Ensemble flood inundation forecasting using emulators of hydraulic models
Floods are among the most damaging natural hazards in the world. Flood inundation forecasting can save lives and reduce impacts. Ensemble forecasting is becoming the state-of-art methods in meteorological and hydrological forecasting to represent forecasting uncertainty. However, it is enormously challenging to produce ensemble forecasts of flood inundation. This is because flood inundation modelling is generally based on using detailed numerical hydraulic models, which are computationally highly demanding to run in real-time. For ensemble forecasting, hundreds of such model runs are required.
In this study, we aim to develop emulators of numerical hydraulic models using statistical or artificial neural network techniques. The emulators are to capture accurately the flood inundation dynamics, but are computationally fast for real-time ensemble forecasting. The emulators will be linked to ensemble river flow forecasting, using hydrological models driven by ensemble meteorological forecasts.
Leader: Q J Wang
Staff: Dr Wenyan Wu A/Prof Rory Nathan
Collaborators: Jeff Perkins (Bureau of Meteorology) Dr Matthew Bethune (Murray Darling Basin Authority)
Sponsors: Bureau of Meteorology Murray Darling Basin Authority
Optimisation of resources and infrastructure
environmental hydrology and water resources; numerical modelling