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

Floods can be caused by heavy rainfall and the consequent overflow of rivers, causing low-lying areas to be affected. Populated regions close to riverbeds are the sectors most affected by these disasters, which requires modelling studies to generate different scenarios. The work focuses on the bibliometric analysis of the search for topics such as flood modelling focused on the research, risk, and assessment of these catastrophes, aiming to determine new trends and tools for their application in the prevention of these natural disasters. The methodology consists of: (i) search criteria and database selection, (ii) pre-processing of the selected data and software, and (iii) analysis and interpretation of the results. The results show a wide range of studies for dimensional analysis in different flood scenarios, which greatly benefit the development of flood prevention and risk strategies. In addition, this work provides insight into the different types of software and modelling for flood analysis and simulation and the various trends and applications for future modelling.

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

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

La température extrême de l’eau influence de nombreuses propriétés physiques, chimiques et biologiques des rivières. l ’ évaluation de l ’ Une prédiction précise de la température de l’eau est importante pour impact environnemental. Dans ce cadre, différents modèles ont été utilisés pour estimer les températures de l ’ linéaires simp eau à différentes échelles spatiales et temporelles, allant des méthodes les pour déterminer l’incertitude à des modèles sophistiqués non linéaires. Cependant, cette variable primordiale n’a pas été traitée dans un contexte probabiliste (ou fréquentiste). Donc, l’estimation des évènements extrêmes thermiques à l’aide des approc hes d’analyse fréquentielle locale (AFL) est importante. Lors de l’estimation des extrêmes thermiques, il est crucial de tenir compte de la forme de la distribution de fréquences considérée. Dans la première partie de la thèse , nous nous concentrons sur la sélection de la distribution de probabilité la plus appropriée des températures des rivières. Le critère d critère d ’ ’ information d ’ Akaike (AIC) et le information bayésien (BIC) sont utilisés pour évaluer la qualité de l distributions statis ’ ajustement des tiques. La validation des distributions candidates appropriées est également effectuée en utilisant l ’ approche de diagramme de rapport des L obtenus montrent que la distribution de Weibull (W2) moments (MRD). Les résultats est celle qui semble s’ajuster le données provenant des stations de haute altitude, tandis que les mieux aux séries d’extrêmes provenant des stations situées dans les régions de basse altitude sont bien adaptées avec la distribution normale (N). Ceci correspond au premier article. L a ’ couverture spatiale des données de température des cours d ’ eau est limitée dans de nombreuses régions du monde. Pour cette raison, une analyse fréquentielle régionale (AFR) permettant d estimer les extrêmes de température des rivières sur des sites non jau gés ou mal surveillés est nécessaire. En général, l’AFR inclut deux étapes principales, la délimitation des régions homogènes (DRH) qui vise à déterminer les sites similaires, et l’estimation régionale (ER) qui transfère l’information depuis les sites déte rminés dans la première étape vers le site cible. Par conséquent, le modèle d’indice thermique (IT) est introduit dans le contexte d’AFR pour estimer les extrêmes du régime thermique. Cette méthode est analogue au modèle d ’ indice de crue (IF) largement uti lisé en hydrologie. Le modèle IT incorpore l’homogénéité de la distribution de fréquence appropriée pour chaque région, ce qui offre une plus grande flexibilité. Dans cette étude, le modèle IT est comparé avec la régression linéaire multiple (MLR). Les rés ultats indiquent que le modèle IT fournit la meilleure performance (Article 2) . Ensuite, l’approche d’analyse canonique des corrélations non linéaires (ACCNL) est intégrée dans la DRH, présentée dans le Chapitre 4 de ce manuscrit (Article 3). Elle permet de considérer la complexité des phénomènes thermiques dans l’étape de DRH. Par la suite, dans le but d’identifier des combinaisons (DRH-ER) plus prometteuses permettant une meilleure estimation, une étude comparative est réalisée. Les combinaisons considérées au niveau des deux étapes de la procédure de l’AFR sont des combinaisons linéaires, semi-linéaires et non linéaires. Les résultats montrent que la meilleure performance globale est présentée par la combinaison non linéaire ACCNL et le modèle additif généralisé (GAM). Finalement, des modèles non paramétriques tels que le foret aléatoire (RF), le boosting de gradient extrême (XGBoost) et le modèle régression multivariée par spline adaptative (MARS) sont introduits dans le contexte de l’AFR pour estimer les quantiles thermiques et les comparer aux quantiles estimés à l’aide du modèle semi-paramétrique GAM. Ces modèles sont combinés avec des approches linéaires et non linéaires dans l’étape DRH, telles que ACC et ACCNL, afin de déterminer leur potentiel prédictif. Les résultats indiquent que ACCNL+GAM est la meilleure, suivie par ACC+MARS. Ceci correspond à l’article 4. <br /><br />Extreme water temperatures have a significant impact on the physical, chemical, and biological properties of the rivers. Environmental impact assessment requires accurate predictions of water temperature. The models used to estimate water temperatures within this framework range from simple linear methods to more complex nonlinear models. However, w ater temperature has not been studied in a probabilistic manner. It is, therefore, essential to estimate extreme thermal events using local frequency analysis (LFA). An LFA aims to predict the frequency and amplitude of these events at a given gauged locat ion. In order to estimate quantiles, it is essential to consider the shape of the frequency distribution being considered. The first part of our study focuses on selecting the most appropriate probability distribution for river water temperatures. The Akai ke information criteria (AIC) and the Bayesian information criteria (BIC) are used to evaluate the goodness of fit of statistical distributions. An Lmoment ratio diagram (MRD) approach is also used to validate sui table candidate distributions. The results good fit for extremes data from the highindicate that the Weibull distribution (W2) provides a altitude stations, while the normal distribution (N) is most appropriate for lowaltitude stations. This corresponds to the first article. In many parts of the world, river temperature data are limited in terms of spatial coverage and size of the series. Therefore, it is necessary to perform a regional frequency analysis (RFA) to estimate river temperature extremes at ungauged or poorly monitored sites. Generall y, RFA involves two main steps: delineation of homogenous regions (DHR), which identifies similar sites, and regional estimation (RE), which transfers information from the identified sites to the target site. The thermal index (TI) model is introduced in t he context of RFA to estimate the extremes of the thermal regime. This method is analogous to the index flood (IF) model commonly used in hydrology. The TI model considers the homogeneity of the appropriate frequency distributions for each region, which pr ovides larger flexibility. This study compares the TI model with multiple linear regression (MLR) approach. Results indicate that the TI model leads to better performances (Article 2). Then, the nonlinear canonical correlations analysis (NLCCA) approach is integrated into the DHR, as presented in Chapter 4 of this manuscript (Article 3). It allows considering the complexity of the thermal phenomena in the DHR step. A comparative study is then conducted to identify more promising combinations (DHR RE), that RFA procedure, linear, semilead to best estimation results. In the two stages of the linear, and nonlinear combinations are considered. The results of this study indicate that the nonlinear combination of the NLCCA and the generalized additive model (GAM ) produces the best overall performances. Finally, nonparametric models such as random forest (RF), extreme gradient boosting (XGBoost), and multivariate adaptive regression splines (MARS) are introduced in the context of RFA in order to estimate thermal q uantiles and compare them to quantiles estimated using the semiparametric GAM model. The predictive potential of these models is determined by combining them with linear and nonlinear approaches, such as CCA and NLCCA, in the DHR step. The results indicat e that NLCCA+GAM is the best, followed by CCA+MARS. This corresponds to article 4.

