Reimagining How Flood Warnings Can Inform Decision-Making and Community Actions

Society faces increasingly severe flood hazards, intensifying demand for flood early warning systems (FEWS) that deliver accurate and actionable information. However, most existing FEWS remain prediction-centric, treating decision-making as a downstream consumer of hazard forecasts while offering limited support for uncertainty interpretation, risk communication, and real-world response. This Perspective presents a vision and blueprint for a novel inland FEWS-decision-making (FEWS-DM) framework that repositions decision-making as an equal partner in the forecasting process—not a passive recipient of its outputs. The framework is built on three tightly coupled, co-evolving thrusts: Physical Science (T1), which advances flood prediction with quantified uncertainty informed by decision relevance; Human Science (T2), which incorporates psychology, behavior, and cultural and institutional context; and Decision Science (T3), which unifies physical predictions and human factors through principled, utility-based decision support with end-to-end uncertainty management. Rather than treating T1 as a solved problem, FEWS-DM recognizes that forecast development itself must be shaped by decision needs through continuous bidirectional feedback. We identify key scientific, behavioral, and operational challenges limiting such integration and discuss the enabling role of AI, while emphasizing human-centered design and community feedback as essential for building trust and improving flood risk management.