Full-scale characterization of the effects of a bioretention system on water quality and quantity following the replacement of a mixed stormwater and combined sewer system - Le Cauchois, Pia; Doucet, Samuel; Bouattour, Ons; McQuaid, Natasha; Beral, Henry; Kõiv-Vainik, Margit; Bichai, Françoise; McCarthy, David; St-Laurent, Julien; Dagenais, Danielle; Bennekrela, Nesrine; Guerra, Juan; et al. (Blue-Green Systems, 2025)
ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.
Keywords: Infolettre_Ete_2025

The Impact of Flood Adaptation Measures on Affected Population’s Mental Health: A mixed method Scoping Review - El-Mousawi, Fatima; Ortiz, Ariel Mundo; Berkat, Rawda; Nasri, Bouchra (Cold Spring Harbor Laboratory, 2023)
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.

Flood inundation modeling in ungauged basins using Unmanned Aerial Vehicles imagery - Yavari, Adel; Homayouni, Saeid; Oubennaceur, Khalid; Chokmani, Karem (Earth Observation and Geomatics Engineering, 2020)
This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion (SfM) technology. To this end, the Flood-Level Marks (FLMs) were created from high-resolution UAV ortho-images and compared to the flood inundated areas simulated using the HEC-RAS hydraulic model. The flood quantiles for 25, 50, 100, and 200 return periods were then estimated by synthetic hydrographs using the Natural Resources Conservation Service (NRCS). The proposed method was applied to UAV image data collected from the Khosban village, in Taleghan County, Iran, in the ungauged sub-basin of the Khosban River. The study area is located along one kilometre of the river in the middle of the village. The results showed that the flood inundation areas modeled by the HEC-RAS were 33%, 19%, and 8% less than those estimated from the UAV’s FLMs for 25, 50, and 100 years return periods, respectively. For return periods of 200 years, this difference was overestimated by more than 6%, compared to the UAV’s FLM. The maximum flood depth in our four proposed scenarios of hydraulic models varied between 2.33 to 2.83 meters. These analyses showed that this method, based on the UAV imagery, is well suited to improve the hydraulic modeling for seasonal inundation in ungauged rivers, thus providing reliable support to flood mitigation strategies

Imaging Floods and Glacier Geohazards with Remote Sensing - Cigna, Francesca; Xie, Hongjie (Remote Sensing, 2020)
Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment - Carreau, J.; Guinot, V. (Advances in Water Resources, 2021)