The canalized southernmost section of Wonscotonach (the Don River) in Tkarón:to (Toronto), also known as The Narrows, is a highly disturbed urban natural landscape. Following the 1886 Don Improvement Project, the Keating Channel, and today the Port Lands Revitalization and Flood Protection Project, these Lands have been harnessed and developed through settler colonization to tame and control the once-winding river. This research-creation—in the form of a curated online exhibition and written thesis—presents a critical (re)reading of the notion of improvement, becoming allied to the pre-colonial landscape and the knowledge it carried. This exhibition and thesis develop the concept of the meander, inspired by the non-linear trajectory of the pre-canalized Don River, as a model for the curatorial. The curatorial process of improvement becomes a wall, and the river meanders still began before the global COVID-19 pandemic and, subsequently, was derailed in March 2020. The exhibition’s final form was unknowable throughout much of the curatorial process. Thus, following the meander as a research-creation technique, the curatorial process, exhibitionary structure, and content had to adapt through lingering uncertainty. This thesis, contributing to the theoretical and practical knowledge of research-creation, looks to intersections with the curatorial following the theoretical underpinnings of Erin Manning and Brian Massumi, Natalie Loveless and Stefanie Springgay and Sarah E. Truman. As a project untethered from institutional timelines and normative requirements to ‘know a project in advance,’ as well as the conventions of a physical exhibition, this research-creation manifested through process-led, creative and exploratory techniques (such as walking and drawing) and slowed pace allowed by the COVID-19 pandemic’s reframing of time. This research-creation exhibition and written thesis develop a responsive and resilient curatorial process deeply indebted to Land-based knowledge.

