Uncertainty Representation and Propagation in Flood Risk Modeling Under Climate Change: A Systematic Review

ABSTRACT This systematic review examines how uncertainty is sampled and propagated through interconnected model chains in climate‐induced flood risk assessments. We focus on top‐down modeling frameworks, where greenhouse gas scenarios drive global and regional climate models, followed by downscaling, bias adjustment, and impact modeling. This sequential approach leads to cumulative uncertainty, also known as uncertainty cascades, that complicate decision‐making in disaster risk management and climate change adaptation. Our review of 143 studies reveals significant variation in model selection and propagation approaches, with no consensus on best practices. While climate model uncertainty is widely sampled, uncertainty in impact, damage, and adaptation models is often less explored. We find that selective sampling and propagation choices can unintentionally increase deep uncertainty, particularly when low‐probability, high‐impact events are excluded. Disciplinary differences in uncertainty treatment further hinder transparency and comparability of model results. We argue that without improved transparency of modeling decisions, ensemble‐based studies risk amplifying uncertainty rather than reducing it. To support robust adaptation planning and improve traceability and confidence in climate impact assessments, we propose a checklist to guide the modeling process. This article is categorized under: Assessing Impacts of Climate Change > Representing Uncertainty Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change Climate Models and Modeling > Knowledge Generation with Models