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 models can integrate diverse data sources, including in-situ measurements, high-resolution satellite observations, and geophysical surveys. Advances in computational resources have also improved our ability to quantify uncertainty in hydrologic models, which is essential to support decision-making under climate disturbances. Uncertainty quantification (UQ) and sensitivity analysis (SA) help evaluate model structure, assess predictive confidence, and identify dominant controls across scales. This session invites contributions on the theory, development, and application of UQ and SA in hydrologic modeling. We encourage studies that integrate multiple datasets to calibrate and/or validate hydrologic models, or that employ AI/ML approaches to enhance model calibration or uncertainty quantification. Submissions addressing groundwater are especially welcome, given persistent challenges with limited subsurface observations and the potential for integrated model-data approaches to yield new insights.