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.

Compte tenu de la nécessité de mettre à jour les cartes d'inondation et de minimiser les coûts associés (collecte de données et ressources humaines), il existe un besoin de méthodes alternatives simplifiées ne reposant pas sur la modélisation hydrodynamique classique. L'une des méthodes simplifiées répondant à ce besoin est HAND (Height Above the Nearest Drainage), une approche qui requiert uniquement un modèle numérique d'altitude (MNA) et un réseau hydrographique. Celle-ci a été mise en œuvre dans PHYSITEL, un système d’information géographique SIG spécialisé pour les modèles hydrologiques distribués. Ainsi, pour une hauteur d’eau donnée dans plusieurs tronçons de rivière, il est possible de faire une délimitation de première instance de la surface inondée le long du réseau hydrographique d’un bassin versant. Par ailleurs, l'utilisation des informations fournies par HAND et l'application de l'équation de Manning permettent également de construire une courbe de tarage synthétique pour tout tronçon de rivière en l’absence de données bathymétriques. Ce mémoire présente l’application de cette approche, qui a été validée précédemment en partie sur de grands bassins, sur deux petits bassins, ceux de la rivière à La Raquette, d’une superficie de 133 km², et de la rivière Saint Charles, d’une superficie de 552 km². Trois stations de jaugeage dans chaque bassin ont fourni les informations de base nécessaires au processus de calage de l’approche. L’efficacité et l’adaptabilité de cette approche ont été évaluées dans ce projet en fonction des données disponibles, du temps de calcul et de la précision mesurée par le biais et l’erreur quadratique moyenne. Les incertitudes et sensibilités de l’approche ont été analysées en tenant compte de la résolution spatiale et du manque de données bathymétriques. De plus, des analyses innovatrices ont été produites dans l’application de HAND. Tels qu’une analyse de sensibilité globale pour informer le processus de calage ainsi que l’application d’un critère basé sur le nombre de Froude afin de permettre de valider le respect des hypothèses sous-jacentes à l’application de l’approche sur chaque tronçon de rivière d’un bassin. En utilisant des MNA à haute résolution(&lt;5 m/pixel), des courbes de tarage synthétiques ont été produites avec des biais inférieurs à ±20 % par rapport à des courbes de tarage in-situ. De plus, la détermination d'un critère de sélection des courbes dans un biais de ± 5% par rapport à la courbe de tarage observée a permis d'obtenir des courbes de tarage synthétiques avec des erreurs quadratiques moyennes normalisées comprises entre 0,03 et 0,62. Ainsi, cette approche a été validée pour dériver des courbes de tarage synthétiques et, par conséquent, pour soutenir la délimitation des zones à risque d'inondation dans les petits bassins versants en tenant compte des incertitudes associées à l'application d'une approche de faible complexité. <br /><br />Given the emergent need to update flood inundation maps and minimize associated financial costs (data collection and human resources), simplified alternative methods to the classical hydrodynamic modelling method, are being developed. One of the simplified methods built to fulfill this need is the terrain-based Height Above the Nearest Drainage (HAND) method, which solely relies on a digital elevation model (DEM) and a river network. This approach was implemented in PHYSITEL, a specialized GIS for distributed hydrological models. For a given river reach and water height, HAND can provide a first-hand delineation of the inundated areas within a watershed. In addition, coupling the information provided by HAND and the Manning equation allows for the construction of a synthetic rating curve for any homogeneous river reach where bathymetric data are not available. Since this synthetic rating curve approach has been validated in part for large watersheds, this study tested this approach onto two small watersheds: the 133- km² La Raquette River watershed and the 552-km² Saint Charles River watershed. Three gauging stations on each basin provided the basic data to perform the calibration process. The effectiveness and adaptability of the approach was assessed as a function of available data, computational time, and accuracy measured using the bias and root mean squared error (RMSE). The uncertainties were quantified in terms of spatial resolution and lack of bathymetry data. In addition, innovative analyses were made on the application of the HAND-synthetic rating curve approach. First, a global sensitivity analysis was done to inform the calibration process, and then a Froude number-based criterion was applied to validate the application of the Manning equation on any river reach of a watershed. Using high-resolution DEMs (&lt;5 m/pixel), we obtained synthetic rating curves with bias less than 20% when compared to in-situ rating curves. Finally, a curve selection criterion was applied to identify those curves having a bias of ± 5%. The selected synthetic rating curves had normalized mean squared errors between 0.03 and 0.62. Thus, the proposed approach was deemed appropriate to derive synthetic rating curves and support the delineation of flood risk areas in small watersheds all the while considering the uncertainties associated with applying a low complexity model.

