Methodological Framework for Analysing Cascading Effects from Flood Events: The Case of Sukhumvit Area, Bangkok, Thailand

This is the final version of the article. Available from MDPI via the DOI in this record.Impacts from floods in urban areas can be diverse and wide ranging. These can include the loss of human life, infrastructure and property damages, as well as other kinds of nuisance and inconvenience to urban life. Hence, the ability to identify and quantify wider ranging effects from floods is of the utmost importance to urban flood managers and infrastructure operators. The present work provides a contribution in this direction and describes a methodological framework for analysing cascading effects from floods that has been applied for the Sukhumvit area in Bangkok (Thailand). It demonstrates that the effects from floods can be much broader in their reach and magnitude than the sole impacts incurred from direct and immediate losses. In Sukhumvit, these include loss of critical services, assets and goods, traffic congestion and delays in transportation, loss of business and income, disturbances and discomfort to the residents, and all these can be traced with the careful analysis of cascading effects. The present work explored the use of different visualization options to present the findings. These include a casual loop diagram, a HAZUR resilience map, a tree diagram and GIS maps.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 603663 for the research project PEARL (Preparing for Extreme and Rare events in coastaL regions). The authors are grateful to Opticits for providing the HAZUR software licence, within the collaboration of the EU H2020 research project RESCCUE (RESilience to cope with Climate Change in Urban arEas—a multisectorial approach focusing on water) Grant Agreement 700174

Infrastructure interdependencies and business-level impacts: a new approach to climate risk assessment

This report examines some of the physical impacts of climate change on the infrastructure sector and the resulting cascade of consequences for the broader economy.The report summarises findings from a workshop conducted in December 2012 by The Climate Institute, Manidis Roberts (a part of the RPS Group) and KPMG, which piloted a process for analysing the climate-related risks associated with interdependent infrastructure systems of a major city. The workshop was informed by a range of sources: a desktop review of academic, business and government documents; analysis from experts in the fields of risk, resilience, sustainability and infrastructure planning; analysis of historical events; interdependency mapping and quantitative modelling.This workshop report follows The Climate Institute’s recently published report Coming Ready or Not: Managing climate risks to Australian infrastructure, which synthesised research on the physical impacts and flow-on consequences of climate change and  analysed preparations for climate change impacts in Australia amongst owners and operators of major infrastructure assets

Behaviour Analysis of Interdependent Critical Infrastructure Components upon Failure

Urban life increasingly depends on intact critical infrastructures (CIs). For this reason, protecting critical infrastructure systems from natural disasters and man-made hazards has become an important topic in urban development research in recent years as a prerequisite for building and optimizing smart cities. To increase efficiency, the connections between CIs have been strengthened increasingly, resulting in highly interdependent large-scale infrastructure systems that are vulnerable to cascading failures. Hence, studying the cascading and feedback effects caused by the failure of a CI component in a given system can help strengthen this system. Understanding the response of the system in the event of a disaster can lead to better disaster management and better planning of critical infrastructures in the future. The population heavily depends on water, electricity, and the transportation network. These three components also depend on each other to function individually. This complex nature of interdependencies must be studied in order to understand the effects induced in one system due to the failure of another. The three systems (water, transport, and electricity) and their interdependencies can be modeled using graph theory. Water, transport, and electricity networks can be further broken down into smaller components. For example, the water network comprises water treatment plants, water storage tanks, pumping stations, sewage treatment, etc. interdependency factors into the model when, for instance, a pumping station depends on electricity. Graph theory can be used to depict the pairwise relationship between the individual components. Each node in the graph represents a critical infrastructure and the edges between these critical infrastructures represent their dependency. The modeled graph is a multigraph (inter-network dependency) and multidirectional (mutual dependence of two or more components). The idea behind building this model is to simulate the response of the interdependent systems upon failure. Building a simulation tool with an underlying interdependency graph model can not only help in understanding the failure response, but can also help in building a robust system for preserving the infrastructures. The data obtained from the simulation results will contribute to a better emergency response in the event of a disaster. The failure response of a system depends largely on the failed component. Hence, three cases are considered to simulate and identify the state of the system upon failure of a component: The failed component can be a node with maximum outward dependencies, a node with maximum inward dependencies, or a random failure of a component. If a component has the maximum number of outward edges, the simulation tool will help visualize the cascading effects, whereas a system with the maximum number of incoming edges will contribute to the understanding of the feedback response as the outward nodes are not affected immediately. Another goal of CI failure analysis is to develop an algorithm for the partial restoration of specific critical services when a CI is not working at full capacity. The selection of critical infrastructure components for restoration is based on the number of people being affected

