Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience.

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Publication Details

Journal
Smart Cities
Published
2024-10-16
DOI
https://doi.org/10.3390/smartcities7050117
Citations
1
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
0.26

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article

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Roman Schotten, Daniel Bachmann
1 citations
Smart Cities
Infrastructure Resilience and Vulnerability Analysis
0.26
article

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Roman Schotten, Daniel Bachmann
article en
1 citations

Abstract

Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience.

Smart CitiesVol. 7(5)
Hochschule Magdeburg-Stendal (DE)
Bundesministerium für Bildung und Forschung
Climate action
Openalex Percentile: Top 44%
Infrastructure Resilience and Vulnerability Analysis
0.26
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