Impacts et coûts indirects des stresseurs secondaires sur la santé biopsychosociale des sinistrés des inondations de 2019 - Maltais, Danielle; Bourdeau-Brien, Michaël; Gilbert, Simon; Normandin, Julie-Maude; Tchassem Pinlap, Jonas; Généreux, Mélissa; Landaverde, Elsa; Boudreault, Mathieu (2023)
Les inondations de 2017 et 2019 au Québec ont affecté respectivement 293 et 240 municipalités. Ces inondations ont généré une cascade d’évènements stressants (stresseurs primaires et secondaires) qui ont eu des effets sur la santé mentale de la population et retardé le processus de rétablissement des individus. Cette période de rétablissement peut s’échelonner sur plusieurs mois voire plusieurs années. Cette étude s’inscrit dans la spécificité de la recherche mixte mise de l’avant à travers trois stratégies de recherche, réalisées de façon séquentielle : 1) sondage populationnelle réalisé auprès de 680 personnes, 2) analyse de documents produits par les organisations participant au processus de rétablissement social des sinistrés, ou sur des analyses externes portant sur ces interventions de rétablissement et 3) entrevues semi-dirigées auprès de 15 propriétaires occupants ayant complété une demande d’indemnisation à la suite des inondations de 2019 et auprès de 11 professionnels et gestionnaires participant au processus de rétablissement social. Les entrevues semi-dirigées et les questionnaires complétés par les personnes sinistrées lors des inondations de 2019 démontrent que les principales sources de stress ayant des impacts sur la santé et le bien-être des répondants sont : 1) l’absence d’avertissement et la vitesse de la montée des eaux; 2) l’obligation de se relocaliser et la peur d’être victime de pillage; 3) le manque de solidarité et d’empathie de la part de certains employés du MSP; 4) la gestion des conflits familiaux; 5) la gestion de problèmes de santé nouveaux ou préexistants; 6) la complexité des demandes d’indemnisation; 7) la lourdeur et les délais des travaux de nettoyage ou de restauration; 8) les indemnités inférieures aux coûts engendrés par l’inondation; 9) les pertes matérielles subies, particulièrement ceux d’une valeur de plus de 50 000 $; et 10) la diminution anticipée de la valeur de sa résidence. À cela s’ajoute l’insatisfaction à l’égard du programme d’indemnisation du gouvernement du Québec (PGIAF) qui fait plus que doubler la prévalence des symptômes de stress post-traumatique. Les inondations entraînent également une perte de satisfaction ou de bien-être statistiquement significative. La valeur monétaire de cette perte de jouissance peut être exprimée en équivalent salaires. En moyenne, cette diminution du bien-être équivaut à une baisse de salaire de 60 000$ pour les individus ayant vécu une première inondation et à 100 000$ pour les individus ayant vécu de multiples inondations. Ces résultats suggèrent que les coûts indirects et intangibles représentent une part importante des dommages découlant des inondations. Ce projet de recherche vise également à analyser l’application du PGIAF et son influence sur les stresseurs vécus par les sinistrés dans le contexte de la pandémie de COVID-19. La principale recommandation de cette étude repose sur une analyse de documents, un sondage populationnel et des entrevues semi-dirigées. Ainsi, s’attaquer à la réduction de principaux stresseurs nécessite 1) d’améliorer la gouvernance du risque d’inondation, 2) d’intensifier la communication et le support aux sinistrés, et 3) de revoir les mécanismes d’indemnisation existants.

A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada - Bourget, Mathilde; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (ASTIN Bulletin: The Journal of the IAA, 2024)
There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Combining quadrat, rake, and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale - Botrel, Morgan; Hudon, Christiane; Biron, Pascale M.; Maranger, Roxane (Limnology and Oceanography: Methods, 2023)
Abstract Measuring freshwater submerged aquatic vegetation (SAV) biomass at large spatial scales is challenging, and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat‐scuba diver technique, fast rake sampling, and large‐scale echosounding. We found that the overall relationship between quadrat and rake biomass is moderately strong (pseudo R 2 = 0.61) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume (pseudo R 2 = 0.57), a biomass proxy derived from echosounding. In addition, the relationship was affected, in decreasing relevance, by SAV growth form, flow velocity, acoustic data quality, depth, and wind conditions. Sequential application of calibrations yielded predictions in agreement with quadrat observations, but echosounding predictions underestimated biomass in shallow areas (< 1 m) while outperforming point estimation in deep areas (> 3 m). Whole‐system quadrat‐equivalent biomass from echosounding differed by a factor of two from point survey estimates, suggesting echosounding is more accurate at larger scales owing to the increased sample size and better representation of spatial heterogeneity. To decide when an individual or a combination of techniques is profitable, we developed a step‐by‐step guideline. Given the risks of quadrat‐scuba diver technique, we recommend developing a one‐time quadrat–rake calibration, followed by the use of rake and echosounding when sampling at larger spatial and temporal scales. In this case, rake sampling becomes a valid ground truthing method for echosounding, also providing valuable species information and estimates in shallow waters where echosounding is inappropriate.

