Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Flooding poses a significant risk to humanity. Thousands of people are displaced each year due to flooding, inx000D
addition to destruction of infrastructure and agriculture. Satellite observations are increasingly being used forx000D
flood monitoring. Satellite-borne synthetic aperture radars (SARs) are a particularly appealing source ofx000D
observations due to their ability to return meaningful information of the earth's surface during all weatherx000D
conditions. With increasing numbers of SAR systems in orbit, and growing data volumes, it is of interest tox000D
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 modelx000D
to provide an improved initial condition for the forecast model. This is the method used to generate weatherx000D
forecasts. For flood forecasting, assimilation of water level observations (WLOs) from SAR imagery is ax000D
relatively new area. Previous studies have obtained the WLOs from SAR using a prescribed geometry of thex000D
ground surface (bathymetry). The proposed research will use a fully coupled hydrological model in which thex000D
bathymetry evolves with the flood to develop a novel approach to retrieve and assimilate WLOs from SARx000D
data. The long-term goal is to improve flood forecasts.