|Title||Improving flood forecast skill using remote sensing data|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Li, Y, Grimaldi, S, Wright, A, Walker, J, Pauwels, V|
|Publisher||Bushfire and Natural Hazards CRC|
Floods are among the most important natural disasters in Australia. The average annual cost of floods in the last 40 years has been estimated to amount to $377 million, with the 2010–2011 Brisbane and south-east Queensland floods alone leading to $2.38 billion in economic damage and 35 confirmed deaths. Flood forecasting systems are the most important tools to limit this damage but are prone to a considerable degree of uncertainty. During the last decades, significant research focusing on the monitoring of the global water cycle through satellite remote sensing has been performed. The strength of remote sensing is the opportunity to provide information at large spatial scales including areas that are difficult or impossible to monitor using on-ground techniques. For these reasons it is believed that the use of remote sensing data can improve the quality of operational flood forecasts. Operational flood forecasting systems typically consist of a hydrologic model, which estimates the amount of water entering a river system, and a hydraulic model, which models the flow of water inside the river system. Remotely sensed soil moisture data is being used to improve the hydrologic model results (i.e. the modeled hydrograph into the river network), while remotely sensed water levels and/or flood extent data are being used to improve the hydraulic model results (i.e. the modeled water velocities, depths, and floodplain extents). The project focusses on two test sites, the Clarence River in New South Wales and the Condamine-Balonne Rriver in Queensland. Figure 1 shows an overview of these test sites.