This section presents an extensive collection of research on hydrologic data assimilation theory and practice, a critical process that integrates observational data with hydrologic model outputs to improve predictions to manage water resources more effectively. The featured studies cover a wide range of topics from uncertainty quantification and parameter estimation to the application of advanced data assimilation techniques in operational hydrology. One core area of focus is the use of sequential data assimilation methods such as the particle filter and ensemble Kalman filter to refine the estimation of model states and parameters. These techniques address the dynamic nature of hydrological processes and their inherent uncertainties, allowing for more accurate forecasting of key hydrologic variables such as snow water equivalent, soil moisture, and streamflow. These variables play crucial roles in flood control, agricultural planning, and effective water resource management, making their accurate prediction essential for operational decision-making. Moreover, the integration of Bayesian Model Averaging (BMA) with data assimilation techniques is explored as a method to reduce model uncertainty. This approach adjusts model weights dynamically, offering a more refined handling of uncertainties compared to traditional methods that rely on fixed probability distributions. The research also delves into the challenges and opportunities of implementing data assimilation in real-world operational settings. It discusses the barriers to integrating the latest scientific advancements into practice, such as the complexity of models and the difficulty in quantifying certain types of uncertainties. Additionally, it outlines potential solutions, including the development of community-based tools and frameworks that could facilitate the adoption of these advanced methods by practitioners. Overall, the papers in this section collectively emphasize the transformative potential of hydrologic data assimilation in enhancing the predictability and management of water resources. They contribute significantly to the field by providing sophisticated tools and methodologies that can lead to more informed and effective hydrological practices and disaster management strategies.