Floods are one of the most devastating natural disasters, hitting various regions in the world each year. In the last decades the potential damage caused by floods has increased. The need for tools, like flood maps and flood models, for real time monitoring and forecasting of floods has also increased. When an upcoming flood is expected, and these preparatory tools are not available, giving an insight into how and when the flood will proceed is difficult. A rapid flood model can assist in decision making and emergency response during threatening flood situations. The concept of a rapid flood is a model which makes prediction of a flood event in 1) a short period with the use of 2) publicly available data 3) for any flood prone area in the world. This thesis evaluates the usability of a rapid flood model for river flooding and crisis management. This study consists of three parts. The first two, a 1D and 2D schematization, determine the most influential parameters and conditions of a flood for different types of river areas. Publicly available data is used for these most influential parameters and conditions to simulate the third study case, the 2011 Thailand flood event. The rapid model can be setup for any flood prone area in the world in 1 or 2 days by an experienced modeler. The publicly available data needs to be supplemented by chosen methods and equations to set up the model. This study shows that the most influential parameters and conditions for the rapid flood model are site specific. For a relative flat area the type of land use is an influential condition, while in a mountainous area the river geometrics are the main influential parameters. The analysis gives an insight into which data the available resources, both time and money, should be spend on, if they are not publicly available. Over all, this evaluation shows that a rapid flood model can provide a reasonable estimate of a flood event in a short amount of time by using only publicly available data. The proper use of the rapid flood model depends highly on the type of river area and using the correct data for the most influential parameters and conditions for these river and area types.