Flooding is a disastrous phenomenon that has adverse impacts on human life, economy and environment across the world. Mitigating these impacts requires a comprehensive characterization of flooding through improvements in computer modeling and prediction, and effective communication with stakeholders and decision makers.

To cover these topics, this session welcomes studies that focus on: (1) innovative techniques (e.g. Machine Learning and other data-driven methods) that improve the understanding of flood characteristics (e.g. magnitude, timing and extent); (2) studies that address the current limitations of design flood estimation, real-time flood inundation and risk modeling (hydrologic, hydrodynamic and loss models); (3) methods that bridge the gaps between science and practice through stakeholder engagement; and (4) the impact of nonstationary stressors (e.g., climate and land cover change) and uncertainties related to flood predictions. Contributions can address various types of flooding, including coastal, fluvial, pluvial and dam/levee break