Towards empirical knowledge as additional information in data-based flood forecasting techniques

Floods are among the most frequent and costly natural disasters in terms of human hardship and economic loss. In recent years Europe suffered over 100 major damaging floods. Since 1998, floods have caused damages of some 700 facilities, the displacement of about half a million people and at least 25 billion Euro in insured economic losses. In the United States, about 90 percent of the damage caused by natural disasters is caused by floods and associated mud and debris flows. Reliable flood forecasting and adequate flood management could help to improve public safety and reduce economic losses. Performance of flood forecasting and flood risk management requires on the one hand technical prerequisites and on the other hand expert knowledge and experience. Thus, a functioning network of real-time climate and streamflow gauging stations, data transmitters, hydrodynamic models, inundation models, to name only a few, are representing the technical part. Water managers, scientists, decision makers etc. are representing the part of expert knowledge and experience sharing different tasks, such as, development of emergency plans, planning of flood protection infrastructure, hydrologic modelling, model development etc.