Trajectoires et visées de l'hydrogéomorphologie au Québec - Buffin‐Bélanger, Thomas; Lachapelle, Félix; Biron, Pascale; Boivin, Maxime (Canadian Geographies / Géographies canadiennes, 2024)
Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Investigation of an Arctic hypertidal estuary under summer and winter conditions : cryo-hydrodynamic and hydrokinetic implications - Mohammadian, Abdolvahid (2021)
La modélisation numérique des estuaires hypertidaux intéresse particulièrement les ingénieurs impliqués dans la navigation maritime et le développement de projets d'énergie marémotrice. Au Québec (Canada), la majorité de ces estuaires à marée extrême sont situés dans des régions isolée de l'Arctique canadien et sont souvent des lieux de résidence des communautés autochtones du Nord canadien. La présente thèse vise à mieux comprendre les processus se manifestent dans ces environnements, avec une emphase particulière sur l'importance (1) de la forte dominance des marées, (2) de l'extrême variabilité bathymétrique et (3) de l'immense forçage climatique. La thèse tente de démontrer comment les modèles numériques peuvent être utilisés pour traiter ces particularités et peuvent être la meilleure méthode disponible pour étudier leurs effets dans des environnements éloignés peu étudies. Premièrement, dans le but d'évaluer le potentiel de courant de marée en eau libre (sans glace) de l'estuaire hypertidal de la rivière Koksoak (KRE), nous avons modélisé le débit de marée en utilisant un model numérique hydrodynamique réputé (Delft3D). Différents aspects de l'hydrodynamique côtière ont été étudiés grâce à la modélisation numérique 1D2D-3D. La variabilité spatio-temporelle de la densité de puissance hydrocinétique disponible a ensuite été quantifiée. Les résultats ont révélé l'énorme potentiel (1000 MW) d'énergie marémotrice présente à plusieurs endroits le long de l'estuaire, ce qui nécessite des études numériques plus approfondies. En mettant davantage l'accent sur la modélisation numérique du site, par exemple la publication d'un Atlas des courants de marée pour aider à la navigation maritime dans le KRE, nous avons constaté que certains problèmes de modélisation des estuaires n'étaient pas abordés. Compte tenu des conditions limites précises et des mesures in situ recueillies au cours de l'hiver 2017-2018, nous avons constaté que les meilleurs résultats pour l'étalonnage du modèle (niveau d'eau) en utilisant les paramètres/options disponibles conduisaient encore à certains ordres d'imprécision. sur les conditions aux limites de formse qualité (campagnes 2017-2018) qui ont effectivement amélioré les résultats numériques, nous avons constaté que les meilleurs résultats pour l'étalonnage du modèle (niveau d'eau) en utilisant les paramètres/options disponibles étaient encore associés à certains ordres d'imprécision. Par conséquent, l'objectif du deuxième travail était d'améliorer l'efficacité de la modélisation hydrodynamique pour les environnements de marée peu profonde. Nous avons introduit quelques hypothèses décrivant pourquoi les modèles de turbulence et de rugosité disponibles ne sont pas bien adaptés à la modélisation des estuaires avec de fortes variabilités spatiales et temporelles des profondeurs de marée. En conséquence (i) un modèle de turbulence k-ε étendu pour la paramétrisation adaptative de la viscosité turbulente en fonction de la profondeur, et une approche basée sur la direction de l'écoulement pour la paramétrisation de la rugosité du lit ont été développés, incorporés dans le modèle hydrodynamique employé (Delft3D). Le modèle modifié a montré une amélioration constante des prévisions du modèle dans les stations de champ proche et de champ lointain, par rapport aux schémas de paramétrage classiques. Enfin, un aspect manquant et mal compris des estuaires de latitude nordique est l'immense impact de l'hiver sur le flux des marées. Situé à la latitude 58°, le KRE subit l'effet intensif du climat arctique pendant la majeure partie de l'année, ce qui entraîne la formation de glace estuarienne rapide sur une grande partie de sa longueur. Plus précisément, et ce qui est le plus pertinent pour cette recherche, il est important de savoir comment le long hiver affecte les potentiels hydrocinétiques des estuaires des régions froides. Ainsi, la surfusion entraîne la formation de frasil et de glace de fond qui peuvent adhérer aux pales des turbines et provoquer leur dysfonctionnement. Dans les estuaires, la surfusion a une nature transitoire complexe car le point de congélation de l'eau salée est une fonction de la salinité et de la profondeur qui est changée par les marées au cours des cycles de marée. En raison du manque de données de terrain en hiver, nous avons collecté des paramètres hydrodynamiques en utilisant de nouvelles campagne de mesures en hiver 2018. Les observations ont montré que le risque de surfusion diminue à l'intérieur de l'estuaire, car en l'absence de débit fluvial, la salinité peut s'infiltrer beaucoup plus loin dans le fleuve. À l'intérieur, une modulation apparente de ∆T (la différence entre la température de l'eau et la température de congélation de l'eau), dépendant de la marée, a été observée avec une augmentation de la température pendant des marées montantes. Cette augmentation retarderait la surfusion, ce qui est un avantage majeur pour turbines. En réglant le module Delft3D-Ice, différents scénarios ont été définis pour l'étendue et l'épaisseur de la couvert de glace, et leurs réponses hydrodynamiques ont été analysées. Il a été démontré que la glace a des impacts complexes et non uniformes sur les caractéristiques hydrodynamiques de la KRE. Surtout, le débit des prismes de marée, qui est la principale source d'élan, peut être modifiée de manière démonstrative par la couverture de glace et la glace de marée plate. Les résultats suggèrent que les zones énergétiques sont légèrement affectées par la glace pendant la plus grande partie de l'hiver. Pendant l'hiver de pointe seulement, la glace pourrait considérablement diminuer densité moyenne de puissance des courants (par exemple, la puissance moyenne est égale ou supérieure à 7 kW m-2). Ces implications cryohydrodynamiques indiquent que l'hiver arctique n'est pas un obstacle à la production d'électricité dans le fleuve Koksoak, et l'énergie marémotrice serait un avantage annuel pour Kuujjuaq

