Signals without action: a value chain analysis of Luxembourg's 2021 flood disaster

Da Costa, J. ORCID: https://orcid.org/0000-0002-1575-1320, Ebert, E. ORCID: https://orcid.org/0000-0001-5284-1651, Hoffmann, D. ORCID: https://orcid.org/0000-0001-5368-1223, Cloke, H. L. ORCID: https://orcid.org/0000-0002-1472-868X and Neumann, J. ORCID: https://orcid.org/0000-0003-3244-2578 (2026) Signals without action: a value chain analysis of Luxembourg's 2021 flood disaster. Natural Hazards and Earth System Sciences, 26 (1). pp. 343-366. ISSN 1684-9981 doi: 10.5194/nhess-26-343-2026

Abstract/Summary

Effective Early Warning Systems are essential for reducing disaster risk, particularly as climate change increases the frequency of extreme events. The July 2021 floods were Luxembourg's most financially costly disaster to date. Although strong early signals were available and forecast products were accessible, these were not consistently translated into timely warnings or coordinated protective measures. While response actions were taken during the event, they occurred too late or at insufficient scale to prevent major impacts. We use a value chain approach to examine how forecast information, institutional responsibilities, and communication processes interacted during the event. Using a structured database questionnaire alongside hydrometeorological data, official documentation, and public communications, the analysis identifies points where early signals did not lead to anticipatory action. The findings show that warning performance was shaped less by technical limitations than by procedural thresholds, institutional fragmentation and timing mismatches across the chain. A new conceptual model, the Waterdrop Model, is introduced to show how forecast signals can be filtered or delayed within systems not designed to process uncertainty collectively. The results demonstrate that forecasting capacity alone is insufficient. Effective early warning depends on integrated procedures, shared interpretation, and governance arrangements that support timely response under uncertainty.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/128076
Identification Number/DOI	10.5194/nhess-26-343-2026
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	European Geosciences Union
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