inondation sous l’effet des changements climatiques. Cette approche vise à susciter l’empathie en rendant les impacts abstraits du changement climatique plus tangibles et personnalisés. En utilisant une image de Google Street View et en la traitant avec \textit{ClimateGAN}, nous générons des images d’inondation physiquement plausibles et visuellement réalistes basées sur l’adaptation de domaine à partir d’un environnement simulé, la prédiction de profondeur et la segmentation sémantique. Ce modèle a été déployé sur un site web dans le but de sensibiliser et d’engager l’action en faveur des changements climatiques. En plus d’aider les gens à mieux visualiser à quoi pourrait ressembler un avenir climatique hors de contrôle, nous étudions également dans cette thèse comment l’apprentissage profond peut améliorer les technologies existantes. Un domaine majeur de recherche dans cette direction est la recherche de nouveaux matériaux. Dans cette thèse, nous explorons plus particulièrement la prédiction des propriétés des matériaux comme moyen d’accélérer la découverte d'électro-catalyseurs, une famille de matériaux impliqués dans le stockage d’énergie à base d’hydrogène. Nous présentons deux contributions, \textit{PhAST} et \textit{FAENet}, qui se concentrent sur l’amélioration du compromis performance/scalabilité dans les réseaux de neurones géométriques de graphe (GNN). Avec \textit{PhAST}, nous introduisons un ensemble de méthodes pour adapter la procédure GNN classique--de la création du graphe d’entrée aux prédictions d’énergie et de forces de sortie--à la tâche spécifique de prédire l’énergie d’un système atomique adsorbant-catalyseur relaxé. Nous démontrons comment, en plus d’améliorer les performances, ces modifications améliorent l’efficacité et permettent un entraînement compétitif des GNN dans des environnements CPU. Dans \textit{FAENet}, nous présentons un nouveau GNN efficace pour les prédictions équivariantes E(3). En particulier, nous transposons la charge de l’équivarience sur la représentation des données afin de réduire les contraintes sur le modèle lui-même. Cette approche nous permet d’introduire une nouvelle architecture légère et expressive visant à faire des prédictions meilleures et plus rapides de diverses propriétés des matériaux. Enfin, nous examinons de manière critique notre propre domaine et discutons des impacts environnementaux associés aux technologies de l’IA. Nous nous penchons sur la façon dont les praticiens peuvent estimer leurs émissions de carbone, quelles mesures ils peuvent prendre aujourd’hui pour les réduire, et quelles autres étapes sont nécessaires pour des déclarations et responsabilités environnementales plus précises.

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.

RÉSUMÉ: Les inondations sont considérées comme l'un des risques naturels les plus dangereux au monde. Plusieurs pays souffrent des conséquences néfastes des inondations. Au Canada, plusieurs provinces ont subi des inondations au cours du siècle dernier. Par exemple, la rivière des Outaouais a été confrontée à de nombreuses inondations comme en 2017 et 2019. La population d'Ottawa continue à augmenter d'une année à l'autre. C'est pour cela que nous avons choisi la rivière des Outaouais comme étude de cas pour ce projet dans le but de protéger la société contre les risques causés par les inondations. Les pays adoptent plusieurs solutions basées sur différentes méthodes afin de minimiser les dommages causés par les crues. La plupart des scientifiques s'accordent que la prévision des crues est la meilleure façon de limiter les conséquences des crues. Les systèmes de prévision des crues sont indispensables dans les pays fréquemment confrontés à des crues. Ils visent à fournir un long délai d'exécution et à fournir aux autorités et aux décideurs des informations suffisantes. Par conséquent, ils auront suffisamment de temps pour prendre les mesures adéquates pour sauver la vie de la population et limiter les catastrophes économiques dues aux inondations. ABSTRACT: Floods are one of the most catastrophic natural disasters in Canada and around the world that can cause loss of life and damages to properties and infrastructures. Saguenay flood (1996), southern Alberta flood (2013), and Ottawa floods (2017, 2019), are a few examples of Canadian floods with tremendous socio-economic impacts. Flood forecasting and predicting its characteristics (e.g., its magnitude and extent) has an important role in preventing and mitigating such flood impacts. Particularly, short-term forecasting is crucial for early warning systems and emergency response to floods. This study presents an integrated hydraulic-hydrologic modeling system for flood prediction. In this system, the Delft3D two-dimensional hydrodynamic model is connected with a HEC-HMS hydrologic model and observation data to provide an automatic exchange of data and results. Delft3D and HEC-HMS were chosen for this study because they were widely used and provided good results. In addition, they were applied in several flood forecasting studies. The prediction weather data and watershed characteristics provide input to the hydrological model to predict streamflow conditions, which are then automatically fed into the hydrodynamic model. The hydrodynamic model simulates the flood characteristics such as water level, 2D depth-averaged velocity field, and flood extent.

