Preprints
https://doi.org/10.5194/egusphere-2025-3892
https://doi.org/10.5194/egusphere-2025-3892
27 Oct 2025
 | 27 Oct 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Signals Without Action: A Value Chain Analysis of Luxembourg’s 2021 Flood Disaster

Jeff Da Costa, Elizabeth Ebert, David Hoffmann, Hannah Louise Cloke, and Jessica Neumann

Abstract. 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. 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Jeff Da Costa, Elizabeth Ebert, David Hoffmann, Hannah Louise Cloke, and Jessica Neumann

Status: open (until 08 Dec 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-3892', Michael Szoenyi, 29 Oct 2025 reply
    • AC1: 'Reply on RC1', Jeff Da Costa, 03 Nov 2025 reply
  • CC1: 'Comment on egusphere-2025-3892', Alexander Fekete, 09 Nov 2025 reply
    • AC2: 'Reply on CC1', Jeff Da Costa, 12 Nov 2025 reply
Jeff Da Costa, Elizabeth Ebert, David Hoffmann, Hannah Louise Cloke, and Jessica Neumann
Jeff Da Costa, Elizabeth Ebert, David Hoffmann, Hannah Louise Cloke, and Jessica Neumann

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Short summary
This paper examines why multiple early indicators of the July 2021 floods in Luxembourg did not lead to better anticipatory action. Using a value chain approach and the Waterdrop Model, it identifies how thresholds, procedures, and institutional responsibilities limited the use of available forecast information under uncertainty. The findings show how aligning information with decision processes can improve timely disaster response.
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