This project facilitates, among other things, the identification of sources of uncertainty associated with traditional water surface profile measurements using real-time kinematic positioning (GNSS-RTK).
The results of the research project will be used by the hydrologic expertise unit to improve the estimation of river flows in Quebec.
Comprehensive knowledge of an area will be made available to the people and organizations concerned, allowing them to plan emergency measures and land use with a view to adapting to climate change.
Data collected in a participatory manner can be fed into models in order to define the flood zones and the alert levels for populations and infrastructure, in particular for the rivers where more traditional data collection is not possible.
The use of the Raven framework will help building several semi-distributed models appropriate to the watersheds of southern Quebec. It will allow the exploration of the different algorithms to simulate snow melting and evapotranspiration.
Contribution to the prioritization of watersheds to be mapped in order to ensure public safety from flooding through the use of the results and the tool.
This project will determine whether assimilating snow in HYDROTEL improves hydrological simulation and forecasting, and if so, under what conditions.
By estimating water levels in ungauged areas, virtual stations contribute to the improvement of these essential tools. Based on free, easily accessible images, the approach will provide data on multiple sectors at an almost daily frequency and at little cost.
Once completed, the methodology developed will make it possible to estimate uncertainties in extreme flow values for different return periods for all gauged sites in southern Québec. This knowledge of uncertainty will provide a better understanding of flood events and a better estimation of flood-risk areas.
