Over the past decades, the scientific community has made significant efforts to simulate flooding conditions using a variety of complex physically based models. Despite all advances, these models still fall short in accuracy and reliability and are often considered computationally intensive to be fully operational. This could be attributed to insufficient comprehension of the causative mechanisms of flood processes, assumptions in model development and inadequate consideration of uncertainties. We suggest adopting an approach that accounts for the influence of human activities, soil saturation, snow processes, topography, river morphology, and land‐use type to enhance our understanding of flood generating mechanisms. We also recommend a transition to the development of innovative earth system modeling frameworks where the interaction among all components of the earth system are simultaneously modeled. Additionally, more nonselective and rigorous studies should be conducted to provide a detailed comparison of physical models and simplified methods for flood inundation mapping. Linking process‐based models with data‐driven/statistical methods offers a variety of opportunities that are yet to be explored and conveyed to researchers and emergency managers. The main contribution of this paper is to notify scientists and practitioners of the latest developments in flood characterization and modeling, identify challenges in understanding flood processes, associated uncertainties and risks in coupled hydrologic and hydrodynamic modeling for forecasting and inundation mapping, and the potential use of state‐of‐the‐art data assimilation and machine learning to tackle the complexities involved in transitioning such developments to operation.

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.