Abstract
Water resources systems are shaped by climate variability, competing demands, and institutional constraints, necessitating approaches that connect hydrology with human systems. This session examines the evolution of systems analysis in water—from early operations research and reservoir simulation to hydro-economic modeling, data-driven methods, and emerging AI applications—building on a tradition of work advanced by researchers including Jay Lund. We invite contributions that explore how these tools inform decisions across sectors under uncertainty. Topics include scenario analysis, coupled human–natural systems, machine learning for prediction and emulation, and decision-support tools that incorporate AI. The session emphasizes transparency, reproducibility, and engagement with users—elements central to the effective use of models in practice. Models are framed as tools to structure dialogue and evaluate tradeoffs, rather than prescribe outcomes. By connecting methodological advances with real-world applications, the session highlights both the potential and limits of systems approaches to support adaptive, equitable, and robust water management.