Understanding the influence of “hot” models in climate impact studies: a hydrological perspective - Rahimpour Asenjan, Mehrad; Brissette, Francois; Martel, Jean-Luc; Arsenault, Richard (Hydrology and Earth System Sciences, 2023)
Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.

Estimation du débit en rivière à l’aide de l’imagerie RADARSAT en utilisant l’apprentissage profond. - Ziadi, Samar (Maîtrise en sciences de l'eau, 2023)
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&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. 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&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.

Déterminants de l'intention d'adoption d'un nouvel outil prévisionnel des crues dans le cadre du projet info-crue - Mutabazi, Jean-Paul (Université du Québec à Rimouski, 2022)
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.

Integrated Modeling of Short-Term Flood Forecasting in the Ottawa River - Ousoukhman, Hamza (Polytechnique Montréal, 2021)
RÉSUMÉ: Les inondations sont considérées comme l'un des risques naturels les plus dangereux au monde. Plusieurs pays souffrent des conséquences néfastes des inondations. Au Canada, plusieurs provinces ont subi des inondations au cours du siècle dernier. Par exemple, la rivière des Outaouais a été confrontée à de nombreuses inondations comme en 2017 et 2019. La population d'Ottawa continue à augmenter d'une année à l'autre. C'est pour cela que nous avons choisi la rivière des Outaouais comme étude de cas pour ce projet dans le but de protéger la société contre les risques causés par les inondations. Les pays adoptent plusieurs solutions basées sur différentes méthodes afin de minimiser les dommages causés par les crues. La plupart des scientifiques s'accordent que la prévision des crues est la meilleure façon de limiter les conséquences des crues. Les systèmes de prévision des crues sont indispensables dans les pays fréquemment confrontés à des crues. Ils visent à fournir un long délai d'exécution et à fournir aux autorités et aux décideurs des informations suffisantes. Par conséquent, ils auront suffisamment de temps pour prendre les mesures adéquates pour sauver la vie de la population et limiter les catastrophes économiques dues aux inondations. ABSTRACT: Floods are one of the most catastrophic natural disasters in Canada and around the world that can cause loss of life and damages to properties and infrastructures. Saguenay flood (1996), southern Alberta flood (2013), and Ottawa floods (2017, 2019), are a few examples of Canadian floods with tremendous socio-economic impacts. Flood forecasting and predicting its characteristics (e.g., its magnitude and extent) has an important role in preventing and mitigating such flood impacts. Particularly, short-term forecasting is crucial for early warning systems and emergency response to floods. This study presents an integrated hydraulic-hydrologic modeling system for flood prediction. In this system, the Delft3D two-dimensional hydrodynamic model is connected with a HEC-HMS hydrologic model and observation data to provide an automatic exchange of data and results. Delft3D and HEC-HMS were chosen for this study because they were widely used and provided good results. In addition, they were applied in several flood forecasting studies. The prediction weather data and watershed characteristics provide input to the hydrological model to predict streamflow conditions, which are then automatically fed into the hydrodynamic model. The hydrodynamic model simulates the flood characteristics such as water level, 2D depth-averaged velocity field, and flood extent.

Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change - Zahmatkesh, Zahra; Han, Shasha; Coulibaly, Paulin (Water, 2021)
An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Flood Risk Assessment under Climate Change: The Petite Nation River Watershed - Oubennaceur, Khalid; Chokmani, Karem; Gauthier, Yves; Ratte-Fortin, Claudie; Homayouni, Saeid; Toussaint, Jean-Patrick (Climate, 2021)
In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Petite Nation River Watershed, located in southern Quebec (Canada). The methodology follows a modelling approach, in which climate projections are derived from the Hydroclimatic Atlas of Southern Quebec following two representative concentration pathways (RCPs) scenarios, i.e., RCP 4.5 and RCP 8.5. These projections are used to predict future river flows. A frequency analysis was carried out with historical data of the peak flow (period 1969–2018) to derive different return periods (2, 20, and 100 years), which were then fed into the GARI tool (Gestion et Analyse du Risque d’Inondation). This tool is used to simulate flood hazard maps and to quantify future flood risk changes. Projected flood hazard (extent and depth) and damage maps were produced for the two municipalities under current and for future scenarios. The results indicate that the flood frequencies are expected to show a minor decrease in peak flows in the basin at the time horizons, 2050 and 2080. In addition, the depth and inundation areas will not significantly change for two time horizons, but instead show a minor decrease. Similarly, the projected flood damage changes in monetary losses are projected to decrease in the future. The results of this study allow one to identify present and future flood hazards and vulnerabilities, and should help decision-makers and the public to better understand the significance of climate change on flood risk in the Petite Nation River watershed.
Keywords: Site_internet_RIISQ

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids - Li, Mingke; McGrath, Heather; Stefanakis, Emmanuel (ISPRS International Journal of Geo-Information, 2022)
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest - Esfandiari, Morteza; Abdi, Ghasem; Jabari, Shabnam; McGrath, Heather; Coleman, David (Remote Sensing, 2020)
Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen’s kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data.

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change - Rees, Ee; Ng, V; Gachon, P; Mawudeku, A; McKenney, D; Pedlar, J; Yemshanov, D; Parmely, J; Knox, J (Canada Communicable Disease Report, 2019)
Keywords: Gachon_Philippe

