The increasing magnitude and frequency of hydrometeorological extremes are exacerbating global flood risks on an unprecedented scale. Changing human-climate dynamics further presents unique challenges for scientific communities to accurately monitor, analyze, forecast, predict, and manage large-scale flood events. Accurate and timely prediction of flood hazards is pivotal for decision-makers in developing flood mitigation, climate resilience, and disaster management strategies. Recent advances in science and technology have led to the development of improved monitoring systems and predictive models that can assimilate observational data into modeling frameworks, including hybrid data-model fusion to provide improved understanding and characterization of flood risks. The evolution of remote sensing technologies and monitoring networks, in conjunction with human feedback, are advancing the predictive capabilities, but challenges remain for large-scale floods. The session invites contributions including but not limited to (1) the development of flood-monitoring, prediction, and forecasting tools (integrated atmospheric-hydrologic-hydrodynamic modeling, data assimilation, uncertainty quantification, and machine learning) and (2) lessons learned from managing big floods from scientific and societal perspectives. We welcome papers that particularly focus on large-scale flood modeling as well as continental and global-scale hydrologic forecasting systems.

Submitters:  Ganesh R Ghimire, Environmental Sciences Division/Water Resources Science and Engineering, ORNL, Oak Ridge, TN; Sudershan Gangrade, Environmental Sciences Division, ORNL, Oak Ridge, TN; Shih-Chieh Kao, Environmental Sciences Division/Water Resources Science and Engineering, ORNL, Oak Ridge, TN and Mario Morales-Hernández, I3A-University of Zaragoza, Zaragoza, Zaragoza, Spain

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