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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.

This study investigates the phenomenon of information (in)sufficiency in the context of flood risks. Individuals’ perception of how much risk information they need is a major trigger and driver of information-seeking behavior, and therefore it is an important part of creating effective preventive risk-communication campaigns. To understand factors that contribute to individuals’ sense of information (in)sufficiency, the roles played by prior experiences of floods and general risk sensitivity were analyzed using survey data from residents in flood-risk zones. The findings highlight that every third respondent reported a state of information sufficiency. Residents with prior experience evaluate their information sufficiency level based on their perception of consequences of future floods. But it is general risk sensitivity that best explains need for more information.

The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow parameters such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The future Terrestrial Snow Mass Mission (TSMM) aims to provide high-resolution spatially distributed SWE information; thus, spatial SWE calibration should be considered along with conventional streamflow calibration for model optimization since the overall water balance is often a key objective in the hydrological modelling. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration approach of hydrological models and attempts to determine whether raw SNODAS data can be utilized for hydrological model calibration. The SPAtial Efficiency (SPAEF) metric is explored for spatially calibrating SWE. The HYDROTEL hydrological model is applied to the Au Saumon River Watershed (∽1120 km2) in Eastern Canada using MSWEP precipitation data and ERA-5 land reanalysis temperature data as input to generate high-resolution SWE and streamflow. Different calibration experiments are performed combining Nash-Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE), and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Multi-Objective Optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance. Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling-Gupta Efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model.

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Abstract. The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020–21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science.

The magnitudes of dissolved organic carbon (DOC) exports from boreal peatlands to streams through lateral subsurface flow vary during the ice-free season. Peatland water table depth and the alternation of low and high flow in peat-draining streams are thought to drive this DOC export variability. However, calculation of the specific DOC exports from a peatland can be challenging considering the multiple potential DOC sources within the catchment. A calculation approach based on the hydrological connectivity between the peat and the stream could help to solve this issue, which is the approach used in the present research. This study took place from June 2018 to October 2019 in a boreal catchment in northeastern Canada, with 76.7 % of the catchment being covered by ombrotrophic peatland. The objectives were to (1) establish relationships between DOC exports from a headwater stream and the peatland hydrology; (2) quantify, at the catchment scale, the amount of DOC laterally exported to the draining stream; and (3) define the patterns of DOC mobilization during high-river-flow events. At the peatland headwater stream outlet, the DOC concentrations were monitored at a high frequency (hourly) using a fluorescent dissolved organic matter (fDOM) sensor, a proxy for DOC concentration. Hydrological variables, such as stream outlet discharge and peatland water table depth (WTD), were continuously monitored at hourly intervals for 2 years. Our results highlight the direct and delayed control of subsurface flow from peat to the stream and associated DOC exports. Rain events raised the peatland WTD, which increased hydrological connectivity between the peatland and the stream. This led to increased stream discharge (Q) and a delayed DOC concentration increase, typical of lateral subsurface flow. The magnitude of the WTD increase played a crucial role in influencing the quantity of DOC exported. Based on the observations that the peatland is the most important contributor to DOC exports at the catchment scale and that other DOC sources were negligible during high-flow periods, we propose a new approach to estimate the specific DOC exports attributable to the peatland by distinguishing between the surfaces used for calculation during high-flow and low-flow periods. In 2018–2019, 92.6 % of DOC was exported during flood events despite the fact that these flood events accounted for 59.1 % of the period. In 2019–2020, 93.8 % of DOC was exported during flood events, which represented 44.1 % of the period. Our analysis of individual flood events revealed three types of events and DOC mobilization patterns. The first type is characterized by high rainfall, leading to an important WTD increase that favours the connection between the peatland and the stream and leading to high DOC exports. The second is characterized by a large WTD increase succeeding a previous event that had depleted DOC available to be transferred to the stream, leading to low DOC exports. The third type corresponds to low rainfall events with an insufficient WTD increase to reconnect the peatland and the stream, leading to low DOC exports. Our results suggest that DOC exports are sensitive to hydroclimatic conditions; moreover, flood events, changes in rainfall regime, ice-free season duration, and porewater temperature may affect the exported DOC and, consequently, partially offset the net carbon sequestration potential of peatlands.

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.

