Primary Convener:
Lijing Wang, University of Connecticut

Convener:
Peishi Jiang, University of Alabama
Juliane Mai, University of Waterloo
Niklas Linde, University of Lausanne
Harrie-Jan Hendricks Franssen, Forschungszentrum Jülich GmbH
Xin Li, Institute of Tibetan Plateau Research, Chinese Academy of Sciences
Peyman Abbaszadeh, Portland State University
Hamid Moradkhani, The University of Alabama

Early Career Convener:
Lijing Wang, University of Connecticut

Chair:
Lijing Wang, University of Connecticut
Peishi Jiang, University of Alabama
Juliane Mai, University of Waterloo
Niklas Linde, University of Lausanne

The increasing availability of process-based models, data-driven methods, and hybrid approaches incorporating AI/ML is transforming hydrology and hydrogeology. These simulation and prediction approaches can integrate diverse data sources, including in-situ measurements, high-resolution satellite observations, and geophysical surveys, to improve model uncertainty quantification and predictive skill. This session invites contributions on the theory and application of physics-informed and data-driven machine learning, generative AI, large language models, and statistical methods (e.g., inverse modeling, Markov Chain Monte Carlo (MCMC), Bayesian inference) for hydrologic and hydrometeorological uncertainty quantification. Specifically, we welcome studies that integrate multiple datasets for model calibration or validation, or that couple machine learning and data assimilation algorithms to address groundwater challenges, surface water-groundwater interactions, and hydroclimatic extremes.

Index Terms
1817 Extreme events
1846 Model calibration
1847 Modeling
1869 Stochastic hydrology
1873 Uncertainty assessment

Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Machine Learning and AI

Neighborhoods:
3. Earth Covering