Data Scarcity in Critical Infrastructure Network Modelling: Impacts on Model Performances and Mitigation Strategies

Natural hazards impact the closely webbed infrastructure networks that keep a modern society functional in its current form. A variety of critical infrastructure network (CIN) modelling methods is available to represent functions and purposes of CI networks on different levels of boundaries. A recurring challenge for all modelling approaches is the availability and accessibility of input and validation data. Those gaps constrain modellers to make assumptions for specific technical parameters. In other cases insular expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. Those assumptions lead to uncertainty and can potentially devalue a per se valuable CIN modelling method.

In the presented work, a schematized workflow for a CIN model generation is defined and the potentially needed input datasets are highlighted and categorised. This categorization features obvious CI data like infrastructure component locations, quantitative measures of the services they are supporting and the relation between natural hazard impacts, functionality thresholds and the degree of disruption to a CI structure. It also tackles less straight-forward relations such as  recovery times after disruptions, interdependencies among CIN and the redundancy of those interdependencies. Invariably, the availability of those datasets is tied to a CIN model’s performance, aptness for answering specific problems, and quality. A range of performance indicators are hence compiled including granularity, fidelity, accuracy, sensitivity and the ability to resemble cascading effects. The relation of these performance indicators and data availability are outlined. Finally, it is suggested to overcome the challenges of data scarcity with participatory methods, anonymized data sharing platforms for CI operators and event based datasets.

With this contribution, we aim to provide systematised orientation to fellow critical infrastructure network modellers on the diverse data needs throughout the modelling chain, from setting up a model to results validation, explore implications of data scarcity, and suggest mitigation strategies.