Uncertainty Representation and Propagation in Flood Risk Modeling Under Climate Change

A Systematic Review

Bibliographic Data

ID12929263
AuthorsVilly Mik‐Meyer (Technical University of Denmark, corresponding author), Emma E H Doyle (0000-0002-2878-0972, Massey University), Morten Andreas Dahl Larsen (0000-0002-7478-5416, Danish Meteorological Institute), Rick Kool (0009-0005-2934-2981, Technical University of Denmark), M Drews (Technical University of Denmark)
Year2026
Volume17
Issue2
Publication date2026-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueWiley Interdisciplinary Reviews Climate Change (JOURNAL)
Journal identifiersISSN: 1757-7780 • E-ISSN: 1757-7799
PublisherWiley (PUBLISHER • GB)
DOI10.1002/wcc.70045
OpenAlexW7136778128
LanguageEN
References cited157

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

Climate change · Climate model · Comparability · Flood myth · Greenhouse gas · Probabilistic logic · Traceability · Transparency (behavior · Uncertainty analysis · Uncertainty quantification · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Sustainability and Climate Change Governance

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