Estimates of potential flood inundation areas and depths are critical to informing the preparedness, response, and investment decisions of many government agencies and private sector organizations, especially under a changing climate. The standard modeling approaches, however, are often either computationally intensive or constrained in their accuracy or applicability. A novel, rivulet-based, 2D model of flooding is described in this article that is 10,000 to 10 million times less computationally complex than the full solution of the shallow water equations, yet achieves inundation area hit rates of between 0.8 and 0.9, relative absolute mean errors of 10%–20% across a wide range of flow depths, and comparable accuracy at forecasting empirical high-water marks. This combination of accuracy and efficiency will significantly enhance real-time depth estimates during flood events, support detailed sensitivity analyses, and allow for the generation of large ensembles to enable complex uncertainty analyses.