Climate Resilience Assessment to Inform Asset Upgrade Strategy Development for Infrastructure Systems

Current infrastructure systems may not have the ability to withstand the future climate, where unprecedented extreme weather events with larger magnitudes and higher frequencies, may happen in new geographical locations, at new times of the year. Considering the centrality of infrastructure systems in society, it is necessary to understand how future climate and the consequential patterns of weather extremes impact infrastructure systems and develop tools to guide investment decisions to minimise disruption in the future. This PhD project addressed the following two research questions. 1) How can the magnitude of future infrastructure service disruption due to climate change be estimated accounting for corresponding uncertainties? 2) How should resources be allocated in the most efficient manner to upgrade infrastructure assets so that the forthcoming service disruptions can be minimised? To answer the first research question, a systemic approach to assess the resilience of infrastructure systems to climate change is formulated. The proposed approach advances the current resilience assessment approaches where failure scenarios are primarily simulated as random or targeted removal of system components, or simulated with manipulations of present-day weather statistics. A case study is then carried out to quantify the resilience of Great Britain’s railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario. A 95-year horizon on the resilience of the railway system is drawn. The results reveal the non-linear responses of the railway system to the increasing temperature and show that models considering random asset failures overestimate the system’s resilience. Two complementary approaches were identified as relevant to the second research question - the node-edge ranking approach in the network science community and the mathematical optimisation approach for resource allocation problems in the field of operational research. From the perspective of node-edge ranking, metrics that capture the topological structure of the network, supply-demand features, and future weather failure patterns are developed to guide resource allocation. The resource allocation problem is formulated as an optimisation problem that takes the future weather pattern as model inputs and aims to maximise the climate resilience of the system. The problem is then analytically solved in a simple model. The analytic solution from the simple model converges to the same expression as one of the developed metrics. The two streams of work implementing the two approaches meet each other. Based on the observed consistency in solutions obtained with the two approaches, a ranking-based resource allocation strategy is developed and applied to a core freight network of Great Britain. The results suggest that the newly proposed approach can effectively tackle the resource allocation problem for large-scale infrastructure systems with low computational costs. The proposed framework and findings from the case studies advance the existing understanding of the climate resilience of infrastructure systems and strategies on resource utilisation for climate change adaptation. The implementation of such strategies is imperative in avoiding the worst consequences of climate change.