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Uncertainty Quantification and Attribution for Resilient Infrastructure Systems

Lookup NU author(s): Dr Anna MurgatroydORCiD, Dr Hannah BloomfieldORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Infrastructure resilience to environmental change is critical for maintaining the delivery of essential services to our society, including power generation, water supply, transport and telecommunication. Computational modelling has become integral to infrastructure planning and management, enabling the exploration of system interdependencies and the testing of adaptation strategies against unprecedented conditions. However, infrastructure model outputs are conditional on a range of uncertain assumptions about the system drivers and properties, both in the present day and in the future under climate change. In this article, we show how global sensitivity analysis can be used to consistently quantify and attribute the uncertainty in infrastructure model outputs as a consequence of input uncertainties, using two examples from the energy and water sector in the United Kingdom (UK). We find that dominant uncertainties vary case by case, as does the relative importance of climate uncertainty. We argue that structured uncertainty and sensitivity analysis should be incorporated more consistently across infrastructure modelling sectors to support resilient infrastructure design.


Publication metadata

Author(s): Salwey S, Murgatroyd A, Bloomfield HC, Coxon G, Pianosi F

Publication type: Article

Publication status: Published

Journal: Climate Resilience and Sustainability

Year: 2026

Volume: 5

Issue: 2

Print publication date: 01/12/2026

Online publication date: 20/08/2026

Acceptance date: 06/08/2026

Date deposited: 25/08/2026

ISSN (electronic): 2692-4587

Publisher: Wiley

URL: https://doi.org/10.1002/cli2.70059

DOI: 10.1002/cli2.70059

Data Access Statement: The code to implement a range of global sensitivity analysis methods, including PAWN, is available at https://github.com/SAFEtoolbox/SAFE- python . The code to apply GSA to the wind power model and repli- cate results presented in this article is available at https://github.com/ SAFEtoolbox/SAFE- on- DAFNI . The code to apply GSA to the Northum- brian water system cannot be made available because of commercial limitations by English water companies.


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Funding

Funder referenceFunder name
European Research Council. Grant Number: 101075354
Newcastle University
UK Research and Innovation. Grant Numbers: ST/Y003713/1, MR/V022857/1

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