Abstract: Accurate spring flow forecasts, primarily driven by snow accumulation and melt processes, are essential for decision-makers aiming to optimize hydro-electricity production and mitigate potential flood damages in snow-dominated regions. By integrating snowpack data from diverse sources (in-situ, remote sensing, and reanalysis) with modeled snow-related state variables through Data Assimilation (DA), there is the potential to leverage both modeling and observations for more accurate estimation of the resulting spring flow. However, challenges such as the lack of sufficient snow station networks and issues with optical and microwave sensors to provide Snow Water Equivalent (SWE) can hinder progress, particularly in regions with heterogeneous snowpack characteristics. Overcoming these challenges is crucial to realizing the full potential of snow DA in advancing spring flow forecasts. This thesis aims to optimize spring flow forecasting effectiveness using snow DA, where snow-related observations are incorporated into hydrological models. It investigates the best hydrological model structure (a lumped model: HSAMI, and a distributed model: HYDROTEL) for leveraging distributed SWE data provided by SNODAS (SNOw Data Assimilation System) dataset, which serves as the observation. This DA framework is applied on a large, heterogeneous northern Québec watershed (Outardes 4). It does so by updating SWE model states of HYDROTEL and HSAMI using the Ensemble Kalman Filter (EnKF) DA method. The simulated spring flow are then compared to observed spring flow data. Another scope of the thesis is to compare two reanalysis products with varying spatial resolutions and SWE representations (SNODAS and ERA5-Land) for improving 1-day spring flow forecasts in HYDROTEL over a smaller southern Québec watershed (Au Saumon). Finally, the thesis assesses the forecasting skill of the optimal model-observation combination over an extended 30-day forecast horizon using various probabilistic and deterministic metrics. The forecasting skills are evaluated in terms of SWE estimations during the snowpack accumulation and melt periods, and their impacts on spring flow. Among the two hydrological models considered (i.e., HYDROTEL and HSAMI), HYDROTEL proves to be a better candidate to unlock the full potential of distributed SNODAS SWE dataset through DA over a large watershed with spatially variable SWE. This is seen by improved 1-day spring flow forecasts metrics over many years (2014-2017). From the observation source point of view, SNODAS DA results in a more consistent 1-day spring flow forecasts compared to ERA5-Land over Au Saumon. However, the improvements in 1-day spring flow forecasts induced by SNODAS DA are comparatively modest over Au Saumon compared to Outardes 4, with NSE changing from 0.44 to 0.45, 0.31 to 0.34, and 0.59 to 0.61 for the 2014-2017 time period. This could be rooted in alignments between the physiographic characteristics of the watershed and the frequency of DA updates. The results obtained from the first two chapters of the thesis provide a snow DA framework with the capability to improve short-term and mid-term SWE forecasts with varying influence over the forecast horizon given the snowpack period considered (i.e., formation and ablation). The improved SWE estimations lead to increased accuracy and better uncertainty representations, as measured by Nash-Sutcliffe Efficiency (NSE), Relative Bias (RB), and Continuous Ranked Probability Score (CRPS), in spring flow forecasts for the Outardes 4 watershed over the study period (2014-2017).

