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Infrastructure Resilience Conference 2018

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Engineering infrastructure systems-level resilience to resource disruption: Case studies from the Shetland Islands and Hurricane Sandy

Extreme weather events can disrupt the flow of goods and services in cities and regions that are crucial to modern living, and enabled by a reliable infrastructure system. To explore the impact of disruption, and test strategies to accelerate recovery, a quantitative spatial resource model has been developed. The approach combines network analysis and input-output modelling to describe the flow of resources (such as materials, water, food etc.) and the infrastructure networks that convey them. Outputs from inundation models describing flood extent and depth can be integrated with the resource model to identify infrastructure connections that are fully or partially cut off, as well as sites of resource production or consumption that cannot operate at full (or even partial) capacity. A preliminary case study, based upon historic data from the Shetland Islands has been set up to compare the effectiveness of alternative resource management strategies such as (i) just in time production, (ii) use of reserve stocks, (iii) batch deliveries of resources, (iv) de-clustering of industries (i.e. not allowing too much of the same industry to co-locate in one space), (v) rationing of resources during events, (vi) construction of new infrastructure links, (vii) flood protection. Data availability in the Shetlands and its relative isolation make it an ideal case study site for model testing, however, observations of impacts and recovery were limited to very specific events. To achieve this, we analysed post-event reports of flooding caused by Hurricane Sandy on food and fuel distribution in New York City, and the model is shown to perform well. Further analysis of 'what-if' scenarios shows how spatial consolidation of resources (e.g. into a small number of large distribution centres – in this case food) significantly increases the impact of extreme events, and how disruption to other sectors can aggravate this further still.

Shaun Brown
Ordnance Survey
United Kingdom

Richard Dawson
Newcastle University
United Kingdom

 

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