La rivière Chaudière, située au sud de la Ville de Québec, est sujette aux inondations provoquées par la formation d'embâcles. Des inondations ont été enregistrées depuis 1896 jusqu'à ce jour malgré la mise en service, en 1967, d'un ouvrage de contrôle des glaces (ICS) à 3 kilomètres en amont de la Ville de Saint-Georges-de-Beauce afin de réduire les inondations causées par la glace dans le secteur le plus à risque de la rivière Chaudière. Les inondations par embâcles demeurent donc un problème récurrent qui affecte régulièrement 8 villes le long du tronçon de 90 kilomètres en aval de l'ICS. Dans le cadre d'un programme gouvernemental d'aléas d'inondation initié par le ministère des Affaires Municipales et de l'Habitation (MAMH), un mandat pour évaluer les cotes de crues en présence de glace de la rivière Chaudière a été confié à l'Université Laval. La modélisation d'embâcles combinée à des données d'observations historiques d'embâcles est utilisée pour déterminer les niveaux d'inondation par embâcles. L'approche préconisée consiste à contrôler un modèle de simulation hydraulique fluviale, plus spécifiquement le module HEC-RAS, avec un script externe en Python pour générer une distribution Monte-Carlo (MOCA) d'évènements d'embâcles le long du secteur de la rivière à l'étude. Les paramètres mécaniques tels que l'angle de frottement, la porosité et les vitesses de contrainte de cisaillement critiques sont également attribués de manière aléatoire par le script dans une plage délimitée. Les paramètres physiques et hydrologiques attribués à chaque évènement sont choisis au hasard en fonction d'une probabilité estimée à partir des observations historiques, soit le débit calculé à l'ICS, l'emplacement de l'embâcle, la longueur de l'embâcle et les degrés-jours de gel (épaisseur de la glace). Les cotes de crues selon les périodes de retour de 2, 20, 100 et 350 ans sont alors déterminées selon une équation statistique empirique de Gringorten, suivie d'une modulation pour tenir compte des facteurs externes non considérés par MOCA. Ces cotes de crues en présence de glace sont comparées à celles en eau libre telles que déterminées par la méthode classique. Le projet démontre que les niveaux d'eau calculés en présence de glace prédominent ceux en eau libre pour les villes en amont de Saint-Joseph-de-Beauce. La combinaison des niveaux d'eau en présence de glace et en eau libre, réalisée à l'aide de l'équation de la FEMA, montre que la probabilité d'atteindre un seuil spécifique d'élévation diminue la période de retour et en conséquence augmente les probabilités reliées aux inondations. Ce mémoire est le premier travail scientifique qui présente une validation complète de l'approche hydrotechnique utilisant les valeurs in situ de débit, de DJGC et de l'emplacement et de la longueur d'embâcles pour la détermination des cotes de crue par embâcles. Les valeurs de cotes de crues calculées avec la méthode MOCA sont comparées avec les données historiques dans le secteur à l'étude de la rivière Chaudière. La présente étude met en évidence les limitations et les conditions nécessaires pour l'utilisation de cette méthode. Ce projet de recherche montre aussi pour la première fois que l'approche hydrotechnique permet de calculer des courbes fréquentielles de niveaux d'eau en présence de glace qui peuvent être utilisées à des fins réglementaires au Québec.

Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.

Abstract Disasters worldwide tend to affect the poorest more severely and increase inequality. Brazil is one of the countries with high income‐inequality rates and has unplanned urbanization issues and an extensive disaster risk profile with little knowledge on how those disasters affect people's welfare. Thus, disasters often hit the poorest hardest, increasing the country's income inequality and poverty rates. This study proposes a method to assess the impact of floods on households spatially based on their income levels by conducting flood analysis and income analysis. The method is applied to the Itapocu River basin (IRB) located in Santa Catarina State, Brazil. The flood is assessed by conducting rainfall analysis and hydrological simulation and generating flood inundation maps. The income is evaluated using downloaded 2010 census data and a dasymetric approach. Flood and income information is combined to analyze flood‐impacted households by income level and flood return period. The results confirm the initial assumption that flood events in the IRB are more likely to affect the lowest‐income households rather than the highest‐income levels, thus, increasing the income inequality.

