Adaptive management of large lakes, coastal systems, and watersheds requires hydroclimate information that supports decision-making across a range of spatiotemporal scales. This includes evaluating management alternatives, anticipating variability and extremes, and understanding the capabilities and limitations of management actions. These systems present challenges, including coupling within the land-water-atmosphere system, complex feedbacks, sparse observational coverage, and the need to integrate data and models across jurisdictions.

Advances in coupled Earth system modeling, in situ and remote sensing observations, and machine learning are improving the ability to characterize and predict hydroclimate variability. This session invites contributions that advance hydroclimate data, models, and analysis frameworks in support of adaptive management, including coupled atmosphere-lake-ice-wave systems, integration of observational datasets, hybrid physics-machine learning and data assimilation approaches, and methods for uncertainty quantification in the context of decision-making.

We encourage contributions that connect methodological advances to decision-making contexts, including hazard mitigation, resilience planning, and management.

• A - Atmospheric Sciences
• OS - Ocean Sciences