Les changements climatiques impactent de plus en plus la vie, le développement et la vulnérabilité de plusieurs communautés à travers le monde, lesquelles devant de plus en plus mitiger les risques naturels. Au Québec, la gestion des risques présente une philosophie de « retour à la normale » qui se penche davantage sur les dimensions d’intervention et de rétablissement. Cependant, à la lumière des incertitudes amenées par les changements climatiques, il est impératif que les communautés québécoises aient les capacités d’augmenter leur résilience face aux risques naturels qui s’accentuent rapidement. Ainsi, la capacité d’adaptation doit se retrouver au cœur de la gestion des risques. Cela dit, il existe peu d’outils d’évaluation de la capacité d’adaptation au Québec, entendue comme l’ensemble des ressources dynamiques disponibles et accessibles qui permettent une augmentation de la résilience et une diminution de la vulnérabilité en transformant positivement une communauté et son environnement. La présente recherche vise ainsi à développer une méthode d’analyse de la capacité d’adaptation des individus et des communautés québécoises touchées par les inondations à l’aide de systèmes d’information géographique (SIG), en utilisant la Ville de Saint-Raymond de Portneuf comme étude de cas. Ce projet se base principalement sur les concepts de vulnérabilité, de résilience et d’adaptation pour recenser des indicateurs pouvant servir à caractériser et évaluer la capacité des personnes et municipalités exposées aux inondations à mobiliser les ressources nécessaires pour non seulement atténuer les risques lors de tels événements, mais aussi mieux les prévenir et s’en préparer. Des données socioéconomiques et d’aménagement du territoire sont notamment mises à profit pour des fins d’analyse de même que des données issues d’un sondage effectué en 2014 à la suite d’une inondation majeure par la CAPSA, l’organisme de bassin versant de la région de Portneuf, en collaboration avec le comité Rivière de la Ville de Saint-Raymond.

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.

Les inondations présentent une grande menace à la sécurité humaine et matérielle. Les effets associés à ces phénomènes naturels risquent d'augmenter encore plus avec les tendances liées aux changements climatiques. Il est donc important de disposer d'outils de prévision et de prévention des crues fiables afin de mitiger les conséquences dévastatrices reliées. La mise en œuvre de ces outils implique des processus physiques assez complexes et nécessite beaucoup de données avec toute l'incertitude associée. Dans cette thèse, on explore les différentes sources d'incertitudes liée à la détermination des niveaux d'eau en rivières principalement dans un contexte de prévision où l'incertitude liée aux données de forçage est très importante. Les analyses conduites sont appliquées à la rivière Chaudière au Québec. En premier lieu, nous avons exploré les différentes sources paramétriques d'incertitude associées à la modélisation hydraulique dans un contexte de simulation avec un accent sur l'amélioration de la calibration du modèle hydraulique. Par la suite, dans un contexte de prévision opérationnel, on a évalué la propagation des sources d'incertitude de la prévision atmosphérique au modèle de rivière en passant par les prévisions hydrologiques avec des techniques probabilistes d'ensemble. La quantification de l'incertitude a montré que les données de forçage sont celles qui contribuent le plus à la description de l'incertitude dans la détermination des niveaux d'eau. L'incertitude paramétrique, dans un contexte de prévision, est quant à elle négligeable. Le recours à des prévisions d'ensemble a permis de produire une prévision de niveau d'eau assez fiable et a montré que celle-ci est fortement liée à la qualité des données qui proviennent de la chaine de prévision hydrométéorologique à l'amont du système de prévision proposé.

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

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

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

The impact of oxidation on mitigation of cyanobacteria and cyanotoxins in drinking water treatment sludge was investigated at the laboratory and treatment plant scales. Two common oxidants, KMnO4 (5 and 10 mg/L) and H2O2 (10 and 20 mg/L) were applied under controlled steady-state conditions. Non-oxidized and oxidized sludge was left to stagnate in the dark for 7 to 38 days. Controlled laboratory trials show that KMnO4 and H2O2 decreased cell counts up to 62% and 77%, respectively. The maximum total MC level reduction achieved after oxidation was 41% and 98% using 20 mg/L H2O2 and 10 mg/L KMnO4, respectively. Stagnation caused cell growth up to 2.6-fold in 8 out of 22 oxidized samples. Microcystin (MC) producer orders as Chroococcales and Synechococcales were persistent while Nostocales was sensitive to combined oxidation and stagnation stresses. In parallel, two on-site shock oxidation treatments were performed in the DWTP’s sludge holding tank using 10 mg/L KMnO4. On-site shock oxidation decreased taxonomic cell counts by up to 43% within 24 h. Stagnation preceded by on-site shock oxidation could increase total cell counts by up to 55% as compared to oxidation alone. The increase of cell counts and mcyD gene copy numbers during stagnation revealed the impact of oxidation/stagnation on cyanobacterial cell growth. These findings show the limitations of sludge oxidation as a strategy to manage cyanobacteria and cyanotoxins in sludge and suggest that alternative approaches to prevent the accumulation and mitigation of cyanobacteria in sludge should be considered.