Data for Critical Infrastructure Network Modelling of Natural Hazard Impacts: Needs and Influence on Model Characteristics

Abstract

Natural hazards impact the interdependent infrastructure networks that keep modern society functional. While a variety of critical infrastructure network (CIN) modelling approaches are available to represent CI networks on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to make assumptions for specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties that these assumptions and extrapolations introduce and their influence on the quality of the modelling outcomes are often poorly understood and are difficult to capture. Additionally, the ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address this challenge, a generic modelling workflow is devised featuring six modelling stages commonly encountered in CIN models. The data requirements of each stage are systematically defined, and literature on potential sources is reviewed to enhance data collection and raise awareness of the issue. The workflow represents model generation and validation as well as natural hazard impact assessment, recovery, and mitigation. The application of the proposed workflow and the assessment of data availability challenges are showcased in three case studies, taking into account their different modelling purposes. From this, a generalised reflection on the relation between data availability, model purposes, model performance, and aptness of the approach is derived. Finally, a discussion on overcoming the challenges of data scarcity, including the use of participatory methods, anonymised data-sharing platforms for CI operators, and event-based impact datasets, is presented.