Programme d’identification et de cartographie des aléas d’inondation

Nom légal du bénéficiaire : South Nation River Conservation Authority (SNC)

Ville : Finch

Province ou territoire du bénéficiaire : Ontario

Valeur de l'entente en CAD : 430 355 $

Objectif du projet et résultats attendus : The recipient will use advanced science to develop accurate flood inundation maps for the South Nation River Watershed, enhancing flood risk decision support systems. These maps will be shared with municipal partners, planners, asset managers, emergency coordinators, and residents to improve flood risk awareness. The project will compare innovative flood mapping techniques with traditional methods and assess flood risks under various climate scenarios. The results will support land use planning and establish new flood mapping techniques, aiding both scientific research and practical decision-making in a rapidly growing region.

Nom légal du bénéficiaire : Water Resources Management Division (WRMD) of the Government of Newfoundland and Labrador (NL)

Ville : St. John’s

Province ou territoire du bénéficiaire : Newfoundland and Labrador

Valeur de l'entente en CAD : 225 000 $

Objectif du projet et résultats attendus : The recipient will enhance capabilities for delivering machine learning (ML)-based solutions for : (1) river ice classification, (2) thickness forecasting, and (3) long-range break up prediction. This enhancement is intended to provide improved inputs to support the ice jam flood risk modeling and mapping outputs conducted by the Proponent. The project will leverage ML-based algorithms developed by the Proponent and its collaborator for river ice classification and ice thickness prediction, freely available satellite imagery, as well as the collaborator’s cloud-based service delivery platform.

Nom légal du bénéficiaire : Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island

Ville : Charlottetown

Province ou territoire du bénéficiaire : Prince Edward Island

Valeur de l'entente en CAD : 459 266 $

Objectif du projet et résultats attendus : The recipient will enhance island-wide pluvial flood modeling for PEI, addressing critical challenges to support the provincial government in developing more robust flood hazard maps amidst climate change. Key research questions include determining coastal boundary conditions for modeling compound flooding risks, integrating hydraulic infrastructures in the model, and assessing model accuracy and associated uncertainties.

Nom légal du bénéficiaire : University of Saskatchewan

Ville : Saskatoon

Province ou territoire du bénéficiaire : Saskatchewan

Valeur de l'entente en CAD : 181 700 $

Objectif du projet et résultats attendus : The recipient will develop three key outputs for this project. First, they will create innovative methods that blend AI, process-based modeling, and remote sensing to generate flood hazard maps, which will be validated and shared through publications and conferences. Second, they will produce detailed flood hazard maps for the Assiniboine River Basin, providing risk categorization and uncertainty measures to support decision-making. Lastly, the recipient will design a user-friendly interface for viewing these maps, incorporating feedback from local collaborators and community workshops to enhance usability and effectiveness.

Nom légal du bénéficiaire : CE Flood Analytics Ltd.

Ville : Steinbach

Province ou territoire du bénéficiaire : Manitoba

Valeur de l'entente en CAD : 143 812 $

Objectif du projet et résultats attendus : The recipient will deliver a tool for local, regional, and national stakeholders to enhance the resolution of existing flood map products using machine learning and NRCan’s high-resolution terrain model, HRDEM. This tool will offer a low-cost solution to create high-resolution flood maps from global or regional models, making it accessible for non-expert stakeholders.

Nom légal du bénéficiaire : The Royal Institution for the Advancement of Learning/McGill University

Ville : Montréal

Province ou territoire du bénéficiaire : Québec

Valeur de l'entente en CAD : 257 600 $

Objectif du projet et résultats attendus : The recipient will evaluate the use of transformer models for predicting water levels in Canadian watersheds, focusing on climate-induced uncertainty. They will deliver optimized probabilistic prediction models for approximately 300 locations (gauges) across Canada, making these tools accessible to researchers, decision-makers, and local communities. The project will also provide Python sample codes for flood mapping using Transformer models and HAND, available on GitHub for reproducibility. Expected results include detailed water level predictions (mean values and confidence levels) for three climate scenarios, yielding around 900 prediction sets in NetCDF format for widespread dissemination and use.

Nom légal du bénéficiaire : Université Concordia

Ville : Montréal

Province ou territoire du bénéficiaire : Québec

Valeur de l'entente en CAD : 773 741 $

Objectif du projet et résultats attendus : Le bénéficiaire améliorera une méthode de modélisation régionale des inondations fluviales, développée initialement au Québec, pour l'appliquer à l'ensemble des bassins versants du Canada. En intégrant l'intelligence artificielle pour détecter les chenaux lors de l'acquisition des données LiDAR, trois bassins pilotes, dont la rivière Rouge au Manitoba, seront analysés. Le projet aboutira à une méthodologie simplifiée pour la cartographie des zones inondables, incluant des programmes informatiques accessibles en libre accès et un guide d'application. Les résultats attendus comprennent des cartes de zones inondables, des modèles hydrauliques, et un rapport final, destinés aux gouvernements et autres intervenants pour améliorer la gestion des risques d'inondation et sensibiliser le public.

Nom légal du bénéficiaire : University of Waterloo

Ville : Waterloo

Province ou territoire du bénéficiaire : Ontario

Valeur de l'entente en CAD : 595 000 $

Objectif du projet et résultats attendus : The recipient will develop advanced techniques for estimating the uncertain magnitude of low-frequency flood events (e.g., 50-year, 100-year, 200-year floods) using hydrological models enhanced by machine learning. They will assess the value of alternative data sources, such as remotely sensed water level observations, to improve predictions of flood magnitudes at a regional scale. The expected outputs include high-resolution strategies for estimating flood discharges and lake levels in both gauged and ungauged basins, integration of machine learning tools like long short-term memory networks (LSTMs) into conventional flood modeling workflows, and methodologies for calibrating regional hydrologic models using river surface elevation and width data, leveraging information from the NASA Surface Water and Ocean Topography (SWOT) mission to enhance the accuracy of flood predictions.

Nom légal du bénéficiaire : Slobodan P. Simonovic Consulting Ltd

Ville : London

Province ou territoire du bénéficiaire : Ontario

Valeur de l'entente en CAD : 45 453 $

Objectif du projet et résultats attendus : The recipient will conduct a focused literature review to advance understanding of global methodologies for creating national-scale total flood hazard maps, identifying approaches suitable for implementation in Canada. This review will cover methods for mapping fluvial, pluvial, and coastal flood hazards at various scales under changing climate conditions. The project will explore the integration of AI and remote sensing to refine regional flood maps to finer scales, expanding the scope to include pluvial and coastal hazards alongside existing expertise in fluvial mapping. The expected outcomes include identifying scalable, climate-informed methods and tools for national flood hazard assessment, supporting Canadian applications.

Nom légal du bénéficiaire : Tribal Chiefs Ventures Inc. (TCVI)

Ville : Edmonton

Province ou territoire du bénéficiaire : Alberta

Valeur de l'entente en CAD : 250 000 $

Objectif du projet et résultats attendus : The recipient will create a robust, culturally informed flood hazard mapping system that integrates traditional Indigenous knowledge with modern GIS and hydrological modeling techniques. The goal is to strengthen community resilience to flooding by improving preparedness, risk identification, and disaster management planning.

Nom légal du bénéficiaire : Seabird Island Band

Ville : Agassiz

Province ou territoire du bénéficiaire : British Columbia

Valeur de l'entente en CAD : 250 000 $

Objectif du projet et résultats attendus : The recipient will enhance resilience and preparedness among Seabird Island Band and neighboring communities against flood hazards by leveraging Indigenous Traditional Knowledge through the development of participatory tools, comprehensive traditional knowledge studies, and inclusive engagement processes. This project aims to integrate Indigenous perspectives into flood hazard mapping and emergency management, identify the impacts of flooding on Indigenous lands and communities, and foster capacity building to empower Indigenous participation in environmental stewardship and sustainable hazard mitigation strategies.

Nom légal du bénéficiaire : Kátł'odeeche First Nation

Ville : Hay River

Province ou territoire du bénéficiaire : Northwest Territories

Valeur de l'entente en CAD : 250 000 $

Objectif du projet et résultats attendus : The recipient will create KFN Flood Hazard datasets that integrate Indigenous Knowledge and Western Science to improve flood data availability and understanding. It will assess flooding's impact on cultural activities, habitat, community safety, and infrastructure. The project will also develop culturally appropriate policy tools for flood mitigation and support knowledge transfer of Indigenous flood response strategies within the community, especially from Elders to Youth.