New survey techniques provide a large amount of high-resolution data, which can be extremely precious for flood inundation modeling. Such data availability raises the issue as to how to exploit their information content to effectively improve flood risk mapping and predictions. In this paper, we will discuss a number of important issues which should be taken into account in works related to flood modeling. These include the large number of uncertainty sources in model structure and available data; the difficult evaluation of model results, due to the scarcity of observed data; computational efficiency; false confidence that can be given by high-resolution outputs, as accuracy is not necessarily increased by higher precision. Finally, we briefly review and discuss a number of existing approaches, such as subgrid parameterization and roughness upscaling methods, which can be used to incorporate high detailed data into flood inundation models, balancing efficiency and reliability.