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

L’estimation du débit en rivières est un paramètre clé pour la gestion des ressources hydriques, la prévention des risques liés aux inondations et la planification des équipements hydroélectriques. Lorsque le débit d’eau est très élevé lors d'évènements extrêmes, les méthodes de jaugeage traditionnelles ne peuvent pas être utilisées. De plus, les stations du réseau hydrométrique sont généralement éparses et leur répartition spatiale n’est pas optimale. Par conséquent, de nombreuses sections de rivières ne peuvent être suivies par des mesures et observations du débit. Pour ces raisons, pendant la dernière décennie, les capteurs satellitaires ont été considérés comme une source d’observation complémentaire aux observations traditionnelles du niveau d’eau et du débit en rivières. L’utilisation d’une telle approche a fourni un moyen de maintenir et d’étendre le réseau d'observation hydrométrique. L’approche avec télédétection permet d’estimer le débit à partir des courbes de tarage qui met en relation le débit instantané (Q) et la géométrie d’une section transversale du chenal (la largeur ou la profondeur effective de la surface d’eau). En revanche, cette méthode est associée à des limitations, notamment, sa dépendance aux courbes de tarage. En effet, en raison de leurs natures empiriques, les courbes de tarage sont limitées à des sections spécifiques et ne peuvent être appliquées dans d’autres rivières. Récemment, des techniques d’apprentissage profond ont été appliquées avec succès dans de nombreux domaines, y compris en hydrologie. Dans le présent travail, l’approche d’apprentissage profond a été choisie, en particulier les réseaux de neurones convolutifs (CNN), pour estimer le débit en rivière. L’objectif principal de ce travail est de développer une approche d’estimation du débit en rivières à partir de l’imagerie RADARSAT 1&amp;2 à l’aide de l’apprentissage profond. La zone d’étude se trouve dans l’ecozone du bouclier boréal à l’Est du Canada. Au total, 39 sites hydrographiques ont fait l’objet de cette étude. Dans le présent travail, une nouvelle architecture de CNN a été a été proposée, elle s'adapte aux données utilisées et permet d’estimer le débit en rivière instantané. Ce modèle donne un résultat du coefficient de détermination (R²) et de Nash-Sutcliffe égale à 0.91, le résultat d’erreur quadratique moyenne égale à 33 m³ /s. Cela démontre que le modèle CNN donne une solution appropriée aux problèmes d’estimation du débit avec des capteurs satellites sans intervention humaine. <br /><br />Estimating river flow is a key parameter for effective water resources management, flood risk prevention and hydroelectric facilities planning. In cases of very high flow of water or extreme events, traditional gauging methods cannot be reliable. In addition, hydrometric network stations are often sparse and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. For these reasons, satellite sensors are considered as a complementary observation source to traditional water level and flow observations in the last decades. The use of this kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate the instantaneous flow (Q) to the geometry of a channel cross-section (the effective width or depth of the water surface). On the other hand, remote sensing is also associated with limitations, notably its dependence on the rating curves. Indeed, due to their empirical nature, rating curves are limited to specific sections and cannot be applied in other rivers. Recently, deep learning techniques have been successfully applied in many fields, including hydrology. In the present work, the deep learning approach has been chosen, in particular convolutional neural networks (CNN), to estimate river flow. The main objective of this work is to develop an approach to estimate river flow from RADARSAT 1&amp;2 imagery using deep learning. In this study, 39 hydrographic sites of the Boreal Shield ecozone in Eastern Canada were considered. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. The achieved results demonstrated a coefficient of determination (R²) and Nash-Sutcliffe values of 0.91, and a root mean square error of 33m³ /s. This indicates the effectiveness of CNN in automatic flow estimation with satellite sensors.

