Flood Hazard Identification and Mapping Program: Projects

Recipient Legal Name: University of Victoria – Pacific Impacts Climate Consortium

Agreement Value in CAD: $150,000

Project Objective: The goal of this project was to test a new method for creating detailed climate change projections for the Fraser River Basin, which can be used in hydrologic models. The project showed how to adapt large-scale climate data for local areas, giving specific information on variables like precipitation, temperature, wind speed, and humidity. The results of the project helped shape a larger national project to provide results for the entire country with the aim of improving flood mapping tools and making them more accurate.

Recipient Legal Name: University of Calgary – Consortium with Laval University, McMaster University, University of Saskatchewan

Agreement Value in CAD: $840,000

Project Objective: This project aimed to improve how climate data is used in water management and identify key data sets that are important for different users. The first goal was to create hydrology models to generate hydrological data for cities across Canada, helping quickly assess future streamflow changes due to climate change. The second goal was to develop new methods for analyzing flood frequency, including a standard way to account for changing conditions like climate change in engineering studies. This new approach will help provide a consistent methodology for flood frequency analysis across Canada.

Recipient Legal Name: University of Saskatchewan

Agreement Value in CAD: $165,000

Project Objective: This project reviewed how Canadian land surface models simulate water movement in soil across sloped terrain. The goal was to improve how these models calculate runoff, focusing on both saturated and unsaturated flows beneath the surface of the earth. The research found limitations in the current methods and suggested improvements for more accurate runoff calculations. These changes will help better assess climate change impacts and improve flood mapping by refining runoff estimates in river networks.

Recipient Legal Name: University of Waterloo

Agreement value in CAD: $160,000

Project Objective: This project improved the understanding of local water systems by creating detailed hydrology models (Raven) for two major regions in Canada: the Great Lakes/Ottawa River Basin and the Fraser River Basin. The models were based on atmospheric data from version 2.1 of ECCC's Canadian Surface Reanalysis (CaSR - computer simulations of past meteorological conditions). Adjusting the data to account for local terrain improved streamflow calculations in the Fraser River Basin and calibrating the model to the basin outlet showed good results at most sites. The results from the model can now be used in hydraulic models and to estimate flood inundation for different scenarios in the region.

Recipient Legal Name: University of Waterloo

Agreement value in CAD: $115,313

Project Objective: This project used the Long Short-Term Memory (LSTM) machine-learning model to simulate peak streamflows across large parts of Canada, using atmospheric data from version 2.1 of ECCC's Canadian Surface Reanalysis (CaSR - computer simulations of past meteorological conditions). Results showed that adding topography information from CaSR slightly improved LSTM's performance in the Fraser River Basin, and LSTM generally performed better than the hydrology model (Raven). The project also introduced a hybrid approach, combining LSTM with the Raven model for more accurate calculations, especially in large watersheds like the Ottawa River. This results from the hybrid model can be used in hydraulic models to estimate flood inundation maps in these regions.

Recipient Legal Name: University of Waterloo

Agreement value in CAD: $90,000

Project Objective: : This project developed the Canadian Lake and River Hydrofabric (CLRH), a detailed dataset of lakes and rivers across Canada, matching the latest data from ECCC's National Hydrometric Network dataset. The two datasets are highly consistent, and the CLRH will soon be available for public download. This dataset is helpful for flood mapping, as it supports the use of a vector-based hydrologic model, required for hydraulic modelling, almost anywhere in Canada.

Recipient Legal Name: Polytechnique Montréal

Agreement value in CAD: $53,000

Project Objective: Flood-prone areas in small watersheds are often estimated using Intensity-Duration-Frequency (IDF) curves, but these curves are typically only available for locations where precipitation is measured, leading to limited coverage. This project developed a method to estimate IDF curves on a regular grid across Eastern Canada. The approach used the Canadian Surface Reanalysis (CaSR - computer simulations of past meteorological conditions) data to improve the accuracy and reliability of these estimates.

Recipient Legal Name: Institut national de la recherche scientifique

Agreement value in CAD: $114,330

Project Objective: The project linked rainfall and flooding by identifying characteristics of rainfall sequences that influence peak streamflows and flood levels. Special focus was on factors affecting the magnitude of spring floods, including winter precipitation, spring liquid precipitation, and surface temperature changes.

Recipient Legal Name: Ouranos

Agreement value in CAD: $246,100

Project Objective: This project developed a new method to correct biases in global climate model data, using it to improve regional climate models for Canada. The aim was to reduce uncertainty in future climate projections, especially when assessing flood hazards. The study resulted in two publications showing the benefits of this method at both continental and regional scales. The findings will help create a large climate-change dataset, which will be produced next year as part of the Climate Research Division's commitment to FHIMP.