Empirical evidence points out that urban form adaptation to climate-induced flooding events—through interventions in land uses and town plans (i. e., street networks, building footprints, and urban blocks)—might exacerbate vulnerabilities and exposures, engendering risk inequalities and climate injustice. We develop a multicriteria model that draws on distributive justice's interconnections with the risk drivers of social vulnerabilities, flood hazard exposures, and the adaptive capacity of urban form (through land uses and town plans). The model assesses “who” is unequally at-risk to flooding events, hence, should be prioritized in adaptation responses; “where” are the high-risk priority areas located; and “how” can urban form adaptive interventions advance climate justice in the priority areas. We test the model in Toronto, Ontario, Canada, where there are indications of increased rainfall events and disparities in social vulnerabilities. Our methodology started with surveying Toronto-based flooding experts who assigned weights to the risk drivers based on their importance. Using ArcGIS, we then mapped and overlayed the risk drivers' values in all the neighborhoods across the city based on the experts' assigned weights. Accordingly, we identified four high-risk tower communities with old infrastructure and vulnerable populations as the priority neighborhoods for adaptation interventions within the urban form. These four neighborhoods are typical of inner-city tower blocks built in the 20 th century across North America, Europe, and Asia based on modern architectural ideas. Considering the lifespan of these blocks, this study calls for future studies to investigate how these types of neighborhoods can be adapted to climate change to advance climate justice.

Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While several recent studies include average annual rainfall and/or temperature, other meteorological information such as snow accumulation and short-term intense rain events that may influence the hydrology of the area under investigation have not been considered. Notably, in Canada, most inland flooding occurs during the freshet, due to the melting of an accumulated snowpack coupled with heavy rainfall. Therefore, in this study the impact of several climate variables along with various hydro-geomorphological (HG) variables were tested to determine the impact of their inclusion. Three tests were run: only HG variables, the addition of annual average temperature and precipitation (HG-PT), and the inclusion of six other meteorological datasets (HG-8M) on five study areas across Canada. In HG-PT, both precipitation and temperature were selected as important in every study area, while in HG-8M a minimum of three meteorological datasets were considered important in each study area. Notably, as the meteorological variables were added, many of the initial HG variables were dropped from the selection set. The accuracy, F1, true skill and Area Under the Curve (AUC) were marginally improved when the meteorological data was added to the a parallel random forest algorithm (parRF). When the model is applied to new data, the estimated accuracy of the prediction is higher in HG-8M, indicating that inclusion of relevant, local meteorological datasets improves the result.

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.

Abstract For years, Japanese knotweed ( Reynoutria japonica ) has been suspected of accelerating riverbank erosion, despite a lack of convincing evidence. The stems of this invasive plant die back following the first autumn frosts, leaving the soil unprotected during winter and spring floods. In Québec (Canada), riverbank erosion may also be accentuated by ice during mechanical ice breakups. The objective of this study was to evaluate the influence of knotweed on riverbank erosion along a river invaded by the species, within a context of floods with ice. The elevation along 120 cross‐sectional riverbank profiles, occupied or not by knotweed, was measured before and after the spring flood of 2019. On average, riverbanks occupied by knotweed had nearly 3 cm more soil erosion than riverbanks without knotweed, a statistically significant difference. Stem density also influenced erosion: the higher the density, the greater the soil loss. Certain riverside conditions, such as the slope of the riverbank or being located on an islet, interacted with knotweed, further accentuating erosion. Soil losses measured between November 2018 and May–June 2019 were particularly pronounced, but the spring flood was also exceptional, with a recurrence interval close to 50 years. On the other hand, soil loss from rivers invaded by knotweed can be expected to increase over time, as this invasive species spreads rapidly in riparian habitats.

While there is a large body of literature focusing on global-level flood hazard management, including preparedness, response, and recovery, there is a lack of research examining the patterns and dynamics of community-level flood management with a focus on local engagement and institutional mechanism. The present research explores how local communities mobilize themselves, both individually and institutionally, to respond to emerging flood-related situations and recover from their impacts. A case study approach was applied to investigate two towns in the Red River Valley of Manitoba, Canada: St. Adolphe and Ste. Agathe. Data collection consisted of in-depth interviews and oral histories provided by local residents, in addition to analysis of secondary official records and documents. The findings revealed that local community-level flood preparedness, response, and recovery in the Province of Manitoba are primarily designed, governed, managed, and evaluated by the provincial government authorities using a top-down approach. The non-participatory nature of this approach makes community members reluctant to engage with precautionary and response measures, which in turn results in undesired losses and damages. It is recommended that the Government of Manitoba develop and implement a collaborative and participatory community-level flood management approach that draws upon the accumulated experiential knowledge of local stakeholders and institutions.

