Chris Leahy

This project investigated the use of remote sensing data to improve modelled flood forecast skill and value. It developed optimal ways to constrain and update hydrologic flood models using remotely sensed soil moisture data. The project also proposed an algorithm for the monitoring of floods under vegetation, and investigated optimal ways to use remote sensing-derived inundation extent and level to implement and calibrate the hydraulic model. The results of this project enable improved predictions of flow depth, extent and velocity in the floodplain.