Recipient Legal Name: Ouranos

Agreement value in CAD: $228,265

Project Objective: This study focused on improving maximum annual snow projections using a specialized weighting method with data from phase 6 of the Coupled Model Intercomparison Project the (CMIP6). The method (ClimWIP) combines two factors: how well the model performed in historical climate and how different it is from other models. The goal was to provide better snow projections, particularly focusing on snow water equivalent. These improved projections will help enhance floodplain mapping and refine projection uncertainty estimates across Canada.

Recipient Legal Name: Université Laval

Agreement value in CAD: $32,045

Project Objective: This project investigated the causes and effects of ice jam flooding using the Chaudière River, Quebec as a case study. The study assessed the use of machine-learning and ensemble approaches to predict the occurrence, timing, severity, and location of ice jam floods. The results of this study were published in Cold Regions Science and Technology and are available free of charge under the open access licence: https://doi.org/10.1016/j.coldregions.2024.104305.

Recipient Legal Name: École Polytechnique de Montréal

Agreement value in CAD: $90,000

Project Objective: Flood-prone areas in small watersheds are often estimated using Intensity-Duration-Frequency (IDF) precipitation curves, but these are usually only available where precipitation data exists, leaving gaps across Canada. According to the Canadian Standards Association, developing IDF curves covering the entire country represents a major challenge. This project used the Canadian Surface Reanalysis (CaSR) spatial explanatory variable to develop a gridded dataset that covers all of Canada. The resulting curves can now be used in flood-prone areas for small watersheds nationwide.

Recipient Legal Name: University of Waterloo

Agreement value in CAD: $90,000

Project Objective: This project enhanced the Canadian Lake and River Hydrofabric (CLRH) dataset, particularly in the prairie potholes region, by incorporating data on multi-outlet lakes. This project will help implement hydrologic models in regions that are traditionally challenging for hydrologic models, such as the Nelson and Churchill River basins.

Recipient Legal Name: Université de Sherbrooke

Agreement value in CAD: $80,500

Project Objective: Ce projet a examiné l’effet de la bathymétrie sur la modélisation hydraulique. Quatre sites d’étude ont été identifiées (un situé sur la Rivière L’Assomption et trois situés sur la Rivière Chaudière) ayant deux sources de données bathymétriques à chaque site d’étude. Deux modèles hydrauliques ont été réalisé à chaque site d’étude en utilisant une seule source de données bathymétriques par modèle. Les résultats du projet ont montré que la source des données bathymétriques a un impact important sur l’exactitude de la modélisation hydraulique. (French only)

Recipient Legal Name: University of British Columbia

Agreement value in CAD: $165,900

Project Objective: This project will advance probabilistic extreme flood predictions across timescales for both gauged and ungauged basins using machine-learning models. An evaluation framework will be developed for the forecasts to properly measure forecast quality. The goal is to create prediction models that give probability-based flow predictions for about 100 locations, mostly in BC and Yukon. These predictions aim to be more accurate and reliable for forecasting extreme events.

Recipient Legal Name: University of Alberta

Agreement value in CAD: $612,400

Project Objective: Researchers will help to develop a framework that integrates hydrological, hydraulic, and ice modelling to better characterize breakup hydrographs, specifically in regions where historical ice jam data is limited. The comprehensive modeling framework will help to improve ice jam flood mapping, while considering the effects of climate change.

Recipient Legal Name: University of Calgary; Consortium with University of Saskatchewan and McMaster University

Agreement value in CAD: $770,200

Project Objective: Researchers will implement comprehensive hydrometeorological and statistical modeling processes required for modelling the hydrologic impacts of climate change throughout Canada. The modeling framework will help increase the understanding of future flood hazards for the purpose of improving floodplain mapping methods and standards. The purpose of the framework is to support more informed decision-making and policy development, while also serving as an essential tool for sharing knowledge between researchers and flood mapping practitioners.

Recipient Legal Name: University of Western Ontario

Agreement value in CAD: $180,000

Project Objective: This project seeks to improve the understanding of compound flood events by investigating the behaviour and impacts of multiple flood drivers in a changing climate. This work will involve the development of a statistical framework designed to characterize potential compound coastal floods and compound inland floods. The project will assess the projected changes in occurrences and intensities of these floods and will evaluate the impacts of compound floods using advanced models to create floodplain maps.

