Effective flood risk management requires a comprehensive characterization of flood hazards through improvements in computer modeling and prediction, an understanding of flood vulnerabilities and risks informed by stakeholders, and advances in decision-making systems that bear on infrastructure investments, early warning systems, community preparedness, and planning decisions. This session welcomes studies focusing on  (1) innovative techniques (e.g. Machine Learning) to enhance understanding of flood characteristics (e.g. magnitude, timing, and extent); (2) studies that address limitations in design flood estimation, and real-time flood inundation modeling (hydrologic and hydrodynamic models); (3) approaches to improve characterization of flood risks, considering physical and social vulnerabilities, (4) approaches and/or case studies where multi-dimensional flood metrics (e.g., economic, social, environmental) are developed and/or integrated into the planning and engineering design. (5) the impact of nonstationary stressors and uncertainties related to flood predictions. Contributions can address various types of flooding, including coastal, fluvial, pluvial, and dam/levee break.

• NH - Natural Hazards