Aquatic Habitat Analysis and Modeling Laboratory
Reliable models are required to determine the impact of changes in the habitat on river biological productivity to assist authorities in the sustainable management of Canadian rivers. In a habitat model, fish preferences for certain combination of physical variables are used to determine the quality index of the habitat. Analysis of these models shows that they have major shortcomings mainly related to the fact that the variables defining fish habitat evolve at different spatial and temporal scales.The objective of this project is to develop models that overcome these shortcomings. The research work will take place within a new laboratory dedicated to the analysis and modeling of aquatic habitats, the only one in Canada and Quebec. In this laboratory, researchers will gather the necessary data to develop the next generation of habitat models. New complex statistical approaches will also be developed to take into account the different spatial and temporal scales involved and different levels of variability. These approaches will help aquatic ecosystem managers in making decisions for the protection of fish. The proposed research will improve our understanding of fish habitats and assist in water resource management mainly in Quebec, but also elsewhere around the world.
Funding: Canadian Fundation fo Innovation – Leaders Fund, Government of Quebec
Collaborators : Normand Bergeron and André St-Hilaire, INRS
Development of knowledge transfer tools to support research, intervention, and monitoring in public health and climate change sectors
A better understanding of the link between mortality and morbidity due to cardiovascular diseases (CVD) and meteorology or climate is important for public health monitoring. Many deliverables have been planned/ published to transfer important information about the CVD Program to the scientific community. However, the scientific concepts and the statistical results are difficult to understand and transfer adequately, even to some experts.As such, additional efforts must be made to promote the use and application of this knowledge in the development of interventions, research protocols, and tools for monitoring public health, and this is the reason behind this project of knowledge transfer. The objective is to improve the knowledge transfer about the health-climate studies, and in particular about the CVD Program. Two deliverables are planned: 1) written reports to synthesize and describe some of the results and analytical methods developed or improved within the CVD Program; 2) development of a software program to automatically identify threshold temperature values at the regional scale.
Funding: Ministère de la Santé et des Services sociaux du Québec
Collaborators : Pierre Gosselin and Diane Bélanger, Institut national de santé publique du Québec
Flexible and optimal statistical methods for regional frequency analysis of hydrological variables
Extreme hydrological events (e.g. floods) have economic and environmental consequences and impacts on the design and management of hydraulic structures. Thorough knowledge of hydrological processes is required to make the right decisions in sizing the structures. It is thus essential to develop adequate models to predict such events in order to reduce related risks.Frequency analysis (FA) is usually used toward this goal. The objective of regional FA (RFA) is to transfer information from gauged to ungauged sites (e.g. the site of a new structure) using a model such as the flood index. Currently used methods in RFA are based on relatively simple models or models having restrictive conditions. The objective of this project is to better understand hydrological processes and to improve the quality of the estimations. The originality of this research lies in the use of new statistical methods for RFA and in the development of innovative approaches in statistical hydrology specific for RFA. The results of this research will help provide managers and engineers with more realistic and robust approaches, and with more precise estimates of hydrological risks.
Funding: NSERC – Discovery Grant
A new data-driven model for urban water demand forecasting
Despite the relative abundance of water in Canada, water suppliers are interested in managing water demand than ever before. In this sense, our partner, Econics, works with local governments, water utilities and municipalities. Efforts in the Canadian urban water (UW) supply sector move towards more integrated and demand side management approaches to control and expand their water supply systems (WSS). To this end, it is recognized the need for new tools. One such tool is state of art and highly accurate, precise and reliable UW demand (UWD) forecasting models. Data-driven models represent the main type of such modeling. However, data-driven models have not been examined for UWD long-term forecasting, especially under different climate changes scenarios (CCS). The latter should be examined and their impact on UWD should be evaluated. The main goal of the project is to develop data-driven models for long-term UWD forecasting, while considering long-term CCS and their expected impacts on UWD for a given municipality. To date, no studies have examined the performance of ANN models for long-term UWD forecasting under CCS. Despite the good performance of ANN models, they have limitations, particularly with non-stationary data. Several studies have shown promising performance outcomes when combined with Wavelets (W-ANN) as well as the use of ensemble ANNs (ENN). The main goal of this project is to develop W-ENN models for the long-term UWD forecasting under different CCS. The new models will provide Econics, and in turn their clients with very useful models that will allow the most accurate, precise and reliable UWD long-term forecasting and will consequently help in effectively and sustainably plan and manage UW supply system.
Funding: NSERC – Engage Grant
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