Every year floods are responsible of a significant number of human losses, many of which could be avoided with a broader implementation of flood forecasting systems. Nevertheless, there are still some technological and economic limitations that impede the creation of these systems in many parts of the world. At the core of many flood forecasting systems is a hydrologic model that transforms the weather forecast into a flow forecast. Using real-time modeling for potential floods poses a series of problems: if the model is complex, the computational power required can be significant, and consequently expensive, and if the model is simple enough to run on regular computers in the time allotted, it is likely that the results will not be accurate enough to be useful. I propose the development of a standardized method for using pre-computed scenarios as an alternative to real-time flood modeling. I explain how pre-computing has been used on other realms in the past, and how it is beginning to be implemented in different branches of hydrology, the prediction coastal flooding due to storms or tsunamis being one of the most developed. My research has focused on answering the questions that arise during the design stage of a flood forecasting system not only for rain or snow driven floods, but also by anthropogenic-produced floods. I analyze the number of parameters and their granularity to be used to create the scenarios, the accuracy of the results, different strategies to implement the systems, etc. Finally, I present some test-cases of the application of the method, and assess their results.