Les instances responsables d’assurer la gestion des risques d’inondations reliés aux embâcles de glace sont toujours à la recherche d’outils visant à prévenir les risques et réduire les conséquences sur les populations et les infrastructures. Grâce à la modélisation de certains paramètres jouant un rôle dans la formation des embâcles de glace comme la prédisposition géomorphologique et les conditions hydrométéorologiques, la prévention de ceux-ci s’est grandement améliorée. Les modèles axés sur la force de mobilisation de la rivière et sa capacité à contraindre l’écoulement gagnerait en pertinence s’ils pouvaient inclure la résistance du couvert de glace. Les outils de télédétection sont une manière efficace de connaitre l’état du couvert de glace tant sur l’ensemble de la rivière qu'à différents endroits ciblés. Ceux-ci peuvent générer différents produits cartographiques utiles avant, pendant et après les événements. La présente thèse vise à intégrer le suivi du couvert de glace dans les méthodes de prévention des embâcles à l’aide d’outils provenant de la télédétection. Pour ce faire, quatre sous-objectifs ont été accomplis 1) créer une approche de suivi du couvert de glace à grande échelle en exploitant les données de télédétection optique, radar et acquises par drone, 2) développer une méthode de cartographie automatique du type de glace par estimation d’ensemble à partir d’imagerie radar, 3) concevoir un modèle de détection automatique des lieux à risque de débâcle en utilisant les connaissances de personnes expertes pour interpréter les cartes du type de glace et 4) intégrer les outils développés aux autres modèles conçus dans le cadre de DAVE (Dispositif d’alertes et de vigilance aux embâcles). Les contributions originales découlant de cette thèse touchent plusieurs aspects du suivi du couvert de glace. Elles incluent la démonstration de la pertinence des indicateurs de suivi de la glace par la télédétection, la conceptualisation d’une méthode de segmentation de la rivière en secteurs de production, transport et accumulation de la glace, l’élaboration d’un modèle de cartographie du type de glace par estimation d’ensemble plus performant et polyvalent que les classificateurs originaux, la construction d’une base de données de dégradation du couvert de glace à partir des connaissances de personnes expertes en cartographie de la glace et un modèle de classification de la dégradation du couvert de glace. Cette thèse se conclut par l’intégration conceptuelle à l’aide d’une analyse multicritères hiérarchique des différents outils développés au sein de DAVE. <br /><br /> The authorities responsible for ice jam flood risk management are always looking for tools to prevent harm and reduce consequences on populations and infrastructure. Ice jam prevention has been greatly improved by modelling certain parameters—such as geomorphic predispositions and hydrometeorological conditions—that are central to ice jam formation. These models focusing on the strength of the river current and its ability to constrain the flow would gain in relevance if they could include ice cover strength. Remote sensing tools are an effective way of knowing the state of the ice cover over the whole river and at different target locations. These can be used to generate different map products to include in monitoring before, during and after hazard events. This dissertation therefore aims to integrate ice cover monitoring into ice jam prevention methods using remote sensing tools. To do so, three main sub-objectives were accomplished: 1) to create a large-scale ice cover monitoring approach using remote sensing data such as optical, radar and drone images, 2) to develop a method for automatic mapping of ice type by ensemble estimation from radar imagery, 3) to design an automatic detection model for breakup risk locations using ice type maps and expert judgment and 4) to integrate the tools developed with other models created within the Dispositif d’alertes et de vigilance des embâcles de glace (DAVE). The original contribution from this work covers multiple aspects of ice cover monitoring. This dissertation demonstrates the relevance of direct indicators of ice monitoring by remote sensing, conceptualizes a method for river segmentation into areas of ice production, transport and accumulation, develops an ensemble-based estimation ice type mapping model more efficient and versatile than the original algorithms, constructs an ice cover degradation database derived from the knowledge of ice mapping experts, and proposes a classification model of ice cover degradation. This dissertation concludes with the conceptual integration by analytic hierarchy process of the different tools developed within DAVE.

