Uncertainty Quantification and Attribution for Resilient Infrastructure Systems

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.