Analyse des liens entre la précipitation extrême et la température : étude de cas dans le bassin versant de la rivière des Outaouais (BVRO) à partir des simulations du modèle climatique régional développé au centre ESCER - Llerena, Ana (2021)
Dans le contexte du réchauffement planétaire, la relation de Clausius Clapeyron (CC) est utilisée comme un indicateur de l’évolution des précipitations extrêmes. Parmi les théories proposées, nous utilisons dans notre recherche une relation exponentielle qui fait le lien entre l’évolution des centiles les plus extrêmes des précipitations et le changement de la température ΔT dans le climat actuel. Selon cette théorie, les précipitations augmentent au même rythme que la capacité de rétention d'humidité dans l’atmosphère, expliquée par la relation de CC, avec un taux de changement d'environ 7 % par degré Celsius pour des valeurs de température et de pression près de la surface. Ainsi, le présent travail vise à vérifier l’existence de liens physiquement plausibles dans la relation entre les précipitations extrêmes et la température de l’air pour la région du Bassin Versant de la Rivière des Outaouais (BVRO) sur la période 1981-2010, à l’aide des simulations du Modèle Régional Canadien du Climat (MRCC) (versions 5 et 6), développé au centre ESCER, et de deux produits de réanalyses du Centre Européen pour les prévisions météorologiques à moyen terme (CEPMMT) à différentes résolutions spatiales. En général, les précipitations quotidiennes suivent un taux de changement inférieur à celui de CC ; tandis que les précipitations horaires augmentent plus rapidement avec la température. Dans ce dernier cas, pour la simulation du MRCC5 à plus haute résolution spatiale, des taux de changement supérieurs à CC ont même été produits, jusqu’à 10,2 %/°C. Ce travail a également mis en évidence qu’au-delà du seuil de 20°C, la capacité de rétention d'humidité de l’atmosphère n’est pas le seul facteur déterminant pour générer des précipitations extrêmes, et que d’autres facteurs sont à considérer, comme la disponibilité de l'humidité au moment de l'événement de précipitation et la présence de mécanismes dynamiques qui favorisent les mouvements verticaux ascendants. Un comportement sous forme de crochet, qui décrit une augmentation des précipitations jusqu'à un seuil de température, est observé dans la saison estivale avec le MRCC5, mais il a disparu avec les simulations du MRCC6, ce qui pourrait être une conséquence d’avoir seulement une année de simulation disponible ou bien d’une conséquence de la très haute résolution du modèle sur les intervalles de température et sur les effets locaux. En conclusion, l'applicabilité de la relation de CC ne doit pas être généralisée quant à l’étude des précipitations extrêmes, il est également important de considérer l'échelle temporelle, la résolution du modèle utilisé et la saison de l'année. L’évolution de cette relation de CC devrait être évaluée avec des simulations à très haute résolution spatiale (version en développement au centre ESCER), et pour d’autres zones climatiques, sachant que les intervalles de températures et les effets locaux exercent un rôle majeur sur les occurrences et les intensités des fortes précipitations. Ces éléments sont essentiels à intégrer dans le contexte des changements climatiques, en raison des conséquences associées aux fortes précipitations, notamment sur l’occurrence des inondations. _____________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Clausius-Clapeyron, évènements extrêmes, aléas météorologiques, risques d’inondation, changements climatiques
Keywords: Gachon_Philippe

Quantitative and qualitative assessment of the impact of climate change on a combined sewer overflow and its receiving water body - Gooré Bi, Eustache; Monette, Frédéric; Gachon, Philippe; Gaspéri, Johnny; Perrodin, Yves (Environmental Science and Pollution Research, 2015)
Projections from the Canadian Regional Climate Model (CRCM) for the southern part of the province of Québec, Canada, suggest an increase in extreme precipitation events for the 2050 horizon (2041–2070). The main goal of this study consisted in a quantitative and qualitative assessment of the impact of the 20 % increase in rainfall intensity that led, in the summer of 2013, to overflows in the “Rolland-Therrien” combined sewer system in the city of Longueuil, Canada. The PCSWMM 2013 model was used to assess the sensitivity of this overflow under current (2013) and future (2050) climate conditions. The simulated quantitative variables (peak flow, QCSO, and volume discharged, VD) served as the basis for deriving ecotoxicological risk indices and event fluxes (EFs) transported to the St. Lawrence (SL) River. Results highlighted 15 to 500 % increases in VD and 13 to 148 % increases in QCSO by 2050 (compared to 2013), based on eight rainfall events measured from May to October. These results show that (i) the relationships between precipitation and combined sewer overflow variables are not linear and (ii) the design criteria for current hydraulic infrastructure must be revised to account for the impact of climate change (CC) arising from changes in precipitation regimes. EFs discharged into the SL River will be 2.24 times larger in the future than they are now (2013) due to large VDs resulting from CC. This will, in turn, lead to excessive inputs of total suspended solids (TSSs) and tracers for numerous urban pollutants (organic matter and nutrients, metals) into the receiving water body. Ecotoxicological risk indices will increase by more than 100 % by 2050 compared to 2013. Given that substantial VDs are at play, and although CC scenarios have many sources of uncertainty, strategies to adapt this drainage network to the effects of CC will have to be developed.
Keywords: Gachon_Philippe

Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada - Salimi, Amirhossein; Noori, Amir; Ebtehaj, Isa; Ghobrial, Tadros; Bonakdari, Hossein (Sustainability, 2024)
Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.
Keywords: Infolettre; Infolettre_Mai_2024