RÉSUMÉ : Le fjord du Saguenay est une vallée glacière de 110 km de long et de 280 m de profondeur maximale qui relie la rivière Saguenay à sa tête à l'estuaire du Saint-Laurent à son embouchure. La bathymétrie du fjord est caractérisée par 3 seuils : le seuil peu profond (~ 20 m) à l'embouchure, un seuil intermédiaire (60 m) à 20 km en amont et un seuil profond (120 m) à 35 km en amont. Ces seuils séparent le fjord en 3 bassins : le bassin extérieur, le bassin intermédiaire et le bassin intérieur. La circulation dans le fjord est forcée par l'apport d'eau douce de la rivière Saguenay à sa tête, de grandes marées (jusqu'à 6 m de marnage) à son embouchure qui apportent de l'eau salée ainsi que par le vent. La circulation à grande échelle a été caractérisée par trois régimes saisonniers au cours desquels les eaux profondes, intermédiaires et de sous-surface du bassin intérieur sont renouvelées respectivement au début de l'hiver, en été et à la fin de l'hiver. Des indications indirectes suggèrent que ces régimes sont déterminés par des processus turbulents qui se produisent localement à chacun de ces trois seuils. Ici, nous présentons les résultats des expériences de terrain que nous avons menées qui visaient à étudier plus directement la dynamique des processus de seuil induits par les courants de marées ainsi que les modifications de masse d'eau qui surviennent aux seuils. À ce jour, nos mesures fournissent la description la plus précise et la plus complète des structures d'écoulement de marée stratifié autour des seuils internes du fjord du Saguenay. Nous avons également observé qu'un ressaut hydraulique interne semble se former lors de chaque marée descendante en aval du seuil intermédiaire, mais pas lors des marées montantes. Des recherches sont toujours en cours pour mieux comprendre cette asymétrie, mais notre hypothèse est que la présence d'une masse d'eau plus salée en amont du seuil intermédiaire empêche la formation d'un ressaut hydraulique, un processus qui pourrait être similaire à celui documenté à Knight Inlet (Colombie-Britannique, Canada). -- Mot(s) clé(s) en français : Fjord du Saguenay, Estuaire du St-Laurent, Processus de Seuil, Mélange Turbulent, Océanographique Côtière. -- ABSTRACT : The Saguenay Fjord is a 110 km long and 280 m deep (max depth) multi-silled glacial valley that connects the Saguenay River at its head with the St. Lawrence Estuary at its mouth. The bathymetry is characterized by 3 sills : a shallow 20-m deep sill at the mouth, an intermediate 60-m deep sill 20 km up-fjord and a deep 120-m sill 35 km up-fjord. These sills separate 3 basins, the outer, the intermediate and the inner basins. The circulation in the fjord is forced by the Saguenay River at its head that brings freshwater, large tides (up to 6 m range) at its mouth that brings salt water and by wind. The large-scale circulation has been characterized by four seasonally dependent regimes during which the deep, intermediate and subsurface waters of the inner basin are being renewed, respectively, during early winter, summer and late winter. There are indirect indications that those regimes are determined by turbulent processes occurring locally at each of these three sills. Here, we carried out field experiments to more directly investigate the detailed dynamics of tidally-driven sill processes and water mass modifications occurring across these three sills. Our measurements provide to date the most accurate and complete description of the stratified tidal flow structures around these sills. We also found that an internal hydraulic jump seems to form every ebb tide on the down-fjord side of the intermediate sill but not during flood tide on the up-fjord side. Research is ongoing to better understand this asymmetry but our hypothesis is that it is the presence of a salty pool up-fjord of the sill that prevents the formation of a hydraulic jump, a process that may be similar to that documented in Knight Inlet (British Columbia, Canada). -- Mot(s) clé(s) en anglais : Saguenay Fjord, St-Lawrence Estuary, Sill Processes, Turbulent Mixing, Coastal Oceanography.

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.

