Synergistic Integration of Flood Inundation Modeling Methods: A Review of Computational, Data‐Driven, Observational and Experimental, and Conceptual Models

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Abstract
Flood inundation models are foundational to a variety of engineering design, risk mitigation, and real‐time decision making and response. The models have evolved, driven primarily by advances in data and computational resources. Despite these advances, modeling methods have increasingly diverged into separate development paths. Rather than experiencing parallel growth, where emerging approaches complement and enhance well‐established approaches, newer methods such as geomorphic and machine learning algorithms have, in some cases, supplanted or stalled the continued advancement of robust, time‐tested methodologies. This trend toward replacement rather than synergistic integration may limit opportunities to leverage the respective strengths of both established and innovative approaches. We define “siloing” as the isolation that occurs when development efforts evolve vertically and concentrate within narrow methodological boundaries, potentially overlooking opportunities for integration across different modeling paradigms. This phenomenon can arise when methods are selected based on convenience, lack of familiarity with parallel tracks, or popular trends. The negative consequences can lead to application of certain methods well beyond their intended scope and hinder progress by underutilization of complementary strengths across different approaches to overcome challenges. This paper first discusses four categories of state‐of‐the‐art flood inundation modeling methods—computational, data‐driven, observational and experimental, and conceptual—alongside their major strengths and limitations, followed by instances of methodological siloing challenges. We then propose a vision for future research emphasizing synergistic integration across all modeling trajectories rather than isolated development. We hope to spur dialog among modelers with the short‐term goal of convergent research and long‐term of integrated practice.
Funding Information
  • National Science Foundation (2203180)
  • National Science Foundation (ICER‐2103754)
  • National Academies of Sciences, Engineering, and Medicine (SCON‐10001211)

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