Abstract Few records of spring paleoclimate are available for boreal Canada, as biological proxies recording the beginning of the warm season are uncommon. Given the spring warming observed during the last decades, and its impact on snowmelt and hydrological processes, searching for spring climate proxies is receiving increasing attention. Tree‐ring anatomical features and intra‐annual widths were used to reconstruct the regional March to May mean air temperature from 1770 to 2016 in eastern boreal Canada. Nested principal component regressions calibrated on 116 years of gridded temperature data were developed from one Fraxinus nigra and 10 Pinus banksiana sites. The reconstruction indicated three distinct phases in spring temperature variability since 1770. Ample phases of multi‐decadal warm and cold springs persisted until the end of the Little Ice Age (1850–1870 CE) and were gradually replaced since the 1940s by decadal to interannual variability associated with an increase in the frequency and magnitude of warm springs. Significant correlations with other paleotemperature records, gridded snow cover extent and runoff support that historical high flooding were associated with late, cold springs with heavy snow cover. Most of the high magnitude spring floods reconstructed for the nearby Harricana River also coincided with the lowest reconstructed spring temperature per decade. However, the last 40 years of observed and reconstructed mean spring temperature showed a reduction in the number of extreme cold springs contrasting with the last few decades of extreme flooding in the eastern Canadian boreal region. This result indicates that warmer late spring mean temperatures on average may contribute, among other factors, to advance the spring break‐up and to likely shift the contribution of snow to rain in spring flooding processes.

With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.

Right after a devastating multi-year drought, a number of flood events with unprecedented spatial extent hit different parts of Iran over the 2-week period of March 17th to April 1st, 2019, causing a human disaster and substantial loss of assets and infrastructure across urban and rural areas. Here, we investigate natural (e.g., rainfall, snow accumulation/melt, soil moisture) and anthropogenic drivers (e.g., deforestation, urbanization, and management practices) of these events using a range of ground-based data and satellite observations. These drivers can range from exceptionally extreme rainfall intensities, to cascades of several extreme and moderate events, and various anthropogenic interventions that exacerbated flooding. Our results reveal strong compounding impacts of natural drivers and anthropogenic triggers in escalating flood risks to unprecedented levels. We argue that a new form of floods, i.e. anthropogenic floods, is becoming more common and should be recognized during the “Anthropocene”. This specific form of floods refers to high to extreme streamflow/runoff events that are primarily caused, or largely exacerbated, by anthropogenic drivers. We demonstrate how the growing risk of anthropogenic floods can be assessed using a wide range of climatic and non-climatic satellite and in-situ data.

In agricultural watersheds, human interventions such as channel straightening have disrupted the hydrologic connectivity between headwater streams and their riparian environment and have thus undermined the ecological services provided by these small streams. Knowledge of the hydrologic connectivity between these streams and their immediate environment (shallow riparian groundwater in the historical floodplain and on adjacent hillslopes) in human-impacted settings is critical for understanding and restoring these hydrological systems but remains largely incomplete. The objective of this research is to investigate the hydrogeomorphological conditions controlling hydrologic connectivity in the historical floodplain of straightened lowland streams. Detailed measurements on the spatiotemporal variability of groundwater-surface water interactions between straightened reaches, historical floodplain including abandoned meanders, and the adjacent hillslopes were obtained using a dense network of piezometers at two sites in the St. Lawrence Lowlands (Quebec, Canada). Results show that the complex mechanisms controlling hydrologic connectivity in naturally meandering lowland rivers also operate in highly disturbed straightened reaches, despite backfilling and agricultural practices. The pre-straightening hydrogeomorphological configuration of the floodplain partly explains the complex patterns of piezometric fluctuations observed at the sites. The apex of the abandoned meanders stands out as a focal area of hydrologic connectivity as water levels indicate pressure transfer that may reflect flows from the stream, the hillslopes, and the surrounding historical floodplain. These unique field observations suggest that abandoned meanders should be promoted as key elements of restoration strategies in lowland agricultural straightened headwater streams.