This thesis examines the main socio-environmental relationships related to recurrent floodings in the Sainte-Anne River watershed by mobilizing the history of land use, local knowledge and risk management policies. From a political ecology perspective, these relationships are part of both social representations of nature, power dynamics associated with the appropriation of ressources and multiple temporalities. By also mobilizing the theoreticals frameworks of the anthropology of disasters and amphibian anthropology, this study allow to retrace step by step what “flood zone” is as a socially constructed space by the authorities, and go back to the source of the advent of “flood” as a catastrophic event, where rising waters have been part of the characteristics of these territories since the beginning of the sedentarization of its inhabitants. Based on 93 semi-directed interviews (76 residents and 17 institutions stakeholders) realized between February to October 2019, non-participant observation and documentary research in municipal and regional archives, this ethnography of the Sainte-Anne River watershed allows a unique incursion with Quebec riverside residents who live with recurrent rising waters. Through four case studies (Saint-Raymond, Saint-Alban, Saint-Casimir and Sainte-Anne-de-la-Pérade), historical contexts of occupation of the territory were documented and significant events were described by focusing on local residents adaptation strategies and anticipated management by institutional stakeholders. The result is a portrait, in a temporal perspective, of the relationship of cohabitation between residents and the river and its overflows. This cohabitation is characterized by tensions and paradoxes associated with different social representations of water and temporality that coexist within the actors, as well as changes in power relations towards the environment. Cette thèse examine les principaux rapports socio-environnementaux liés aux inondations récurrentes dans le bassin versant de la rivière Sainte-Anne en mobilisant l’histoire de l’occupation du territoire, les savoirs locaux et les politiques de gestion des risques. Dans une perspective d’écologie politique, ces rapports s’inscrivent à la fois dans les représentations sociales de la nature, les dynamiques de pouvoir associées à l’appropriation des ressources et des temporalités multiples. En puisant également dans les cadres théoriques de l’anthropologie des catastrophes et de l’anthropologie amphibienne, cette étude permet notamment de retracer pas à pas ce qu’est la « zone inondable » en tant qu’espace construit socialement par les autorités, et de remonter à la source de l’avènement de « l’inondation » comme étant un événement catastrophique, alors que la montée des eaux fait partie des caractéristiques de ces territoires depuis le début de la sédentarisation des habitants. Basée sur 93 entrevues semi-dirigées (76 riverains et 17 acteurs institutionnels) menées de février à octobre 2019, de l’observation non participante et une recherche documentaire dans les archives municipales et régionales, cette ethnographie du bassin versant de la rivière Sainte-Anne permet une incursion unique auprès de citoyens québécois qui vivent avec la montée récurrente des eaux. Pour quatre municipalités riveraines (Saint-Raymond, Saint-Alban, Saint-Casimir et Sainte-Anne-de-la-Pérade), les contextes historiques de l’occupation du territoire ont été documentés et les événements significatifs ont été décrits en focalisant sur les stratégies d’adaptation des résidents et la gestion menée par des acteurs institutionnels. En résulte un portrait, dans une perspective temporelle, de la relation de cohabitation entre les riverains et la rivière et ses débordements. Cette cohabitation est caractérisée par des tensions et des paradoxes associés aux différentes représentations sociales de l’eau et de la temporalité qui coexistent au sein des acteurs, ainsi qu’aux changements dans les rapports de pouvoir envers l’environnement.

The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, multiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g. tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of four days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Abstract Floods are among the most devastating natural hazards worldwide. While rainfall is the primary trigger of floods, human activities and climate change can exacerbate the impacts of floods and lead to more significant economic and social consequences. In this research, fluvial flood fatalities in the 1951–2020 period have been studied, analyzing the information reported in the Emergency Database (EM‐DAT). The EM‐DAT data were classified into five categories in terms of the number of events and fatalities connected with riverine floods, considering only events that caused more than 10 fatalities. The results show that the severity of flood‐related fatalities is not equally distributed worldwide, but presents specific geographical patterns. The flood fatality coefficient, which represents the ratio between the total number of fatalities and the number of flood events, calculated for different countries, identified that the Southern, Eastern, and South‐Eastern regions of Asia have the deadliest floods in the world. The number of flood events has been increasing since 1951 and peaked in 2007, following a relative decline since then. Though, the resulting fatalities do not follow a statistically significant trend. An analysis of the number of flood events in different decades shows that the 2001–2010 decade saw the highest number of events, which corresponds to the largest precipitation anomaly in the world. The lethality of riverine floods decreased over time, from 412 per flood in 1951–1960 to 67 in the 2011–2020 decade. This declining trend is probably a consequence of a more resilient environment and better risk reduction strategies. Based on the presented data and using regression analysis, relationships between flood fatalities and the number of flood events with population density and gross domestic product are developed and discussed.

Many regions are becoming subject to successive flooding and with climate change taking its toll, it is no surprise that we observe a growing interest for risk avoidance strategies such as relocation. Cost-benefit analysis is the dominant tool used by decision-makers to assess flood risk avoidance projects. Yet, few guidelines are available about how to implement such analysis. This paper advocates for a probabilistic cost-benefit analysis and details a step-by-step procedure via a real-world example. The results show that relocation can be a cost-effective strategy for many high-risk properties and neighborhoods. The level of indemnities and the inclusion of intangible losses are two key drivers of profitability. The paper also analyzes three distinct designs of relocation programs. The results reveal that proactive and innovative schemes, such as managed retreat and usufruct arrangements, constitute worthwhile alternatives to a more conventional post-flood response design.