Potential impact of climate change on the risk of windthrow in eastern Canada’s forests - Saad, Christian; Boulanger, Yan; Beaudet, Marilou; Gachon, Philippe; Ruel, Jean-Claude; Gauthier, Sylvie (Climatic Change, 2017)
Climate change is likely to affect windthrow risks at northern latitudes by potentially changing high wind probabilities and soil frost duration. Here, we evaluated the effect of climate change on windthrow risk in eastern Canada’s balsam fir (Abies balsamea [L.] Mill.) forests using a methodology that accounted for changes in both wind speed and soil frost duration. We used wind speed and soil temperature projections at the regional scale from the CRCM5 regional climate model (RCM) driven by the CanESM2 global climate model (GCM) under two representative concentration pathways (RCP4.5, RCP8.5), for a baseline (1976–2005) and two future periods (2041–2070, 2071–2100). A hybrid mechanistic model (ForestGALES) that considers species resistance to uprooting and wind speed distribution was used to calculate windthrow risk. An increased risk of windthrow (3 to 30%) was predicted for the future mainly due to an increased duration of unfrozen soil conditions (by up to 2 to 3 months by the end of the twenty-first century under RCP8.5). In contrast, wind speed did not vary markedly with a changing climate. Strong regional variations in wind speeds translated into regional differences in windthrow risk, with the easternmost region (Atlantic provinces) having the strongest winds and the highest windthrow risk. Because of the inherent uncertainties associated with climate change projections, especially regarding wind climate, further research is required to assess windthrow risk from the optimum combination of RCM/GCM ensemble simulations.
Keywords: Gachon_Philippe

Will Evolving Climate Conditions Increase the Risk of Floods of the Large U.S.‐Canada Transboundary Richelieu River Basin? - Lucas‐Picher, Philippe; Lachance‐Cloutier, Simon; Arsenault, Richard; Poulin, Annie; Ricard, Simon; Turcotte, Richard; Brissette, François (JAWRA Journal of the American Water Resources Association, 2021)
Abstract In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.

Projected changes in the dynamics of flood hazard in the Grand River Basin, Canada. - Gaur, Abhishek; Simonovic, Slobodan P. (British Journal of Environment and Climate Change, 2015)
In this study future flooding frequencies have been estimated for the Grand River catchment located in south - western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter - comparison Project - 3 climate models are bias - corrected and downscaled before they are used to obtain mid - and end of 21 st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature record s it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10 - 100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.

Harmonizing and Extending Fragmented 100 Year Flood Hazard Maps in Canada’s Capital Region Using Random Forest Classification - Bhuiyan, Shelina A.; Bataille, Clement P.; McGrath, Heather (Water, 2022)
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.

Flood occurrence and impact models for socioeconomic applications over Canada and the United States - Grenier, Manuel; Boudreault, Mathieu; Carozza, David A.; Boudreault, Jérémie; Raymond, Sébastien (Natural Hazards and Earth System Sciences, 2024)
Abstract. Large-scale socioeconomic studies of the impacts of floods are difficult and costly for countries such as Canada and the United States due to the large number of rivers and size of watersheds. Such studies are however very important for analyzing spatial patterns and temporal trends to inform large-scale flood risk management decisions and policies. In this paper, we present different flood occurrence and impact models based upon statistical and machine learning methods of over 31 000 watersheds spread across Canada and the US. The models can be quickly calibrated and thereby easily run predictions over thousands of scenarios in a matter of minutes. As applications of the models, we present the geographical distribution of the modelled average annual number of people displaced due to flooding in Canada and the US, as well as various scenario analyses. We find for example that an increase of 10 % in average precipitation yields an increase in the displaced population of 18 % in Canada and 14 % in the US. The model can therefore be used by a broad range of end users ranging from climate scientists to economists who seek to translate climate and socioeconomic scenarios into flood probabilities and impacts measured in terms of the displaced population.
Keywords: Infolettre; Infolettre_Decembre_2024

A random forest machine learning model to detect fluvial hazards - Gava, Marco; Biron, Pascale M.; Buffin‐Bélanger, Thomas (River Research and Applications, 2024)
Abstract Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.
Keywords: Infolettre; Infolettre_Septembre_2024

Future Changes in Flood Hazards across Canada under a Changing Climate - Gaur, Ayushi; Gaur, Abhishek; Simonovic, Slobodan P. (Water, 2018)
Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.