AGU24: H110 - Recent Advances in Large-Scale, High-Resolution Hydrologic & Flood Modeling: Assessing Hydroclimatic Extremes

[Ganesh Ghimire]

Dear colleagues,

It's that time of the year again! We invite you to submit abstracts to our AGU 2024 Fall Meeting Session: "H110 - Recent Advances in Large-Scale, High-Resolution Hydrologic & Flood Modeling: Assessing Hydroclimatic Extremes ”. We have been convening this session successfully for the last five years. We have two excellent Invited Speakers lined up for the session: Dr. Daniel Wright from the University of Wisconsin-Madison, and Dr. Vidya Samadi from Clemson University.

 

Abstract submission link: 

https://agu.confex.com/agu/agu24/prelim.cgi/Session/226959 

 

Conveners

Sudershan Gangrade (Oak Ridge National Laboratory)

Ganesh Ghimire (Oak Ridge National Laboratory)

Shih-Chieh Kao (Oak Ridge National Laboratory)

Mario Morales-Hernández (University of Zaragoza)

Session description:

As extreme flood events become more intense and frequent in a changing environment, the need for accurate and timely flood risk quantification for emergency preparedness, flood mitigation, and climate resilience efforts becomes increasingly evident. Recent advances in hydrologic, hydraulic, and machine learning models and their coupling have improved our understanding and characterization of elevated flood risks. This session invites presentations that demonstrate the use of large-scale, high-resolution modeling tools to analyze hydroclimate extremes and their impacts. We welcome studies focusing on, but not limited to:

• Advances in coupled atmospheric, hydrologic, and hydraulic models for enhanced flood risk assessments
• Prediction, projection, and characterization of extreme hydrologic events including probable maximum precipitation and flood
• Real-time streamflow or flood forecasting applications
• Computational advances in hydrologic or inundation modeling
• Machine learning-based applications, including hybrid data-model fusion in enhancing predictive capabilities
• Uncertainty quantification using multi-forcing, model, and parameter ensemble
• Frameworks for integrated flood risk management