Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions
Year: 2026
Type: article
Abstract: 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.
Source: Earth s Future
DOI: 10.1029/2026ef008857
Authors: Vinh Ngoc Tran, Xun Huan, Anindya Das Antar, Nikola Banovic, Jeff H. Bednar, Shannon Marie Bergt, Chen Cheng, Francina Dominguez, Simone Fatichi, Richard Gonzalez, Kevin Gray, Brian Jewett, Jongho Kim, Phong V.V. Le, Dan Lu, Snehal Prabhudesai, Deffi Putri, Sudhansu Rath, Khachik Sargsyan, Sarah H. Whitaker, Daniel B. Wright, Donghui Xu, John P. Ziker, Valeriy Y. Ivanov
Institutions: Oak Ridge National Laboratory, University of Michigan, United States Army, University of Illinois Urbana-Champaign, National University of Singapore, University of Ulsan, Sandia National Laboratories California, Sandia National Laboratories, Boise State University, University of Wisconsin–Madison, Pacific Northwest National Laboratory
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,180
Funder: U.S. Department of Defense
OpenAlex ID: W7165679685