Recipient Legal Name: Queen’s University – consortium with Dalhousie University

Agreement value in CAD: $350,800

Project Objective: The goal of this research is to improve floodplain modeling and mapping by developing coastal numerical models to predict flood inundation from extreme storms along Atlantic coastlines. The models will be validated using field observations, and will consider coastal variables such as surface waves, storm surges, and tides, all of which lead to flooding and erosion along coastlines. The research will produce detailed field datasets, maps, and calibrated numerical models, leading to new floodplain mapping methods and results that can be applied in practice. The results will help predict future coastal hazards under changing environmental conditions.

Recipient Legal Name: Université Laval

Agreement value in CAD: $232,200

Project Objective: Researchers will combine remote sensing techniques and geospatial modeling to improve the estimation of key parameters associated with ice jam flood hazard mapping in the Peace and Athabasca Rivers (Alberta) and the Chaudière River (Québec). The project aims to help communities mitigate the cost and environmental impacts associated with ice-jam flooding events. This project will also assess how specific remote sensing technologies can measure water levels on ice-covered waterbodies, leading to an enhanced understanding of winter water levels and ice conditions. A simplified geospatial index will be created to identify areas prone to ice jams, improving the accuracy and predictive capabilities of flood models and leading to more reliable flood risk assessments.

Recipient Legal Name: Polytechnique Montréal

Agreement value in CAD: $272,900

Project Objective: Researchers will use information from existing intensity-duration-frequency (IDF) curves along with spatial information from the Canadian Surface Reanalysis (CaSR - computer simulations of past meteorological conditions) dataset to develop IDF curves for any location in Canada. The IDF curves are used to predict the likelihood of a range of extreme rainfall events under historical and future potential climatic conditions. The project outputs will include an open dataset with access to the generated IDF curves, along with the computer codes used to produce this data.

Recipient Legal Name: Polytechnique Montréal

Agreement value in CAD: $330,900

Project Objective: This project aims to develop a hybrid modelling framework that combines hydrological, hydraulic, and physics-based models with advanced machine learning techniques to provide fast and accurate prediction of streamflow, particularly in cold-region rivers. This project will significantly improve flood modeling, infrastructure planning, and environmental studies, benefiting federal and provincial agencies, municipalities, and researchers involved in hydrodynamics and flood management.

Recipient Legal Name: Ouranos

Agreement value in CAD: $365,400

Project Objective: This project supports the integration of Environment and Climate Change Canada’s Surface Prediction System into the Government of Quebec's flood mapping program. This integration will reduce uncertainty in hydrological modeling, improving our ability to assess flooding risks in a changing climate. This research will produce regional projections for all of Canada at a resolution of approximately 2 kilometres, simulate river flows for southern Quebec, and integrate annual maximum flow indicators into a tool for engineers to help analyze future climate hazard probabilities.

Recipient Legal Name: South Nation River Conservation Authority

Agreement value in CAD: $430,355

Project Objective: 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.

Recipient Legal Name: University of Prince Edward Island – Canadian Centre for Climate Change and Adaptation

Agreement value in CAD: $459,266

Project Objective: This project 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.

Recipient Legal Name: University of Saskatchewan

Agreement value in CAD: $181,700

Project Objective: First, the project will create innovative methods to generate flood hazard maps, which will be verified and shared through publications and conferences. Second, they will produce detailed flood hazard maps for the Assiniboine River Basin to help decision-makers better understand risk and uncertainty. Lastly, the recipient will design a user-friendly interface to view the maps.

Recipient Legal Name: CE Flood Analytics Ltd.

Agreement value in CAD: $143,812

Project Objective: The project 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.

Recipient Legal Name: McGill University – The Royal Institution for the Advancement of Learning

Agreement value in CAD: $257,000

Project Objective: The project will evaluate the use of transformer models for predicting water levels in Canadian watersheds focusing on climate-induced uncertainty. They will deliver improved probabilistic prediction models for approximately 300 locations (gauges) across Canada, making these tools accessible to researchers, decision-makers, and local communities. Expected results include detailed water level predictions (mean values and confidence levels) for three climate scenarios.

Recipient Legal Name: Université Concordia

Agreement value in CAD: $773,741

Project Objective: 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.

Recipient Legal Name: University of Waterloo

Agreement value in CAD: $595,000

Project Objective: The project 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.

Recipient Legal Name: Slobodan P. Simonovic Consulting Ltd

Agreement value in CAD: $45,453

Project Objective: The recipient will conduct a focused literature review to advance understanding of global methodologies for creating national-scale total flood hazard maps to identify 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.

Recipient Legal Name: Tribal Chief Ventures Inc.

Agreement value in CAD: $250,000

Project Objective: 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.

Recipient Legal Name: Seabird Island Band

Agreement value in CAD: $250,000

Project Objective: The project 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.