Critical Infrastructure Protection Approaches: Analytical Outlook on Capacity Responsiveness to Dynamic Trends

Overview: Critical infrastructures (CIs) – any asset with a functionality that is critical to normal societal functions, safety, security, economic or social wellbeing of people, and disruption or destruction of which would have a very significant negative societal impact. CIs are clearly central to the normal functioning of a nation’s economy and require to be protected from both intentional and unintentional sabotages. It is important to correctly discern and aptly manage security risks within CI domains. The protection (security) of CIs and their networks can provide clear benefits to owner organizations and nations including: enabling the attainment of a properly functioning social environment and economic market, improving service security, enabling integration to external markets, and enabling service recipients (consumers, clients, and users) to benefit from new and emerging technological developments. To effectively secure CI system, firstly, it is crucial to understand three things - what can happen, how likely it is to happen, and the consequences of such happenings. One way to achieve this is through modelling and simulations of CI attributes, functionalities, operations, and behaviours to support security analysis perspectives, and especially considering the dynamics in trends and technological adoptions. Despite the availability of several security-related CI modelling approaches (tools and techniques), trends such as inter-networking, internet and IoT integrations raise new issues. Part of the issues relate to how to effectively (more precisely and realistically) model the complex behavior of interconnected CIs and their protection as system of systems (SoS). This report attempts to address the broad goal around this issue by reviewing a sample of critical infrastructure protection approaches; comprising tools, techniques, and frameworks (methodologies). The analysis covers contexts relating to the types of critical infrastructures, applicable modelling techniques, risk management scope covered, considerations for resilience, interdependency, and policy and regulations factors. Key Findings: This research presents the following key findings: 1. There is not a single specific Critical Infrastructure Protection (CIP) approach – tool, technique, methodology or framework – that exists or emerges as a ‘fit-for-all’; to allow the modelling and simulation of cyber security risks, resilience, dependency, and impact attributes in all critical infrastructure set-ups. 2. Typically, two or more modelling techniques can be (need to be) merged to cover a broader scope and context of modelling and simulation applications (areas) to achieve desirable highlevel protection and security for critical infrastructures. 3. Empirical-based, network-based, agent-based, and system dynamics-based modelling techniques are more widely used, and all offer gains for their use. 4. The deciding factors for choosing modelling techniques often rest on; complexity of use, popularity of approach, types and objectives of user Organisation and sector. 5. The scope of modelling functions and operations also help to strike the balance between ‘specificity’ and ‘generality’ of modelling technique and approach for the gains of in-depth analysis and wider coverage respectively. 6. Interdependency and resilience modelling and simulations in critical infrastructure operations, as well as associated security and safety risks; are crucial characteristics that need to be considered and explored in revising existing or developing new CIP modelling approaches. Recommendations: Key recommendations from this research include: 1. Other critical infrastructure sectors such as emergency services, food & agriculture, and dams; need to draw lessons from the energy and transportation sectors for the successive benefits of: i. Amplifying the drive and efforts towards evaluating and understanding security risks to their infrastructure and operations. ii. Support better understanding of any associated dependencies and cascading impacts. iii. Learning how to establish effective security and resilience. iv. Support the decision-making process linked with measuring the effectiveness of preparedness activities and investments. v. Improve the behavioural security-related responses of CI to disturbances or disruptions. 2. Security-related critical infrastructure modelling approaches should be developed or revised to include wider scopes of security risk management – from identification to effectiveness evaluations, to support: i. Appropriate alignment and responsiveness to the dynamic trends introduced by new technologies such as IoT and IIoT. ii. Dynamic security risk management – especially the assessment section needs to be more dynamic than static, to address the recurrent and impactful risks that emerge in critical infrastructures

Critical infrastructure, panarchies and the vulnerability paths of cascading disasters

Cascading effects and cascading disasters are emerging fields of scientific research. The widespread diffusion of functional networks increases the complexity of interdependent systems and their vulnerability to large-scale disruptions. Although in recent years studies of interconnections and chain effects have improved significantly, cascading phenomena are often associated with the ‘‘toppling domino metaphor’’, or with high-impact, low-probability events. This paper aimed to support a paradigm shift in the state of the art by proposing a new theoretical approach to cascading events in terms of their root causes and lack of predictability. By means of interdisciplinary theory building, we demonstrate how cascades reflect the ways in which panarchies collapse. We suggest that the vulnerability of critical infrastructure may orientate the progress of events in relation to society’s feedback loops, rather than merely being an effect of natural triggers. Our conclusions point to a paradigm shift in the preparedness phase that could include escalation points and social nodes, but that also reveals a brand new field of research for disaster scholars