Reference number : GC-2017-Q4-01616, amendment number : , agreement type : G, recipient legal name : Scott, Andrea (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Flooding poses a significant risk to humanity. Thousands of people are displaced each year due to flooding, in_x000D_
addition to destruction of infrastructure and agriculture. Satellite observations are increasingly being used for_x000D_
flood monitoring. Satellite-borne synthetic aperture radars (SARs) are a particularly appealing source of_x000D_
observations due to their ability to return meaningful information of the earth's surface during all weather_x000D_
conditions. With increasing numbers of SAR systems in orbit, and growing data volumes, it is of interest to_x000D_
develop methods to use these data in automated processes._x000D_
One such process, known as data assimilation, combines observational data with output from a forecast model_x000D_
to provide an improved initial condition for the forecast model. This is the method used to generate weather_x000D_
forecasts. For flood forecasting, assimilation of water level observations (WLOs) from SAR imagery is a_x000D_
relatively new area. Previous studies have obtained the WLOs from SAR using a prescribed geometry of the_x000D_
ground surface (bathymetry). The proposed research will use a fully coupled hydrological model in which the_x000D_
bathymetry evolves with the flood to develop a novel approach to retrieve and assimilate WLOs from SAR_x000D_
data. The long-term goal is to improve flood forecasts., program purpose french : Flooding poses a significant risk to humanity. Thousands of people are displaced each year due to flooding, in_x000D_
addition to destruction of infrastructure and agriculture. Satellite observations are increasingly being used for_x000D_
flood monitoring. Satellite-borne synthetic aperture radars (SARs) are a particularly appealing source of_x000D_
observations due to their ability to return meaningful information of the earth's surface during all weather_x000D_
conditions. With increasing numbers of SAR systems in orbit, and growing data volumes, it is of interest to_x000D_
develop methods to use these data in automated processes._x000D_
One such process, known as data assimilation, combines observational data with output from a forecast model_x000D_
to provide an improved initial condition for the forecast model. This is the method used to generate weather_x000D_
forecasts. For flood forecasting, assimilation of water level observations (WLOs) from SAR imagery is a_x000D_
relatively new area. Previous studies have obtained the WLOs from SAR using a prescribed geometry of the_x000D_
ground surface (bathymetry). The proposed research will use a fully coupled hydrological model in which the_x000D_
bathymetry evolves with the flood to develop a novel approach to retrieve and assimilate WLOs from SAR_x000D_
data. The long-term goal is to improve flood forecasts., agreement title english : Toward a novel approach for assimilation of SAR-based flood observations in a fully coupled hydrological model, agreement title french : Toward a novel approach for assimilation of SAR-based flood observations in a fully coupled hydrological model, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada