Description:

The increasing frequency and magnitude of hydrometeorological extremes are exacerbating the risks of global flood events on an unprecedented scale. In particular, changing human-climate dynamics present unique challenges for scientific communities to accurately monitor, predict, and forecast large-scale flood events. Accurate and timely prediction of flood hazards is pivotal for decision-makers in developing flood mitigation and climate resilience strategies. Recent advances in atmospheric, hydrologic, hydrodynamic, and machine learning models, and their coupling, provide an excellent opportunity to better understand and characterize elevated flood risks. The evolution of remote sensing and low-cost sensor technologies, in conjunction with human feedback, further complements the advances in modeling. Centered on the challenges related to the modeling, monitoring, and forecasting of large-scale flood events, this session invites submissions including but not limited to, integrated atmospheric-hydrologic modeling, hydrodynamic simulation, and inundation mapping, remote-sensing and sensor technologies, data assimilation, decision support tools and web applications, and uncertainty quantification. The session will provide an opportunity for participants to exchange their experiences and jointly discuss future research needs.

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